PredictionModel_v2.ipynb

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      "source": [
        "# Intro To Machine Learning\n",
        "<br>\n",
        "<center>\n",
        "<img src=\"https://pbs.twimg.com/profile_images/1127109408406659072/_uGDKgap_400x400.jpg\" align='center' width='20%'><br>\n",
        "Explore ML Workshop\n",
        "</center>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SH7GQi9CnJqw",
        "colab_type": "text"
      },
      "source": [
        "## Importing Datasets\n",
        "\n",
        "The following file can be imported using pandas library:\n",
        "\n",
        " - csv files\n",
        " - excel files and many more..\n",
        "\n",
        "<img src=\"https://github.com/Geek-ubaid/Machine-Learning-Make-computers-think/blob/master/files/pandas.png?raw=1\" align='center'>\n",
        "\n",
        "Refer to the documentation here: <a href=\"https://pandas.pydata.org/pandas-docs/stable/\"> Pandas Documentation </a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CmKujXFKnJqy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import os"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zCUfxAmfnJq4",
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      "source": [
        "path = os.path.join('Data','Iris.csv')\n",
        "df = pd.read_csv(path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LtsWWogZnJq7",
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      },
      "source": [
        "***Tip:*** In Jupyter notebooks, contents of an object can be viewed by simply typing the variable name as the final expression in a cell, like so: "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        "outputId": "cdc2f9a6-19d2-4a00-dcbe-272e2c33023e"
      },
      "source": [
        "df"
      ],
      "execution_count": 0,
      "outputs": [
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              "      <th>136</th>\n",
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              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>138</td>\n",
              "      <td>6.4</td>\n",
              "      <td>3.1</td>\n",
              "      <td>5.5</td>\n",
              "      <td>1.8</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>139</td>\n",
              "      <td>6.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.8</td>\n",
              "      <td>1.8</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>140</td>\n",
              "      <td>6.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.1</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>141</td>\n",
              "      <td>6.7</td>\n",
              "      <td>3.1</td>\n",
              "      <td>5.6</td>\n",
              "      <td>2.4</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>142</td>\n",
              "      <td>6.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>5.1</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142</th>\n",
              "      <td>143</td>\n",
              "      <td>5.8</td>\n",
              "      <td>2.7</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.9</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>144</td>\n",
              "      <td>6.8</td>\n",
              "      <td>3.2</td>\n",
              "      <td>5.9</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>144</th>\n",
              "      <td>145</td>\n",
              "      <td>6.7</td>\n",
              "      <td>3.3</td>\n",
              "      <td>5.7</td>\n",
              "      <td>2.5</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>146</td>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>147</td>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>148</td>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>149</td>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>150</td>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
              "0      1            5.1           3.5            1.4           0.2   \n",
              "1      2            4.9           3.0            1.4           0.2   \n",
              "2      3            4.7           3.2            1.3           0.2   \n",
              "3      4            4.6           3.1            1.5           0.2   \n",
              "4      5            5.0           3.6            1.4           0.2   \n",
              "5      6            5.4           3.9            1.7           0.4   \n",
              "6      7            4.6           3.4            1.4           0.3   \n",
              "7      8            5.0           3.4            1.5           0.2   \n",
              "8      9            4.4           2.9            1.4           0.2   \n",
              "9     10            4.9           3.1            1.5           0.1   \n",
              "10    11            5.4           3.7            1.5           0.2   \n",
              "11    12            4.8           3.4            1.6           0.2   \n",
              "12    13            4.8           3.0            1.4           0.1   \n",
              "13    14            4.3           3.0            1.1           0.1   \n",
              "14    15            5.8           4.0            1.2           0.2   \n",
              "15    16            5.7           4.4            1.5           0.4   \n",
              "16    17            5.4           3.9            1.3           0.4   \n",
              "17    18            5.1           3.5            1.4           0.3   \n",
              "18    19            5.7           3.8            1.7           0.3   \n",
              "19    20            5.1           3.8            1.5           0.3   \n",
              "20    21            5.4           3.4            1.7           0.2   \n",
              "21    22            5.1           3.7            1.5           0.4   \n",
              "22    23            4.6           3.6            1.0           0.2   \n",
              "23    24            5.1           3.3            1.7           0.5   \n",
              "24    25            4.8           3.4            1.9           0.2   \n",
              "25    26            5.0           3.0            1.6           0.2   \n",
              "26    27            5.0           3.4            1.6           0.4   \n",
              "27    28            5.2           3.5            1.5           0.2   \n",
              "28    29            5.2           3.4            1.4           0.2   \n",
              "29    30            4.7           3.2            1.6           0.2   \n",
              "..   ...            ...           ...            ...           ...   \n",
              "120  121            6.9           3.2            5.7           2.3   \n",
              "121  122            5.6           2.8            4.9           2.0   \n",
              "122  123            7.7           2.8            6.7           2.0   \n",
              "123  124            6.3           2.7            4.9           1.8   \n",
              "124  125            6.7           3.3            5.7           2.1   \n",
              "125  126            7.2           3.2            6.0           1.8   \n",
              "126  127            6.2           2.8            4.8           1.8   \n",
              "127  128            6.1           3.0            4.9           1.8   \n",
              "128  129            6.4           2.8            5.6           2.1   \n",
              "129  130            7.2           3.0            5.8           1.6   \n",
              "130  131            7.4           2.8            6.1           1.9   \n",
              "131  132            7.9           3.8            6.4           2.0   \n",
              "132  133            6.4           2.8            5.6           2.2   \n",
              "133  134            6.3           2.8            5.1           1.5   \n",
              "134  135            6.1           2.6            5.6           1.4   \n",
              "135  136            7.7           3.0            6.1           2.3   \n",
              "136  137            6.3           3.4            5.6           2.4   \n",
              "137  138            6.4           3.1            5.5           1.8   \n",
              "138  139            6.0           3.0            4.8           1.8   \n",
              "139  140            6.9           3.1            5.4           2.1   \n",
              "140  141            6.7           3.1            5.6           2.4   \n",
              "141  142            6.9           3.1            5.1           2.3   \n",
              "142  143            5.8           2.7            5.1           1.9   \n",
              "143  144            6.8           3.2            5.9           2.3   \n",
              "144  145            6.7           3.3            5.7           2.5   \n",
              "145  146            6.7           3.0            5.2           2.3   \n",
              "146  147            6.3           2.5            5.0           1.9   \n",
              "147  148            6.5           3.0            5.2           2.0   \n",
              "148  149            6.2           3.4            5.4           2.3   \n",
              "149  150            5.9           3.0            5.1           1.8   \n",
              "\n",
              "            Species  \n",
              "0       Iris-setosa  \n",
              "1       Iris-setosa  \n",
              "2       Iris-setosa  \n",
              "3       Iris-setosa  \n",
              "4       Iris-setosa  \n",
              "5       Iris-setosa  \n",
              "6       Iris-setosa  \n",
              "7       Iris-setosa  \n",
              "8       Iris-setosa  \n",
              "9       Iris-setosa  \n",
              "10      Iris-setosa  \n",
              "11      Iris-setosa  \n",
              "12      Iris-setosa  \n",
              "13      Iris-setosa  \n",
              "14      Iris-setosa  \n",
              "15      Iris-setosa  \n",
              "16      Iris-setosa  \n",
              "17      Iris-setosa  \n",
              "18      Iris-setosa  \n",
              "19      Iris-setosa  \n",
              "20      Iris-setosa  \n",
              "21      Iris-setosa  \n",
              "22      Iris-setosa  \n",
              "23      Iris-setosa  \n",
              "24      Iris-setosa  \n",
              "25      Iris-setosa  \n",
              "26      Iris-setosa  \n",
              "27      Iris-setosa  \n",
              "28      Iris-setosa  \n",
              "29      Iris-setosa  \n",
              "..              ...  \n",
              "120  Iris-virginica  \n",
              "121  Iris-virginica  \n",
              "122  Iris-virginica  \n",
              "123  Iris-virginica  \n",
              "124  Iris-virginica  \n",
              "125  Iris-virginica  \n",
              "126  Iris-virginica  \n",
              "127  Iris-virginica  \n",
              "128  Iris-virginica  \n",
              "129  Iris-virginica  \n",
              "130  Iris-virginica  \n",
              "131  Iris-virginica  \n",
              "132  Iris-virginica  \n",
              "133  Iris-virginica  \n",
              "134  Iris-virginica  \n",
              "135  Iris-virginica  \n",
              "136  Iris-virginica  \n",
              "137  Iris-virginica  \n",
              "138  Iris-virginica  \n",
              "139  Iris-virginica  \n",
              "140  Iris-virginica  \n",
              "141  Iris-virginica  \n",
              "142  Iris-virginica  \n",
              "143  Iris-virginica  \n",
              "144  Iris-virginica  \n",
              "145  Iris-virginica  \n",
              "146  Iris-virginica  \n",
              "147  Iris-virginica  \n",
              "148  Iris-virginica  \n",
              "149  Iris-virginica  \n",
              "\n",
              "[150 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J_8uGHCanJrA",
        "colab_type": "text"
      },
      "source": [
        "## Data Exploration"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WC-DiI65nJrB",
        "colab_type": "text"
      },
      "source": [
        "It is very important to get to know your data before you start working on it. Pandas dataframe objects have attributes that help with this. Try executing the code block below:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8cFQFcGTnJrC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.head(6)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jfHt1c1GnJrE",
        "colab_type": "text"
      },
      "source": [
        "- **object.head(n)** displays the first n rows in dataframe object\n",
        "- **object.tail(n)** displays last n rows in dataframe object"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IYVG-MtEnJrF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## TO-DO: Write command to view last 10 rows of dataset below ##\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "coFKlMEnnJrI",
        "colab_type": "text"
      },
      "source": [
        "Other useful attributes and properties are demonstrated below:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sJ3klJKLnJrJ",
        "colab_type": "code",
        "colab": {},
        "outputId": "603c41d9-51bc-485d-de8e-bb6efe8b442a"
      },
      "source": [
        "df.sample(5)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "      <th>Species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>83</td>\n",
              "      <td>5.8</td>\n",
              "      <td>2.7</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1.2</td>\n",
              "      <td>Iris-versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>89</td>\n",
              "      <td>5.6</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.1</td>\n",
              "      <td>1.3</td>\n",
              "      <td>Iris-versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>53</td>\n",
              "      <td>6.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>4.9</td>\n",
              "      <td>1.5</td>\n",
              "      <td>Iris-versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123</th>\n",
              "      <td>124</td>\n",
              "      <td>6.3</td>\n",
              "      <td>2.7</td>\n",
              "      <td>4.9</td>\n",
              "      <td>1.8</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>105</td>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.8</td>\n",
              "      <td>2.2</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
              "82    83            5.8           2.7            3.9           1.2   \n",
              "88    89            5.6           3.0            4.1           1.3   \n",
              "52    53            6.9           3.1            4.9           1.5   \n",
              "123  124            6.3           2.7            4.9           1.8   \n",
              "104  105            6.5           3.0            5.8           2.2   \n",
              "\n",
              "             Species  \n",
              "82   Iris-versicolor  \n",
              "88   Iris-versicolor  \n",
              "52   Iris-versicolor  \n",
              "123   Iris-virginica  \n",
              "104   Iris-virginica  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BgmaR5_BnJrN",
        "colab_type": "code",
        "colab": {},
        "outputId": "3c177ddb-53e3-4d44-bfb3-52fe6981a27c"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 150 entries, 0 to 149\n",
            "Data columns (total 6 columns):\n",
            "Id               150 non-null int64\n",
            "SepalLengthCm    150 non-null float64\n",
            "SepalWidthCm     150 non-null float64\n",
            "PetalLengthCm    150 non-null float64\n",
            "PetalWidthCm     150 non-null float64\n",
            "Species          150 non-null object\n",
            "dtypes: float64(4), int64(1), object(1)\n",
            "memory usage: 7.1+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kLxLZpRLnJrR",
        "colab_type": "code",
        "colab": {},
        "outputId": "e8691c5e-e14a-42ce-934b-17113c8c6967"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id                 int64\n",
              "SepalLengthCm    float64\n",
              "SepalWidthCm     float64\n",
              "PetalLengthCm    float64\n",
              "PetalWidthCm     float64\n",
              "Species           object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aJaMcWZenJrU",
        "colab_type": "code",
        "colab": {},
        "outputId": "f702630f-a5ef-424a-9512-93e33688a2a1"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a8H4ZedAnJrX",
        "colab_type": "code",
        "colab": {},
        "outputId": "41bb4ef6-1ed0-4492-c668-ccf9783905d5"
      },
      "source": [
        "df.index"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RangeIndex(start=0, stop=150, step=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KT_u6ruRnJra",
        "colab_type": "code",
        "colab": {},
        "outputId": "9b6baf38-0fd9-459a-ba65-103d96284e87"
      },
      "source": [
        "df['Species'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Iris-versicolor    50\n",
              "Iris-setosa        50\n",
              "Iris-virginica     50\n",
              "Name: Species, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ap4Vh4r0nJrc",
        "colab_type": "text"
      },
      "source": [
        "### Selection in dataset\n",
        "Pandas dataframes have following attributes that help in selection:\n",
        "- **object.loc[rows, columns]** to select using row indices and column names. \n",
        "- **object.iloc[rows, columns]** to select using numeric indices.\n",
        "\n",
        "Try executing the code blocks below:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-0-qmN4RnJrd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.loc[:,'Species']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tsN1EuFTnJrg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.iloc[:,-1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F2a8c_mHnJrj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.loc[:,['Species','SepalLengthCm']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "60SsjgFmnJrl",
        "colab_type": "text"
      },
      "source": [
        "### Numeric Vs Categorical Data\n",
        "\n",
        "#### Requirements for working with data in scikit-learn\n",
        "\n",
        "1. Features and response are **separate objects**\n",
        "2. Features and response should be **numeric**\n",
        "3. Features and response should be **NumPy arrays**\n",
        "4. Features and response should have **specific shapes**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "6QQPkqe-nJrm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_num = df.select_dtypes(include=['float64'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "QksItpu5nJrp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_cat = df.select_dtypes(include=['object'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5z9DTDwsnJrs",
        "colab_type": "code",
        "colab": {},
        "outputId": "bf59a1a5-f817-488d-845b-bdad908f0823"
      },
      "source": [
        "df_cat.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Species\n",
              "0  Iris-setosa\n",
              "1  Iris-setosa\n",
              "2  Iris-setosa\n",
              "3  Iris-setosa\n",
              "4  Iris-setosa"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ymsX-8y9nJru",
        "colab_type": "code",
        "colab": {},
        "outputId": "6499513b-52e7-4994-9271-d7b61510c542"
      },
      "source": [
        "df_num.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
              "0            5.1           3.5            1.4           0.2\n",
              "1            4.9           3.0            1.4           0.2\n",
              "2            4.7           3.2            1.3           0.2\n",
              "3            4.6           3.1            1.5           0.2\n",
              "4            5.0           3.6            1.4           0.2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lGccBpBznJr0",
        "colab_type": "text"
      },
      "source": [
        "### Merging and joining the dataframe\n",
        "\n",
        "Multible tables can be joined using the following 2 commands:\n",
        "\n",
        "- merge\n",
        "- join"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GKfJi8kxnJr2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_join = df_num.join(df_cat)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oU6tq9kBnJr4",
        "colab_type": "code",
        "colab": {},
        "outputId": "59718404-8b06-4e03-88b0-9d6d78e64049"
      },
      "source": [
        "df_join"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "      <th>Species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.4</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>4.4</td>\n",
              "      <td>2.9</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.1</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.7</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>4.8</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>4.8</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.1</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>4.3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>5.8</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>5.7</td>\n",
              "      <td>4.4</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>5.7</td>\n",
              "      <td>3.8</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.3</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.8</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.3</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.7</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.3</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.5</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>4.8</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.4</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>5.2</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>5.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120</th>\n",
              "      <td>6.9</td>\n",
              "      <td>3.2</td>\n",
              "      <td>5.7</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>5.6</td>\n",
              "      <td>2.8</td>\n",
              "      <td>4.9</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>7.7</td>\n",
              "      <td>2.8</td>\n",
              "      <td>6.7</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.7</td>\n",
              "      <td>4.9</td>\n",
              "      <td>1.8</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.3</td>\n",
              "      <td>5.7</td>\n",
              "      <td>2.1</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125</th>\n",
              "      <td>7.2</td>\n",
              "      <td>3.2</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.8</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>6.2</td>\n",
              "      <td>2.8</td>\n",
              "      <td>4.8</td>\n",
              "      <td>1.8</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>6.1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.9</td>\n",
              "      <td>1.8</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>6.4</td>\n",
              "      <td>2.8</td>\n",
              "      <td>5.6</td>\n",
              "      <td>2.1</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>7.2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.8</td>\n",
              "      <td>1.6</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>7.4</td>\n",
              "      <td>2.8</td>\n",
              "      <td>6.1</td>\n",
              "      <td>1.9</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>7.9</td>\n",
              "      <td>3.8</td>\n",
              "      <td>6.4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>6.4</td>\n",
              "      <td>2.8</td>\n",
              "      <td>5.6</td>\n",
              "      <td>2.2</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.8</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>6.1</td>\n",
              "      <td>2.6</td>\n",
              "      <td>5.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>7.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6.1</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>6.3</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.6</td>\n",
              "      <td>2.4</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>6.4</td>\n",
              "      <td>3.1</td>\n",
              "      <td>5.5</td>\n",
              "      <td>1.8</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>6.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.8</td>\n",
              "      <td>1.8</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>6.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.1</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.1</td>\n",
              "      <td>5.6</td>\n",
              "      <td>2.4</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>6.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>5.1</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142</th>\n",
              "      <td>5.8</td>\n",
              "      <td>2.7</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.9</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>6.8</td>\n",
              "      <td>3.2</td>\n",
              "      <td>5.9</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>144</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.3</td>\n",
              "      <td>5.7</td>\n",
              "      <td>2.5</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm         Species\n",
              "0              5.1           3.5            1.4           0.2     Iris-setosa\n",
              "1              4.9           3.0            1.4           0.2     Iris-setosa\n",
              "2              4.7           3.2            1.3           0.2     Iris-setosa\n",
              "3              4.6           3.1            1.5           0.2     Iris-setosa\n",
              "4              5.0           3.6            1.4           0.2     Iris-setosa\n",
              "5              5.4           3.9            1.7           0.4     Iris-setosa\n",
              "6              4.6           3.4            1.4           0.3     Iris-setosa\n",
              "7              5.0           3.4            1.5           0.2     Iris-setosa\n",
              "8              4.4           2.9            1.4           0.2     Iris-setosa\n",
              "9              4.9           3.1            1.5           0.1     Iris-setosa\n",
              "10             5.4           3.7            1.5           0.2     Iris-setosa\n",
              "11             4.8           3.4            1.6           0.2     Iris-setosa\n",
              "12             4.8           3.0            1.4           0.1     Iris-setosa\n",
              "13             4.3           3.0            1.1           0.1     Iris-setosa\n",
              "14             5.8           4.0            1.2           0.2     Iris-setosa\n",
              "15             5.7           4.4            1.5           0.4     Iris-setosa\n",
              "16             5.4           3.9            1.3           0.4     Iris-setosa\n",
              "17             5.1           3.5            1.4           0.3     Iris-setosa\n",
              "18             5.7           3.8            1.7           0.3     Iris-setosa\n",
              "19             5.1           3.8            1.5           0.3     Iris-setosa\n",
              "20             5.4           3.4            1.7           0.2     Iris-setosa\n",
              "21             5.1           3.7            1.5           0.4     Iris-setosa\n",
              "22             4.6           3.6            1.0           0.2     Iris-setosa\n",
              "23             5.1           3.3            1.7           0.5     Iris-setosa\n",
              "24             4.8           3.4            1.9           0.2     Iris-setosa\n",
              "25             5.0           3.0            1.6           0.2     Iris-setosa\n",
              "26             5.0           3.4            1.6           0.4     Iris-setosa\n",
              "27             5.2           3.5            1.5           0.2     Iris-setosa\n",
              "28             5.2           3.4            1.4           0.2     Iris-setosa\n",
              "29             4.7           3.2            1.6           0.2     Iris-setosa\n",
              "..             ...           ...            ...           ...             ...\n",
              "120            6.9           3.2            5.7           2.3  Iris-virginica\n",
              "121            5.6           2.8            4.9           2.0  Iris-virginica\n",
              "122            7.7           2.8            6.7           2.0  Iris-virginica\n",
              "123            6.3           2.7            4.9           1.8  Iris-virginica\n",
              "124            6.7           3.3            5.7           2.1  Iris-virginica\n",
              "125            7.2           3.2            6.0           1.8  Iris-virginica\n",
              "126            6.2           2.8            4.8           1.8  Iris-virginica\n",
              "127            6.1           3.0            4.9           1.8  Iris-virginica\n",
              "128            6.4           2.8            5.6           2.1  Iris-virginica\n",
              "129            7.2           3.0            5.8           1.6  Iris-virginica\n",
              "130            7.4           2.8            6.1           1.9  Iris-virginica\n",
              "131            7.9           3.8            6.4           2.0  Iris-virginica\n",
              "132            6.4           2.8            5.6           2.2  Iris-virginica\n",
              "133            6.3           2.8            5.1           1.5  Iris-virginica\n",
              "134            6.1           2.6            5.6           1.4  Iris-virginica\n",
              "135            7.7           3.0            6.1           2.3  Iris-virginica\n",
              "136            6.3           3.4            5.6           2.4  Iris-virginica\n",
              "137            6.4           3.1            5.5           1.8  Iris-virginica\n",
              "138            6.0           3.0            4.8           1.8  Iris-virginica\n",
              "139            6.9           3.1            5.4           2.1  Iris-virginica\n",
              "140            6.7           3.1            5.6           2.4  Iris-virginica\n",
              "141            6.9           3.1            5.1           2.3  Iris-virginica\n",
              "142            5.8           2.7            5.1           1.9  Iris-virginica\n",
              "143            6.8           3.2            5.9           2.3  Iris-virginica\n",
              "144            6.7           3.3            5.7           2.5  Iris-virginica\n",
              "145            6.7           3.0            5.2           2.3  Iris-virginica\n",
              "146            6.3           2.5            5.0           1.9  Iris-virginica\n",
              "147            6.5           3.0            5.2           2.0  Iris-virginica\n",
              "148            6.2           3.4            5.4           2.3  Iris-virginica\n",
              "149            5.9           3.0            5.1           1.8  Iris-virginica\n",
              "\n",
              "[150 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0_oY-z3WnURW",
        "colab_type": "text"
      },
      "source": [
        "### Descriptive Statistics\n",
        "\n",
        "The descriptive statistics include varrious operation:\n",
        "- mean\n",
        "- maximum\n",
        "- Standard Deviation\n",
        "- variance\n",
        "- median"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gbDY8UiXnURX",
        "colab_type": "code",
        "outputId": "f0224362-797d-4138-a4e2-c49981eeb4c8",
        "colab": {}
      },
      "source": [
        "df_num[df_num.columns.values[0]].mean()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5.843333333333335"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SOmBvRBHnURZ",
        "colab_type": "code",
        "outputId": "7d03dbf2-513b-4ab8-f32e-dedd0c70bed6",
        "colab": {}
      },
      "source": [
        "df_num[df_num.columns.values[0]].median()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5.8"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-7IQJa7OnURc",
        "colab_type": "code",
        "outputId": "ad30d3ef-c4ba-427e-86f4-756472b34226",
        "colab": {}
      },
      "source": [
        "df_num[df_num.columns.values[0]].std()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8280661279778629"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "54KDuOlOnURe",
        "colab_type": "code",
        "outputId": "f444874c-e298-42e2-c712-a8b2d4dcc562",
        "colab": {}
      },
      "source": [
        "df_num[df_num.columns.values[0]].var()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6856935123042505"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BMN594N8nURh",
        "colab_type": "code",
        "outputId": "6a0ee48c-2939-4650-f8cc-b8d86b3570f7",
        "colab": {}
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>75.500000</td>\n",
              "      <td>5.843333</td>\n",
              "      <td>3.054000</td>\n",
              "      <td>3.758667</td>\n",
              "      <td>1.198667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>43.445368</td>\n",
              "      <td>0.828066</td>\n",
              "      <td>0.433594</td>\n",
              "      <td>1.764420</td>\n",
              "      <td>0.763161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.300000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>38.250000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>0.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>75.500000</td>\n",
              "      <td>5.800000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.350000</td>\n",
              "      <td>1.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>112.750000</td>\n",
              "      <td>6.400000</td>\n",
              "      <td>3.300000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>1.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>7.900000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>6.900000</td>\n",
              "      <td>2.500000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
              "count  150.000000     150.000000    150.000000     150.000000    150.000000\n",
              "mean    75.500000       5.843333      3.054000       3.758667      1.198667\n",
              "std     43.445368       0.828066      0.433594       1.764420      0.763161\n",
              "min      1.000000       4.300000      2.000000       1.000000      0.100000\n",
              "25%     38.250000       5.100000      2.800000       1.600000      0.300000\n",
              "50%     75.500000       5.800000      3.000000       4.350000      1.300000\n",
              "75%    112.750000       6.400000      3.300000       5.100000      1.800000\n",
              "max    150.000000       7.900000      4.400000       6.900000      2.500000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eopdKCMInURk",
        "colab_type": "code",
        "outputId": "fb59a1f5-bf95-4f14-9f67-91f82cb7276c",
        "colab": {}
      },
      "source": [
        "df.describe(include='all')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "      <th>Species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Iris-versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>75.500000</td>\n",
              "      <td>5.843333</td>\n",
              "      <td>3.054000</td>\n",
              "      <td>3.758667</td>\n",
              "      <td>1.198667</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>43.445368</td>\n",
              "      <td>0.828066</td>\n",
              "      <td>0.433594</td>\n",
              "      <td>1.764420</td>\n",
              "      <td>0.763161</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.300000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>38.250000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>75.500000</td>\n",
              "      <td>5.800000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.350000</td>\n",
              "      <td>1.300000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>112.750000</td>\n",
              "      <td>6.400000</td>\n",
              "      <td>3.300000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>1.800000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>7.900000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>6.900000</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
              "count   150.000000     150.000000    150.000000     150.000000    150.000000   \n",
              "unique         NaN            NaN           NaN            NaN           NaN   \n",
              "top            NaN            NaN           NaN            NaN           NaN   \n",
              "freq           NaN            NaN           NaN            NaN           NaN   \n",
              "mean     75.500000       5.843333      3.054000       3.758667      1.198667   \n",
              "std      43.445368       0.828066      0.433594       1.764420      0.763161   \n",
              "min       1.000000       4.300000      2.000000       1.000000      0.100000   \n",
              "25%      38.250000       5.100000      2.800000       1.600000      0.300000   \n",
              "50%      75.500000       5.800000      3.000000       4.350000      1.300000   \n",
              "75%     112.750000       6.400000      3.300000       5.100000      1.800000   \n",
              "max     150.000000       7.900000      4.400000       6.900000      2.500000   \n",
              "\n",
              "                Species  \n",
              "count               150  \n",
              "unique                3  \n",
              "top     Iris-versicolor  \n",
              "freq                 50  \n",
              "mean                NaN  \n",
              "std                 NaN  \n",
              "min                 NaN  \n",
              "25%                 NaN  \n",
              "50%                 NaN  \n",
              "75%                 NaN  \n",
              "max                 NaN  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MIqU48TVnURo",
        "colab_type": "text"
      },
      "source": [
        "### Data Visualization\n",
        "\n",
        "Let's Dive into more data visualization and learn about various type of visual analysis of data.\n",
        "\n",
        "Analysis is of 2 types:\n",
        "\n",
        "- **Univariate analysis**\n",
        "- **Multivariate analysis**\n",
        "\n",
        "Packages used here are:\n",
        "\n",
        "- **seaborn**\n",
        "- **matplotlib**\n",
        "\n",
        "<img src='https://github.com/Geek-ubaid/Machine-Learning-Make-computers-think/blob/master/files/download.png?raw=1'><br>\n",
        "<img src='https://github.com/Geek-ubaid/Machine-Learning-Make-computers-think/blob/master/files/images.png?raw=1'>\n",
        "\n",
        "#### Univariate Analysis "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-JlsdSDqnURp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zKQ9fns4nURx",
        "colab_type": "text"
      },
      "source": [
        "#### Line plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OgVA3epenURz",
        "colab_type": "code",
        "outputId": "54523d55-feb4-49e6-98f9-3989b09a52c7",
        "colab": {}
      },
      "source": [
        "df_num[df_num.columns.values[0]].plot(kind='line')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x27caa7506a0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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V2fvasm10Uxvh6Jgw64LXb0y4o5u8veah+QkAwIE5RwU7MWEr1LPxoRv3YKKc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\nEwRBEESbIBEmCIIgiDbx/wFpI5lM3sejDwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x27caa73b8d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P1OpwLpMnUR1",
        "colab_type": "text"
      },
      "source": [
        "#### KDE plot (For distribution)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NVxQNLYjnUR2",
        "colab_type": "code",
        "outputId": "038862cf-1525-495d-f9cf-4c64fd439469",
        "colab": {}
      },
      "source": [
        "sns.kdeplot(df['SepalLengthCm'],shade=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x27caa825278>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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eb85Ji5e5EooVI13bFri8AZzscMpdDtFVCXvlvHTpUlRWVmL16tWQJAnl5eXY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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x27caa7a0630>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tXjVTOBpnUR4",
        "colab_type": "text"
      },
      "source": [
        "#### Countplot(Works with categorical data)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WJlMBJwlnUR5",
        "colab_type": "code",
        "outputId": "151631b4-d6fd-40c6-cd17-94dad58b7cfc",
        "colab": {}
      },
      "source": [
        "sns.countplot(df['Species'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "C:\\Anaconda\\lib\\site-packages\\seaborn\\categorical.py:1428: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
            "  stat_data = remove_na(group_data)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x27caa748c18>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x27caa7e8ba8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vLs8h8_snUR7",
        "colab_type": "text"
      },
      "source": [
        "#### Histogram"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cTsoqObjnUR8",
        "colab_type": "code",
        "outputId": "1d7c0d87-7988-43f9-f0bc-f5ac96577c21",
        "colab": {}
      },
      "source": [
        "df['SepalLengthCm'].plot(kind='hist')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x27caa882320>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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27Nmjxx9/PFqHBgDAF6IW7aKiIi1fvlzjx49XOBxWZmZmtA4NAIAvePrweHp6ujZv3ixJ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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x27caa98e6a0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wVeUM5minUSg",
        "colab_type": "text"
      },
      "source": [
        "### Correlation\n",
        "- Gives quantifiable relationship between features\n",
        "- Corr is calculated using various methods like 'pearson','spearman' etc\n",
        "- Corr can be represented using correlation heatmaps"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QdidGwPWnUSi",
        "colab_type": "code",
        "outputId": "7e26738c-2012-4cbf-8b63-6bec1883a607",
        "colab": {}
      },
      "source": [
        "df.corr(method='spearman')\n",
        "# using default method as pearson"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Id</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.734054</td>\n",
              "      <td>-0.411539</td>\n",
              "      <td>0.868498</td>\n",
              "      <td>0.879045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <td>0.734054</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.159457</td>\n",
              "      <td>0.881386</td>\n",
              "      <td>0.834421</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <td>-0.411539</td>\n",
              "      <td>-0.159457</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.303421</td>\n",
              "      <td>-0.277511</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <td>0.868498</td>\n",
              "      <td>0.881386</td>\n",
              "      <td>-0.303421</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.936003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PetalWidthCm</th>\n",
              "      <td>0.879045</td>\n",
              "      <td>0.834421</td>\n",
              "      <td>-0.277511</td>\n",
              "      <td>0.936003</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  \\\n",
              "Id             1.000000       0.734054     -0.411539       0.868498   \n",
              "SepalLengthCm  0.734054       1.000000     -0.159457       0.881386   \n",
              "SepalWidthCm  -0.411539      -0.159457      1.000000      -0.303421   \n",
              "PetalLengthCm  0.868498       0.881386     -0.303421       1.000000   \n",
              "PetalWidthCm   0.879045       0.834421     -0.277511       0.936003   \n",
              "\n",
              "               PetalWidthCm  \n",
              "Id                 0.879045  \n",
              "SepalLengthCm      0.834421  \n",
              "SepalWidthCm      -0.277511  \n",
              "PetalLengthCm      0.936003  \n",
              "PetalWidthCm       1.000000  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BryGXsYJnUSk",
        "colab_type": "text"
      },
      "source": [
        "### Correlation Heatmap"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0SoowBE7nUSl",
        "colab_type": "code",
        "outputId": "5fa29a6c-d0ac-4c5d-a2f3-ee1ace717681",
        "colab": {}
      },
      "source": [
        "sns.heatmap(df.corr(),annot=True,)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x27cada5e2b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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75jAKFs54/QRu+Z7jOzaRlpKCd/nKtOv3Lo5Oztbuyr+XD+4saHEq88UXX9Cn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\nrkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x27cad4c4748>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G3D-0dB9nUSo",
        "colab_type": "text"
      },
      "source": [
        "### Handling outliers\n",
        "\n",
        "Methods involved:\n",
        "\n",
        "- visual outlier detection using boxplot and scatterplot\n",
        "- Interquartile Range/Z-score\n",
        "- Local outlier factor(Unsupervised Method)\n",
        "- Pyod"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jbYzBk1FnUSp",
        "colab_type": "code",
        "outputId": "f14a6caf-8b89-41a1-cac1-59bfb2be7483",
        "colab": {}
      },
      "source": [
        "sns.boxplot(x='Species',y='SepalLengthCm',data=df)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "C:\\Anaconda\\lib\\site-packages\\seaborn\\categorical.py:454: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
            "  box_data = remove_na(group_data)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x27cadb44748>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5nU5NnDhR+fn5ZmcDAADX4FF5T506VQ8++KDOnz+vsLAw1a1b133CFgAA4F0elffx48eV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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x27cad379080>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "7SLnnSeBnUSs",
        "colab_type": "code",
        "outputId": "59189d57-e646-4dfb-be67-559c28b57836",
        "colab": {}
      },
      "source": [
        "from scipy import stats\n",
        "import numpy as np\n",
        "z = np.abs(stats.zscore(df_num))\n",
        "print(z)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[9.00681170e-01 1.03205722e+00 1.34127240e+00 1.31297673e+00]\n",
            " [1.14301691e+00 1.24957601e-01 1.34127240e+00 1.31297673e+00]\n",
            " [1.38535265e+00 3.37848329e-01 1.39813811e+00 1.31297673e+00]\n",
            " [1.50652052e+00 1.06445364e-01 1.28440670e+00 1.31297673e+00]\n",
            " [1.02184904e+00 1.26346019e+00 1.34127240e+00 1.31297673e+00]\n",
            " [5.37177559e-01 1.95766909e+00 1.17067529e+00 1.05003079e+00]\n",
            " [1.50652052e+00 8.00654259e-01 1.34127240e+00 1.18150376e+00]\n",
            " [1.02184904e+00 8.00654259e-01 1.28440670e+00 1.31297673e+00]\n",
            " [1.74885626e+00 3.56360566e-01 1.34127240e+00 1.31297673e+00]\n",
            " [1.14301691e+00 1.06445364e-01 1.28440670e+00 1.44444970e+00]\n",
            " [5.37177559e-01 1.49486315e+00 1.28440670e+00 1.31297673e+00]\n",
            " [1.26418478e+00 8.00654259e-01 1.22754100e+00 1.31297673e+00]\n",
            " [1.26418478e+00 1.24957601e-01 1.34127240e+00 1.44444970e+00]\n",
            " [1.87002413e+00 1.24957601e-01 1.51186952e+00 1.44444970e+00]\n",
            " [5.25060772e-02 2.18907205e+00 1.45500381e+00 1.31297673e+00]\n",
            " [1.73673948e-01 3.11468391e+00 1.28440670e+00 1.05003079e+00]\n",
            " [5.37177559e-01 1.95766909e+00 1.39813811e+00 1.05003079e+00]\n",
            " [9.00681170e-01 1.03205722e+00 1.34127240e+00 1.18150376e+00]\n",
            " [1.73673948e-01 1.72626612e+00 1.17067529e+00 1.18150376e+00]\n",
            " [9.00681170e-01 1.72626612e+00 1.28440670e+00 1.18150376e+00]\n",
            " [5.37177559e-01 8.00654259e-01 1.17067529e+00 1.31297673e+00]\n",
            " [9.00681170e-01 1.49486315e+00 1.28440670e+00 1.05003079e+00]\n",
            " [1.50652052e+00 1.26346019e+00 1.56873522e+00 1.31297673e+00]\n",
            " [9.00681170e-01 5.69251294e-01 1.17067529e+00 9.18557817e-01]\n",
            " [1.26418478e+00 8.00654259e-01 1.05694388e+00 1.31297673e+00]\n",
            " [1.02184904e+00 1.24957601e-01 1.22754100e+00 1.31297673e+00]\n",
            " [1.02184904e+00 8.00654259e-01 1.22754100e+00 1.05003079e+00]\n",
            " [7.79513300e-01 1.03205722e+00 1.28440670e+00 1.31297673e+00]\n",
            " [7.79513300e-01 8.00654259e-01 1.34127240e+00 1.31297673e+00]\n",
            " [1.38535265e+00 3.37848329e-01 1.22754100e+00 1.31297673e+00]\n",
            " [1.26418478e+00 1.06445364e-01 1.22754100e+00 1.31297673e+00]\n",
            " [5.37177559e-01 8.00654259e-01 1.28440670e+00 1.05003079e+00]\n",
            " [7.79513300e-01 2.42047502e+00 1.28440670e+00 1.44444970e+00]\n",
            " [4.16009689e-01 2.65187798e+00 1.34127240e+00 1.31297673e+00]\n",
            " [1.14301691e+00 1.06445364e-01 1.28440670e+00 1.44444970e+00]\n",
            " [1.02184904e+00 3.37848329e-01 1.45500381e+00 1.31297673e+00]\n",
            " [4.16009689e-01 1.03205722e+00 1.39813811e+00 1.31297673e+00]\n",
            " [1.14301691e+00 1.06445364e-01 1.28440670e+00 1.44444970e+00]\n",
            " [1.74885626e+00 1.24957601e-01 1.39813811e+00 1.31297673e+00]\n",
            " [9.00681170e-01 8.00654259e-01 1.28440670e+00 1.31297673e+00]\n",
            " [1.02184904e+00 1.03205722e+00 1.39813811e+00 1.18150376e+00]\n",
            " [1.62768839e+00 1.74477836e+00 1.39813811e+00 1.18150376e+00]\n",
            " [1.74885626e+00 3.37848329e-01 1.39813811e+00 1.31297673e+00]\n",
            " [1.02184904e+00 1.03205722e+00 1.22754100e+00 7.87084847e-01]\n",
            " [9.00681170e-01 1.72626612e+00 1.05694388e+00 1.05003079e+00]\n",
            " [1.26418478e+00 1.24957601e-01 1.34127240e+00 1.18150376e+00]\n",
            " [9.00681170e-01 1.72626612e+00 1.22754100e+00 1.31297673e+00]\n",
            " [1.50652052e+00 3.37848329e-01 1.34127240e+00 1.31297673e+00]\n",
            " [6.58345429e-01 1.49486315e+00 1.28440670e+00 1.31297673e+00]\n",
            " [1.02184904e+00 5.69251294e-01 1.34127240e+00 1.31297673e+00]\n",
            " [1.40150837e+00 3.37848329e-01 5.35295827e-01 2.64698913e-01]\n",
            " [6.74501145e-01 3.37848329e-01 4.21564419e-01 3.96171883e-01]\n",
            " [1.28034050e+00 1.06445364e-01 6.49027235e-01 3.96171883e-01]\n",
            " [4.16009689e-01 1.74477836e+00 1.37235899e-01 1.33225943e-01]\n",
            " [7.95669016e-01 5.87763531e-01 4.78430123e-01 3.96171883e-01]\n",
            " [1.73673948e-01 5.87763531e-01 4.21564419e-01 1.33225943e-01]\n",
            " [5.53333275e-01 5.69251294e-01 5.35295827e-01 5.27644853e-01]\n",
            " [1.14301691e+00 1.51337539e+00 2.60824029e-01 2.61192967e-01]\n",
            " [9.16836886e-01 3.56360566e-01 4.78430123e-01 1.33225943e-01]\n",
            " [7.79513300e-01 8.19166497e-01 8.03701950e-02 2.64698913e-01]\n",
            " [1.02184904e+00 2.43898725e+00 1.47092621e-01 2.61192967e-01]\n",
            " [6.86617933e-02 1.24957601e-01 2.50967307e-01 3.96171883e-01]\n",
            " [1.89829664e-01 1.97618132e+00 1.37235899e-01 2.61192967e-01]\n",
            " [3.10997534e-01 3.56360566e-01 5.35295827e-01 2.64698913e-01]\n",
            " [2.94841818e-01 3.56360566e-01 9.02269170e-02 1.33225943e-01]\n",
            " [1.03800476e+00 1.06445364e-01 3.64698715e-01 2.64698913e-01]\n",
            " [2.94841818e-01 1.24957601e-01 4.21564419e-01 3.96171883e-01]\n",
            " [5.25060772e-02 8.19166497e-01 1.94101603e-01 2.61192967e-01]\n",
            " [4.32165405e-01 1.97618132e+00 4.21564419e-01 3.96171883e-01]\n",
            " [2.94841818e-01 1.28197243e+00 8.03701950e-02 1.29719997e-01]\n",
            " [6.86617933e-02 3.37848329e-01 5.92161531e-01 7.90590793e-01]\n",
            " [3.10997534e-01 5.87763531e-01 1.37235899e-01 1.33225943e-01]\n",
            " [5.53333275e-01 1.28197243e+00 6.49027235e-01 3.96171883e-01]\n",
            " [3.10997534e-01 5.87763531e-01 5.35295827e-01 1.75297293e-03]\n",
            " [6.74501145e-01 3.56360566e-01 3.07833011e-01 1.33225943e-01]\n",
            " [9.16836886e-01 1.24957601e-01 3.64698715e-01 2.64698913e-01]\n",
            " [1.15917263e+00 5.87763531e-01 5.92161531e-01 2.64698913e-01]\n",
            " [1.03800476e+00 1.24957601e-01 7.05892939e-01 6.59117823e-01]\n",
            " [1.89829664e-01 3.56360566e-01 4.21564419e-01 3.96171883e-01]\n",
            " [1.73673948e-01 1.05056946e+00 1.47092621e-01 2.61192967e-01]\n",
            " [4.16009689e-01 1.51337539e+00 2.35044910e-02 1.29719997e-01]\n",
            " [4.16009689e-01 1.51337539e+00 3.33612130e-02 2.61192967e-01]\n",
            " [5.25060772e-02 8.19166497e-01 8.03701950e-02 1.75297293e-03]\n",
            " [1.89829664e-01 8.19166497e-01 7.62758643e-01 5.27644853e-01]\n",
            " [5.37177559e-01 1.24957601e-01 4.21564419e-01 3.96171883e-01]\n",
            " [1.89829664e-01 8.00654259e-01 4.21564419e-01 5.27644853e-01]\n",
            " [1.03800476e+00 1.06445364e-01 5.35295827e-01 3.96171883e-01]\n",
            " [5.53333275e-01 1.74477836e+00 3.64698715e-01 1.33225943e-01]\n",
            " [2.94841818e-01 1.24957601e-01 1.94101603e-01 1.33225943e-01]\n",
            " [4.16009689e-01 1.28197243e+00 1.37235899e-01 1.33225943e-01]\n",
            " [4.16009689e-01 1.05056946e+00 3.64698715e-01 1.75297293e-03]\n",
            " [3.10997534e-01 1.24957601e-01 4.78430123e-01 2.64698913e-01]\n",
            " [5.25060772e-02 1.05056946e+00 1.37235899e-01 1.75297293e-03]\n",
            " [1.02184904e+00 1.74477836e+00 2.60824029e-01 2.61192967e-01]\n",
            " [2.94841818e-01 8.19166497e-01 2.50967307e-01 1.33225943e-01]\n",
            " [1.73673948e-01 1.24957601e-01 2.50967307e-01 1.75297293e-03]\n",
            " [1.73673948e-01 3.56360566e-01 2.50967307e-01 1.33225943e-01]\n",
            " [4.32165405e-01 3.56360566e-01 3.07833011e-01 1.33225943e-01]\n",
            " [9.00681170e-01 1.28197243e+00 4.31421141e-01 1.29719997e-01]\n",
            " [1.73673948e-01 5.87763531e-01 1.94101603e-01 1.33225943e-01]\n",
            " [5.53333275e-01 5.69251294e-01 1.27454998e+00 1.71090158e+00]\n",
            " [5.25060772e-02 8.19166497e-01 7.62758643e-01 9.22063763e-01]\n",
            " [1.52267624e+00 1.24957601e-01 1.21768427e+00 1.18500970e+00]\n",
            " [5.53333275e-01 3.56360566e-01 1.04708716e+00 7.90590793e-01]\n",
            " [7.95669016e-01 1.24957601e-01 1.16081857e+00 1.31648267e+00]\n",
            " [2.12851559e+00 1.24957601e-01 1.61574420e+00 1.18500970e+00]\n",
            " [1.14301691e+00 1.28197243e+00 4.21564419e-01 6.59117823e-01]\n",
            " [1.76501198e+00 3.56360566e-01 1.44514709e+00 7.90590793e-01]\n",
            " [1.03800476e+00 1.28197243e+00 1.16081857e+00 7.90590793e-01]\n",
            " [1.64384411e+00 1.26346019e+00 1.33141568e+00 1.71090158e+00]\n",
            " [7.95669016e-01 3.37848329e-01 7.62758643e-01 1.05353673e+00]\n",
            " [6.74501145e-01 8.19166497e-01 8.76490051e-01 9.22063763e-01]\n",
            " [1.15917263e+00 1.24957601e-01 9.90221459e-01 1.18500970e+00]\n",
            " [1.73673948e-01 1.28197243e+00 7.05892939e-01 1.05353673e+00]\n",
            " [5.25060772e-02 5.87763531e-01 7.62758643e-01 1.57942861e+00]\n",
            " [6.74501145e-01 3.37848329e-01 8.76490051e-01 1.44795564e+00]\n",
            " [7.95669016e-01 1.24957601e-01 9.90221459e-01 7.90590793e-01]\n",
            " [2.24968346e+00 1.72626612e+00 1.67260991e+00 1.31648267e+00]\n",
            " [2.24968346e+00 1.05056946e+00 1.78634131e+00 1.44795564e+00]\n",
            " [1.89829664e-01 1.97618132e+00 7.05892939e-01 3.96171883e-01]\n",
            " [1.28034050e+00 3.37848329e-01 1.10395287e+00 1.44795564e+00]\n",
            " [2.94841818e-01 5.87763531e-01 6.49027235e-01 1.05353673e+00]\n",
            " [2.24968346e+00 5.87763531e-01 1.67260991e+00 1.05353673e+00]\n",
            " [5.53333275e-01 8.19166497e-01 6.49027235e-01 7.90590793e-01]\n",
            " [1.03800476e+00 5.69251294e-01 1.10395287e+00 1.18500970e+00]\n",
            " [1.64384411e+00 3.37848329e-01 1.27454998e+00 7.90590793e-01]\n",
            " [4.32165405e-01 5.87763531e-01 5.92161531e-01 7.90590793e-01]\n",
            " [3.10997534e-01 1.24957601e-01 6.49027235e-01 7.90590793e-01]\n",
            " [6.74501145e-01 5.87763531e-01 1.04708716e+00 1.18500970e+00]\n",
            " [1.64384411e+00 1.24957601e-01 1.16081857e+00 5.27644853e-01]\n",
            " [1.88617985e+00 5.87763531e-01 1.33141568e+00 9.22063763e-01]\n",
            " [2.49201920e+00 1.72626612e+00 1.50201279e+00 1.05353673e+00]\n",
            " [6.74501145e-01 5.87763531e-01 1.04708716e+00 1.31648267e+00]\n",
            " [5.53333275e-01 5.87763531e-01 7.62758643e-01 3.96171883e-01]\n",
            " [3.10997534e-01 1.05056946e+00 1.04708716e+00 2.64698913e-01]\n",
            " [2.24968346e+00 1.24957601e-01 1.33141568e+00 1.44795564e+00]\n",
            " [5.53333275e-01 8.00654259e-01 1.04708716e+00 1.57942861e+00]\n",
            " [6.74501145e-01 1.06445364e-01 9.90221459e-01 7.90590793e-01]\n",
            " [1.89829664e-01 1.24957601e-01 5.92161531e-01 7.90590793e-01]\n",
            " [1.28034050e+00 1.06445364e-01 9.33355755e-01 1.18500970e+00]\n",
            " [1.03800476e+00 1.06445364e-01 1.04708716e+00 1.57942861e+00]\n",
            " [1.28034050e+00 1.06445364e-01 7.62758643e-01 1.44795564e+00]\n",
            " [5.25060772e-02 8.19166497e-01 7.62758643e-01 9.22063763e-01]\n",
            " [1.15917263e+00 3.37848329e-01 1.21768427e+00 1.44795564e+00]\n",
            " [1.03800476e+00 5.69251294e-01 1.10395287e+00 1.71090158e+00]\n",
            " [1.03800476e+00 1.24957601e-01 8.19624347e-01 1.44795564e+00]\n",
            " [5.53333275e-01 1.28197243e+00 7.05892939e-01 9.22063763e-01]\n",
            " [7.95669016e-01 1.24957601e-01 8.19624347e-01 1.05353673e+00]\n",
            " [4.32165405e-01 8.00654259e-01 9.33355755e-01 1.44795564e+00]\n",
            " [6.86617933e-02 1.24957601e-01 7.62758643e-01 7.90590793e-01]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hBHH0YRSnUSy",
        "colab_type": "code",
        "outputId": "10512d50-a76e-4c2a-de93-9e05861028a3",
        "colab": {}
      },
      "source": [
        "threshold = 3\n",
        "print(np.where(z > 3), end='\\n\\n')\n",
        "\n",
        "print(\"Possible Outlier:\", feature[15][1])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(array([15], dtype=int64), array([1], dtype=int64))\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'feature' is not defined",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[1;32m<ipython-input-47-ac6a366b05e6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mz\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mend\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'\\n\\n'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Possible Outlier:\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeature\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m15\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[1;31mNameError\u001b[0m: name 'feature' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1MvGU3QBnUS0",
        "colab_type": "text"
      },
      "source": [
        "#### IQR Score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xqjcBxhGnUS1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Q1 = df_num.quantile(0.25)\n",
        "Q3 = df_num.quantile(0.75)\n",
        "IQR = Q3 - Q1\n",
        "print(IQR)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "0-68UnppnUS3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "print(df_num < (Q1 - 1.5 * IQR)) or (df_num > (Q3 + 1.5 * IQR))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sjQPZ8DFnJr7",
        "colab_type": "text"
      },
      "source": [
        "## Data Preprocessing\n",
        "\n",
        "We will be having various preprocessing steps involved in this section:\n",
        "\n",
        "- **Standarization**\n",
        "- **Normalization**\n",
        "- **Dummy Variables**\n",
        "- **Label Encoding vs OHE**\n",
        "- **DictVectorizer**\n",
        "\n",
        "The packages to be used in this section will be:\n",
        "<img src='https://github.com/Geek-ubaid/Machine-Learning-Make-computers-think/blob/master/files/sklearn.png?raw=1'>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EVUvzGaKnJr8",
        "colab_type": "text"
      },
      "source": [
        "### Standarization\n",
        "\n",
        "- used when you want to convert data into gaussian distribution meaning mean=0 and variance=1\n",
        "- less sensitive to Outliers\n",
        "- More deviation in data\n",
        "\n",
        "**Formula used:**\n",
        "\n",
        "<img src='https://github.com/Geek-ubaid/Machine-Learning-Make-computers-think/blob/master/files/stand.png?raw=1' width= '30%'>\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nM9aG8QrnJr8",
        "colab_type": "code",
        "colab": {},
        "outputId": "c25e54f5-2a39-4f9b-eecd-fe84f5d5cccd"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "import numpy as np\n",
        "\n",
        "scaler = StandardScaler()\n",
        "\n",
        "reshaped_data = np.array(df['SepalLengthCm']).reshape((-1,1))\n",
        "\n",
        "scaler.fit(reshaped_data)\n",
        "\n",
        "scaled = scaler.transform(reshaped_data)\n",
        "\n",
        "print(\"Variance:\",scaled.var(),\"Mean:\",scaled.mean(),sep=' ')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Variance: 1.0 Mean: -4.736951571734001e-16\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WXT2G9efnJr_",
        "colab_type": "code",
        "colab": {},
        "outputId": "f60490f7-ff97-451c-8d69-30d193e4d0ff"
      },
      "source": [
        "scaled_df = pd.DataFrame(columns=list(df_num.columns.values))\n",
        "for i in df_num.columns:\n",
        "    scaled_df[i] = scaler.fit_transform(df_num[i].values.reshape(-1,1)).reshape(-1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "C:\\Anaconda\\lib\\site-packages\\sklearn\\preprocessing\\data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
            "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
            "C:\\Anaconda\\lib\\site-packages\\sklearn\\preprocessing\\data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
            "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
            "C:\\Anaconda\\lib\\site-packages\\sklearn\\preprocessing\\data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
            "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
            "C:\\Anaconda\\lib\\site-packages\\sklearn\\preprocessing\\data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
            "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
            "C:\\Anaconda\\lib\\site-packages\\sklearn\\preprocessing\\data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
            "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
            "C:\\Anaconda\\lib\\site-packages\\sklearn\\preprocessing\\data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
            "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
            "C:\\Anaconda\\lib\\site-packages\\sklearn\\preprocessing\\data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
            "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
            "C:\\Anaconda\\lib\\site-packages\\sklearn\\preprocessing\\data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
            "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oQOUCk8FnJsB",
        "colab_type": "code",
        "colab": {},
        "outputId": "3aaa2912-1271-4180-e02c-61a4662fde1b"
      },
      "source": [
        "scaled_df.var()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SepalLengthCm    1.006711\n",
              "SepalWidthCm     1.006711\n",
              "PetalLengthCm    1.006711\n",
              "PetalWidthCm     1.006711\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "i7UgcOmKnJsE",
        "colab_type": "code",
        "colab": {},
        "outputId": "edf24d4a-2606-469b-983c-150c7c00e6c5"
      },
      "source": [
        "scaled_df.mean()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SepalLengthCm   -2.775558e-16\n",
              "SepalWidthCm    -5.140333e-16\n",
              "PetalLengthCm    1.154632e-16\n",
              "PetalWidthCm     9.251859e-16\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GEO61mCwnJsG",
        "colab_type": "text"
      },
      "source": [
        "### Normalization\n",
        "\n",
        "- Also known as minmax scaler and reduce data between (0 to 1) or (-1 to 1 in case of negative values)\n",
        "- Sensitive to outliers \n",
        "- Ideally used when the deviation is small or data distribution is not gaussian\n",
        "- Works better where standarization is not working fine.\n",
        "\n",
        "**Formula Used:**\n",
        "\n",
        "<img src=\"https://github.com/Geek-ubaid/Machine-Learning-Make-computers-think/blob/master/files/norm'.png?raw=1\">"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3OddHWNknJsH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "mnmxscaler = MinMaxScaler()\n",
        "\n",
        "'''\n",
        "TO-DO: \n",
        "1) fit the scaler to 'SepalLengthCm' column from dataset\n",
        "2) scale this column using the scaler object and store the result in \n",
        "a new variable called 'normalized_data'.\n",
        "If in doubt, feel free to refer to the code block on standardization above!\n",
        "\n",
        "(~ 1-2 lines of code)\n",
        "'''"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X8jqMrbXnJsJ",
        "colab_type": "text"
      },
      "source": [
        "Many useful methods and properties exist in libraries like sklearn, pandas and numpy to make your life easier; however you need not memorize their parameters to use them! Documentation for these libraries is easily available online. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P1riABs0nJsK",
        "colab_type": "text"
      },
      "source": [
        "### Handling missing values\n",
        "\n",
        "Methods/Techniques used to handle missing values in the dataset\n",
        "\n",
        "- **dropna**\n",
        "- **fillna**\n",
        "- **Impute**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l-tDiyZrnJsL",
        "colab_type": "code",
        "colab": {},
        "outputId": "b3cdd048-fdf6-4431-9fcf-b6ee62e75148"
      },
      "source": [
        "from sklearn.impute import SimpleImputer\n",
        "\n",
        "missing_values_handler = SimpleImputer(strategy='mean')\n",
        "\n",
        "missing_values_handler.fit_transform(df_num['SepalLengthCm'].values.reshape(-1,1))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
            "  DeprecationWarning)\n",
            "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
            "  DeprecationWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[5.1, 4.9, 4.7, 4.6, 5. , 5.4, 4.6, 5. , 4.4, 4.9, 5.4, 4.8, 4.8,\n",
              "        4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4, 5.1, 4.6, 5.1, 4.8, 5. ,\n",
              "        5. , 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5, 4.9, 5. , 5.5, 4.9, 4.4,\n",
              "        5.1, 5. , 4.5, 4.4, 5. , 5.1, 4.8, 5.1, 4.6, 5.3, 5. , 7. , 6.4,\n",
              "        6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2, 5. , 5.9, 6. , 6.1, 5.6,\n",
              "        6.7, 5.6, 5.8, 6.2, 5.6, 5.9, 6.1, 6.3, 6.1, 6.4, 6.6, 6.8, 6.7,\n",
              "        6. , 5.7, 5.5, 5.5, 5.8, 6. , 5.4, 6. , 6.7, 6.3, 5.6, 5.5, 5.5,\n",
              "        6.1, 5.8, 5. , 5.6, 5.7, 5.7, 6.2, 5.1, 5.7, 6.3, 5.8, 7.1, 6.3,\n",
              "        6.5, 7.6, 4.9, 7.3, 6.7, 7.2, 6.5, 6.4, 6.8, 5.7, 5.8, 6.4, 6.5,\n",
              "        7.7, 7.7, 6. , 6.9, 5.6, 7.7, 6.3, 6.7, 7.2, 6.2, 6.1, 6.4, 7.2,\n",
              "        7.4, 7.9, 6.4, 6.3, 6.1, 7.7, 6.3, 6.4, 6. , 6.9, 6.7, 6.9, 5.8,\n",
              "        6.8, 6.7, 6.7, 6.3, 6.5, 6.2, 5.9]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ouk4j181nJsN",
        "colab_type": "code",
        "colab": {},
        "outputId": "b70a9671-283e-49fe-a549-8095b3ffd980"
      },
      "source": [
        "df.isnull().any()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id               False\n",
              "SepalLengthCm    False\n",
              "SepalWidthCm     False\n",
              "PetalLengthCm    False\n",
              "PetalWidthCm     False\n",
              "Species          False\n",
              "dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z6DwRfJpnJsR",
        "colab_type": "code",
        "colab": {},
        "outputId": "90092c3f-adb0-4051-f232-93017424b895"
      },
      "source": [
        "df.dropna()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "      <th>Species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>5.4</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.4</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>4.4</td>\n",
              "      <td>2.9</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>4.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.1</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11</td>\n",
              "      <td>5.4</td>\n",
              "      <td>3.7</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>12</td>\n",
              "      <td>4.8</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>13</td>\n",
              "      <td>4.8</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.1</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>14</td>\n",
              "      <td>4.3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>15</td>\n",
              "      <td>5.8</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>16</td>\n",
              "      <td>5.7</td>\n",
              "      <td>4.4</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>17</td>\n",
              "      <td>5.4</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>18</td>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>19</td>\n",
              "      <td>5.7</td>\n",
              "      <td>3.8</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.3</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>20</td>\n",
              "      <td>5.1</td>\n",
              "      <td>3.8</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.3</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>21</td>\n",
              "      <td>5.4</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>22</td>\n",
              "      <td>5.1</td>\n",
              "      <td>3.7</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>23</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>24</td>\n",
              "      <td>5.1</td>\n",
              "      <td>3.3</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.5</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>25</td>\n",
              "      <td>4.8</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>26</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>27</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.4</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>28</td>\n",
              "      <td>5.2</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>29</td>\n",
              "      <td>5.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>30</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120</th>\n",
              "      <td>121</td>\n",
              "      <td>6.9</td>\n",
              "      <td>3.2</td>\n",
              "      <td>5.7</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>122</td>\n",
              "      <td>5.6</td>\n",
              "      <td>2.8</td>\n",
              "      <td>4.9</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>123</td>\n",
              "      <td>7.7</td>\n",
              "      <td>2.8</td>\n",
              "      <td>6.7</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123</th>\n",
              "      <td>124</td>\n",
              "      <td>6.3</td>\n",
              "      <td>2.7</td>\n",
              "      <td>4.9</td>\n",
              "      <td>1.8</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>125</td>\n",
              "      <td>6.7</td>\n",
              "      <td>3.3</td>\n",
              "      <td>5.7</td>\n",
              "      <td>2.1</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125</th>\n",
              "      <td>126</td>\n",
              "      <td>7.2</td>\n",
              "      <td>3.2</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.8</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>127</td>\n",
              "      <td>6.2</td>\n",
              "      <td>2.8</td>\n",
              "      <td>4.8</td>\n",
              "      <td>1.8</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>128</td>\n",
              "      <td>6.1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.9</td>\n",
              "      <td>1.8</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>129</td>\n",
              "      <td>6.4</td>\n",
              "      <td>2.8</td>\n",
              "      <td>5.6</td>\n",
              "      <td>2.1</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>130</td>\n",
              "      <td>7.2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.8</td>\n",
              "      <td>1.6</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>131</td>\n",
              "      <td>7.4</td>\n",
              "      <td>2.8</td>\n",
              "      <td>6.1</td>\n",
              "      <td>1.9</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>132</td>\n",
              "      <td>7.9</td>\n",
              "      <td>3.8</td>\n",
              "      <td>6.4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>133</td>\n",
              "      <td>6.4</td>\n",
              "      <td>2.8</td>\n",
              "      <td>5.6</td>\n",
              "      <td>2.2</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>134</td>\n",
              "      <td>6.3</td>\n",
              "      <td>2.8</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>135</td>\n",
              "      <td>6.1</td>\n",
              "      <td>2.6</td>\n",
              "      <td>5.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>136</td>\n",
              "      <td>7.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6.1</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>137</td>\n",
              "      <td>6.3</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.6</td>\n",
              "      <td>2.4</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>138</td>\n",
              "      <td>6.4</td>\n",
              "      <td>3.1</td>\n",
              "      <td>5.5</td>\n",
              "      <td>1.8</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>139</td>\n",
              "      <td>6.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.8</td>\n",
              "      <td>1.8</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>140</td>\n",
              "      <td>6.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.1</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>141</td>\n",
              "      <td>6.7</td>\n",
              "      <td>3.1</td>\n",
              "      <td>5.6</td>\n",
              "      <td>2.4</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>142</td>\n",
              "      <td>6.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>5.1</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142</th>\n",
              "      <td>143</td>\n",
              "      <td>5.8</td>\n",
              "      <td>2.7</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.9</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>144</td>\n",
              "      <td>6.8</td>\n",
              "      <td>3.2</td>\n",
              "      <td>5.9</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>144</th>\n",
              "      <td>145</td>\n",
              "      <td>6.7</td>\n",
              "      <td>3.3</td>\n",
              "      <td>5.7</td>\n",
              "      <td>2.5</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>146</td>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>147</td>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>148</td>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>149</td>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>150</td>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
              "0      1            5.1           3.5            1.4           0.2   \n",
              "1      2            4.9           3.0            1.4           0.2   \n",
              "2      3            4.7           3.2            1.3           0.2   \n",
              "3      4            4.6           3.1            1.5           0.2   \n",
              "4      5            5.0           3.6            1.4           0.2   \n",
              "5      6            5.4           3.9            1.7           0.4   \n",
              "6      7            4.6           3.4            1.4           0.3   \n",
              "7      8            5.0           3.4            1.5           0.2   \n",
              "8      9            4.4           2.9            1.4           0.2   \n",
              "9     10            4.9           3.1            1.5           0.1   \n",
              "10    11            5.4           3.7            1.5           0.2   \n",
              "11    12            4.8           3.4            1.6           0.2   \n",
              "12    13            4.8           3.0            1.4           0.1   \n",
              "13    14            4.3           3.0            1.1           0.1   \n",
              "14    15            5.8           4.0            1.2           0.2   \n",
              "15    16            5.7           4.4            1.5           0.4   \n",
              "16    17            5.4           3.9            1.3           0.4   \n",
              "17    18            5.1           3.5            1.4           0.3   \n",
              "18    19            5.7           3.8            1.7           0.3   \n",
              "19    20            5.1           3.8            1.5           0.3   \n",
              "20    21            5.4           3.4            1.7           0.2   \n",
              "21    22            5.1           3.7            1.5           0.4   \n",
              "22    23            4.6           3.6            1.0           0.2   \n",
              "23    24            5.1           3.3            1.7           0.5   \n",
              "24    25            4.8           3.4            1.9           0.2   \n",
              "25    26            5.0           3.0            1.6           0.2   \n",
              "26    27            5.0           3.4            1.6           0.4   \n",
              "27    28            5.2           3.5            1.5           0.2   \n",
              "28    29            5.2           3.4            1.4           0.2   \n",
              "29    30            4.7           3.2            1.6           0.2   \n",
              "..   ...            ...           ...            ...           ...   \n",
              "120  121            6.9           3.2            5.7           2.3   \n",
              "121  122            5.6           2.8            4.9           2.0   \n",
              "122  123            7.7           2.8            6.7           2.0   \n",
              "123  124            6.3           2.7            4.9           1.8   \n",
              "124  125            6.7           3.3            5.7           2.1   \n",
              "125  126            7.2           3.2            6.0           1.8   \n",
              "126  127            6.2           2.8            4.8           1.8   \n",
              "127  128            6.1           3.0            4.9           1.8   \n",
              "128  129            6.4           2.8            5.6           2.1   \n",
              "129  130            7.2           3.0            5.8           1.6   \n",
              "130  131            7.4           2.8            6.1           1.9   \n",
              "131  132            7.9           3.8            6.4           2.0   \n",
              "132  133            6.4           2.8            5.6           2.2   \n",
              "133  134            6.3           2.8            5.1           1.5   \n",
              "134  135            6.1           2.6            5.6           1.4   \n",
              "135  136            7.7           3.0            6.1           2.3   \n",
              "136  137            6.3           3.4            5.6           2.4   \n",
              "137  138            6.4           3.1            5.5           1.8   \n",
              "138  139            6.0           3.0            4.8           1.8   \n",
              "139  140            6.9           3.1            5.4           2.1   \n",
              "140  141            6.7           3.1            5.6           2.4   \n",
              "141  142            6.9           3.1            5.1           2.3   \n",
              "142  143            5.8           2.7            5.1           1.9   \n",
              "143  144            6.8           3.2            5.9           2.3   \n",
              "144  145            6.7           3.3            5.7           2.5   \n",
              "145  146            6.7           3.0            5.2           2.3   \n",
              "146  147            6.3           2.5            5.0           1.9   \n",
              "147  148            6.5           3.0            5.2           2.0   \n",
              "148  149            6.2           3.4            5.4           2.3   \n",
              "149  150            5.9           3.0            5.1           1.8   \n",
              "\n",
              "            Species  \n",
              "0       Iris-setosa  \n",
              "1       Iris-setosa  \n",
              "2       Iris-setosa  \n",
              "3       Iris-setosa  \n",
              "4       Iris-setosa  \n",
              "5       Iris-setosa  \n",
              "6       Iris-setosa  \n",
              "7       Iris-setosa  \n",
              "8       Iris-setosa  \n",
              "9       Iris-setosa  \n",
              "10      Iris-setosa  \n",
              "11      Iris-setosa  \n",
              "12      Iris-setosa  \n",
              "13      Iris-setosa  \n",
              "14      Iris-setosa  \n",
              "15      Iris-setosa  \n",
              "16      Iris-setosa  \n",
              "17      Iris-setosa  \n",
              "18      Iris-setosa  \n",
              "19      Iris-setosa  \n",
              "20      Iris-setosa  \n",
              "21      Iris-setosa  \n",
              "22      Iris-setosa  \n",
              "23      Iris-setosa  \n",
              "24      Iris-setosa  \n",
              "25      Iris-setosa  \n",
              "26      Iris-setosa  \n",
              "27      Iris-setosa  \n",
              "28      Iris-setosa  \n",
              "29      Iris-setosa  \n",
              "..              ...  \n",
              "120  Iris-virginica  \n",
              "121  Iris-virginica  \n",
              "122  Iris-virginica  \n",
              "123  Iris-virginica  \n",
              "124  Iris-virginica  \n",
              "125  Iris-virginica  \n",
              "126  Iris-virginica  \n",
              "127  Iris-virginica  \n",
              "128  Iris-virginica  \n",
              "129  Iris-virginica  \n",
              "130  Iris-virginica  \n",
              "131  Iris-virginica  \n",
              "132  Iris-virginica  \n",
              "133  Iris-virginica  \n",
              "134  Iris-virginica  \n",
              "135  Iris-virginica  \n",
              "136  Iris-virginica  \n",
              "137  Iris-virginica  \n",
              "138  Iris-virginica  \n",
              "139  Iris-virginica  \n",
              "140  Iris-virginica  \n",
              "141  Iris-virginica  \n",
              "142  Iris-virginica  \n",
              "143  Iris-virginica  \n",
              "144  Iris-virginica  \n",
              "145  Iris-virginica  \n",
              "146  Iris-virginica  \n",
              "147  Iris-virginica  \n",
              "148  Iris-virginica  \n",
              "149  Iris-virginica  \n",
              "\n",
              "[150 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NvEw08rmnJsT",
        "colab_type": "text"
      },
      "source": [
        "### Handling Categorical Data\n",
        "\n",
        "Methods involved in handling categorical data\n",
        "\n",
        "- Label Encoder: works well for ordinal data(egs: scale, value items)\n",
        "- dummy_variables: for generating dummy data for replacing categorical data\n",
        "- OHE: works well with nominal data(egs: male, female)\n",
        "\n",
        "- Dict vectorizer - use to combine steps label encoder and OHE"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FAtwMDdynJsb",
        "colab_type": "code",
        "colab": {},
        "outputId": "01b9cc92-f442-44ea-cbc7-d90b093ebd97"
      },
      "source": [
        "df_dummy = pd.get_dummies(df_cat)\n",
        "\n",
        "df_dummy"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Species_Iris-setosa</th>\n",
              "      <th>Species_Iris-versicolor</th>\n",
              "      <th>Species_Iris-virginica</th>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>144</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Species_Iris-setosa  Species_Iris-versicolor  Species_Iris-virginica\n",
              "0                      1                        0                       0\n",
              "1                      1                        0                       0\n",
              "2                      1                        0                       0\n",
              "3                      1                        0                       0\n",
              "4                      1                        0                       0\n",
              "5                      1                        0                       0\n",
              "6                      1                        0                       0\n",
              "7                      1                        0                       0\n",
              "8                      1                        0                       0\n",
              "9                      1                        0                       0\n",
              "10                     1                        0                       0\n",
              "11                     1                        0                       0\n",
              "12                     1                        0                       0\n",
              "13                     1                        0                       0\n",
              "14                     1                        0                       0\n",
              "15                     1                        0                       0\n",
              "16                     1                        0                       0\n",
              "17                     1                        0                       0\n",
              "18                     1                        0                       0\n",
              "19                     1                        0                       0\n",
              "20                     1                        0                       0\n",
              "21                     1                        0                       0\n",
              "22                     1                        0                       0\n",
              "23                     1                        0                       0\n",
              "24                     1                        0                       0\n",
              "25                     1                        0                       0\n",
              "26                     1                        0                       0\n",
              "27                     1                        0                       0\n",
              "28                     1                        0                       0\n",
              "29                     1                        0                       0\n",
              "..                   ...                      ...                     ...\n",
              "120                    0                        0                       1\n",
              "121                    0                        0                       1\n",
              "122                    0                        0                       1\n",
              "123                    0                        0                       1\n",
              "124                    0                        0                       1\n",
              "125                    0                        0                       1\n",
              "126                    0                        0                       1\n",
              "127                    0                        0                       1\n",
              "128                    0                        0                       1\n",
              "129                    0                        0                       1\n",
              "130                    0                        0                       1\n",
              "131                    0                        0                       1\n",
              "132                    0                        0                       1\n",
              "133                    0                        0                       1\n",
              "134                    0                        0                       1\n",
              "135                    0                        0                       1\n",
              "136                    0                        0                       1\n",
              "137                    0                        0                       1\n",
              "138                    0                        0                       1\n",
              "139                    0                        0                       1\n",
              "140                    0                        0                       1\n",
              "141                    0                        0                       1\n",
              "142                    0                        0                       1\n",
              "143                    0                        0                       1\n",
              "144                    0                        0                       1\n",
              "145                    0                        0                       1\n",
              "146                    0                        0                       1\n",
              "147                    0                        0                       1\n",
              "148                    0                        0                       1\n",
              "149                    0                        0                       1\n",
              "\n",
              "[150 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ONqCCchKnJse",
        "colab_type": "text"
      },
      "source": [
        "Compare with original data before transformation:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o6ajXeq7nJsf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_cat"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_OS5zob2nJsh",
        "colab_type": "code",
        "colab": {},
        "outputId": "6cac8854-cfd0-4c91-ebc6-3afea14d912f"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "labelencode = LabelEncoder()\n",
        "transformed_df_cat = labelencode.fit_transform(df_cat)\n",
        "\n",
        "print('Data after transformation:\\n', transformed_df_cat)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "C:\\Anaconda\\lib\\site-packages\\sklearn\\preprocessing\\label.py:129: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], dtype=int64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nDYJM_dlnJsk",
        "colab_type": "code",
        "colab": {},
        "outputId": "f9cc61d3-befd-4dca-fe8b-479ccaaa8e8c"
      },
      "source": [
        "data_for_ohe = labelencode.fit_transform(np.array(df_cat).reshape((-1,1)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "C:\\Anaconda\\lib\\site-packages\\sklearn\\preprocessing\\label.py:129: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "slle5DoRnJsm",
        "colab_type": "code",
        "colab": {},
        "outputId": "6bb5da5a-8850-47a6-ef4b-9e5361924e65"
      },
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from keras.utils import to_categorical\n",
        "\n",
        "oheencode = OneHotEncoder()\n",
        "\n",
        "oheencode.fit(data_for_ohe.reshape((-1,1)))\n",
        "\n",
        "print(\"****************** OHE using sklearn OneHotEncoder ******************\")\n",
        "print(oheencode.transform(data_for_ohe.reshape((-1,1))).toarray())\n",
        "print(\"****************** OHE using keras to_categorical ******************\")\n",
        "print(to_categorical(data_for_ohe))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "****************** OHE using sklearn OneHotEncoder ******************\n",
            "[[1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 0. 1.]\n",
            " [0. 0. 1.]\n",
            " [0. 0. 1.]\n",
            " [0. 0. 1.]\n",
            " [0. 0. 1.]\n",
            " [0. 0. 1.]\n",
            " [0. 0. 1.]\n",
            " [0. 0. 1.]\n",
            " [0. 0. 1.]\n",
            " [0. 0. 1.]\n",
            " [0. 0. 1.]\n",
            " [0. 0. 1.]\n",
            " [0. 0. 1.]\n",
            " [0. 0. 1.]\n",
            " [0. 0. 1.]\n",
            " [0. 0. 1.]\n",
            " [0. 0. 1.]\n",
            " [0. 0. 1.]\n",
            " [0. 0. 1.]\n",
            " [0. 0. 1.]\n",
            " [0. 0. 1.]\n",
            " [0. 0. 1.]\n",
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            " [0. 0. 1.]]\n",
            "****************** OHE using keras to_categorical ******************\n",
            "[[1. 0. 0.]\n",
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            " [0. 0. 1.]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "37PYJiP9nJsp",
        "colab_type": "text"
      },
      "source": [
        "## Data preparation for modelling\n",
        "\n",
        "- preparing the data for modelling\n",
        "- Creating Train/Test/validation sets\n",
        "- Managing the shapes of data\n",
        "\n",
        "The iris dataset is actually a standard dataset provided by sci-kit learn in sklearn.datasets <br>\n",
        "This version of the dataset has already handled many of the pre-processing tasks we just performed, so we can dive into training our first model -"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fT9oMTWvnJsp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn import datasets\n",
        "iris = datasets.load_iris()\n",
        "\n",
        "feature = iris.data\n",
        "\n",
        "target = iris.target"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Lo7JeZcnJss",
        "colab_type": "text"
      },
      "source": [
        "### Train and test  and validation split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YePycnMTnJss",
        "colab_type": "code",
        "colab": {},
        "outputId": "527dcef1-b6c5-454c-8f89-7bb652ae64e5"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "x_train, x_left, y_train, y_left = train_test_split(feature, target, test_size=0.4, random_state=32)\n",
        "\n",
        "print(\"Training Set shape: \", x_train.shape, y_train.shape, sep=' ')\n",
        "\n",
        "x_test, x_val, y_test, y_val = train_test_split(x_left, y_left, test_size=0.5, random_state=32)\n",
        "\n",
        "print(\"Testing Set shape: \", x_test.shape, y_test.shape, sep=' ')\n",
        "print(\"Validation Set shape: \", x_val.shape, y_val.shape, sep=' ')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Set shape:  (90, 4) (90,)\n",
            "Testing Set shape:  (30, 4) (30,)\n",
            "Validation Set shape:  (30, 4) (30,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j-_CLr9inJsv",
        "colab_type": "text"
      },
      "source": [
        "## Data modelling\n",
        "\n",
        "- Tree based and non tree based modelling\n",
        "- baseline model and baseline accuracy\n",
        "- Trying fitting data to different model\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GSxW51oZnJsw",
        "colab_type": "text"
      },
      "source": [
        "### Supervised models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L5k1eW92nJsx",
        "colab_type": "code",
        "colab": {},
        "outputId": "b769232c-c6fc-4640-b95f-377a79057a0a"
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "\n",
        "model = SVC()\n",
        "model.fit(x_train,y_train)\n",
        "\n",
        "y_pred = model.predict(x_test)\n",
        "print(y_pred)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[2 0 1 0 1 0 0 2 0 0 2 0 1 2 2 0 1 1 2 0 0 1 0 0 1 0 1 1 1 2]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C7DPDLvmnJsz",
        "colab_type": "text"
      },
      "source": [
        "### Unsupervised Models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dC_z2PpXnJs0",
        "colab_type": "code",
        "colab": {},
        "outputId": "60da2ce8-134a-4db6-f18b-70590323427c"
      },
      "source": [
        "from sklearn.cluster import KMeans\n",
        "\n",
        "kmeans = KMeans(n_clusters=3)\n",
        "kmeans.fit(feature)\n",
        "y_kmeans = kmeans.predict(feature)\n",
        "\n",
        "df_temp = df\n",
        "df_temp['labels'] = y_kmeans\n",
        "df_temp"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>25</td>\n",
              "      <td>4.8</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>26</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>27</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.4</td>\n",
              "      <td>Iris-setosa</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>28</td>\n",
              "      <td>5.2</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>29</td>\n",
              "      <td>5.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>30</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120</th>\n",
              "      <td>121</td>\n",
              "      <td>6.9</td>\n",
              "      <td>3.2</td>\n",
              "      <td>5.7</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>122</td>\n",
              "      <td>5.6</td>\n",
              "      <td>2.8</td>\n",
              "      <td>4.9</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>123</td>\n",
              "      <td>7.7</td>\n",
              "      <td>2.8</td>\n",
              "      <td>6.7</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123</th>\n",
              "      <td>124</td>\n",
              "      <td>6.3</td>\n",
              "      <td>2.7</td>\n",
              "      <td>4.9</td>\n",
              "      <td>1.8</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>125</td>\n",
              "      <td>6.7</td>\n",
              "      <td>3.3</td>\n",
              "      <td>5.7</td>\n",
              "      <td>2.1</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125</th>\n",
              "      <td>126</td>\n",
              "      <td>7.2</td>\n",
              "      <td>3.2</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.8</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>127</td>\n",
              "      <td>6.2</td>\n",
              "      <td>2.8</td>\n",
              "      <td>4.8</td>\n",
              "      <td>1.8</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>128</td>\n",
              "      <td>6.1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.9</td>\n",
              "      <td>1.8</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>129</td>\n",
              "      <td>6.4</td>\n",
              "      <td>2.8</td>\n",
              "      <td>5.6</td>\n",
              "      <td>2.1</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>130</td>\n",
              "      <td>7.2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.8</td>\n",
              "      <td>1.6</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>131</td>\n",
              "      <td>7.4</td>\n",
              "      <td>2.8</td>\n",
              "      <td>6.1</td>\n",
              "      <td>1.9</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>132</td>\n",
              "      <td>7.9</td>\n",
              "      <td>3.8</td>\n",
              "      <td>6.4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>133</td>\n",
              "      <td>6.4</td>\n",
              "      <td>2.8</td>\n",
              "      <td>5.6</td>\n",
              "      <td>2.2</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>134</td>\n",
              "      <td>6.3</td>\n",
              "      <td>2.8</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>135</td>\n",
              "      <td>6.1</td>\n",
              "      <td>2.6</td>\n",
              "      <td>5.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>136</td>\n",
              "      <td>7.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6.1</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>137</td>\n",
              "      <td>6.3</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.6</td>\n",
              "      <td>2.4</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>138</td>\n",
              "      <td>6.4</td>\n",
              "      <td>3.1</td>\n",
              "      <td>5.5</td>\n",
              "      <td>1.8</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>139</td>\n",
              "      <td>6.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.8</td>\n",
              "      <td>1.8</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>140</td>\n",
              "      <td>6.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.1</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>141</td>\n",
              "      <td>6.7</td>\n",
              "      <td>3.1</td>\n",
              "      <td>5.6</td>\n",
              "      <td>2.4</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>142</td>\n",
              "      <td>6.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>5.1</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142</th>\n",
              "      <td>143</td>\n",
              "      <td>5.8</td>\n",
              "      <td>2.7</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.9</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>144</td>\n",
              "      <td>6.8</td>\n",
              "      <td>3.2</td>\n",
              "      <td>5.9</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>144</th>\n",
              "      <td>145</td>\n",
              "      <td>6.7</td>\n",
              "      <td>3.3</td>\n",
              "      <td>5.7</td>\n",
              "      <td>2.5</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>146</td>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>147</td>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>148</td>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>149</td>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>150</td>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
              "0      1            5.1           3.5            1.4           0.2   \n",
              "1      2            4.9           3.0            1.4           0.2   \n",
              "2      3            4.7           3.2            1.3           0.2   \n",
              "3      4            4.6           3.1            1.5           0.2   \n",
              "4      5            5.0           3.6            1.4           0.2   \n",
              "5      6            5.4           3.9            1.7           0.4   \n",
              "6      7            4.6           3.4            1.4           0.3   \n",
              "7      8            5.0           3.4            1.5           0.2   \n",
              "8      9            4.4           2.9            1.4           0.2   \n",
              "9     10            4.9           3.1            1.5           0.1   \n",
              "10    11            5.4           3.7            1.5           0.2   \n",
              "11    12            4.8           3.4            1.6           0.2   \n",
              "12    13            4.8           3.0            1.4           0.1   \n",
              "13    14            4.3           3.0            1.1           0.1   \n",
              "14    15            5.8           4.0            1.2           0.2   \n",
              "15    16            5.7           4.4            1.5           0.4   \n",
              "16    17            5.4           3.9            1.3           0.4   \n",
              "17    18            5.1           3.5            1.4           0.3   \n",
              "18    19            5.7           3.8            1.7           0.3   \n",
              "19    20            5.1           3.8            1.5           0.3   \n",
              "20    21            5.4           3.4            1.7           0.2   \n",
              "21    22            5.1           3.7            1.5           0.4   \n",
              "22    23            4.6           3.6            1.0           0.2   \n",
              "23    24            5.1           3.3            1.7           0.5   \n",
              "24    25            4.8           3.4            1.9           0.2   \n",
              "25    26            5.0           3.0            1.6           0.2   \n",
              "26    27            5.0           3.4            1.6           0.4   \n",
              "27    28            5.2           3.5            1.5           0.2   \n",
              "28    29            5.2           3.4            1.4           0.2   \n",
              "29    30            4.7           3.2            1.6           0.2   \n",
              "..   ...            ...           ...            ...           ...   \n",
              "120  121            6.9           3.2            5.7           2.3   \n",
              "121  122            5.6           2.8            4.9           2.0   \n",
              "122  123            7.7           2.8            6.7           2.0   \n",
              "123  124            6.3           2.7            4.9           1.8   \n",
              "124  125            6.7           3.3            5.7           2.1   \n",
              "125  126            7.2           3.2            6.0           1.8   \n",
              "126  127            6.2           2.8            4.8           1.8   \n",
              "127  128            6.1           3.0            4.9           1.8   \n",
              "128  129            6.4           2.8            5.6           2.1   \n",
              "129  130            7.2           3.0            5.8           1.6   \n",
              "130  131            7.4           2.8            6.1           1.9   \n",
              "131  132            7.9           3.8            6.4           2.0   \n",
              "132  133            6.4           2.8            5.6           2.2   \n",
              "133  134            6.3           2.8            5.1           1.5   \n",
              "134  135            6.1           2.6            5.6           1.4   \n",
              "135  136            7.7           3.0            6.1           2.3   \n",
              "136  137            6.3           3.4            5.6           2.4   \n",
              "137  138            6.4           3.1            5.5           1.8   \n",
              "138  139            6.0           3.0            4.8           1.8   \n",
              "139  140            6.9           3.1            5.4           2.1   \n",
              "140  141            6.7           3.1            5.6           2.4   \n",
              "141  142            6.9           3.1            5.1           2.3   \n",
              "142  143            5.8           2.7            5.1           1.9   \n",
              "143  144            6.8           3.2            5.9           2.3   \n",
              "144  145            6.7           3.3            5.7           2.5   \n",
              "145  146            6.7           3.0            5.2           2.3   \n",
              "146  147            6.3           2.5            5.0           1.9   \n",
              "147  148            6.5           3.0            5.2           2.0   \n",
              "148  149            6.2           3.4            5.4           2.3   \n",
              "149  150            5.9           3.0            5.1           1.8   \n",
              "\n",
              "            Species  labels  \n",
              "0       Iris-setosa       1  \n",
              "1       Iris-setosa       1  \n",
              "2       Iris-setosa       1  \n",
              "3       Iris-setosa       1  \n",
              "4       Iris-setosa       1  \n",
              "5       Iris-setosa       1  \n",
              "6       Iris-setosa       1  \n",
              "7       Iris-setosa       1  \n",
              "8       Iris-setosa       1  \n",
              "9       Iris-setosa       1  \n",
              "10      Iris-setosa       1  \n",
              "11      Iris-setosa       1  \n",
              "12      Iris-setosa       1  \n",
              "13      Iris-setosa       1  \n",
              "14      Iris-setosa       1  \n",
              "15      Iris-setosa       1  \n",
              "16      Iris-setosa       1  \n",
              "17      Iris-setosa       1  \n",
              "18      Iris-setosa       1  \n",
              "19      Iris-setosa       1  \n",
              "20      Iris-setosa       1  \n",
              "21      Iris-setosa       1  \n",
              "22      Iris-setosa       1  \n",
              "23      Iris-setosa       1  \n",
              "24      Iris-setosa       1  \n",
              "25      Iris-setosa       1  \n",
              "26      Iris-setosa       1  \n",
              "27      Iris-setosa       1  \n",
              "28      Iris-setosa       1  \n",
              "29      Iris-setosa       1  \n",
              "..              ...     ...  \n",
              "120  Iris-virginica       2  \n",
              "121  Iris-virginica       0  \n",
              "122  Iris-virginica       2  \n",
              "123  Iris-virginica       0  \n",
              "124  Iris-virginica       2  \n",
              "125  Iris-virginica       2  \n",
              "126  Iris-virginica       0  \n",
              "127  Iris-virginica       0  \n",
              "128  Iris-virginica       2  \n",
              "129  Iris-virginica       2  \n",
              "130  Iris-virginica       2  \n",
              "131  Iris-virginica       2  \n",
              "132  Iris-virginica       2  \n",
              "133  Iris-virginica       0  \n",
              "134  Iris-virginica       2  \n",
              "135  Iris-virginica       2  \n",
              "136  Iris-virginica       2  \n",
              "137  Iris-virginica       2  \n",
              "138  Iris-virginica       0  \n",
              "139  Iris-virginica       2  \n",
              "140  Iris-virginica       2  \n",
              "141  Iris-virginica       2  \n",
              "142  Iris-virginica       0  \n",
              "143  Iris-virginica       2  \n",
              "144  Iris-virginica       2  \n",
              "145  Iris-virginica       2  \n",
              "146  Iris-virginica       0  \n",
              "147  Iris-virginica       2  \n",
              "148  Iris-virginica       2  \n",
              "149  Iris-virginica       0  \n",
              "\n",
              "[150 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "btvLCSc4nJs2",
        "colab_type": "text"
      },
      "source": [
        "### Visualize the KMeans Algorithm"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_zXiMzNtnJs6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotli.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ddWKyyMEnJs8",
        "colab_type": "code",
        "colab": {},
        "outputId": "a1b4a889-0e48-4c8c-f73e-01eaff0d71a1"
      },
      "source": [
        "from sklearn.datasets.samples_generator import make_blobs\n",
        "X1, y_true = make_blobs(n_samples=300, centers=4,\n",
        "                       cluster_std=0.60, random_state=0)\n",
        "plt.scatter(X1[:, 0], X1[:, 1], s=50);"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wcLzkb2alshOpLHr6pFdN6ukbLlTd6pn+ojbOe/h0aTVv+fH8j0+twq69vejp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\nTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x27cb127bef0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "T3LrgFmMnJs-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "kmeans = KMeans(n_clusters=4)\n",
        "kmeans.fit(X1)\n",
        "y_kmeans = kmeans.predict(X1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VtZVHx-RnJtA",
        "colab_type": "text"
      },
      "source": [
        "Try the code block below to visualize how our KMeans model clusters the data:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iz1BdoHPnJtB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "plt.scatter(X1[:, 0], X1[:, 1], c=y_kmeans, s=50, cmap='viridis')\n",
        "centers = kmeans.cluster_centers_\n",
        "plt.scatter(centers[:, 0], centers[:, 1], c='black', s=200, alpha=0.5);"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tQCzdbItnJtG",
        "colab_type": "text"
      },
      "source": [
        "## Model Validation\n",
        "\n",
        "- Metrics for validation of data\n",
        "- Strategies for improving the model accuracy\n",
        "- choosing the right metric and loss function"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_vfEKVDenJtG",
        "colab_type": "text"
      },
      "source": [
        "#### Metrics used\n",
        "\n",
        "- accuracy\n",
        "- confusion matrix\n",
        "- precision\n",
        "- recall\n",
        "- F1 score\n",
        "- AUC score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RF_btV7knJtH",
        "colab_type": "code",
        "colab": {},
        "outputId": "382f5d09-6d5d-45dc-d32d-eab2ce6b268b"
      },
      "source": [
        "from sklearn.metrics import accuracy_score,classification_report, precision_score, recall_score, f1_score, auc,confusion_matrix\n",
        "\n",
        "accuracy_score(y_pred,y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "IDu5NbmKnJtK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cm = confusion_matrix(model.predict(x_test), y_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OSOYVeg_nJtM",
        "colab_type": "code",
        "colab": {},
        "outputId": "ba6c489d-d8b0-41eb-ee81-32dd38520578"
      },
      "source": [
        "cm_df = pd.DataFrame(cm,\n",
        "                     index = ['setosa','versicolor','virginica'], \n",
        "                     columns = ['setosa','versicolor','virginica'])\n",
        "\n",
        "plt.figure(figsize=(15,10))\n",
        "sns.heatmap(cm_df, annot=True)\n",
        "plt.title('SVM \\nAccuracy:{}'.format(accuracy_score(y_pred, y_test)))\n",
        "plt.ylabel('True label')\n",
        "plt.xlabel('Predicted label')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qqrnllluywQYbpKbm773QHnvssUL7pLiampo+d3tNm7L1wcD/8nl/vW1sbKxA\nJdD6+blHS6upsihlqYPzy2u11VbL9OnTM3369OZtmpTWq0/v3nl+2ovNjz+YNStdu3bJKh07VrAq\nqC5zPpidrqt3bX68ao9V89dZf61gRdB6+bkH5VW2dn/UqFH55je/mb59++boo4/OqFGjyrUUBbD7\nrjvn+Wkv5q0///3c94m/uDt77dm3wlVBdZn21IvZeb+dU1NTkw6dOmT7vbbLtN9Nq3RZ0Cr5uQfl\ntUxJSn19ff785z9ns802y7x587LKKqss9T1jx47Nfffdl2222SY//elPs99+++W4445b4YIpptW7\nd88PR34/p531/TQ0NGSdtdfKReeNrHRZUFWe+uXT6bHm6vnej09Lm7Zt8vR9z2TG869Xuixolfzc\no6VV2dleS29Snn766YwcOTKLFi3K+PHjc9BBB+Xyyy9f6qlb9913X2677ba0bds2DQ0NOfzwwzUp\nrdye/7R79vyn3StdBlSV8ZdOaP53Y2Nj7r7mngpWA9XFzz0on6We7jVmzJjcfvvt6dq1a3r16pVb\nb701l1566VJ33NTUlLZt/94DtWvXLu3atVvxagEAgFZvqUlKY2Njevbs2fx44403XqYd77jjjjn5\n5JOz44475rnnnsv222+//FUCAABVY6lNSp8+ffL444+nVCrlk08+yW233ZY111xzqTs+88wz8+tf\n/zozZszI4MGD069fv5VSMAAAVJtqu0noUpuUCy64IBdeeGHee++97L333tl1111zwQUXLPH1EyZM\nWOxx586d8/7772fChAkZOnToilcMAAC0akttUlZfffWMGTNmmXc4a9asFSoIAABYXJUFKUtvUvr3\n7/+58dKjjz76ua//n3eq//Wvf51XX301G2ywQfbee+8VKBMAAKgWS21Sxo4d2/zvhQsX5le/+lUW\nLFiw1B2PHj06b731VnbYYYfcfffdee6553LmmWeuWLUAAFCFSjXVFaUstUlZa621Fnt8/PHH59BD\nD82IESP+4fueffbZjB8/PklyzDHH5LDDDluBMgEAgGqx1Cbl2Wefbf53U1NTXn311cyfP3+pO164\ncGEaGxtTU1OTpqamqrsiAQAArCzV9qv0UpuUK6+8svnfpVIpq622Wi6++OKl7nj//ffPsGHDsu22\n2+b555/P/vvvv2KVAgAAVWGpTcp+++2XI4444gvv+Nhjj80ee+yR119/PYMHD85mm222XAUCAADV\npWZpL7j55fjTAAAfnUlEQVT99tuXa8cTJ07MXXfdlX333TeXXHJJ7r777uXaDwAAUF2W6Y7zRx99\ndLbddtu0b9++efv/vNTw5xk3blzuuOOOJMkNN9yQo446KoMGDVrBcgEAoPpU23z3UpuU7bbbbrl2\nXFNTk7Zt/777du3aVd03FgAAWD5LbFLuuuuuHHLIIUtNTJZkwIABOeKII7LNNtvkxRdfTP/+/Ze7\nSAAAoHossUn5+c9/nkMOOWS5dzxixIjstddeeeONNzJo0KBsvvnmy70vAACoZtV2UtJSB+e/qP+a\nQxk9enQefPDBTJ8+PQ888EDGjBmzspcCAABaoSUmKa+++moGDBjwme3/dWPGRx999HPf16dPnyTJ\nhhtuuJJKBACA6lZt891LbFLWW2+93HjjjV94h3379k2S7LPPPvnkk0/Spk2bTJw40ZW9AACAZbLE\nJqVdu3ZZa621lnvHJ598coYNG5b//M//zMYbb5yRI0fmJz/5yXLvDwAAqlWVBSlLnknZYYcdVmjH\nf/vb39K/f/+8//77+fa3v51Fixat0P4AAIDqsMQmZeTIkSu044aGhtxyyy350pe+lNdeey3z5s1b\nof0BAADVYaVf3eu/nHnmmfnggw9y4okn5plnnsn3v//9ci0FAAC0Iku94/zyuu222zJ69OgkyVFH\nHVWuZQAAoPWrsqGUsiUpCxYsyPTp0zN//vwsWLAgCxYsKNdSAABAK1K2JOXNN9/MiBEjmh//o3ur\nAAAAS+Y+KSvJvffemySZPXt2unXrVnXfWAAAYPmUrUl59tlnc/7552fRokXZd999s+aaa2bIkCHl\nWg4AAFqtavt7f9lmUn70ox/l1ltvTY8ePXLCCSdk3Lhx5VoKAABoRcqWpJRKpebTvNq3b59OnTqV\naykAAGjVSjXVFaWULUlZb731Mnr06MyePTs33nhj1lxzzXItBQAAtCJla1I+/PDD9OnTJzvuuGNW\nWWWV/PCHPyzXUgAAQCtStiblO9/5TmbMmJE//OEPmT17dj766KNyLQUAALQiZZtJ2WqrrbLVVlvl\nr3/9a84777x89atfzbRp08q1HAAA0EqUrUmZMmVKJk2alBdeeCH77rtvzjzzzHItBQAArVq1XYK4\nbE3KLbfckiFDhuTCCy90I0cAAGCZla1Jueqqq8q1awAAqCrV9kf/sg3OAwAALI+yJSkAAMDKUWVB\niiQFAAAoFkkKAAAUnJkUAACACtKkAAAAhaJJAQAACsVMCgAAFFyVjaRIUgAAgGKRpAAAQMG5uhcA\nAEAFaVIAAIBCcboXAAAUXZVFC1V2uAAAQNFJUgAAoOAMzgMAAFSQJgUAACgUTQoAAFAoZlIAAKDg\nijSScsghh6Rz585JkrXXXjujRo1qfu6xxx7LNddck7Zt22bw4ME57LDDlmsNTQoAALBM5s+fn6am\npowdO/YzzzU0NGTUqFG5884707FjxwwbNiz9+/dPjx49vvA6TvcCAICCK5VKLfb1j0yfPj3z5s3L\nsccem6OPPjpTp05tfm7GjBlZd911s+qqq6a2tjY77rhjnn322eU6XkkKAACwTDp06JDjjjsuQ4YM\nyZtvvplvfetbeeihh9K2bdvU1dWlS5cuza/t1KlT6urqlmsdTQoAABRcUWZSNthgg6y33noplUrZ\nYIMN0q1bt8yaNStrrLFGOnfunLlz5za/du7cuYs1LV+E070AAIBlcuedd+biiy9OksycOTN1dXXp\n2bNnkmSjjTbKW2+9lTlz5mTBggWZMmVKtt9+++VaR5ICAABFV5Ao5etf/3rOPvvsDBs2LKVSKRdd\ndFEefPDB1NfXZ+jQoTnrrLNy3HHHpampKYMHD07v3r2Xax1NCgAAsExqa2szevToxbbtsMMOzf/u\n379/+vfvv8LrON0LAAAoFE0KAABQKJoUAACgUMykAABAwZVqijE431IkKQAAQKFIUgAAoOAKcgXi\nFiNJAQAACkWSAgAABVeqsihFkgIAABSKJAUAAAquyoIUSQoAAFAsmhQAAKBQNCkAAEChmEkBAICi\nq7KhFEkKAABQKJIUAAAouFKNJAUAAKBiJCkAAFBwVTaSIkkBAACKRZMCAAAUitO9AACg6KrsfC9J\nCgAAUCiaFAAAoFCc7gU0u/jusytdAlSlB8+9vdIlQNU6+MrvVLoEPocmBQAACq7KRlKc7gUAABSL\nJAUAAAquVFNdUYokBQAAKBRJCgAAFFypyoZSJCkAAEChSFIAAKDoqitIkaQAAADFokkBAAAKRZMC\nAAAUipkUAAAoOFf3AgAAqCBNCgAAUChO9wIAgIJzuhcAAEAFSVIAAKDoqixaqLLDBQAAik6SAgAA\nBWcmBQAAoII0KQAAQKFoUgAAgEIxkwIAAAVnJgUAAKCCJCkAAFB01RWkSFIAAIBikaQAAEDBlWqq\nK0qRpAAAAIUiSQEAgKJzdS8AAIDK0aQAAACFokkBAAAKRZMCAAAUisF5AAAouCqbm5ekAAAAxSJJ\nAQCAgitVWZQiSQEAAApFkgIAAEVXI0kBAACoGEkKAAAUnJkUAACACtKkAAAAhaJJAQAACsVMCgAA\nFF11jaRIUgAAgGKRpAAAQMG5uhcAAEAFaVIAAIBCcboXAAAUXKnG6V4AAAAVI0kBAICiMzgPAABQ\nOZIUAAAoOJcgBgAAqCBNCgAAUCiaFAAAoFDMpAAAQNFV10iKJAUAACgWSQoAABScO84DAABUkCQF\nAACKzn1SAAAAKkeSAgAABeeO8wAAABWkSQEAAApFkwIAABSKJgUAACgUg/MAAFB0buYIAABQOZIU\nAAAoOJcgBgAAqCBJCgAAFF11BSmSFAAAoFgkKQAAUHBFmUlpaGjIOeeck3feeScLFizIiSeemAED\nBjQ//7Of/Sx33HFHunfvniQ5//zzs+GGG37hdTQpAADAMrnnnnvSrVu3XHbZZZkzZ04GDRq0WJMy\nbdq0XHLJJdlqq61WaB1NCgAAsEz23Xff7LPPPkmSpqamtGnTZrHnX3zxxdx4442ZNWtWvvKVr+Sf\n//mfl2sdTQoAALBMOnXqlCSpq6vLySefnFNOOWWx5w844IAcccQR6dy5c0466aQ8/vjj2Wuvvb7w\nOgbnAQCg6GpKLfe1FO+9916OPvroHHzwwRk4cGDz9qamphxzzDHp3r17amtr069fv7z00kvLd7jL\n9S4AAKDqfPjhhzn22GNz+umn5+tf//piz9XV1eXAAw/M3Llz09TUlMmTJy/3bIrTvQAAoOCKcnWv\n66+/Pp988kmuvfbaXHvttUmSIUOGZN68eRk6dGhOPfXUHH300amtrc1uu+2Wfv36Ldc6paampqaV\nWfjKsOCTjypdAgC0mAfPvb3SJUDVOvjK71S6hGUy87e/brG1evf9SouttSSSFAAAKLqCJCktxUwK\nAABQKJoUAACgUJzuBQAABVeUwfmWIkkBAAAKRZLCSvPE757Mj665Pg0LGrLJJhvlgnPPSefOnSpd\nFrR6PnvQ8tb58ubZaK/tmh+37Vibjt065+GRN2f+p/MqWBm0DpIUVoqPZ8/ODy64MFdcclHu/cX4\nrL3WmvnR1ddWuixo9Xz2oDL+8uz0/PrS8fn1pePzm8snZv4n9Xn+jt9oUGAl0aSwUjz1zO/zpS23\nyHrrrpMkGTr40Nz/0MMp4G14oFXx2YPK22TvHTK/bl7eeurFSpdCa1ZTarmvAijb6V4LFy7MCy+8\nkIULF6apqSkffPBBDjzwwHItR4W9P3Nm+vTu3fy4d6+eqZs7N3Pn1jvtBMrIZw8qq7ZTh2zUf/v8\n5tIJlS4FWpWyNSknnXRSGhoa8sEHH2TRokXp1auXJqUVW9JfbWvaCOugnHz2oLLW232rvP/CG6n/\n+JNKl0Ir5+peK8ns2bPzk5/8JNtss00mTZqU+fPnl2spCqBP796Z9eGHzY8/mDUrXbt2ySodO1aw\nKmj9fPagstbaYZP8efJLlS4DWp2yNSkdOnRIksybNy8dOnSouu6v2uy+6855ftqLeevPf0mSTPzF\n3dlrz74VrgpaP589qJx2HdunU49V8/Hr71e6FKpBqdRyXwVQttO9vva1r+Waa67J5ptvnsMOOyyr\nrLJKuZaiAFbv3j0/HPn9nHbW99PQ0JB11l4rF503stJlQavnsweV06nnqvnbJ3PT1NhY6VKg1Sk1\nlfESMHPnzk2nTp3y7LPPZuutt25OV5ZmwScflaskACicB8+9vdIlQNU6+MrvVLqEZfLhs0+12Fo9\nvrx7i621JGU73evqq6/ODTfckCQZO3Zsfv7zn5drKQAAoBUpW5Py2GOP5bTTTkuSXHnllXnsscfK\ntRQAANCKlK1JKZVKWbBgQZKkoaHBjcUAAIBlUrbB+cMPPzwDBw7Mpptumtdffz3HH398uZYCAIDW\nrSBX3WopZWtShgwZkgEDBuQvf/lL1llnnXTv3r1cSwEAAK3ISm9Srr322owYMSKnnXbaZ+6NMnr0\n6JW9HAAA0Mqs9Calf//+Sf5+uhcAALDiqu3G6Cu9Sdl8882TJFtuuWWeeOKJ5uF5AACAZVG2mZQR\nI0akV69eWWONNZJUX/cHAAArTZX9Ll22JqWpqSmXX355uXYPAAC0UmVrUjbbbLP88Y9/zBZbbNG8\nrba2tlzLAQBAq1WqkaSsFL///e8Xu8t8qVTKo48+Wq7lAACAVqJsTco999xTrl0DAACt2EpvUi64\n4IKMHDkyQ4cO/cyw/Pjx41f2cgAAQCuz0puUESNGJEnGjBmzsncNAADVydW9VkyPHj2SJFdfffVi\n29u1a5c+ffrkyCOPzKqrrrqylwUAAFqJmnLteP78+enVq1f233//rLXWWpk5c2YWLFiQM888s1xL\nAgBA61QqtdxXAZStSfn4449z6qmnpm/fvjnppJPS0NCQU045JZ9++mm5lgQAAFqBsjUpdXV1mTFj\nRpJkxowZmTt3bmbPnp36+vpyLQkAAK1SqVRqsa8iKNsliEeOHJnTTz89H3zwQdZYY42MHDkyDzzw\nQE444YRyLQkAALQCZWtSnn322UyaNGmxbVtvvXW5lgMAgNaryu44X7bTvX7zm99k0aJF5do9AADQ\nSpUtSZk9e3b69u2btddeu/n8NjdzBAAAlqZsTcr1119frl0DAACt2EpvUu64444MGTIkEyZM+Mxz\np5122speDgAAaGVWepPSp0+fJMntt9+eU089NR06dEhNTdlGXwAAoNUrlarr9+mVfrR9+/ZNktx8\n882ZMWNGbrnllrz99tvZZZddVvZSAABAK1S2mZStt946W2+9df7617/mvPPOy9e+9rVMmzatXMsB\nAEDrVZCbLLaUsuVGU6ZMyTnnnJOjjjoqG2+8cR555JFyLQUAALQiZUtSbrnllgwZMiQXXnhhSlXW\n+QEAwMpUbb9Pl61Jueqqq8q1awAAoBUrW5MCAACsJDXVlaRU17XMAACAwtOkAAAAhaJJAQAACsVM\nCgAAFFy1Xd1LkgIAABSKJAUAAIpOkgIAAFA5mhQAAKBQnO4FAABFV6qubKG6jhYAACg8SQoAABRc\nqcbgPAAAQMVoUgAAgELRpAAAAIViJgUAAIrOzRwBAAAqR5ICAAAFV5KkAAAAVI4kBQAAis4d5wEA\nACpHkgIAAAXnjvMAAAAVpEkBAAAKRZMCAAAUiiYFAAAoFIPzAABQdG7mCAAAUDmSFAAAKLiSJAUA\nAKByJCkAAFB0perKFqrraAEAgMKTpAAAQNHVmEkBAACoGE0KAABQKJoUAACgUMykAABAwblPCgAA\nQAVJUgAAoOjcJwUAAKByJCkAAFBwZlIAAAAqSJMCAAAUitO9AACg6AzOAwAAVI4mBQAAKBRNCgAA\nUChmUgAAoOBKNS5BDAAAUDGSFAAAKDo3cwQAAKgcSQoAABRcyX1SAAAAPquxsTEjR47M0KFDM3z4\n8Lz11luLPf/YY49l8ODBGTp0aCZOnLjc60hSAACg6Aoyk/LII49kwYIFmTBhQqZOnZqLL7441113\nXZKkoaEho0aNyp133pmOHTtm2LBh6d+/f3r06PGF15GkAAAAy+S5555L3759kyTbbbddpk2b1vzc\njBkzsu6662bVVVdNbW1tdtxxxzz77LPLtU4hk5TarqtXugQAaDEHX/mdSpcAFFxRfj+uq6tL586d\nmx+3adMmCxcuTNu2bVNXV5cuXbo0P9epU6fU1dUt1zqSFAAAYJl07tw5c+fObX7c2NiYtm3bfu5z\nc+fOXaxp+SI0KQAAwDLZYYcd8sQTTyRJpk6dmk033bT5uY022ihvvfVW5syZkwULFmTKlCnZfvvt\nl2udUlNTU9NKqRgAAGjVGhsbc9555+VPf/pTmpqactFFF+Wll15KfX19hg4dmsceeyzXXHNNmpqa\nMnjw4Bx55JHLtY4mBQAAKBSnewEAAIWiSQEAAApFk8IKeeWVV5b7+tfAinviiScyYcKEL/Seq666\nKuPGjStTRfB/1xf5PM2aNSvnnXfeEp9/+eWXc/XVV6+kyqD6mElhhVx11VXp0aNHhg0bVulSgGXk\ncwtA0RXyZo5U3htvvJGzzz47bdu2TWNjY0aPHp3bb789U6ZMSWNjY77xjW9khx12yF133ZV27drl\nS1/6Uj799NP86Ec/Svv27dOtW7dcdNFFWbhwYU455ZQ0NTVl/vz5Of/887PFFltk9OjRmTZtWubM\nmZPNN988o0aNqvQhQ4s66aSTcvTRR2fnnXfOCy+80Nw4vPXWW2lsbMwpp5ySXXbZJQceeGDWX3/9\ntGvXLkcddVQuueSStG3bNh07dsx//Md/5OGHH87rr7+e733ve7n22mvzyCOPZNGiRRk2bFgOP/zw\n/PSnP83999+ftm3bZqeddsrpp5++WB0XX3xxnnvuuSTJgQcemGOOOSZnnXVW5syZkzlz5uSGG27I\nqquuWolvEZTd//4cfuMb32j+7Jx44onp1q1b9txzz+yyyy45//zz06lTp6y++upp3759TjrppJx2\n2mmZOHFiBg4cmJ133jmvvPJKSqVSrr322rz00ksZP358rrjiitxxxx0ZN25cGhsb079//5x88sm5\n9dZb8/DDD2fevHlZbbXVcvXVV6e2trbS3xIoDE0Kn+upp57KNttsk9NPPz1TpkzJI488krfffjvj\nxo3L/Pnzc9hhh2Xs2LE55JBD0qNHj2y99dYZMGBAxo0bl969e+eWW27Jddddl1122SXdunXLpZde\nmtdeey319fWpq6tL165dc/PNN6exsTEHHHBAZs6cmd69e1f6sKHFDBkyJHfddVd23nnnTJo0KX37\n9s3777+fiy66KLNnz85RRx2V+++/P/X19RkxYkS23HLLXHLJJdlvv/1yzDHH5LHHHssnn3zSvL+X\nXnopTzzxRO64444sWrQoY8aMySuvvJIHH3ww48ePT9u2bfOd73wnjz/+ePN7Hn/88bz99tuZOHFi\nFi5cmCOOOCK77rprkmTXXXfNN77xjZb+tkCL+t+fw1NPPTXvv/9+kr+fzvWLX/witbW1OeSQQ3Lp\npZdmk002yRVXXJGZM2cutp+5c+fmgAMOyA9+8IN897vfzRNPPJEePXokST766KPcdNNNueeee9K+\nffuMHj06dXV1mTNnTn72s5+lpqYmxx13XF544YXsuOOOLf49gKLSpPC5vv71r+emm27K8ccfny5d\numTzzTfPiy++mOHDhydJFi5cmHfeeaf59bNnz07nzp2bG40vf/nLGTNmTE4//fS8+eabGTFiRNq2\nbZsTTzwx7du3z8cff5zTTjstq6yySurr69PQ0FCR44RK6du3by677LLMmTOnOaH8wx/+kOeffz7J\n3z9jH3/8cZJkgw02SJKccMIJuf7663PMMcekd+/e2WabbZr398Ybb2SbbbZJmzZt0qZNm5x11ll5\n8MEHs+2226Zdu3ZJkp122imvvvpq83tmzJiRnXbaKaVSKe3atcu2226bGTNmLLYmtGb/+3O45ZZb\nNj+39tprNycbH3zwQTbZZJMkyY477pgHHnjgM/v6r/euscYamT9/fvP2v/zlL9lkk03SoUOHJMn3\nvve9JEm7du2afw6+//77WbhwYXkOEv6PMjjP53r0/2vvzmJj7v44jr9/7TOqNfJY2oZIFSVtGRVK\nCEHIXLREi8ZWFUtCYk2QxpLpQiv2JbX0wpYxaulFtagGfVwQ25SIJUiIEE1oY4u0JdP21/+FmDxF\n/ZFi8vi87uac8zvnzEl+M/Odc87v/PMPsbGxOJ1O4uLiKCgoYODAgbhcLpxOJ/Hx8YSFhWEYBqZp\n0rZtW6qqqqisrATA7XbTpUsXrl69SmhoKPv27WPu3Lls2bKF8+fP8+zZM7Zs2cKSJUt4//492hol\nfxo/Pz/i4uLIzMzEbrcTERHB6NGjcblc7N69m7i4ONq0aeMtC3D8+HHGjRuHy+WiR48e5Ofne+vr\n1q0bd+/exTRNamtrmTlzJl27duXWrVvU1dXR0NBAWVlZo+AjIiLCu9SrtraWGzduEB4eDoBhGL9q\nKER+m0/vQ39//0Z5H3Xo0IGHDx8CcPPmzS/W1dQ907lzZx49eoTH4wFg0aJFuN1uSktL2bZtG2lp\naZimqe9BkU9oJkW+yGazsWzZMnJzczFNk5ycHE6cOEFycjI1NTXY7XasVis2m40NGzYQERFBdnY2\nCxcuxDAM/v77b9auXYthGCxZsoTDhw9TV1fH/PnziYyMZNeuXUydOhXDMAgLC6OyspKwsLDf/bZF\nfqmkpCTsdjunT58mNDQUh8NBSkoKVVVVJCcnN/qRBBATE4PD4SAwMBA/Pz9Wr17tfbpedHQ0Q4cO\nZcqUKZimyZQpU4iKiiI+Pt6bFhsbi91u5/79+wCMGDECt9vNpEmTqK2tJS4ujl69ev3ycRD5nf59\nH7rd7i+WycjIYOXKlQQFBWGxWL5reXK7du2YPXs2KSkpGIbBiBEj6N27N4GBgUyePBmAkJAQ7598\nIvKBnu4lIiIi8hV5eXnEx8fTrl07tm7disViYcGCBb+7WyL/aZpJEREREfmK9u3bM2vWLIKCgmjd\nujXr1q373V0S+c/TTIqIiIiIiPgUbZwXERERERGfoiBFRERERER8ioIUERERERHxKQpSRER+QHl5\nOTabjcTERMaOHcvo0aOZOXOm97TqH1FQUMDy5csBmD179menWv9bTk4O165d+676IyMjP0vbvn07\n27dv/+p1I0eOpLy8/Jvb+ZY6RUREvkZBiojIDwoNDaWoqIjCwkKKi4ux2WxkZWU1S927d+/+6lkM\nZWVl1NfXN0tbIiIivkaPIBYRaSb9+/fn3LlzwIfZh5iYGO7du8ehQ4e4cOECTqcT0zTp1asXGRkZ\nBAQEUFhYSG5uLlarlU6dOhEUFOS9/sCBA4SEhLBq1SquX7+OxWJh3rx5eDwe7ty5g8PhYMeOHbRs\n2ZLMzEzevHlDy5YtSUtLo2fPnpSXl5OamkpNTQ19+vT5v/0/ePAgRUVFvHv3DsMw2LZtGxEREQDs\n2LGD+/fvExAQwKpVq4iKiuLFixekp6fz/PlzDMNg6dKlDB48+OcNsIiI/DE0kyIi0gxqa2spKSmh\nX79+3rRhw4Zx+vRpXr16RX5+PkeOHKGoqIj27duzd+9eKioq2LRpE3l5eRw9epTq6urP6nW5XNTU\n1FBSUsL+/fvZuXMno0aNwmazkZ2dTWRkJMuWLSM1NZVjx46RlZXF4sWLAcjKymL8+PEUFRU16teX\nVFVVUVpaisvl4uTJk9jtdg4dOuTNDw8Pp7CwkHnz5nmXpK1Zs4akpCQKCgrIzc0lPT2dqqqq5hhO\nERH5w2kmRUTkB1VWVpKYmAiAx+MhJiaGpUuXevM/zl5cvXqVJ0+eMHHiROBDQNOzZ09u3LhB3759\nCQ4OBmDMmDFcuXKlURtlZWVMnDgRPz8/QkJCKC4ubpRfXV3NnTt3WLFihTetpqaG169f43a72bx5\nMwAJCQk4HI4m34vVamXz5s0UFxfz+PFjLly4QHR0tDd/woQJAAwfPpzU1FTevn3LpUuXePToETk5\nOQDU1dXx9OnT7xhBERGRL1OQIiLygz7uSWlKQEAAAPX19cTHx3uDhOrqaurr67l8+TKmaXrL//XX\n5x/Jn6Y9efKEjh07el+bpkmLFi0a9eP58+e0adMGgI/n9RqGgWEYTfb12bNnTJs2jZSUFIYNG0Zw\ncDD37t3z5vv7+zcqb7FYME0Tp9PpbauiooLg4GBKS0ubbEdERORbaLmXiMhPNnDgQM6ePcvLly9p\naGggMzMTp9NJbGwsN2/epKKiAtM0OXXq1GfXDhgwgJKSEhoaGnj58iUpKSl4PB78/f2pr6+ndevW\ndOnSxRukXLx4kalTpwIwePBgjh8/DsCZM2fweDxN9vH27duEh4czY8YM+vTpw/nz5xttzD9x4gQA\nZ8+epVu3bgQGBjJo0CDvkrCHDx+SkJDAu3fvmmfQRETkj6aZFBGRnywqKooFCxYwffp0TNMkOjqa\nOXPmEBAQgMPhYMaMGQQGBtK9e/fPrk1OTiY7O5uEhAQA0tLSsFqtDB06lIyMDNavX8/GjRvJzMxk\nz549WCwWtm7dimEYpKenk5qaypEjR+jduzetWrVqso9Dhgzh8OHDjBo1ihYtWhATE8ODBw+8+Y8f\nPyYxMZFWrVqxbt06ABwOB+np6YwZMwaADRs2YLVam3PoRETkD2U0fFwLICIiIiIi4gO03EtERERE\nRHyKghQREREREfEpClJERERERMSnKEgRERERERGfoiBFRERERER8ioIUERERERHxKQpSRERERETE\npyhIERERERERn/I/Hm5v4M2AMVUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x27cb1844f98>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eizXb43ZnJtO",
        "colab_type": "text"
      },
      "source": [
        "In both precision score and recall score default value of average is binary so it will not work for multiclass classification. We need to change the average value to either None or micro to get the score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bhfbijd8nJtP",
        "colab_type": "code",
        "colab": {},
        "outputId": "6a7c3bab-129a-47c8-85a5-6cb2bb9a8dfd"
      },
      "source": [
        "precision_score(y_true=y_test,y_pred=y_pred,average='micro')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IgYhWKzRnJtR",
        "colab_type": "code",
        "colab": {},
        "outputId": "cbe4a2f2-abba-4b43-84af-8c77055cddba"
      },
      "source": [
        "recall_score(y_true=y_test,y_pred=y_pred,average='micro')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "drxaL7-ZnJtT",
        "colab_type": "text"
      },
      "source": [
        "#### Checking and fitting various other classification algorithm"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JX01CdovnJtU",
        "colab_type": "text"
      },
      "source": [
        "We now train multiple classifiers to compare their performances:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GfhRipPwnJtU",
        "colab_type": "code",
        "colab": {},
        "outputId": "7bee26d1-17b1-4e49-bf37-2e685e8ad0b4"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import AdaBoostClassifier,RandomForestClassifier,ExtraTreesClassifier,BaggingClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.linear_model import LogisticRegression,RidgeClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier    \n",
        "from sklearn.naive_bayes import GaussianNB,BernoulliNB,MultinomialNB\n",
        "\n",
        "#Instantiating all our classifiers:\n",
        "rf_clf = RandomForestClassifier()\n",
        "et_clf = ExtraTreesClassifier()\n",
        "knn_clf = KNeighborsClassifier()\n",
        "svc_clf = SVC()\n",
        "rg_clf = RidgeClassifier()\n",
        "lr_clf = LogisticRegression()\n",
        "dt_clf = DecisionTreeClassifier()\n",
        "adab_clf = AdaBoostClassifier()\n",
        "gnb_clf = GaussianNB()\n",
        "mnb_clf = MultinomialNB()\n",
        "bnb_clf = BernoulliNB()\n",
        "\n",
        "classifiers = [('RandomForestClassifier',rf_clf),\\\n",
        "               ('ExtraTreesClassifier', et_clf),\\\n",
        "               ('KNeighborsClassifier', knn_clf),\\\n",
        "               ( 'SVC', svc_clf),\\\n",
        "               ('RidgeClassifier', rg_clf),\\\n",
        "               ('LogisticRegression', lr_clf),\\\n",
        "               ('DecisionTreeClassifier', dt_clf),\\\n",
        "               ('AdaBoostClassifier', adab_clf),\\\n",
        "               ('GaussianNB', gnb_clf),\\\n",
        "               ('MultinomialNB', mnb_clf),\\\n",
        "               ('BernoulliNB', bnb_clf)]\n",
        "\n",
        "\n",
        "model_summary = pd.DataFrame(columns=['clf_name','accuracy','precision','recall'])\n",
        "model_precision= []\n",
        "model_accuracy = []\n",
        "model_recall = []\n",
        "model_name = []\n",
        "\n",
        "for i in classifiers:\n",
        "    i[1].fit(x_train, y_train)\n",
        "    model_name.append(i[0])\n",
        "    model_accuracy.append(accuracy_score(i[1].predict(x_test), y_test))\n",
        "    model_recall.append(recall_score(i[1].predict(x_test), y_test, average='micro'))\n",
        "    model_precision.append(precision_score(i[1].predict(x_test), y_test, average='micro'))\n",
        "    \n",
        "model_summary['clf_name'] = model_name\n",
        "model_summary['accuracy'] = model_accuracy\n",
        "model_summary['recall'] = model_recall\n",
        "model_summary['precision'] = model_precision\n",
        "\n",
        "model_summary"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>clf_name</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>RandomForestClassifier</td>\n",
              "      <td>0.966667</td>\n",
              "      <td>0.966667</td>\n",
              "      <td>0.966667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ExtraTreesClassifier</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>KNeighborsClassifier</td>\n",
              "      <td>0.966667</td>\n",
              "      <td>0.966667</td>\n",
              "      <td>0.966667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>SVC</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>RidgeClassifier</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.900000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>LogisticRegression</td>\n",
              "      <td>0.966667</td>\n",
              "      <td>0.966667</td>\n",
              "      <td>0.966667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>DecisionTreeClassifier</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>AdaBoostClassifier</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>GaussianNB</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>MultinomialNB</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>0.833333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>BernoulliNB</td>\n",
              "      <td>0.233333</td>\n",
              "      <td>0.233333</td>\n",
              "      <td>0.233333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  clf_name  accuracy  precision    recall\n",
              "0   RandomForestClassifier  0.966667   0.966667  0.966667\n",
              "1     ExtraTreesClassifier  1.000000   1.000000  1.000000\n",
              "2     KNeighborsClassifier  0.966667   0.966667  0.966667\n",
              "3                      SVC  1.000000   1.000000  1.000000\n",
              "4          RidgeClassifier  0.900000   0.900000  0.900000\n",
              "5       LogisticRegression  0.966667   0.966667  0.966667\n",
              "6   DecisionTreeClassifier  1.000000   1.000000  1.000000\n",
              "7       AdaBoostClassifier  1.000000   1.000000  1.000000\n",
              "8               GaussianNB  1.000000   1.000000  1.000000\n",
              "9            MultinomialNB  0.833333   0.833333  0.833333\n",
              "10             BernoulliNB  0.233333   0.233333  0.233333"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B9rsq7ONnJtX",
        "colab_type": "text"
      },
      "source": [
        "### Miscellaneous Methods:\n",
        "\n",
        "- using the validation data to find the correct probability\n",
        "- Cross validation technique"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5mphkPIZnJtZ",
        "colab_type": "code",
        "colab": {},
        "outputId": "e0e0f316-641c-4902-bbfc-fb4526180979"
      },
      "source": [
        "print(\" The validation data set details are:\\n Feature Shape: {}\\n Label Shape: {}\".format(x_val.shape,y_val.shape))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " The validation data set details are:\n",
            " Feature Shape: (30, 4)\n",
            " Label Shape: (30,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KjDFFjx-nJtc",
        "colab_type": "code",
        "colab": {},
        "outputId": "a060010a-1775-4b6c-eee5-1c1b24a8fdc7"
      },
      "source": [
        "print(accuracy_score(model.predict(x_val), y_val))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9666666666666667\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7B4JoPGTnJtd",
        "colab_type": "text"
      },
      "source": [
        "This is the real accuracy of the real world data model and is much less than the test and train data accuracy. The validation set gives you the correct idea about how your model is doing. This data is hidden from the model during train and thus reflects how the model performs on data it has not seen."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1aUjAPjfnJte",
        "colab_type": "text"
      },
      "source": [
        "### Cross validation\n",
        "\n",
        "This method is used when:\n",
        "\n",
        "> We want to do a sequence of fits where each subset of the data is used both as a training set and as a validation set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JWMWSUNXnJte",
        "colab_type": "code",
        "colab": {},
        "outputId": "8a0fffa6-5214-4f3b-df29-5cb34b4af8d8"
      },
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "accuracies = cross_val_score(estimator= model, X = feature, y = target, cv=5, n_jobs=-1)\n",
        "acc_mean = accuracies.mean()\n",
        "acc_std = accuracies.std()\n",
        "\n",
        "print(\"Avg. Accuracy: \", round(acc_mean*100),\"% \", \" Std: \", round(acc_std*100,2),\"%\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Avg. Accuracy:  98.0 %   Std:  1.63 %\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}