Training_And_Model_Evaluation.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_fSi3N81EpZ0"
      },
      "source": [
        "# Training and Model Evaluation\n",
        "Training the data (csv file) with the Multiple Model after applying Feature Reduction techniques like **Principal Component Analysis** and **Standard Scaler** to negate and reduce the Dimensionality of the data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FiERseNipHnr",
        "outputId": "ddf3c7e0-2d6b-4e54-a2c9-9434fce043f6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EaEiIrjPEpZ7"
      },
      "source": [
        "## Importing Packages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "vcyeHsXJO1C1"
      },
      "outputs": [],
      "source": [
        "import pickle\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
        "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
        "from sklearn.metrics import accuracy_score, classification_report"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yDNtn07yEpZ9"
      },
      "source": [
        "## Data collection (CSV File)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "tjghcqUIPlpe"
      },
      "outputs": [],
      "source": [
        "data=pd.read_csv(\"/content/drive/MyDrive/Github/Medicinal Plant Detection Using Machine Learning/Data/ExtractedFeatures.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 878
        },
        "id": "wcnPVOlalDR7",
        "outputId": "c741adc8-6b27-42c0-98e3-51cba7f7430e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      LBP_Feature_1  LBP_Feature_2  LBP_Feature_3  LBP_Feature_4  \\\n",
              "0          0.027994       0.069837       0.018527       0.094523   \n",
              "1          0.035107       0.077375       0.020802       0.091493   \n",
              "2          0.024717       0.068902       0.015160       0.095858   \n",
              "3          0.055080       0.102960       0.030774       0.078403   \n",
              "4          0.027034       0.067895       0.016363       0.093636   \n",
              "...             ...            ...            ...            ...   \n",
              "1811       0.012088       0.017700       0.009490       0.014483   \n",
              "1812       0.006046       0.010284       0.005926       0.011109   \n",
              "1813       0.007656       0.014827       0.009302       0.020337   \n",
              "1814       0.007229       0.013236       0.006933       0.014331   \n",
              "1815       0.021928       0.028826       0.014745       0.020396   \n",
              "\n",
              "      LBP_Feature_5  LBP_Feature_6  LBP_Feature_7  LBP_Feature_8  \\\n",
              "0          0.070229       0.161165       0.069341       0.097861   \n",
              "1          0.069175       0.147587       0.072821       0.100051   \n",
              "2          0.061750       0.165130       0.071451       0.098987   \n",
              "3          0.062366       0.105536       0.084563       0.104321   \n",
              "4          0.064696       0.161530       0.067177       0.098934   \n",
              "...             ...            ...            ...            ...   \n",
              "1811       0.013968       0.020210       0.014567       0.018409   \n",
              "1812       0.012515       0.017398       0.010056       0.011053   \n",
              "1813       0.022640       0.029843       0.017621       0.016470   \n",
              "1814       0.013291       0.021095       0.013214       0.014211   \n",
              "1815       0.018506       0.025711       0.021582       0.029510   \n",
              "\n",
              "      LBP_Feature_9  GLCM_Feature_Dissimilarity  ...     Mean_R        Var_R  \\\n",
              "0          0.390523                    1.315370  ...  15.862588   568.844559   \n",
              "1          0.385590                    1.541173  ...  20.941332   799.555744   \n",
              "2          0.398044                    1.015514  ...  13.047573   438.424098   \n",
              "3          0.375998                    1.848266  ...  14.164750   532.220768   \n",
              "4          0.402735                    1.276336  ...  12.249544   436.688477   \n",
              "...             ...                         ...  ...        ...          ...   \n",
              "1811       0.879084                    0.640441  ...  13.839704  1072.098142   \n",
              "1812       0.915614                    0.375903  ...   9.076615   785.043005   \n",
              "1813       0.861305                    0.526366  ...  17.027024  1486.579019   \n",
              "1814       0.896459                    0.398143  ...  12.089983  1014.167195   \n",
              "1815       0.818795                    0.971415  ...  22.330943  1566.468517   \n",
              "\n",
              "        Skew_R     Mean_G        Var_G    Skew_G     Mean_B       Var_B  \\\n",
              "0     1.797773  21.068242   867.985941  1.153764   9.720084  304.414832   \n",
              "1     1.192635  25.101884  1079.680481  0.888754  11.037473  338.580671   \n",
              "2     1.899293  17.258159   687.776815  1.276937   7.574794  212.321602   \n",
              "3     1.809078  17.819565   779.015558  1.344897   7.084845  219.530270   \n",
              "4     2.245516  16.183751   663.958488  1.485639   7.494372  236.579771   \n",
              "...        ...        ...          ...       ...        ...         ...   \n",
              "1811  2.428298  15.748247  1347.237405  2.177858   4.592833  231.230836   \n",
              "1812  3.481602  10.705626  1027.139284  2.981260   4.022584  230.583898   \n",
              "1813  2.121477  19.979577  1996.953958  1.946036   5.280974  248.553693   \n",
              "1814  2.662396  14.855094  1485.915583  2.394971   3.701817  181.054805   \n",
              "1815  1.546010  27.444230  2295.093611  1.334677   6.903499  281.590495   \n",
              "\n",
              "         Skew_B                                  Label  \n",
              "0      3.367585                 Bellyache bush (Green)  \n",
              "1      3.163192                 Bellyache bush (Green)  \n",
              "2      4.026185                 Bellyache bush (Green)  \n",
              "3      4.779979                 Bellyache bush (Green)  \n",
              "4      4.175034                 Bellyache bush (Green)  \n",
              "...         ...                                    ...  \n",
              "1811   8.805814  Trigonella Foenum-graecum (Fenugreek)  \n",
              "1812   7.690323  Trigonella Foenum-graecum (Fenugreek)  \n",
              "1813   8.235564  Trigonella Foenum-graecum (Fenugreek)  \n",
              "1814  10.495481  Trigonella Foenum-graecum (Fenugreek)  \n",
              "1815   7.542093  Trigonella Foenum-graecum (Fenugreek)  \n",
              "\n",
              "[1816 rows x 62 columns]"
            ],
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>LBP_Feature_1</th>\n",
              "      <th>LBP_Feature_2</th>\n",
              "      <th>LBP_Feature_3</th>\n",
              "      <th>LBP_Feature_4</th>\n",
              "      <th>LBP_Feature_5</th>\n",
              "      <th>LBP_Feature_6</th>\n",
              "      <th>LBP_Feature_7</th>\n",
              "      <th>LBP_Feature_8</th>\n",
              "      <th>LBP_Feature_9</th>\n",
              "      <th>GLCM_Feature_Dissimilarity</th>\n",
              "      <th>...</th>\n",
              "      <th>Mean_R</th>\n",
              "      <th>Var_R</th>\n",
              "      <th>Skew_R</th>\n",
              "      <th>Mean_G</th>\n",
              "      <th>Var_G</th>\n",
              "      <th>Skew_G</th>\n",
              "      <th>Mean_B</th>\n",
              "      <th>Var_B</th>\n",
              "      <th>Skew_B</th>\n",
              "      <th>Label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.027994</td>\n",
              "      <td>0.069837</td>\n",
              "      <td>0.018527</td>\n",
              "      <td>0.094523</td>\n",
              "      <td>0.070229</td>\n",
              "      <td>0.161165</td>\n",
              "      <td>0.069341</td>\n",
              "      <td>0.097861</td>\n",
              "      <td>0.390523</td>\n",
              "      <td>1.315370</td>\n",
              "      <td>...</td>\n",
              "      <td>15.862588</td>\n",
              "      <td>568.844559</td>\n",
              "      <td>1.797773</td>\n",
              "      <td>21.068242</td>\n",
              "      <td>867.985941</td>\n",
              "      <td>1.153764</td>\n",
              "      <td>9.720084</td>\n",
              "      <td>304.414832</td>\n",
              "      <td>3.367585</td>\n",
              "      <td>Bellyache bush (Green)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.035107</td>\n",
              "      <td>0.077375</td>\n",
              "      <td>0.020802</td>\n",
              "      <td>0.091493</td>\n",
              "      <td>0.069175</td>\n",
              "      <td>0.147587</td>\n",
              "      <td>0.072821</td>\n",
              "      <td>0.100051</td>\n",
              "      <td>0.385590</td>\n",
              "      <td>1.541173</td>\n",
              "      <td>...</td>\n",
              "      <td>20.941332</td>\n",
              "      <td>799.555744</td>\n",
              "      <td>1.192635</td>\n",
              "      <td>25.101884</td>\n",
              "      <td>1079.680481</td>\n",
              "      <td>0.888754</td>\n",
              "      <td>11.037473</td>\n",
              "      <td>338.580671</td>\n",
              "      <td>3.163192</td>\n",
              "      <td>Bellyache bush (Green)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.024717</td>\n",
              "      <td>0.068902</td>\n",
              "      <td>0.015160</td>\n",
              "      <td>0.095858</td>\n",
              "      <td>0.061750</td>\n",
              "      <td>0.165130</td>\n",
              "      <td>0.071451</td>\n",
              "      <td>0.098987</td>\n",
              "      <td>0.398044</td>\n",
              "      <td>1.015514</td>\n",
              "      <td>...</td>\n",
              "      <td>13.047573</td>\n",
              "      <td>438.424098</td>\n",
              "      <td>1.899293</td>\n",
              "      <td>17.258159</td>\n",
              "      <td>687.776815</td>\n",
              "      <td>1.276937</td>\n",
              "      <td>7.574794</td>\n",
              "      <td>212.321602</td>\n",
              "      <td>4.026185</td>\n",
              "      <td>Bellyache bush (Green)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.055080</td>\n",
              "      <td>0.102960</td>\n",
              "      <td>0.030774</td>\n",
              "      <td>0.078403</td>\n",
              "      <td>0.062366</td>\n",
              "      <td>0.105536</td>\n",
              "      <td>0.084563</td>\n",
              "      <td>0.104321</td>\n",
              "      <td>0.375998</td>\n",
              "      <td>1.848266</td>\n",
              "      <td>...</td>\n",
              "      <td>14.164750</td>\n",
              "      <td>532.220768</td>\n",
              "      <td>1.809078</td>\n",
              "      <td>17.819565</td>\n",
              "      <td>779.015558</td>\n",
              "      <td>1.344897</td>\n",
              "      <td>7.084845</td>\n",
              "      <td>219.530270</td>\n",
              "      <td>4.779979</td>\n",
              "      <td>Bellyache bush (Green)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.027034</td>\n",
              "      <td>0.067895</td>\n",
              "      <td>0.016363</td>\n",
              "      <td>0.093636</td>\n",
              "      <td>0.064696</td>\n",
              "      <td>0.161530</td>\n",
              "      <td>0.067177</td>\n",
              "      <td>0.098934</td>\n",
              "      <td>0.402735</td>\n",
              "      <td>1.276336</td>\n",
              "      <td>...</td>\n",
              "      <td>12.249544</td>\n",
              "      <td>436.688477</td>\n",
              "      <td>2.245516</td>\n",
              "      <td>16.183751</td>\n",
              "      <td>663.958488</td>\n",
              "      <td>1.485639</td>\n",
              "      <td>7.494372</td>\n",
              "      <td>236.579771</td>\n",
              "      <td>4.175034</td>\n",
              "      <td>Bellyache bush (Green)</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\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>1811</th>\n",
              "      <td>0.012088</td>\n",
              "      <td>0.017700</td>\n",
              "      <td>0.009490</td>\n",
              "      <td>0.014483</td>\n",
              "      <td>0.013968</td>\n",
              "      <td>0.020210</td>\n",
              "      <td>0.014567</td>\n",
              "      <td>0.018409</td>\n",
              "      <td>0.879084</td>\n",
              "      <td>0.640441</td>\n",
              "      <td>...</td>\n",
              "      <td>13.839704</td>\n",
              "      <td>1072.098142</td>\n",
              "      <td>2.428298</td>\n",
              "      <td>15.748247</td>\n",
              "      <td>1347.237405</td>\n",
              "      <td>2.177858</td>\n",
              "      <td>4.592833</td>\n",
              "      <td>231.230836</td>\n",
              "      <td>8.805814</td>\n",
              "      <td>Trigonella Foenum-graecum (Fenugreek)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1812</th>\n",
              "      <td>0.006046</td>\n",
              "      <td>0.010284</td>\n",
              "      <td>0.005926</td>\n",
              "      <td>0.011109</td>\n",
              "      <td>0.012515</td>\n",
              "      <td>0.017398</td>\n",
              "      <td>0.010056</td>\n",
              "      <td>0.011053</td>\n",
              "      <td>0.915614</td>\n",
              "      <td>0.375903</td>\n",
              "      <td>...</td>\n",
              "      <td>9.076615</td>\n",
              "      <td>785.043005</td>\n",
              "      <td>3.481602</td>\n",
              "      <td>10.705626</td>\n",
              "      <td>1027.139284</td>\n",
              "      <td>2.981260</td>\n",
              "      <td>4.022584</td>\n",
              "      <td>230.583898</td>\n",
              "      <td>7.690323</td>\n",
              "      <td>Trigonella Foenum-graecum (Fenugreek)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1813</th>\n",
              "      <td>0.007656</td>\n",
              "      <td>0.014827</td>\n",
              "      <td>0.009302</td>\n",
              "      <td>0.020337</td>\n",
              "      <td>0.022640</td>\n",
              "      <td>0.029843</td>\n",
              "      <td>0.017621</td>\n",
              "      <td>0.016470</td>\n",
              "      <td>0.861305</td>\n",
              "      <td>0.526366</td>\n",
              "      <td>...</td>\n",
              "      <td>17.027024</td>\n",
              "      <td>1486.579019</td>\n",
              "      <td>2.121477</td>\n",
              "      <td>19.979577</td>\n",
              "      <td>1996.953958</td>\n",
              "      <td>1.946036</td>\n",
              "      <td>5.280974</td>\n",
              "      <td>248.553693</td>\n",
              "      <td>8.235564</td>\n",
              "      <td>Trigonella Foenum-graecum (Fenugreek)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1814</th>\n",
              "      <td>0.007229</td>\n",
              "      <td>0.013236</td>\n",
              "      <td>0.006933</td>\n",
              "      <td>0.014331</td>\n",
              "      <td>0.013291</td>\n",
              "      <td>0.021095</td>\n",
              "      <td>0.013214</td>\n",
              "      <td>0.014211</td>\n",
              "      <td>0.896459</td>\n",
              "      <td>0.398143</td>\n",
              "      <td>...</td>\n",
              "      <td>12.089983</td>\n",
              "      <td>1014.167195</td>\n",
              "      <td>2.662396</td>\n",
              "      <td>14.855094</td>\n",
              "      <td>1485.915583</td>\n",
              "      <td>2.394971</td>\n",
              "      <td>3.701817</td>\n",
              "      <td>181.054805</td>\n",
              "      <td>10.495481</td>\n",
              "      <td>Trigonella Foenum-graecum (Fenugreek)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1815</th>\n",
              "      <td>0.021928</td>\n",
              "      <td>0.028826</td>\n",
              "      <td>0.014745</td>\n",
              "      <td>0.020396</td>\n",
              "      <td>0.018506</td>\n",
              "      <td>0.025711</td>\n",
              "      <td>0.021582</td>\n",
              "      <td>0.029510</td>\n",
              "      <td>0.818795</td>\n",
              "      <td>0.971415</td>\n",
              "      <td>...</td>\n",
              "      <td>22.330943</td>\n",
              "      <td>1566.468517</td>\n",
              "      <td>1.546010</td>\n",
              "      <td>27.444230</td>\n",
              "      <td>2295.093611</td>\n",
              "      <td>1.334677</td>\n",
              "      <td>6.903499</td>\n",
              "      <td>281.590495</td>\n",
              "      <td>7.542093</td>\n",
              "      <td>Trigonella Foenum-graecum (Fenugreek)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1816 rows × 62 columns</p>\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_4b0d45a3-c0b9-405b-a672-4d1f11872e00\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
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              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('data')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "97kzs6oK6uAC",
        "outputId": "4dcf5e24-c81d-4aea-ba78-3c88036e0bb6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "29"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "len(list(data['Label'].unique()))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NzNeDsBfEpZ-"
      },
      "source": [
        "## Data Split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "kM2fCUFyCDdw"
      },
      "outputs": [],
      "source": [
        "X = data.drop('Label', axis=1)\n",
        "y = data['Label']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "jqF-hyxZCB5z"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.05, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CJ6TX3ESPovn",
        "outputId": "dbd74d98-1637-4b78-ddab-b2f51fca1a82"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Bellyache bush (Green)', 'Alpinia Galanga (Rasna)',\n",
              "       'Amaranthus Viridis (Arive-Dantu)',\n",
              "       'Artocarpus Heterophyllus (Jackfruit)',\n",
              "       'Azadirachta Indica (Neem)', 'Basella Alba (Basale)',\n",
              "       'Brassica Juncea (Indian Mustard)', 'Carissa Carandas (Karanda)',\n",
              "       'Citrus Limon (Lemon)', 'Ficus Auriculata (Roxburgh fig)',\n",
              "       'Ficus Religiosa (Peepal Tree)', 'Hibiscus Rosa-sinensis',\n",
              "       'Jasminum (Jasmine)', 'Mangifera Indica (Mango)', 'Mentha (Mint)',\n",
              "       'Moringa Oleifera (Drumstick)',\n",
              "       'Muntingia Calabura (Jamaica Cherry-Gasagase)',\n",
              "       'Murraya Koenigii (Curry)', 'Nerium Oleander (Oleander)',\n",
              "       'Nyctanthes Arbor-tristis (Parijata)', 'Piper Betle (Betel)',\n",
              "       'Pongamia Pinnata (Indian Beech)', 'Psidium Guajava (Guava)',\n",
              "       'Punica Granatum (Pomegranate)', 'Santalum Album (Sandalwood)',\n",
              "       'Syzygium Cumini (Jamun)', 'Syzygium Jambos (Rose Apple)',\n",
              "       'Tabernaemontana Divaricata (Crape Jasmine)',\n",
              "       'Trigonella Foenum-graecum (Fenugreek)'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "y.unique()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H021hCjVEpaA"
      },
      "source": [
        "## Feature Reduction and Multiple Model Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3JhOx2dREpaA"
      },
      "source": [
        "**Principal Component Analysis** and **Standard Scaler** is applied to pick the Correlated Feature and Train along Multiple Models: Random Forest, **Support Vector Machine**, K-Nearest Neighbors, Neural Network (MLP), Logistic Regression, Decision Tree, Naive Bayes (Gaussian), AdaBoost, Linear Discriminant Analysis, Quadratic Discriminant Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_tI1srwJEpaA"
      },
      "source": [
        "Best Model is chosen with the derived result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ExQfFqcqSbz6",
        "outputId": "949d9f5a-fb25-4d00-f962-74b8c3907ada"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest 0.967032967032967\n",
            "Support Vector Machine 0.989010989010989\n",
            "K-Nearest Neighbors 0.9560439560439561\n",
            "Neural Network (MLP) 0.967032967032967\n",
            "Logistic Regression 0.978021978021978\n",
            "Decision Tree 0.7582417582417582\n",
            "Naive Bayes (Gaussian) 0.9340659340659341\n",
            "AdaBoost 0.10989010989010989\n",
            "Linear Discriminant Analysis 0.9230769230769231\n",
            "Quadratic Discriminant Analysis 0.03296703296703297\n",
            "Best Classifier :  Support Vector Machine\n",
            "Accuracy : 0.989010989010989\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/discriminant_analysis.py:935: UserWarning: Variables are collinear\n",
            "  warnings.warn(\"Variables are collinear\")\n"
          ]
        }
      ],
      "source": [
        "n_components = 53\n",
        "pca = PCA(n_components=n_components)\n",
        "\n",
        "X_train_pca = pca.fit_transform(X_train)\n",
        "X_test_pca = pca.transform(X_test)\n",
        "\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train_pca)\n",
        "X_test_scaled = scaler.transform(X_test_pca)\n",
        "\n",
        "classifiers = {\n",
        "    'Random Forest': RandomForestClassifier(random_state=42),\n",
        "    'Support Vector Machine' : SVC(kernel='linear',C=10, gamma=0.1),\n",
        "    'K-Nearest Neighbors': KNeighborsClassifier(),\n",
        "    'Neural Network (MLP)': MLPClassifier(hidden_layer_sizes=(50, 50), max_iter=1000, random_state=42),\n",
        "    'Logistic Regression': LogisticRegression(max_iter=1000, random_state=42),\n",
        "    'Decision Tree': DecisionTreeClassifier(random_state=42),\n",
        "    'Naive Bayes (Gaussian)': GaussianNB(),\n",
        "    'AdaBoost': AdaBoostClassifier(random_state=42),\n",
        "    'Linear Discriminant Analysis': LinearDiscriminantAnalysis(),\n",
        "    'Quadratic Discriminant Analysis': QuadraticDiscriminantAnalysis()\n",
        "}\n",
        "\n",
        "accuracy_results = {}\n",
        "di={}\n",
        "maxacc=0\n",
        "for classifier_name, classifier in classifiers.items():\n",
        "    classifier.fit(X_train_scaled, y_train)\n",
        "    predictions = classifier.predict(X_test_scaled)\n",
        "    accuracy = accuracy_score(y_test, predictions)\n",
        "    di[classifier_name]=accuracy\n",
        "    if(accuracy>maxacc):\n",
        "      maxacc=accuracy\n",
        "      best_classifier=classifier\n",
        "      best_classifier_name=classifier_name\n",
        "    print(classifier_name,accuracy)\n",
        "\n",
        "print(\"Best Classifier : \",best_classifier_name)\n",
        "print(\"Accuracy :\",maxacc)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qwMBXtudEpaB"
      },
      "source": [
        "## Visualization of the Model Performance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "insWRc93BRxJ",
        "outputId": "6a5736fc-5925-4c0b-8174-b12d731e7ac9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 650x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "names = list(di.keys())\n",
        "values = list(di.values())\n",
        "plt.figure(figsize=(6.5, 6))\n",
        "  # Adjust figure size as needed\n",
        "bars = plt.bar(names, values, color='purple')\n",
        "\n",
        "# Display y-values at the top of each bar\n",
        "for bar, value in zip(bars, values):\n",
        "    plt.text(bar.get_x() + bar.get_width() / 2, value,'{:.2f}'.format(value), ha='center', va='bottom')\n",
        "\n",
        "plt.xticks(rotation=90)\n",
        "plt.tight_layout()\n",
        "plt.ylim(0, max(values) * 1.1)\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gDadUPTb9GK8",
        "outputId": "036efaa9-3c08-454b-e0cf-33a8b81e6458"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Support Vector Machine\n",
            "Accuracy: 0.989010989010989\n",
            "Classification Report:\n",
            "                                              precision    recall  f1-score   support\n",
            "\n",
            "                     Alpinia Galanga (Rasna)       1.00      1.00      1.00         2\n",
            "            Amaranthus Viridis (Arive-Dantu)       0.83      1.00      0.91         5\n",
            "        Artocarpus Heterophyllus (Jackfruit)       1.00      1.00      1.00         4\n",
            "                   Azadirachta Indica (Neem)       1.00      1.00      1.00         2\n",
            "                       Basella Alba (Basale)       1.00      1.00      1.00         6\n",
            "                      Bellyache bush (Green)       1.00      1.00      1.00         3\n",
            "            Brassica Juncea (Indian Mustard)       1.00      1.00      1.00         2\n",
            "                  Carissa Carandas (Karanda)       1.00      1.00      1.00         7\n",
            "                        Citrus Limon (Lemon)       1.00      1.00      1.00         4\n",
            "             Ficus Auriculata (Roxburgh fig)       1.00      1.00      1.00         3\n",
            "               Ficus Religiosa (Peepal Tree)       1.00      1.00      1.00         2\n",
            "                      Hibiscus Rosa-sinensis       1.00      1.00      1.00         2\n",
            "                          Jasminum (Jasmine)       1.00      1.00      1.00         2\n",
            "                    Mangifera Indica (Mango)       1.00      1.00      1.00         3\n",
            "                               Mentha (Mint)       1.00      1.00      1.00         3\n",
            "                Moringa Oleifera (Drumstick)       1.00      1.00      1.00         3\n",
            "Muntingia Calabura (Jamaica Cherry-Gasagase)       1.00      0.75      0.86         4\n",
            "                    Murraya Koenigii (Curry)       1.00      1.00      1.00         4\n",
            "                  Nerium Oleander (Oleander)       1.00      1.00      1.00         5\n",
            "         Nyctanthes Arbor-tristis (Parijata)       1.00      1.00      1.00         1\n",
            "                         Piper Betle (Betel)       1.00      1.00      1.00         2\n",
            "             Pongamia Pinnata (Indian Beech)       1.00      1.00      1.00         6\n",
            "                     Psidium Guajava (Guava)       1.00      1.00      1.00         3\n",
            "               Punica Granatum (Pomegranate)       1.00      1.00      1.00         4\n",
            "                 Santalum Album (Sandalwood)       1.00      1.00      1.00         1\n",
            "                     Syzygium Cumini (Jamun)       1.00      1.00      1.00         2\n",
            "                Syzygium Jambos (Rose Apple)       1.00      1.00      1.00         2\n",
            "  Tabernaemontana Divaricata (Crape Jasmine)       1.00      1.00      1.00         4\n",
            "\n",
            "                                    accuracy                           0.99        91\n",
            "                                   macro avg       0.99      0.99      0.99        91\n",
            "                                weighted avg       0.99      0.99      0.99        91\n",
            "\n"
          ]
        }
      ],
      "source": [
        "n_components = 53\n",
        "pca = PCA(n_components=n_components)\n",
        "X_train_pca = pca.fit_transform(X_train)\n",
        "X_test_pca = pca.transform(X_test)\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train_pca)\n",
        "X_test_scaled = scaler.transform(X_test_pca)\n",
        "\n",
        "svm = SVC(kernel='linear',C=10, gamma=0.1)\n",
        "\n",
        "svm.fit(X_train_scaled, y_train)\n",
        "\n",
        "y_pred = svm.predict(X_test_scaled)\n",
        "\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "report = classification_report(y_test, y_pred)\n",
        "\n",
        "print(\"Support Vector Machine\")\n",
        "print(f'Accuracy: {accuracy}')\n",
        "print(f'Classification Report:\\n{report}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iW1UP81GEpaD"
      },
      "source": [
        "## Saving the Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "JqFSgUkcP9wd"
      },
      "outputs": [],
      "source": [
        "with open('/content/Best_Model.pkl', 'wb') as files:\n",
        "    pickle.dump(best_classifier, files)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "btQV0OO1QA51"
      },
      "outputs": [],
      "source": [
        "with open('/content/pca.pkl', 'wb') as pca_file:\n",
        "    pickle.dump(pca, pca_file)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "ubAIimODQDMA"
      },
      "outputs": [],
      "source": [
        "with open('/content/scaler.pkl', 'wb') as scaler_file:\n",
        "    pickle.dump(scaler, scaler_file)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}