Rainfall_Prediction.ipynb

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      "cell_type": "markdown",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/sarthakksingh2/Sarthak-ML/blob/main/Rainfall_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "metadata": {
        "id": "UAZnefOih-ue"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import sklearn as sk\n",
        "from sklearn.linear_model import LinearRegression\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(\"austin_weather.csv\")"
      ],
      "metadata": {
        "id": "CAIKz6UiiUi4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.drop(['Events', 'Date', 'SeaLevelPressureHighInches', 'SeaLevelPressureLowInches'], axis = 1)"
      ],
      "metadata": {
        "id": "JPmz9Y5filQo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.replace('T',0.0)"
      ],
      "metadata": {
        "id": "Uu2WipNAjqqk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.replace('-',0.0)"
      ],
      "metadata": {
        "id": "kXiQ6DDHj0Ie"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.to_csv('austin_final.csv')"
      ],
      "metadata": {
        "id": "SxirXsTrj8OF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(\"austin_final.csv\")"
      ],
      "metadata": {
        "id": "jKfpL7OCkIuH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = data.drop(['PrecipitationSumInches'], axis = 1)\n",
        "y = data['PrecipitationSumInches']"
      ],
      "metadata": {
        "id": "Sol1vlaikuht"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = y.values.reshape(-1,1)"
      ],
      "metadata": {
        "id": "reczuJBDlIPL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "day_index = 798\n",
        "days = [i for i in range(y.size)]"
      ],
      "metadata": {
        "id": "RsAlZGDtlPbV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clf = LinearRegression()\n",
        "clf.fit(x,y)"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
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        "id": "-_8R6kTKlm6-",
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              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inp = np.array([[74],[60],[45],[67],[49],[43],[33],[45],[57],[29.68],[10],[7],[2],[0],[20],[4],[31]])\n",
        "inp = inp.reshape(1,-1)"
      ],
      "metadata": {
        "id": "m-ANSzQLmAAW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Precipitation in inches for the input is:',clf.predict(inp))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HLL7qY2Amrhd",
        "outputId": "03404731-1d76-4295-f996-936fd3e8320c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precipitation in inches for the input is: [[1.33868402]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('The Precipitation trend graph : ')\n",
        "plt.scatter(days,y,color='black')\n",
        "plt.scatter(days[day_index],y[day_index],color='r')\n",
        "plt.title('Precipitation level')\n",
        "plt.xlabel('Days')\n",
        "plt.ylabel('Precipitation in inches')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "69Vcak4Wm6pW",
        "outputId": "984ec8dc-a8a3-48fa-cbfc-b8ea416d36ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Precipitation trend graph : \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}