CropPrediction(Regression).ipynb

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    "colab": {
      "name": "CropPrediction(Regression).ipynb",
      "version": "0.3.2",
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      "collapsed_sections": [],
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/arghac14/CropYield-prediction/blob/master/CropPrediction(Regression).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6-muHbPWRFvq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OnoZ-JkYUhjD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 580
        },
        "outputId": "c65c3800-318c-4517-bf72-a26f57daf270"
      },
      "source": [
        "# df=pd.read_csv(\"https://github.com/arghac14/CropYield-prediction/blob/master/dataset.csv\")\n",
        "# df\n",
        "data={\n",
        "    'Dist':['Bidar','Bangalore','Belgaum','Bellary','Kolar','Dharwad','Koppal','Chikmagalur','Chitradurga','Haveri','Chamrajanagar','Mandya','Mysore','Raichur','Kodaku','Hassan','Devanagare','Gulbarga'],\n",
        "    'Precipitation':[622.438,748.194,1334.194, 574.325,724.545,1623.548,724.545,1923.742,674.17,1473.343,1334.754,1477.249,2242.378,450.113,1691.933,2200.349,1060.343, 525.402],\n",
        "    'Min_Temp':[27.643,25.263,21.254,26.407,25.464,26.148,47.029,26.459,25.214,25.817,25.089,24.567,25.76766667,27.42241667,25.426,25.348,25.509,27.851],\n",
        "    'Cloud_Cover':[35.241,49.134,39.728,38.466,47.029,38.267,41.039,44.842,41.364,41.292,50.77,49.54775,50.57941667,35.76258333,46.353,47.12,40.929,35.109],\n",
        "    'Vapour_pressure':[17.953,21.56, 22.5509,20.008,20.63,23.652,19.992,24.717,20.82,23.168,23.079,22.234,24.64266667,18.93741667,23.975,24.008,22.042,18.662],\n",
        "    'Area':[4709,18790,4398,3768,2278,8395,3084,1650,3026,10659,3485,11349,3462,4586,17856,10487,2459,10487],\n",
        "    'Production':[9043,20981,6054,5903,2759,10986,3952,2958,3325,9865, 4120,18957, 4539,6145,15362,7586,1865,7895]\n",
        "}      #Final dataset\n",
        "df=pd.DataFrame(data)\n",
        "df"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\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>Dist</th>\n",
              "      <th>Precipitation</th>\n",
              "      <th>Min_Temp</th>\n",
              "      <th>Cloud_Cover</th>\n",
              "      <th>Vapour_pressure</th>\n",
              "      <th>Area</th>\n",
              "      <th>Production</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Bidar</td>\n",
              "      <td>622.438</td>\n",
              "      <td>27.643000</td>\n",
              "      <td>35.241000</td>\n",
              "      <td>17.953000</td>\n",
              "      <td>4709</td>\n",
              "      <td>9043</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Bangalore</td>\n",
              "      <td>748.194</td>\n",
              "      <td>25.263000</td>\n",
              "      <td>49.134000</td>\n",
              "      <td>21.560000</td>\n",
              "      <td>18790</td>\n",
              "      <td>20981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Belgaum</td>\n",
              "      <td>1334.194</td>\n",
              "      <td>21.254000</td>\n",
              "      <td>39.728000</td>\n",
              "      <td>22.550900</td>\n",
              "      <td>4398</td>\n",
              "      <td>6054</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Bellary</td>\n",
              "      <td>574.325</td>\n",
              "      <td>26.407000</td>\n",
              "      <td>38.466000</td>\n",
              "      <td>20.008000</td>\n",
              "      <td>3768</td>\n",
              "      <td>5903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Kolar</td>\n",
              "      <td>724.545</td>\n",
              "      <td>25.464000</td>\n",
              "      <td>47.029000</td>\n",
              "      <td>20.630000</td>\n",
              "      <td>2278</td>\n",
              "      <td>2759</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Dharwad</td>\n",
              "      <td>1623.548</td>\n",
              "      <td>26.148000</td>\n",
              "      <td>38.267000</td>\n",
              "      <td>23.652000</td>\n",
              "      <td>8395</td>\n",
              "      <td>10986</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Koppal</td>\n",
              "      <td>724.545</td>\n",
              "      <td>47.029000</td>\n",
              "      <td>41.039000</td>\n",
              "      <td>19.992000</td>\n",
              "      <td>3084</td>\n",
              "      <td>3952</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Chikmagalur</td>\n",
              "      <td>1923.742</td>\n",
              "      <td>26.459000</td>\n",
              "      <td>44.842000</td>\n",
              "      <td>24.717000</td>\n",
              "      <td>1650</td>\n",
              "      <td>2958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Chitradurga</td>\n",
              "      <td>674.170</td>\n",
              "      <td>25.214000</td>\n",
              "      <td>41.364000</td>\n",
              "      <td>20.820000</td>\n",
              "      <td>3026</td>\n",
              "      <td>3325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Haveri</td>\n",
              "      <td>1473.343</td>\n",
              "      <td>25.817000</td>\n",
              "      <td>41.292000</td>\n",
              "      <td>23.168000</td>\n",
              "      <td>10659</td>\n",
              "      <td>9865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Chamrajanagar</td>\n",
              "      <td>1334.754</td>\n",
              "      <td>25.089000</td>\n",
              "      <td>50.770000</td>\n",
              "      <td>23.079000</td>\n",
              "      <td>3485</td>\n",
              "      <td>4120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Mandya</td>\n",
              "      <td>1477.249</td>\n",
              "      <td>24.567000</td>\n",
              "      <td>49.547750</td>\n",
              "      <td>22.234000</td>\n",
              "      <td>11349</td>\n",
              "      <td>18957</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Mysore</td>\n",
              "      <td>2242.378</td>\n",
              "      <td>25.767667</td>\n",
              "      <td>50.579417</td>\n",
              "      <td>24.642667</td>\n",
              "      <td>3462</td>\n",
              "      <td>4539</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Raichur</td>\n",
              "      <td>450.113</td>\n",
              "      <td>27.422417</td>\n",
              "      <td>35.762583</td>\n",
              "      <td>18.937417</td>\n",
              "      <td>4586</td>\n",
              "      <td>6145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Kodaku</td>\n",
              "      <td>1691.933</td>\n",
              "      <td>25.426000</td>\n",
              "      <td>46.353000</td>\n",
              "      <td>23.975000</td>\n",
              "      <td>17856</td>\n",
              "      <td>15362</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Hassan</td>\n",
              "      <td>2200.349</td>\n",
              "      <td>25.348000</td>\n",
              "      <td>47.120000</td>\n",
              "      <td>24.008000</td>\n",
              "      <td>10487</td>\n",
              "      <td>7586</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Devanagare</td>\n",
              "      <td>1060.343</td>\n",
              "      <td>25.509000</td>\n",
              "      <td>40.929000</td>\n",
              "      <td>22.042000</td>\n",
              "      <td>2459</td>\n",
              "      <td>1865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Gulbarga</td>\n",
              "      <td>525.402</td>\n",
              "      <td>27.851000</td>\n",
              "      <td>35.109000</td>\n",
              "      <td>18.662000</td>\n",
              "      <td>10487</td>\n",
              "      <td>7895</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Dist  Precipitation   Min_Temp  ...  Vapour_pressure   Area  Production\n",
              "0           Bidar        622.438  27.643000  ...        17.953000   4709        9043\n",
              "1       Bangalore        748.194  25.263000  ...        21.560000  18790       20981\n",
              "2         Belgaum       1334.194  21.254000  ...        22.550900   4398        6054\n",
              "3         Bellary        574.325  26.407000  ...        20.008000   3768        5903\n",
              "4           Kolar        724.545  25.464000  ...        20.630000   2278        2759\n",
              "5         Dharwad       1623.548  26.148000  ...        23.652000   8395       10986\n",
              "6          Koppal        724.545  47.029000  ...        19.992000   3084        3952\n",
              "7     Chikmagalur       1923.742  26.459000  ...        24.717000   1650        2958\n",
              "8     Chitradurga        674.170  25.214000  ...        20.820000   3026        3325\n",
              "9          Haveri       1473.343  25.817000  ...        23.168000  10659        9865\n",
              "10  Chamrajanagar       1334.754  25.089000  ...        23.079000   3485        4120\n",
              "11         Mandya       1477.249  24.567000  ...        22.234000  11349       18957\n",
              "12         Mysore       2242.378  25.767667  ...        24.642667   3462        4539\n",
              "13        Raichur        450.113  27.422417  ...        18.937417   4586        6145\n",
              "14         Kodaku       1691.933  25.426000  ...        23.975000  17856       15362\n",
              "15         Hassan       2200.349  25.348000  ...        24.008000  10487        7586\n",
              "16     Devanagare       1060.343  25.509000  ...        22.042000   2459        1865\n",
              "17       Gulbarga        525.402  27.851000  ...        18.662000  10487        7895\n",
              "\n",
              "[18 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mqwB-0vKx7GB",
        "colab_type": "text"
      },
      "source": [
        "## Linear Regression:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pT-eZ3t0x91c",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        },
        "outputId": "264fdc28-25bb-45f1-fb8b-d0218be5154e"
      },
      "source": [
        "from sklearn import linear_model\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import r2_score\n",
        "\n",
        "X = df[['Area']] \n",
        "Y = df['Production']\n",
        " \n",
        "x_train,x_test,y_train,y_test=train_test_split(X,Y, test_size=0.3, random_state=1)\n",
        "reg=linear_model.LinearRegression()\n",
        "reg.fit(x_train,y_train)\n",
        "  \n",
        "\n",
        "\n",
        "\n",
        "# prediction\n",
        "y_pred=reg.predict(x_test)\n",
        "print('Predicted Production:',y_pred,'\\n')\n",
        "\n",
        "# Coefficients\n",
        "print('\\nCoefficients: ', reg.coef_,'\\n')\n",
        "\n",
        "# R-squared score\n",
        "print('\\nR-squared score: ', r2_score(y_test,y_pred),'\\n')"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Predicted Production: [ 3834.0464198   4561.42499993  5431.30172296  5231.37895532\n",
            " 19542.87154515  2309.10360706] \n",
            "\n",
            "\n",
            "Coefficients:  [1.06341898] \n",
            "\n",
            "\n",
            "R-squared score:  0.7865979189963656 \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RAGyH5Ez0ixS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "8312f554-a1fd-46e0-fe07-945d3d0774e8"
      },
      "source": [
        "\n",
        "plt.scatter(x_test,y_test, color='blue')\n",
        "plt.plot(x_test,y_pred, color='green')\n",
        "plt.xlabel('Area')\n",
        "plt.ylabel('Production')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q3slSVLaUIpb",
        "colab_type": "text"
      },
      "source": [
        "## Multivariate Linear Regression:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ncyZbKgUWTx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        },
        "outputId": "3ce7e34b-de00-48d6-db72-08912e25579e"
      },
      "source": [
        "from sklearn import linear_model\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import r2_score\n",
        "\n",
        "X = df[['Precipitation'\t,'Min_Temp'\t,'Cloud_Cover'\t,'Vapour_pressure'\t,'Area']] \n",
        "Y = df['Production']\n",
        " \n",
        "x_train,x_test,y_train,y_test=train_test_split(X,Y, test_size=0.2, random_state=1)\n",
        "reg=linear_model.LinearRegression()\n",
        "reg.fit(x_train,y_train)\n",
        "  \n",
        "\n",
        "\n",
        "\n",
        "# prediction\n",
        "y_pred=reg.predict(x_test)\n",
        "print('Predicted Production:',y_pred,'\\n')\n",
        "\n",
        "# Coefficients\n",
        "print('\\nCoefficients: ', reg.coef_,'\\n')\n",
        "\n",
        "# R-squared score\n",
        "print('\\nR-squared score: ', r2_score(y_test,y_pred),'\\n')"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Predicted Production: [-17654.76855091   4572.38847303   5020.76059205   9681.20127861] \n",
            "\n",
            "\n",
            "Coefficients:  [ 2.49292762e+00 -1.07420796e+03  6.84243914e+01 -1.18000389e+03\n",
            "  9.41906438e-01] \n",
            "\n",
            "\n",
            "R-squared score:  -146.22783288559427 \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}