CROP_RECOMMENDATIONS_TO_FARMERS_Copy1_(1).ipynb

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        "<a href=\"https://colab.research.google.com/github/Josephat-Onkoba/-Study-Python/blob/master/CROP_RECOMMENDATIONS_TO_FARMERS_Copy1_(1).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "execution_count": null,
      "id": "43dac9ea",
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        "id": "43dac9ea"
      },
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      "source": [
        "#importing relevant packages\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "360d688a",
      "metadata": {
        "id": "360d688a"
      },
      "outputs": [],
      "source": [
        "# loading the dataset\n",
        "data=pd.read_csv(r'Desktop/crop_recommendation.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c7b32aa7",
      "metadata": {
        "id": "c7b32aa7",
        "outputId": "5122283c-2009-4048-abd7-b953255d4bd5"
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      "outputs": [
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              "      <th></th>\n",
              "      <th>N</th>\n",
              "      <th>P</th>\n",
              "      <th>K</th>\n",
              "      <th>temperature</th>\n",
              "      <th>humidity</th>\n",
              "      <th>ph</th>\n",
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              "      <td>20.879744</td>\n",
              "      <td>82.002744</td>\n",
              "      <td>6.502985</td>\n",
              "      <td>202.935536</td>\n",
              "      <td>rice</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>85</td>\n",
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              "      <td>80.319644</td>\n",
              "      <td>7.038096</td>\n",
              "      <td>226.655537</td>\n",
              "      <td>rice</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>82.320763</td>\n",
              "      <td>7.840207</td>\n",
              "      <td>263.964248</td>\n",
              "      <td>rice</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>74</td>\n",
              "      <td>35</td>\n",
              "      <td>40</td>\n",
              "      <td>26.491096</td>\n",
              "      <td>80.158363</td>\n",
              "      <td>6.980401</td>\n",
              "      <td>242.864034</td>\n",
              "      <td>rice</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>78</td>\n",
              "      <td>42</td>\n",
              "      <td>42</td>\n",
              "      <td>20.130175</td>\n",
              "      <td>81.604873</td>\n",
              "      <td>7.628473</td>\n",
              "      <td>262.717340</td>\n",
              "      <td>rice</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "    N   P   K  temperature   humidity        ph    rainfall label\n",
              "0  90  42  43    20.879744  82.002744  6.502985  202.935536  rice\n",
              "1  85  58  41    21.770462  80.319644  7.038096  226.655537  rice\n",
              "2  60  55  44    23.004459  82.320763  7.840207  263.964248  rice\n",
              "3  74  35  40    26.491096  80.158363  6.980401  242.864034  rice\n",
              "4  78  42  42    20.130175  81.604873  7.628473  262.717340  rice"
            ]
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              "      <th>N</th>\n",
              "      <th>P</th>\n",
              "      <th>K</th>\n",
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              "      <td>2200.000000</td>\n",
              "      <td>2200.000000</td>\n",
              "      <td>2200.000000</td>\n",
              "      <td>2200.000000</td>\n",
              "      <td>2200.000000</td>\n",
              "      <td>2200.000000</td>\n",
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              "      <th>mean</th>\n",
              "      <td>50.551818</td>\n",
              "      <td>53.362727</td>\n",
              "      <td>48.149091</td>\n",
              "      <td>25.616244</td>\n",
              "      <td>71.481779</td>\n",
              "      <td>6.469480</td>\n",
              "      <td>103.463655</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>36.917334</td>\n",
              "      <td>32.985883</td>\n",
              "      <td>50.647931</td>\n",
              "      <td>5.063749</td>\n",
              "      <td>22.263812</td>\n",
              "      <td>0.773938</td>\n",
              "      <td>54.958389</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>8.825675</td>\n",
              "      <td>14.258040</td>\n",
              "      <td>3.504752</td>\n",
              "      <td>20.211267</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>21.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>22.769375</td>\n",
              "      <td>60.261953</td>\n",
              "      <td>5.971693</td>\n",
              "      <td>64.551686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>37.000000</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>25.598693</td>\n",
              "      <td>80.473146</td>\n",
              "      <td>6.425045</td>\n",
              "      <td>94.867624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>84.250000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>28.561654</td>\n",
              "      <td>89.948771</td>\n",
              "      <td>6.923643</td>\n",
              "      <td>124.267508</td>\n",
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              "      <td>145.000000</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>43.675493</td>\n",
              "      <td>99.981876</td>\n",
              "      <td>9.935091</td>\n",
              "      <td>298.560117</td>\n",
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            "text/plain": [
              "                 N            P            K  temperature     humidity  \\\n",
              "count  2200.000000  2200.000000  2200.000000  2200.000000  2200.000000   \n",
              "mean     50.551818    53.362727    48.149091    25.616244    71.481779   \n",
              "std      36.917334    32.985883    50.647931     5.063749    22.263812   \n",
              "min       0.000000     5.000000     5.000000     8.825675    14.258040   \n",
              "25%      21.000000    28.000000    20.000000    22.769375    60.261953   \n",
              "50%      37.000000    51.000000    32.000000    25.598693    80.473146   \n",
              "75%      84.250000    68.000000    49.000000    28.561654    89.948771   \n",
              "max     140.000000   145.000000   205.000000    43.675493    99.981876   \n",
              "\n",
              "                ph     rainfall  \n",
              "count  2200.000000  2200.000000  \n",
              "mean      6.469480   103.463655  \n",
              "std       0.773938    54.958389  \n",
              "min       3.504752    20.211267  \n",
              "25%       5.971693    64.551686  \n",
              "50%       6.425045    94.867624  \n",
              "75%       6.923643   124.267508  \n",
              "max       9.935091   298.560117  "
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          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2200 entries, 0 to 2199\n",
            "Data columns (total 8 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   N            2200 non-null   int64  \n",
            " 1   P            2200 non-null   int64  \n",
            " 2   K            2200 non-null   int64  \n",
            " 3   temperature  2200 non-null   float64\n",
            " 4   humidity     2200 non-null   float64\n",
            " 5   ph           2200 non-null   float64\n",
            " 6   rainfall     2200 non-null   float64\n",
            " 7   label        2200 non-null   object \n",
            "dtypes: float64(4), int64(3), object(1)\n",
            "memory usage: 137.6+ KB\n"
          ]
        }
      ],
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        "data.info()"
      ]
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    {
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              "(2200, 8)"
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          "execution_count": 6,
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              "N                int64\n",
              "P                int64\n",
              "K                int64\n",
              "temperature    float64\n",
              "humidity       float64\n",
              "ph             float64\n",
              "rainfall       float64\n",
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              "N              0\n",
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              "      <td>0.026504</td>\n",
              "      <td>0.190688</td>\n",
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              "      <td>-0.127541</td>\n",
              "      <td>-0.118734</td>\n",
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              "      <th>K</th>\n",
              "      <td>-0.140512</td>\n",
              "      <td>0.736232</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.160387</td>\n",
              "      <td>0.190859</td>\n",
              "      <td>-0.169503</td>\n",
              "      <td>-0.053461</td>\n",
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              "    <tr>\n",
              "      <th>temperature</th>\n",
              "      <td>0.026504</td>\n",
              "      <td>-0.127541</td>\n",
              "      <td>-0.160387</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.205320</td>\n",
              "      <td>-0.017795</td>\n",
              "      <td>-0.030084</td>\n",
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              "    <tr>\n",
              "      <th>humidity</th>\n",
              "      <td>0.190688</td>\n",
              "      <td>-0.118734</td>\n",
              "      <td>0.190859</td>\n",
              "      <td>0.205320</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.008483</td>\n",
              "      <td>0.094423</td>\n",
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              "      <th>ph</th>\n",
              "      <td>0.096683</td>\n",
              "      <td>-0.138019</td>\n",
              "      <td>-0.169503</td>\n",
              "      <td>-0.017795</td>\n",
              "      <td>-0.008483</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.109069</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rainfall</th>\n",
              "      <td>0.059020</td>\n",
              "      <td>-0.063839</td>\n",
              "      <td>-0.053461</td>\n",
              "      <td>-0.030084</td>\n",
              "      <td>0.094423</td>\n",
              "      <td>-0.109069</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "</div>"
            ],
            "text/plain": [
              "                    N         P         K  temperature  humidity        ph  \\\n",
              "N            1.000000 -0.231460 -0.140512     0.026504  0.190688  0.096683   \n",
              "P           -0.231460  1.000000  0.736232    -0.127541 -0.118734 -0.138019   \n",
              "K           -0.140512  0.736232  1.000000    -0.160387  0.190859 -0.169503   \n",
              "temperature  0.026504 -0.127541 -0.160387     1.000000  0.205320 -0.017795   \n",
              "humidity     0.190688 -0.118734  0.190859     0.205320  1.000000 -0.008483   \n",
              "ph           0.096683 -0.138019 -0.169503    -0.017795 -0.008483  1.000000   \n",
              "rainfall     0.059020 -0.063839 -0.053461    -0.030084  0.094423 -0.109069   \n",
              "\n",
              "             rainfall  \n",
              "N            0.059020  \n",
              "P           -0.063839  \n",
              "K           -0.053461  \n",
              "temperature -0.030084  \n",
              "humidity     0.094423  \n",
              "ph          -0.109069  \n",
              "rainfall     1.000000  "
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "corr=data.drop(['label'],axis=1).corr()\n",
        "corr"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "89f4aa04",
      "metadata": {
        "id": "89f4aa04"
      },
      "outputs": [],
      "source": [
        "crop_dict = {\n",
        "    'rice': 1,\n",
        "    'maize': 2,\n",
        "    'jute': 3,\n",
        "    'cotton': 4,\n",
        "    'coconut': 5,\n",
        "    'papaya': 6,\n",
        "    'orange': 7,\n",
        "    'apple': 8,\n",
        "    'muskmelon': 9,\n",
        "    'watermelon': 10,\n",
        "    'grapes': 11,\n",
        "    'mango': 12,\n",
        "    'banana': 13,\n",
        "    'pomegranate': 14,\n",
        "    'lentil': 15,\n",
        "    'blackgram': 16,\n",
        "    'mungbean': 17,\n",
        "    'mothbeans': 18,\n",
        "     'pigeonpeas': 19,\n",
        "    'kidneybeans': 20,\n",
        "    'chickpea': 21,\n",
        "    'coffee': 22\n",
        "}\n",
        "data['crop_num']=data['label'].map(crop_dict)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6c619bf9",
      "metadata": {
        "id": "6c619bf9"
      },
      "outputs": [],
      "source": [
        "data=data.drop('label',axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "408831a8",
      "metadata": {
        "id": "408831a8",
        "outputId": "d62cedd6-72b3-4674-ac37-9df9119a52d6"
      },
      "outputs": [
        {
          "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>N</th>\n",
              "      <th>P</th>\n",
              "      <th>K</th>\n",
              "      <th>temperature</th>\n",
              "      <th>humidity</th>\n",
              "      <th>ph</th>\n",
              "      <th>rainfall</th>\n",
              "      <th>crop_num</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>90</td>\n",
              "      <td>42</td>\n",
              "      <td>43</td>\n",
              "      <td>20.879744</td>\n",
              "      <td>82.002744</td>\n",
              "      <td>6.502985</td>\n",
              "      <td>202.935536</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>85</td>\n",
              "      <td>58</td>\n",
              "      <td>41</td>\n",
              "      <td>21.770462</td>\n",
              "      <td>80.319644</td>\n",
              "      <td>7.038096</td>\n",
              "      <td>226.655537</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>60</td>\n",
              "      <td>55</td>\n",
              "      <td>44</td>\n",
              "      <td>23.004459</td>\n",
              "      <td>82.320763</td>\n",
              "      <td>7.840207</td>\n",
              "      <td>263.964248</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>74</td>\n",
              "      <td>35</td>\n",
              "      <td>40</td>\n",
              "      <td>26.491096</td>\n",
              "      <td>80.158363</td>\n",
              "      <td>6.980401</td>\n",
              "      <td>242.864034</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>78</td>\n",
              "      <td>42</td>\n",
              "      <td>42</td>\n",
              "      <td>20.130175</td>\n",
              "      <td>81.604873</td>\n",
              "      <td>7.628473</td>\n",
              "      <td>262.717340</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    N   P   K  temperature   humidity        ph    rainfall  crop_num\n",
              "0  90  42  43    20.879744  82.002744  6.502985  202.935536         1\n",
              "1  85  58  41    21.770462  80.319644  7.038096  226.655537         1\n",
              "2  60  55  44    23.004459  82.320763  7.840207  263.964248         1\n",
              "3  74  35  40    26.491096  80.158363  6.980401  242.864034         1\n",
              "4  78  42  42    20.130175  81.604873  7.628473  262.717340         1"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "82f1f8e5",
      "metadata": {
        "id": "82f1f8e5",
        "outputId": "2edb434f-2ada-40cb-ac95-86d267d4689f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10,8))\n",
        "sns.heatmap(corr,annot=True,cbar=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c45f75c9",
      "metadata": {
        "id": "c45f75c9"
      },
      "source": [
        "**TRAIN TEST SPLIT**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7e3990d5",
      "metadata": {
        "id": "7e3990d5"
      },
      "outputs": [],
      "source": [
        "X=data.drop('crop_num',axis=1)\n",
        "y=data['crop_num']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2c026a69",
      "metadata": {
        "id": "2c026a69"
      },
      "outputs": [],
      "source": [
        "#importing train test split package\n",
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "69990690",
      "metadata": {
        "id": "69990690"
      },
      "outputs": [],
      "source": [
        "# data splitting to train set and test set\n",
        "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "25761f6f",
      "metadata": {
        "id": "25761f6f"
      },
      "outputs": [],
      "source": [
        "# Data normalization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "aa1741c5",
      "metadata": {
        "id": "aa1741c5"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f7d918f6",
      "metadata": {
        "id": "f7d918f6"
      },
      "outputs": [],
      "source": [
        "scaler=StandardScaler()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "35ba28f8",
      "metadata": {
        "id": "35ba28f8",
        "outputId": "e5fba217-e5f9-4cb9-8039-0e6d82e49412"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StandardScaler()</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 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 sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "StandardScaler()"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#stardardizing x_train set\n",
        "scaler.fit(X_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f81c16cb",
      "metadata": {
        "id": "f81c16cb"
      },
      "outputs": [],
      "source": [
        "#transforming x_train\n",
        "scaled_data=scaler.transform(X_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fa73de3b",
      "metadata": {
        "id": "fa73de3b"
      },
      "outputs": [],
      "source": [
        "#transforming x_train\n",
        "test_transformed=scaler.transform(X_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4f659476",
      "metadata": {
        "id": "4f659476"
      },
      "source": [
        "**MODELS**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3c20f80f",
      "metadata": {
        "id": "3c20f80f"
      },
      "outputs": [],
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d4b1b440",
      "metadata": {
        "id": "d4b1b440"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.tree import ExtraTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.ensemble import BaggingClassifier\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "from sklearn.metrics import accuracy_score"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1e861b66",
      "metadata": {
        "id": "1e861b66"
      },
      "source": [
        "**Logistic Regression**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8e81a543",
      "metadata": {
        "id": "8e81a543"
      },
      "outputs": [],
      "source": [
        "logmodel=LogisticRegression()\n",
        "logmodel.fit(X_train,y_train)\n",
        "predict1=logmodel.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2ab22dc4",
      "metadata": {
        "id": "2ab22dc4",
        "outputId": "97843ad7-f10b-4b6b-8cfb-293940a08a61"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "94.54545454545455"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "accuracy_score(y_test,predict1)*100"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "841a69e9",
      "metadata": {
        "id": "841a69e9"
      },
      "source": [
        "**Random Forest Classifier**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e5acae93",
      "metadata": {
        "id": "e5acae93"
      },
      "outputs": [],
      "source": [
        "classifier=RandomForestClassifier()\n",
        "classifier.fit(X_train,y_train)\n",
        "ypred=classifier.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7bf6baa7",
      "metadata": {
        "id": "7bf6baa7",
        "outputId": "124afc8a-6183-4cd8-da6a-7a97db7ca048"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "99.31818181818181"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "accuracy_score(y_test,ypred)*100"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "22cda3cb",
      "metadata": {
        "id": "22cda3cb"
      },
      "source": [
        "**Gradient Boosting**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0bfbf18b",
      "metadata": {
        "id": "0bfbf18b"
      },
      "outputs": [],
      "source": [
        "Gbc=GradientBoostingClassifier()\n",
        "Gbc.fit(X_train,y_train)\n",
        "predict4=Gbc.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "142c12db",
      "metadata": {
        "id": "142c12db",
        "outputId": "fc0d72c3-e89c-4e7f-fecb-3d58395b6250"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "98.18181818181819"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "accuracy_score(y_test,predict4)*100"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3db90d52",
      "metadata": {
        "id": "3db90d52"
      },
      "source": [
        "**MODEL VALIDATION**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "27d966a3",
      "metadata": {
        "id": "27d966a3"
      },
      "outputs": [],
      "source": [
        "def recommendation(N,P,k,temperature,humidity,ph,rainfal):\n",
        "    features = np.array([[N,P,k,temperature,humidity,ph,rainfal]])\n",
        "    prediction = classifier.predict(features).reshape(1,-1)\n",
        "    predictions = classifier.predict_proba(features)[0]\n",
        "    predictions = np.argsort(predictions)[::-1]\n",
        "    top_3 = predictions[:3]\n",
        "    return prediction[0],top_3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8322297e",
      "metadata": {
        "id": "8322297e"
      },
      "outputs": [],
      "source": [
        "def get_crop(indices):\n",
        "    crop_dict = {1: \"Rice\", 2: \"Maize\", 3: \"Jute\", 4: \"Cotton\", 5: \"Coconut\", 6: \"Papaya\", 7: \"Orange\",\n",
        "                 8: \"Apple\", 9: \"Muskmelon\", 10: \"Watermelon\", 11: \"Grapes\", 12: \"Mango\", 13: \"Banana\",\n",
        "              14: \"Pomegranate\", 15: \"Lentil\", 16: \"Blackgram\", 17: \"Mungbean\", 18: \"Mothbeans\",\n",
        "                 19: \"Pigeonpeas\", 20: \"Kidneybeans\", 21: \"Chickpea\", 22: \"Coffee\"}\n",
        "    return crop_dict[indices + 1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "38a947eb",
      "metadata": {
        "id": "38a947eb",
        "outputId": "f82b12a2-bedd-489f-b200-e919a2dd6ad7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['Mango', 'Chickpea', 'Grapes']\n"
          ]
        }
      ],
      "source": [
        "N = 2.24\n",
        "P = 0\n",
        "k = 283\n",
        "temperature = 40.0\n",
        "humidity = 20\n",
        "ph = 5.94\n",
        "rainfall = 100\n",
        "\n",
        "predict,recomended = recommendation(N,P,k,temperature,humidity,ph,rainfall)\n",
        "\n",
        "\n",
        "crop_dict = {1: \"Rice\", 2: \"Maize\", 3: \"Jute\", 4: \"Cotton\", 5: \"Coconut\", 6: \"Papaya\", 7: \"Orange\",\n",
        "                 8: \"Apple\", 9: \"Muskmelon\", 10: \"Watermelon\", 11: \"Grapes\", 12: \"Mango\", 13: \"Banana\",\n",
        "              14: \"Pomegranate\", 15: \"Lentil\", 16: \"Blackgram\", 17: \"Mungbean\", 18: \"Mothbeans\",\n",
        "                 19: \"Pigeonpeas\", 20: \"Kidneybeans\", 21: \"Chickpea\", 22: \"Coffee\"}\n",
        "\n",
        "# if predict[0] in crop_dict:\n",
        "#     crop = crop_dict[predict[0]]\n",
        "#     print(\"{} is a best crop to be cultivated \".format(crop))\n",
        "# else:\n",
        "#     print(\"Sorry are not able to recommend a proper crop for this environment\")\n",
        "\n",
        "recomendations = [get_crop(crop_index) for crop_index in recomended]\n",
        "print(recomendations)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fd4f6efa",
      "metadata": {
        "id": "fd4f6efa",
        "outputId": "7929812f-24f9-4c41-ea5a-74fb8175da49"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([12], dtype=int64)"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "predict"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "39c6cf2a",
      "metadata": {
        "id": "39c6cf2a"
      },
      "source": [
        "**MODEL DEPLOYMENT**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b6147a03",
      "metadata": {
        "id": "b6147a03"
      },
      "outputs": [],
      "source": [
        "#saving the model\n",
        "import pickle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "89d4364f",
      "metadata": {
        "id": "89d4364f"
      },
      "outputs": [],
      "source": [
        "with open('pi.pkl','wb') as file:\n",
        "    pickle.dump(classifier,file)\n",
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a64c607c",
      "metadata": {
        "id": "a64c607c"
      },
      "outputs": [],
      "source": [
        "with open('pi.pkl','rb') as file:\n",
        "    model=pickle.load(file)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "33b8d0ff",
      "metadata": {
        "id": "33b8d0ff"
      },
      "outputs": [],
      "source": [
        "mkdir MkulimaSmarts"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "160bffb8",
      "metadata": {
        "id": "160bffb8",
        "outputId": "c3c282a4-c2c0-4833-8bdb-1251232304cb"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "<!DOCTYPE html>\n",
              "<html lang=\"en\">\n",
              "<head>\n",
              "    <meta charset=\"UTF-8\">\n",
              "    <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\n",
              "    <title>M-kulimaSmart</title>\n",
              "\n",
              "    <style>\n",
              "        body {\n",
              "            font-family: Arial, sans-serif;\n",
              "            background-color: #f4f4f4;\n",
              "            margin: 0;\n",
              "            padding: 0;\n",
              "            display: flex;\n",
              "            flex-direction: column;\n",
              "            align-items: center;\n",
              "            justify-content: center;\n",
              "            height: 100vh;\n",
              "        }\n",
              "\n",
              "        h1 {\n",
              "            color: #333;\n",
              "        }\n",
              "\n",
              "        form {\n",
              "            background-color: #fff;\n",
              "            padding: 20px;\n",
              "            border-radius: 8px;\n",
              "            box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);\n",
              "            width: 300px;\n",
              "        }\n",
              "\n",
              "        label {\n",
              "            display: block;\n",
              "            margin: 10px 0 5px;\n",
              "            color: #555;\n",
              "        }\n",
              "\n",
              "        input {\n",
              "            width: 100%;\n",
              "            padding: 8px;\n",
              "            margin-bottom: 10px;\n",
              "            box-sizing: border-box;\n",
              "            border: 1px solid #ccc;\n",
              "            border-radius: 4px;\n",
              "        }\n",
              "\n",
              "        button {\n",
              "            background-color: #4caf50;\n",
              "            color: #fff;\n",
              "            padding: 10px;\n",
              "            border: none;\n",
              "            border-radius: 4px;\n",
              "            cursor: pointer;\n",
              "        }\n",
              "\n",
              "        button:hover {\n",
              "            background-color: #45a049;\n",
              "        }\n",
              "    </style>\n",
              "</head>\n",
              "<body>\n",
              "    <h1>Data Collection Form</h1>\n",
              "    \n",
              "    <form action=\"/predict\" method=\"post\">\n",
              "        <label for=\"nitrogen\">Nitrogen (N):</label>\n",
              "        <input type=\"text\" id=\"nitrogen\" name=\"nitrogen\" required>\n",
              "\n",
              "        <label for=\"phosphorus\">Phosphorus (P):</label>\n",
              "        <input type=\"text\" id=\"phosphorus\" name=\"phosphorus\" required>\n",
              "\n",
              "        <label for=\"potassium\">Potassium (K):</label>\n",
              "        <input type=\"text\" id=\"potassium\" name=\"potassium\" required>\n",
              "\n",
              "        <label for=\"temperature\">Temperature:</label>\n",
              "        <input type=\"text\" id=\"temperature\" name=\"temperature\" required>\n",
              "\n",
              "        <label for=\"humidity\">Humidity:</label>\n",
              "        <input type=\"text\" id=\"humidity\" name=\"humidity\" required>\n",
              "\n",
              "        <label for=\"ph\">pH:</label>\n",
              "        <input type=\"text\" id=\"ph\" name=\"ph\" required>\n",
              "\n",
              "        <label for=\"rainfall\">Rainfall:</label>\n",
              "        <input type=\"text\" id=\"rainfall\" name=\"rainfall\" required>\n",
              "\n",
              "        <button type=\"submit\">Submit</button>\n",
              "    </form>\n",
              "</body>\n",
              "</html>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "%%html\n",
        "\n",
        "<!DOCTYPE html>\n",
        "<html lang=\"en\">\n",
        "<head>\n",
        "    <meta charset=\"UTF-8\">\n",
        "    <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\n",
        "    <title>M-kulimaSmart</title>\n",
        "\n",
        "    <style>\n",
        "        body {\n",
        "            font-family: Arial, sans-serif;\n",
        "            background-color: #f4f4f4;\n",
        "            margin: 0;\n",
        "            padding: 0;\n",
        "            display: flex;\n",
        "            flex-direction: column;\n",
        "            align-items: center;\n",
        "            justify-content: center;\n",
        "            height: 100vh;\n",
        "        }\n",
        "\n",
        "        h1 {\n",
        "            color: #333;\n",
        "        }\n",
        "\n",
        "        form {\n",
        "            background-color: #fff;\n",
        "            padding: 20px;\n",
        "            border-radius: 8px;\n",
        "            box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);\n",
        "            width: 300px;\n",
        "        }\n",
        "\n",
        "        label {\n",
        "            display: block;\n",
        "            margin: 10px 0 5px;\n",
        "            color: #555;\n",
        "        }\n",
        "\n",
        "        input {\n",
        "            width: 100%;\n",
        "            padding: 8px;\n",
        "            margin-bottom: 10px;\n",
        "            box-sizing: border-box;\n",
        "            border: 1px solid #ccc;\n",
        "            border-radius: 4px;\n",
        "        }\n",
        "\n",
        "        button {\n",
        "            background-color: #4caf50;\n",
        "            color: #fff;\n",
        "            padding: 10px;\n",
        "            border: none;\n",
        "            border-radius: 4px;\n",
        "            cursor: pointer;\n",
        "        }\n",
        "\n",
        "        button:hover {\n",
        "            background-color: #45a049;\n",
        "        }\n",
        "    </style>\n",
        "</head>\n",
        "<body>\n",
        "    <h1>Data Collection Form</h1>\n",
        "\n",
        "    <form action=\"/predict\" method=\"post\">\n",
        "        <label for=\"nitrogen\">Nitrogen (N):</label>\n",
        "        <input type=\"text\" id=\"nitrogen\" name=\"nitrogen\" required>\n",
        "\n",
        "        <label for=\"phosphorus\">Phosphorus (P):</label>\n",
        "        <input type=\"text\" id=\"phosphorus\" name=\"phosphorus\" required>\n",
        "\n",
        "        <label for=\"potassium\">Potassium (K):</label>\n",
        "        <input type=\"text\" id=\"potassium\" name=\"potassium\" required>\n",
        "\n",
        "        <label for=\"temperature\">Temperature:</label>\n",
        "        <input type=\"text\" id=\"temperature\" name=\"temperature\" required>\n",
        "\n",
        "        <label for=\"humidity\">Humidity:</label>\n",
        "        <input type=\"text\" id=\"humidity\" name=\"humidity\" required>\n",
        "\n",
        "        <label for=\"ph\">pH:</label>\n",
        "        <input type=\"text\" id=\"ph\" name=\"ph\" required>\n",
        "\n",
        "        <label for=\"rainfall\">Rainfall:</label>\n",
        "        <input type=\"text\" id=\"rainfall\" name=\"rainfall\" required>\n",
        "\n",
        "        <button type=\"submit\">Submit</button>\n",
        "    </form>\n",
        "</body>\n",
        "</html>"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.5"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}