Crop_Yield_Prediction (1).ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "ew25g9VyfdYd"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s6whDbtYlVi9"
      },
      "source": [
        "#**Predicting Crops Yield : A Machine Learning Approach**\n",
        "## Crop yield prediction is an important agricultural problem.It is a regression problem.\n",
        "\n",
        "The crops included in dataset are :\n",
        "\n",
        "Cassava\n",
        "Maize\n",
        "Plantains and others\n",
        "Potatoes\n",
        "Rice, paddy\n",
        "Sorghum\n",
        "Soybeans\n",
        "Sweet potatoes\n",
        "Wheat\n",
        "Yams\n",
        "\n",
        "\n",
        "In the project, Machine Learning methods are applied to predict crop yield using publicly available data from FAO and World Data Bank."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "ZqSqIpvxlyI9"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Machine learning (ML) is used for **crop yield prediction** to improve the accuracy and flexibility of predictions compared to traditional methods. Here's why:\n",
        "\n",
        "### Why Use ML for Crop Yield Prediction?\n",
        "\n",
        "1. **Complex Relationships**:\n",
        "   - Crop yield is influenced by a wide range of factors like **rainfall, temperature, pesticides, soil conditions**, and more. These relationships are often complex and non-linear. Machine learning algorithms like **Random Forest** and **Gradient Boosting** can model these non-linear relationships better than simple statistical analysis.\n",
        "\n",
        "2. **Feature Interactions**:\n",
        "   - In crop yield, certain features interact with each other (e.g., **rainfall might affect crop yield differently depending on temperature**). ML algorithms like **Decision Trees** can capture such interactions more effectively.\n",
        "\n",
        "3. **Large Datasets**:\n",
        "   - When working with a large dataset that includes multiple countries, crops, and factors like weather and pesticides, ML models can handle and process this massive amount of data efficiently, offering better scalability.\n",
        "\n",
        "4. **Automated Feature Engineering**:\n",
        "   - ML models like **Random Forest** can automatically handle feature importance and selection. For example, a **Decision Tree** will automatically find the most important factors (like **rainfall, temperature**) that impact crop yield, saving you from manually selecting the features.\n",
        "\n",
        "5. **High Predictive Power**:\n",
        "   - Traditional methods like simple statistical analysis may not be able to achieve the same predictive power as advanced ML algorithms. In your case, the **Decision Tree Regressor** achieved an accuracy of 96.04%, which might be challenging to match with simpler methods.\n",
        "\n",
        "### Why Not Just Use Simple Analysis?\n",
        "\n",
        "- **Simple statistical analysis** like linear regression may assume a linear relationship between features and crop yield. In the real world, however, the relationship is often much more complex. For example, the effect of rainfall on yield might not be the same across all regions or crops.\n",
        "  \n",
        "- ML models are **adaptive**. They can learn from more complex data patterns without needing prior knowledge of the relationships between variables, whereas traditional methods might miss those subtleties.\n",
        "\n",
        "### ML in Your Project:\n",
        "\n",
        "In your project, you used algorithms like:\n",
        "- **Random Forest Regressor**: Aggregates many decision trees, making it robust and preventing overfitting.\n",
        "- **Gradient Boosting Regressor**: Sequentially builds models to minimize errors, offering a highly accurate predictive model.\n",
        "- **Decision Tree Regressor**: Provides visual and interpretable decision paths for each prediction.\n",
        "- **Support Vector Regressor (SVM)**: Works well with high-dimensional data and can capture more complex patterns.\n",
        "\n",
        "These algorithms make ML a powerful tool for improving crop yield predictions."
      ],
      "metadata": {
        "id": "vvRdJoVb1fFU"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KRInFcY4mMlI",
        "outputId": "1cf0288e-2212-4fb7-872e-1eee463db5ef"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Unnamed: 0     Area         Item  Year  hg/ha_yield  \\\n",
            "0           0  Albania        Maize  1990        36613   \n",
            "1           1  Albania     Potatoes  1990        66667   \n",
            "2           2  Albania  Rice, paddy  1990        23333   \n",
            "3           3  Albania      Sorghum  1990        12500   \n",
            "4           4  Albania     Soybeans  1990         7000   \n",
            "\n",
            "   average_rain_fall_mm_per_year  pesticides_tonnes  avg_temp  \n",
            "0                         1485.0              121.0     16.37  \n",
            "1                         1485.0              121.0     16.37  \n",
            "2                         1485.0              121.0     16.37  \n",
            "3                         1485.0              121.0     16.37  \n",
            "4                         1485.0              121.0     16.37  \n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 28242 entries, 0 to 28241\n",
            "Data columns (total 8 columns):\n",
            " #   Column                         Non-Null Count  Dtype  \n",
            "---  ------                         --------------  -----  \n",
            " 0   Unnamed: 0                     28242 non-null  int64  \n",
            " 1   Area                           28242 non-null  object \n",
            " 2   Item                           28242 non-null  object \n",
            " 3   Year                           28242 non-null  int64  \n",
            " 4   hg/ha_yield                    28242 non-null  int64  \n",
            " 5   average_rain_fall_mm_per_year  28242 non-null  float64\n",
            " 6   pesticides_tonnes              28242 non-null  float64\n",
            " 7   avg_temp                       28242 non-null  float64\n",
            "dtypes: float64(3), int64(3), object(2)\n",
            "memory usage: 1.7+ MB\n",
            "                      Area  Year  hg/ha_yield  average_rain_fall_mm_per_year  \\\n",
            "Item                                                                           \n",
            "Cassava               2045  2045         2045                           2045   \n",
            "Maize                 4121  4121         4121                           4121   \n",
            "Plantains and others   556   556          556                            556   \n",
            "Potatoes              4276  4276         4276                           4276   \n",
            "Rice, paddy           3388  3388         3388                           3388   \n",
            "Sorghum               3039  3039         3039                           3039   \n",
            "Soybeans              3223  3223         3223                           3223   \n",
            "Sweet potatoes        2890  2890         2890                           2890   \n",
            "Wheat                 3857  3857         3857                           3857   \n",
            "Yams                   847   847          847                            847   \n",
            "\n",
            "                      pesticides_tonnes  avg_temp  \n",
            "Item                                               \n",
            "Cassava                            2045      2045  \n",
            "Maize                              4121      4121  \n",
            "Plantains and others                556       556  \n",
            "Potatoes                           4276      4276  \n",
            "Rice, paddy                        3388      3388  \n",
            "Sorghum                            3039      3039  \n",
            "Soybeans                           3223      3223  \n",
            "Sweet potatoes                     2890      2890  \n",
            "Wheat                              3857      3857  \n",
            "Yams                                847       847  \n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "yeild_df = pd.read_csv('/content/sample_data/yield_df.csv')\n",
        "print(yeild_df.head())\n",
        "yeild_df.info()\n",
        "yeild_df.drop('Unnamed: 0',inplace=True,axis=1)\n",
        "yeild_df.isnull().sum()\n",
        "yeild_df.describe()\n",
        "yeild_df.shape\n",
        "\n",
        "yeild_df.dropna(inplace=True)\n",
        "print(yeild_df.groupby('Item').count())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "-FWes_anpCu-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "6c6b6da7-d823-42c4-b90c-e27cc6b5e191"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Year    hg/ha_yield  average_rain_fall_mm_per_year  \\\n",
              "count  28242.000000   28242.000000                    28242.00000   \n",
              "mean    2001.544296   77053.332094                     1149.05598   \n",
              "std        7.051905   84956.612897                      709.81215   \n",
              "min     1990.000000      50.000000                       51.00000   \n",
              "25%     1995.000000   19919.250000                      593.00000   \n",
              "50%     2001.000000   38295.000000                     1083.00000   \n",
              "75%     2008.000000  104676.750000                     1668.00000   \n",
              "max     2013.000000  501412.000000                     3240.00000   \n",
              "\n",
              "       pesticides_tonnes      avg_temp  \n",
              "count       28242.000000  28242.000000  \n",
              "mean        37076.909344     20.542627  \n",
              "std         59958.784665      6.312051  \n",
              "min             0.040000      1.300000  \n",
              "25%          1702.000000     16.702500  \n",
              "50%         17529.440000     21.510000  \n",
              "75%         48687.880000     26.000000  \n",
              "max        367778.000000     30.650000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0e3bec3d-27ee-4332-881f-9c904cf2fc83\" class=\"colab-df-container\">\n",
              "    <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>Year</th>\n",
              "      <th>hg/ha_yield</th>\n",
              "      <th>average_rain_fall_mm_per_year</th>\n",
              "      <th>pesticides_tonnes</th>\n",
              "      <th>avg_temp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>28242.000000</td>\n",
              "      <td>28242.000000</td>\n",
              "      <td>28242.00000</td>\n",
              "      <td>28242.000000</td>\n",
              "      <td>28242.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2001.544296</td>\n",
              "      <td>77053.332094</td>\n",
              "      <td>1149.05598</td>\n",
              "      <td>37076.909344</td>\n",
              "      <td>20.542627</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>7.051905</td>\n",
              "      <td>84956.612897</td>\n",
              "      <td>709.81215</td>\n",
              "      <td>59958.784665</td>\n",
              "      <td>6.312051</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1990.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>51.00000</td>\n",
              "      <td>0.040000</td>\n",
              "      <td>1.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1995.000000</td>\n",
              "      <td>19919.250000</td>\n",
              "      <td>593.00000</td>\n",
              "      <td>1702.000000</td>\n",
              "      <td>16.702500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2001.000000</td>\n",
              "      <td>38295.000000</td>\n",
              "      <td>1083.00000</td>\n",
              "      <td>17529.440000</td>\n",
              "      <td>21.510000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2008.000000</td>\n",
              "      <td>104676.750000</td>\n",
              "      <td>1668.00000</td>\n",
              "      <td>48687.880000</td>\n",
              "      <td>26.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2013.000000</td>\n",
              "      <td>501412.000000</td>\n",
              "      <td>3240.00000</td>\n",
              "      <td>367778.000000</td>\n",
              "      <td>30.650000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0e3bec3d-27ee-4332-881f-9c904cf2fc83')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-0e3bec3d-27ee-4332-881f-9c904cf2fc83 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-0e3bec3d-27ee-4332-881f-9c904cf2fc83');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-05747502-e0f6-424f-ac20-bdffee63db4e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-05747502-e0f6-424f-ac20-bdffee63db4e')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\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",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-05747502-e0f6-424f-ac20-bdffee63db4e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"yeild_df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9404.109252355267,\n        \"min\": 7.05190528539453,\n        \"max\": 28242.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2001.5442957297641,\n          2001.0,\n          28242.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hg/ha_yield\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 163398.39696078157,\n        \"min\": 50.0,\n        \"max\": 501412.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          77053.33209404434,\n          38295.0,\n          28242.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"average_rain_fall_mm_per_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9603.207715748134,\n        \"min\": 51.0,\n        \"max\": 28242.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1149.055980454642,\n          1083.0,\n          28242.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pesticides_tonnes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 122107.72892356533,\n        \"min\": 0.04,\n        \"max\": 367778.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          37076.909343530904,\n          17529.44,\n          28242.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_temp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9978.846266500655,\n        \"min\": 1.3,\n        \"max\": 28242.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          20.542626584519507,\n          21.51,\n          28242.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "yeild_df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It can be noticed the high variance in the values for each columns, later on I'll account for that will scaling."
      ],
      "metadata": {
        "id": "a4Z2zQNKrlDR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "yeild_df['Area'].nunique()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gv3VAue_rXPz",
        "outputId": "1f0ab4ed-3cd8-4218-ba29-ac7559497250"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "101"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The dataframe has 101 Countries, ordering these by 20 the highest yield production:"
      ],
      "metadata": {
        "id": "6oJAWCtZr0hx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "yeild_df.groupby(['Area'],sort=True)['hg/ha_yield'].sum().nlargest(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 742
        },
        "id": "cWdlHG9Vr21j",
        "outputId": "e50db53d-71a3-4395-d43f-b9ad908a0835"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Area\n",
              "India                 327420324\n",
              "Brazil                167550306\n",
              "Mexico                130788528\n",
              "Japan                 124470912\n",
              "Australia             109111062\n",
              "Pakistan               73897434\n",
              "Indonesia              69193506\n",
              "United Kingdom         55419990\n",
              "Turkey                 52263950\n",
              "Spain                  46773540\n",
              "South Africa           41333132\n",
              "Germany                38780463\n",
              "Egypt                  36828848\n",
              "Canada                 34706922\n",
              "Argentina              32864032\n",
              "Italy                  32280700\n",
              "Colombia               26927138\n",
              "Dominican Republic     25312166\n",
              "Morocco                22173010\n",
              "Ecuador                21315591\n",
              "Name: hg/ha_yield, dtype: int64"
            ],
            "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>hg/ha_yield</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Area</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>India</th>\n",
              "      <td>327420324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Brazil</th>\n",
              "      <td>167550306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mexico</th>\n",
              "      <td>130788528</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Japan</th>\n",
              "      <td>124470912</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Australia</th>\n",
              "      <td>109111062</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pakistan</th>\n",
              "      <td>73897434</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Indonesia</th>\n",
              "      <td>69193506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>United Kingdom</th>\n",
              "      <td>55419990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Turkey</th>\n",
              "      <td>52263950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Spain</th>\n",
              "      <td>46773540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>South Africa</th>\n",
              "      <td>41333132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Germany</th>\n",
              "      <td>38780463</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Egypt</th>\n",
              "      <td>36828848</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Canada</th>\n",
              "      <td>34706922</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Argentina</th>\n",
              "      <td>32864032</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Italy</th>\n",
              "      <td>32280700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Colombia</th>\n",
              "      <td>26927138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dominican Republic</th>\n",
              "      <td>25312166</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Morocco</th>\n",
              "      <td>22173010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ecuador</th>\n",
              "      <td>21315591</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "yeild_df.groupby(['Item','Area'],sort=True)['hg/ha_yield'].sum().nlargest(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 742
        },
        "id": "itzZjeJlscfi",
        "outputId": "1449541b-3152-4996-b571-f6117a37bc41"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Item            Area          \n",
              "Cassava         India             142810624\n",
              "Potatoes        India              92122514\n",
              "                Brazil             49602168\n",
              "                United Kingdom     46705145\n",
              "                Australia          45670386\n",
              "Sweet potatoes  India              44439538\n",
              "Potatoes        Japan              42918726\n",
              "                Mexico             42053880\n",
              "Sweet potatoes  Mexico             35808592\n",
              "                Australia          35550294\n",
              "Cassava         Brazil             33671231\n",
              "Potatoes        Pakistan           32969754\n",
              "Sweet potatoes  Japan              32794236\n",
              "Potatoes        Turkey             30530955\n",
              "Yams            Japan              29165394\n",
              "Sweet potatoes  Brazil             28266502\n",
              "Potatoes        South Africa       27341980\n",
              "                Germany            26672181\n",
              "Yams            Brazil             23472053\n",
              "Sweet potatoes  Pakistan           21687615\n",
              "Name: hg/ha_yield, dtype: int64"
            ],
            "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></th>\n",
              "      <th>hg/ha_yield</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Item</th>\n",
              "      <th>Area</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Cassava</th>\n",
              "      <th>India</th>\n",
              "      <td>142810624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">Potatoes</th>\n",
              "      <th>India</th>\n",
              "      <td>92122514</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Brazil</th>\n",
              "      <td>49602168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>United Kingdom</th>\n",
              "      <td>46705145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Australia</th>\n",
              "      <td>45670386</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sweet potatoes</th>\n",
              "      <th>India</th>\n",
              "      <td>44439538</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Potatoes</th>\n",
              "      <th>Japan</th>\n",
              "      <td>42918726</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mexico</th>\n",
              "      <td>42053880</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Sweet potatoes</th>\n",
              "      <th>Mexico</th>\n",
              "      <td>35808592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Australia</th>\n",
              "      <td>35550294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cassava</th>\n",
              "      <th>Brazil</th>\n",
              "      <td>33671231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Potatoes</th>\n",
              "      <th>Pakistan</th>\n",
              "      <td>32969754</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sweet potatoes</th>\n",
              "      <th>Japan</th>\n",
              "      <td>32794236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Potatoes</th>\n",
              "      <th>Turkey</th>\n",
              "      <td>30530955</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yams</th>\n",
              "      <th>Japan</th>\n",
              "      <td>29165394</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sweet potatoes</th>\n",
              "      <th>Brazil</th>\n",
              "      <td>28266502</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Potatoes</th>\n",
              "      <th>South Africa</th>\n",
              "      <td>27341980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Germany</th>\n",
              "      <td>26672181</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yams</th>\n",
              "      <th>Brazil</th>\n",
              "      <td>23472053</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sweet potatoes</th>\n",
              "      <th>Pakistan</th>\n",
              "      <td>21687615</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "India is the highest for production of cassava and potatoes. Potatoes seems to be the dominated crop in the dataset, being the highest in 4 countries.\n",
        "\n",
        "The final dataframe starts from 1990 and ends in 2013, that's 23 years worth of data for 101 countries.\n",
        "\n",
        "Now, exploring the relationships between the colunms of the dataframe, a good way to quickly check correlations among columns is by visualizing the correlation matrix as a heatmap."
      ],
      "metadata": {
        "id": "zJtg3uJjsxBL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import sklearn\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "metadata": {
        "id": "8G5ivuZXtFxG"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "corr_data = yeild_df.select_dtypes(include=[np.number]).corr()\n",
        "\n",
        "mask = np.zeros_like(corr_data, dtype=bool)\n",
        "mask[np.triu_indices_from(mask)] = True\n",
        "f,ax = plt.subplots(figsize=(11,9))\n",
        "\n",
        "# Generate a custom diverging colormap\n",
        "cmap = sns.light_palette(\"navy\")\n",
        "\n",
        "# to draw the heatmap with the mask and correct aspect ratio\n",
        "sns.heatmap(corr_data,mask=mask,cmap=cmap,vmax=3,center=0,\n",
        "            square=True, linewidth=.5, cbar_kws={\"shrink\" : .5});"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 950
        },
        "id": "i-Y1PxsD4Ym9",
        "outputId": "086217b4-53ce-4eae-ef6a-4bc4cff47d18"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1100x900 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This code generates a heatmap of the **correlation matrix** with specific customizations using **seaborn** and **matplotlib**. Let's break down the different parts of the code and how each contributes to the final heatmap visualization:\n",
        "\n",
        "### Code Breakdown:\n",
        "\n",
        "1. **`corr_data = yield_df.select_dtypes(include=[np.number]).corr()`**\n",
        "   - This computes the correlation matrix for all the numeric columns in the DataFrame `yield_df`. This matrix will later be visualized as a heatmap.\n",
        "\n",
        "2. **`mask = np.zeros_like(corr_data, dtype=np.bool)`**\n",
        "   - Creates an array of `False` values of the same shape as the correlation matrix. This will be used as a mask to hide parts of the heatmap (the upper triangle in this case).\n",
        "\n",
        "3. **`mask[np.triu_indices_from(mask)] = True`**\n",
        "   - Marks the upper triangle of the `mask` array as `True`, meaning that part of the heatmap will be hidden. This is done to avoid showing redundant information, as the correlation matrix is symmetric.\n",
        "\n",
        "4. **`f, ax = plt.subplots(figsize=(11, 9))`**\n",
        "   - Creates a figure (`f`) and a set of axes (`ax`) using `matplotlib`. The figure size is set to 11x9 inches for better readability of the heatmap.\n",
        "\n",
        "5. **`cmap = sns.light_palette(\"navy\")`**\n",
        "   - This generates a custom color palette (colormap) using **navy** as the base color. The `light_palette()` function generates a series of lighter shades of the base color.\n",
        "\n",
        "6. **`sns.heatmap(corr_data, mask=mask, cmap=cmap, vmax=.3, center=0, square=True, linewidths=.5, cbar_kws={\"shrink\": .5})`**\n",
        "   - **`corr_data`**: The correlation matrix is passed to the heatmap function.\n",
        "   - **`mask=mask`**: Applies the mask to hide the upper triangle of the heatmap.\n",
        "   - **`cmap=cmap`**: Uses the custom diverging colormap created with `sns.light_palette(\"navy\")`.\n",
        "   - **`vmax=.3`**: Limits the maximum value for the colormap. Values above 0.3 will be clipped, which can help emphasize smaller correlations.\n",
        "   - **`center=0`**: The center of the colormap is set at `0`, ensuring that zero correlations are represented by a neutral color.\n",
        "   - **`square=True`**: Forces the cells in the heatmap to be square-shaped, improving visual consistency.\n",
        "   - **`linewidths=.5`**: Adds a small gap of 0.5 pixels between cells, making the grid clearer.\n",
        "   - **`cbar_kws={\"shrink\": .5}`**: This shrinks the color bar by 50%, making it smaller and more proportionate to the heatmap.\n",
        "\n",
        "### What This Heatmap Achieves:\n",
        "- **Visualizes Correlations**: The heatmap provides an intuitive way to visualize the pairwise correlations between features in the dataset.\n",
        "- **Hides Redundant Information**: By masking the upper triangle, the heatmap only shows the lower triangle of the correlation matrix, reducing redundancy.\n",
        "- **Custom Colors**: The navy-blue color palette is used, with lighter shades representing weaker correlations and stronger shades representing stronger correlations.\n",
        "- **Focuses on Specific Range**: The `vmax=0.3` argument clips high correlation values, ensuring that the heatmap emphasizes lower and more relevant correlation values, depending on the context of the analysis.\n",
        "\n",
        "### Example Output:\n",
        "The resulting heatmap will visually show correlations between features (e.g., crop yield, rainfall, pesticide usage), with lighter to darker navy shades indicating the strength and direction of the correlations.\n",
        "\n",
        "This visualization helps you quickly identify:\n",
        "- **Strong correlations** between features (important for feature selection).\n",
        "- **Multicollinearity** if two independent variables are highly correlated.\n",
        "- **Unimportant variables** (if a variable shows little correlation with the target, e.g., crop yield).\n",
        "\n",
        "### Improvements and Customizations:\n",
        "- **Change Color Palette**: You can experiment with different palettes by adjusting `sns.light_palette()` or using `sns.diverging_palette()` for diverging color maps.\n",
        "- **Adjust `vmax` and `center`**: Tuning these parameters can help highlight specific ranges of correlation values.\n"
      ],
      "metadata": {
        "id": "LqFRuKxK9hCw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# It can be seen from the above correlation map that there is no correlation between any of the columns in the dataframe."
      ],
      "metadata": {
        "id": "sFhBDXovAdhl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "yeild_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "dVkRksCdAYdT",
        "outputId": "39c7dbf2-1586-4641-f55d-af3f8354e325"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Area         Item  Year  hg/ha_yield  average_rain_fall_mm_per_year  \\\n",
              "0  Albania        Maize  1990        36613                         1485.0   \n",
              "1  Albania     Potatoes  1990        66667                         1485.0   \n",
              "2  Albania  Rice, paddy  1990        23333                         1485.0   \n",
              "3  Albania      Sorghum  1990        12500                         1485.0   \n",
              "4  Albania     Soybeans  1990         7000                         1485.0   \n",
              "\n",
              "   pesticides_tonnes  avg_temp  \n",
              "0              121.0     16.37  \n",
              "1              121.0     16.37  \n",
              "2              121.0     16.37  \n",
              "3              121.0     16.37  \n",
              "4              121.0     16.37  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-023f8cb0-bd9d-4e58-9b97-cf0c4741e2da\" class=\"colab-df-container\">\n",
              "    <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>Area</th>\n",
              "      <th>Item</th>\n",
              "      <th>Year</th>\n",
              "      <th>hg/ha_yield</th>\n",
              "      <th>average_rain_fall_mm_per_year</th>\n",
              "      <th>pesticides_tonnes</th>\n",
              "      <th>avg_temp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Albania</td>\n",
              "      <td>Maize</td>\n",
              "      <td>1990</td>\n",
              "      <td>36613</td>\n",
              "      <td>1485.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>16.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Albania</td>\n",
              "      <td>Potatoes</td>\n",
              "      <td>1990</td>\n",
              "      <td>66667</td>\n",
              "      <td>1485.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>16.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Albania</td>\n",
              "      <td>Rice, paddy</td>\n",
              "      <td>1990</td>\n",
              "      <td>23333</td>\n",
              "      <td>1485.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>16.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Albania</td>\n",
              "      <td>Sorghum</td>\n",
              "      <td>1990</td>\n",
              "      <td>12500</td>\n",
              "      <td>1485.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>16.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Albania</td>\n",
              "      <td>Soybeans</td>\n",
              "      <td>1990</td>\n",
              "      <td>7000</td>\n",
              "      <td>1485.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>16.37</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-023f8cb0-bd9d-4e58-9b97-cf0c4741e2da')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-023f8cb0-bd9d-4e58-9b97-cf0c4741e2da button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-023f8cb0-bd9d-4e58-9b97-cf0c4741e2da');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-5fec24bb-70c1-41a0-a4d0-35f85dc76638\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5fec24bb-70c1-41a0-a4d0-35f85dc76638')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\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",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5fec24bb-70c1-41a0-a4d0-35f85dc76638 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "yeild_df",
              "summary": "{\n  \"name\": \"yeild_df\",\n  \"rows\": 28242,\n  \"fields\": [\n    {\n      \"column\": \"Area\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 101,\n        \"samples\": [\n          \"South Africa\",\n          \"Lithuania\",\n          \"Namibia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Item\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Plantains and others\",\n          \"Potatoes\",\n          \"Wheat\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 1990,\n        \"max\": 2013,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2006,\n          1999,\n          1990\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hg/ha_yield\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 84956,\n        \"min\": 50,\n        \"max\": 501412,\n        \"num_unique_values\": 11514,\n        \"samples\": [\n          27368,\n          110413,\n          98893\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"average_rain_fall_mm_per_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 709.8121499492202,\n        \"min\": 51.0,\n        \"max\": 3240.0,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          1162.0,\n          788.0,\n          2280.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pesticides_tonnes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 59958.78466505808,\n        \"min\": 0.04,\n        \"max\": 367778.0,\n        \"num_unique_values\": 1673,\n        \"samples\": [\n          14156.16,\n          43720.04,\n          10777.08\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_temp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.312050836049793,\n        \"min\": 1.3,\n        \"max\": 30.65,\n        \"num_unique_values\": 1831,\n        \"samples\": [\n          8.76,\n          15.98,\n          13.97\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Encoding Categorical Variables:"
      ],
      "metadata": {
        "id": "idbTEb6jCSfH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder"
      ],
      "metadata": {
        "id": "hpyvDkYcCRh0"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "yeild_df_onehot = pd.get_dummies(yeild_df,columns=['Area','Item'],prefix = ['Country','Item'])\n",
        "print(yeild_df_onehot.head())\n",
        "features = yeild_df_onehot.loc[:,yeild_df_onehot.columns !='hg/ha_yield']\n",
        "label = yeild_df['hg/ha_yield']\n",
        "print(features.head())\n",
        "print(label.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UsCyk7c2CfH5",
        "outputId": "8d5a56f0-c640-4b86-aa3f-4c253f5b51e9"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Year  hg/ha_yield  average_rain_fall_mm_per_year  pesticides_tonnes  \\\n",
            "0  1990        36613                         1485.0              121.0   \n",
            "1  1990        66667                         1485.0              121.0   \n",
            "2  1990        23333                         1485.0              121.0   \n",
            "3  1990        12500                         1485.0              121.0   \n",
            "4  1990         7000                         1485.0              121.0   \n",
            "\n",
            "   avg_temp  Country_Albania  Country_Algeria  Country_Angola  \\\n",
            "0     16.37             True            False           False   \n",
            "1     16.37             True            False           False   \n",
            "2     16.37             True            False           False   \n",
            "3     16.37             True            False           False   \n",
            "4     16.37             True            False           False   \n",
            "\n",
            "   Country_Argentina  Country_Armenia  ...  Item_Cassava  Item_Maize  \\\n",
            "0              False            False  ...         False        True   \n",
            "1              False            False  ...         False       False   \n",
            "2              False            False  ...         False       False   \n",
            "3              False            False  ...         False       False   \n",
            "4              False            False  ...         False       False   \n",
            "\n",
            "   Item_Plantains and others  Item_Potatoes  Item_Rice, paddy  Item_Sorghum  \\\n",
            "0                      False          False             False         False   \n",
            "1                      False           True             False         False   \n",
            "2                      False          False              True         False   \n",
            "3                      False          False             False          True   \n",
            "4                      False          False             False         False   \n",
            "\n",
            "   Item_Soybeans  Item_Sweet potatoes  Item_Wheat  Item_Yams  \n",
            "0          False                False       False      False  \n",
            "1          False                False       False      False  \n",
            "2          False                False       False      False  \n",
            "3          False                False       False      False  \n",
            "4           True                False       False      False  \n",
            "\n",
            "[5 rows x 116 columns]\n",
            "   Year  average_rain_fall_mm_per_year  pesticides_tonnes  avg_temp  \\\n",
            "0  1990                         1485.0              121.0     16.37   \n",
            "1  1990                         1485.0              121.0     16.37   \n",
            "2  1990                         1485.0              121.0     16.37   \n",
            "3  1990                         1485.0              121.0     16.37   \n",
            "4  1990                         1485.0              121.0     16.37   \n",
            "\n",
            "   Country_Albania  Country_Algeria  Country_Angola  Country_Argentina  \\\n",
            "0             True            False           False              False   \n",
            "1             True            False           False              False   \n",
            "2             True            False           False              False   \n",
            "3             True            False           False              False   \n",
            "4             True            False           False              False   \n",
            "\n",
            "   Country_Armenia  Country_Australia  ...  Item_Cassava  Item_Maize  \\\n",
            "0            False              False  ...         False        True   \n",
            "1            False              False  ...         False       False   \n",
            "2            False              False  ...         False       False   \n",
            "3            False              False  ...         False       False   \n",
            "4            False              False  ...         False       False   \n",
            "\n",
            "   Item_Plantains and others  Item_Potatoes  Item_Rice, paddy  Item_Sorghum  \\\n",
            "0                      False          False             False         False   \n",
            "1                      False           True             False         False   \n",
            "2                      False          False              True         False   \n",
            "3                      False          False             False          True   \n",
            "4                      False          False             False         False   \n",
            "\n",
            "   Item_Soybeans  Item_Sweet potatoes  Item_Wheat  Item_Yams  \n",
            "0          False                False       False      False  \n",
            "1          False                False       False      False  \n",
            "2          False                False       False      False  \n",
            "3          False                False       False      False  \n",
            "4           True                False       False      False  \n",
            "\n",
            "[5 rows x 115 columns]\n",
            "0    36613\n",
            "1    66667\n",
            "2    23333\n",
            "3    12500\n",
            "4     7000\n",
            "Name: hg/ha_yield, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features = features.drop(['Year'], axis=1)\n",
        "features.info()\n",
        "print(features.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zPZQCUZlEH7p",
        "outputId": "1c0af9d0-0c37-4027-d426-d707e5525382"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 28242 entries, 0 to 28241\n",
            "Columns: 114 entries, average_rain_fall_mm_per_year to Item_Yams\n",
            "dtypes: bool(111), float64(3)\n",
            "memory usage: 3.6 MB\n",
            "   average_rain_fall_mm_per_year  pesticides_tonnes  avg_temp  \\\n",
            "0                         1485.0              121.0     16.37   \n",
            "1                         1485.0              121.0     16.37   \n",
            "2                         1485.0              121.0     16.37   \n",
            "3                         1485.0              121.0     16.37   \n",
            "4                         1485.0              121.0     16.37   \n",
            "\n",
            "   Country_Albania  Country_Algeria  Country_Angola  Country_Argentina  \\\n",
            "0             True            False           False              False   \n",
            "1             True            False           False              False   \n",
            "2             True            False           False              False   \n",
            "3             True            False           False              False   \n",
            "4             True            False           False              False   \n",
            "\n",
            "   Country_Armenia  Country_Australia  Country_Austria  ...  Item_Cassava  \\\n",
            "0            False              False            False  ...         False   \n",
            "1            False              False            False  ...         False   \n",
            "2            False              False            False  ...         False   \n",
            "3            False              False            False  ...         False   \n",
            "4            False              False            False  ...         False   \n",
            "\n",
            "   Item_Maize  Item_Plantains and others  Item_Potatoes  Item_Rice, paddy  \\\n",
            "0        True                      False          False             False   \n",
            "1       False                      False           True             False   \n",
            "2       False                      False          False              True   \n",
            "3       False                      False          False             False   \n",
            "4       False                      False          False             False   \n",
            "\n",
            "   Item_Sorghum  Item_Soybeans  Item_Sweet potatoes  Item_Wheat  Item_Yams  \n",
            "0         False          False                False       False      False  \n",
            "1         False          False                False       False      False  \n",
            "2         False          False                False       False      False  \n",
            "3          True          False                False       False      False  \n",
            "4         False           True                False       False      False  \n",
            "\n",
            "[5 rows x 114 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "# Scaling Features:"
      ],
      "metadata": {
        "id": "g80qlZ6THCHL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "features = scaler.fit_transform(features)\n",
        "print(features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t5TBr5-YHFch",
        "outputId": "8b4d0635-ee08-4a94-fcd8-506e5cfefc0f"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[4.49670743e-01 3.28894097e-04 5.13458262e-01 ... 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00]\n",
            " [4.49670743e-01 3.28894097e-04 5.13458262e-01 ... 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00]\n",
            " [4.49670743e-01 3.28894097e-04 5.13458262e-01 ... 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00]\n",
            " ...\n",
            " [1.90028222e-01 6.93361288e-03 6.28960818e-01 ... 0.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00]\n",
            " [1.90028222e-01 6.93361288e-03 6.28960818e-01 ... 1.00000000e+00\n",
            "  0.00000000e+00 0.00000000e+00]\n",
            " [1.90028222e-01 6.93361288e-03 6.28960818e-01 ... 0.00000000e+00\n",
            "  1.00000000e+00 0.00000000e+00]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Training Data:**"
      ],
      "metadata": {
        "id": "tqaB0_tjHh55"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test = train_test_split(features,label,test_size=0.2,random_state=4)\n"
      ],
      "metadata": {
        "id": "JBii5U-_HlJa"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Model Comparison & Selection**\n",
        "For this project, we'll compare between the following models :\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "*   Gradient Boosting Regressor\n",
        "*   Random Forest Regressor\n",
        "*   SVM\n",
        "*   Decision Tree Regressor\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "Ic0bfA9jIhWS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score\n",
        "def compare_models(model):\n",
        "    model_name = model.__class__.__name__\n",
        "    fit = model.fit(x_train,y_train)\n",
        "    y_pred = fit.predict(x_test)\n",
        "    r2 = r2_score(y_test,y_pred)\n",
        "    return([model_name,r2])"
      ],
      "metadata": {
        "id": "iJGkzHOQMA3L"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "This function, `compare_models`, is designed to evaluate the performance of a machine learning model, particularly on the task of predicting crop yield in your project. Let's break it down step by step:\n",
        "\n",
        "\n",
        "\n",
        "### **1. `model_name = model.__class__.__name__`**\n",
        "- **What it does**: This line extracts the name of the model’s class (e.g., `RandomForestRegressor`, `GradientBoostingRegressor`, etc.).\n",
        "- **Purpose**: This allows the function to keep track of which model it is evaluating. For example, if you pass a `RandomForestRegressor` model, `model_name` will store `\"RandomForestRegressor\"`. This is useful for comparison between multiple models.\n",
        "\n",
        "### **2. `fit = model.fit(x_train, y_train)`**\n",
        "- **What it does**: The model is trained (or \"fit\") using the **training data** (`x_train` and `y_train`), where:\n",
        "  - `x_train`: This represents the features (inputs) for training (like rainfall, temperature, pesticides, etc.).\n",
        "  - `y_train`: This represents the target variable, which in this case is the **crop yield** (e.g., `hg/ha_yield`).\n",
        "- **Purpose**: This is the step where the model learns the relationship between the input features and the target crop yield.\n",
        "\n",
        "### **3. `y_pred = fit.predict(x_test)`**\n",
        "- **What it does**: After the model has been trained, it makes predictions on the **test data** (`x_test`), which is a separate set of input features that the model hasn’t seen before.\n",
        "  - `x_test`: These are the input features from the test dataset (rainfall, temperature, etc.).\n",
        "  - `y_pred`: This is the model's predicted crop yield for each row in `x_test`.\n",
        "- **Purpose**: This step allows you to see how well the model generalizes to unseen data, giving you a sense of its real-world predictive power.\n",
        "\n",
        "### **4. `r2 = r2_score(y_test, y_pred)`**\n",
        "- **What it does**: The **R² score** (or coefficient of determination) is calculated between the true values (`y_test`) and the predicted values (`y_pred`).\n",
        "  - `y_test`: The actual crop yield values from the test data.\n",
        "  - `y_pred`: The predicted crop yield values from the model.\n",
        "- **Purpose**: The R² score measures how well the predictions match the actual outcomes. It indicates the proportion of variance in the target variable (crop yield) that can be explained by the input features (rainfall, temperature, etc.). An R² score closer to 1 means better predictions, while a score closer to 0 indicates poor performance.\n",
        "\n",
        "### **5. `return([model_name, r2])`**\n",
        "- **What it does**: The function returns a list containing two elements:\n",
        "  1. `model_name`: The name of the model class (e.g., `RandomForestRegressor`, `DecisionTreeRegressor`).\n",
        "  2. `r2`: The R² score, which tells you how well the model performed on the test set.\n",
        "- **Purpose**: This makes it easy to compare multiple models by their R² score and know which model performed better.\n",
        "\n",
        "### **How This Helps in Your Crop Yield Prediction Project**\n",
        "This function allows you to compare how different regression models (e.g., Random Forest, Gradient Boosting, Decision Tree, etc.) perform on the same crop yield prediction task. After training each model and evaluating it on the test set, the R² score tells you which model best predicts the crop yield based on features like **rainfall, temperature, and pesticide usage**.\n",
        "\n",
        "#### Example:\n",
        "- If you call this function with different models like `RandomForestRegressor()` or `DecisionTreeRegressor()`, it will return their respective R² scores:\n",
        "  ```python\n",
        "  rf_result = compare_models(RandomForestRegressor())\n",
        "  dt_result = compare_models(DecisionTreeRegressor())\n",
        "  ```\n",
        "- The R² scores will help you decide which model performs best for predicting crop yields."
      ],
      "metadata": {
        "id": "ZkN6csN24GCr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "from sklearn import svm\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "\n",
        "models = [\n",
        "    GradientBoostingRegressor(n_estimators=200, max_depth = 3,random_state = 0 ),\n",
        "    RandomForestRegressor(n_estimators=200,max_depth=3,random_state=0),\n",
        "    svm.SVR(gamma='auto'),\n",
        "    DecisionTreeRegressor()\n",
        "]\n",
        "\n"
      ],
      "metadata": {
        "id": "7HLWbEzJIdai"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "This code sets up a list of four different machine learning models that will be used for crop yield prediction. Each model belongs to a different algorithm type, and the parameters are specified to control how the models work. Let's break down each model and its parameters:\n",
        "\n",
        "### 1. **GradientBoostingRegressor**\n",
        "\n",
        "```python\n",
        "GradientBoostingRegressor(n_estimators=200, max_depth=3, random_state=0)\n",
        "```\n",
        "\n",
        "- **Algorithm Type**: Gradient Boosting is an ensemble learning method that combines the predictions of multiple decision trees in a sequential manner. Each tree corrects the errors of the previous tree.\n",
        "  \n",
        "- **Parameters**:\n",
        "  - **`n_estimators=200`**: This means the model will build **200 trees** sequentially. The more trees, the more complex and accurate the model can become, but it can also increase training time.\n",
        "  - **`max_depth=3`**: This restricts the maximum depth of each tree to 3 levels. A shallower tree (smaller depth) prevents overfitting but might underfit.\n",
        "  - **`random_state=0`**: Ensures reproducibility by setting the seed for random number generation.\n",
        "\n",
        "- **Purpose**: This model will learn the relationship between the input features (rainfall, temperature, pesticide usage, etc.) and the crop yield by minimizing errors over several iterations.\n",
        "\n",
        "---\n",
        "\n",
        "### 2. **RandomForestRegressor**\n",
        "\n",
        "```python\n",
        "RandomForestRegressor(n_estimators=200, max_depth=3, random_state=0)\n",
        "```\n",
        "\n",
        "- **Algorithm Type**: Random Forest is an ensemble learning method that builds multiple decision trees independently (in parallel) and averages their predictions to improve accuracy and reduce overfitting.\n",
        "\n",
        "- **Parameters**:\n",
        "  - **`n_estimators=200`**: The number of decision trees in the forest. Having more trees increases stability but also computational complexity.\n",
        "  - **`max_depth=3`**: Limits the depth of each tree to 3 levels to avoid overfitting.\n",
        "  - **`random_state=0`**: Ensures consistent results when re-running the model.\n",
        "\n",
        "- **Purpose**: This model will aggregate the predictions of multiple trees for a more stable and accurate crop yield prediction.\n",
        "\n",
        "---\n",
        "\n",
        "### 3. **Support Vector Regressor (SVR)**\n",
        "\n",
        "```python\n",
        "svm.SVR(gamma='auto')\n",
        "```\n",
        "\n",
        "- **Algorithm Type**: Support Vector Regression (SVR) is a regression model based on Support Vector Machines (SVM). It tries to fit a hyperplane within a certain margin that best predicts the target variable (crop yield).\n",
        "\n",
        "- **Parameters**:\n",
        "  - **`gamma='auto'`**: This controls how far the influence of a single training example reaches. In this case, `gamma='auto'` uses 1/n_features (number of features) as the default value for gamma, determining the influence of each point.\n",
        "\n",
        "- **Purpose**: SVR is well-suited for capturing non-linear relationships in the dataset (e.g., complex dependencies between rainfall, temperature, and crop yield). However, it is sensitive to the feature scaling and requires proper tuning for large datasets.\n",
        "\n",
        "---\n",
        "\n",
        "### 4. **Decision Tree Regressor**\n",
        "\n",
        "```python\n",
        "DecisionTreeRegressor()\n",
        "```\n",
        "\n",
        "- **Algorithm Type**: Decision Tree Regressor builds a single decision tree that splits the dataset based on feature values to predict the target (crop yield).\n",
        "\n",
        "- **Parameters**:\n",
        "  - **No parameters explicitly set**: Here, the model uses default parameters, which might result in a fully grown tree (no limit on depth, which can lead to overfitting).\n",
        "\n",
        "- **Purpose**: This is a simpler, more interpretable model. However, decision trees tend to overfit if no depth restrictions are applied.\n",
        "\n",
        "---\n",
        "\n",
        "### **How These Models Work Together**\n",
        "\n",
        "You’ve created a list of four different models (`models`), each from a different algorithm type. These models can now be compared using the `compare_models` function you shared earlier. Each model will be trained on the same training data and evaluated based on its R² score to see which one performs best for crop yield prediction.\n",
        "\n",
        "For example:\n",
        "\n",
        "```python\n",
        "for model in models:\n",
        "    result = compare_models(model)\n",
        "    print(result)\n",
        "```\n",
        "\n",
        "This will run each model through the `compare_models` function and print the model name along with its performance (R² score) on the test data.\n",
        "\n",
        "### **Why Different Models Are Used**\n",
        "- **Gradient Boosting** and **Random Forest** are both ensemble methods, but Gradient Boosting improves sequentially while Random Forest uses independent trees.\n",
        "- **SVR** is good for capturing complex non-linear relationships, but it might not scale well for large datasets.\n",
        "- **Decision Tree** is a simple model, easy to interpret but prone to overfitting. It can serve as a baseline.\n",
        "\n",
        "Comparing these models allows you to identify which approach gives the best balance of accuracy and performance for your crop yield prediction task."
      ],
      "metadata": {
        "id": "CYFGpizH4Lrm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_train = list(map(compare_models,models))"
      ],
      "metadata": {
        "id": "KXBAfkYdP8Ok"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The line of code:\n",
        "\n",
        "```python\n",
        "model_train = list(map(compare_models, models))\n",
        "```\n",
        "\n",
        "is used to apply the `compare_models` function to each model in the `models` list and store the results in `model_train`.\n",
        "\n",
        "Here’s a breakdown of how this line works:\n",
        "\n",
        "### **1. `map(compare_models, models)`**\n",
        "- **`map` function**: The `map()` function applies a given function (`compare_models` in this case) to every item in a list or iterable (`models` in this case).\n",
        "  - **`compare_models`**: This is the function you defined earlier, which trains the model, makes predictions, calculates the R² score, and returns the model name and the R² score.\n",
        "  - **`models`**: This is the list of models that you created (`GradientBoostingRegressor`, `RandomForestRegressor`, `SVR`, `DecisionTreeRegressor`).\n",
        "  \n",
        "- **What it does**: The `map()` function iterates over the `models` list, and for each model, it applies `compare_models(model)`. So, for every model in the list, the `compare_models` function will:\n",
        "  1. Train the model on the training data.\n",
        "  2. Predict the crop yield on the test data.\n",
        "  3. Calculate the R² score.\n",
        "  4. Return the model name and R² score as a list.\n",
        "\n",
        "### **2. `list(map(compare_models, models))`**\n",
        "- **Converts the result of `map` to a list**: The `map()` function returns an iterator (or \"lazy\" map object). By wrapping it in `list()`, you convert this into a list, so that you can store and access the results immediately.\n",
        "  \n",
        "### **3. `model_train`**\n",
        "- **Stores the output**: The results of applying `compare_models` to all the models in `models` are stored in the variable `model_train`.\n",
        "  - `model_train` will be a list of lists, where each inner list contains two elements:\n",
        "    1. The name of the model.\n",
        "    2. The R² score of that model.\n",
        "\n",
        "### **Example Output**:\n",
        "Let’s assume you have these models in your `models` list: `GradientBoostingRegressor`, `RandomForestRegressor`, `SVR`, and `DecisionTreeRegressor`. The result might look something like this:\n",
        "\n",
        "```python\n",
        "[['GradientBoostingRegressor', 0.85],\n",
        " ['RandomForestRegressor', 0.83],\n",
        " ['SVR', 0.60],\n",
        " ['DecisionTreeRegressor', 0.78]]\n",
        "```\n",
        "\n",
        "### **How It Helps in Your Crop Yield Prediction Project**:\n",
        "- This single line of code evaluates all the models in the list efficiently.\n",
        "- It provides a quick comparison between the different regression models based on the R² score, so you can easily determine which model is performing best for predicting crop yield based on your features like rainfall, temperature, and pesticide use.\n",
        "  \n",
        "After running this, you can simply inspect `model_train` to see which model performed the best."
      ],
      "metadata": {
        "id": "SvuI_uXC6ORN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(*model_train,sep = \"\\n\")"
      ],
      "metadata": {
        "id": "2aiO3NXKQmZ7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "18efd16d-dc29-4cef-8237-84cadeca4c5d"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['GradientBoostingRegressor', 0.8963259512674945]\n",
            "['RandomForestRegressor', 0.679853645519567]\n",
            "['SVR', -0.21553813895075247]\n",
            "['DecisionTreeRegressor', 0.9667020395602147]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The evaluation metric is based on **R^2 (coefficient of determination)** regression score function, that will represents the proportion of the variance for items (crops) in the regression model.**R^2** score shows how well terms (data points) fit a curve or line.\n",
        "\n",
        "**R^2** is a statistical measure between 0 and 1 which calculates how similar a regression line is to the data it’s fitted to. If it’s a 1, the model 100% predicts the data variance; if it’s a 0, the model predicts none of the variance. I will calculate the adjusted **R^2 (coefficient of determination)**.R-squared is a modified version of R-squared that has been adjusted for the number of predictors in the model. The adjusted R-squared increases only if the new term improves the model more than would be expected by chance. It decreases when a predictor improves the model by less than expected by chance."
      ],
      "metadata": {
        "id": "URm9g15yRIJb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "yeild_df_onehot = yeild_df_onehot.drop(['Year'], axis=1)\n"
      ],
      "metadata": {
        "id": "15vTcP8KRguE"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "yeild_df_onehot.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "jxnC72ZHR1_V",
        "outputId": "cb8be3bd-1ae9-4b30-922f-9d384f90b22f"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   hg/ha_yield  average_rain_fall_mm_per_year  pesticides_tonnes  avg_temp  \\\n",
              "0        36613                         1485.0              121.0     16.37   \n",
              "1        66667                         1485.0              121.0     16.37   \n",
              "2        23333                         1485.0              121.0     16.37   \n",
              "3        12500                         1485.0              121.0     16.37   \n",
              "4         7000                         1485.0              121.0     16.37   \n",
              "\n",
              "   Country_Albania  Country_Algeria  Country_Angola  Country_Argentina  \\\n",
              "0             True            False           False              False   \n",
              "1             True            False           False              False   \n",
              "2             True            False           False              False   \n",
              "3             True            False           False              False   \n",
              "4             True            False           False              False   \n",
              "\n",
              "   Country_Armenia  Country_Australia  ...  Item_Cassava  Item_Maize  \\\n",
              "0            False              False  ...         False        True   \n",
              "1            False              False  ...         False       False   \n",
              "2            False              False  ...         False       False   \n",
              "3            False              False  ...         False       False   \n",
              "4            False              False  ...         False       False   \n",
              "\n",
              "   Item_Plantains and others  Item_Potatoes  Item_Rice, paddy  Item_Sorghum  \\\n",
              "0                      False          False             False         False   \n",
              "1                      False           True             False         False   \n",
              "2                      False          False              True         False   \n",
              "3                      False          False             False          True   \n",
              "4                      False          False             False         False   \n",
              "\n",
              "   Item_Soybeans  Item_Sweet potatoes  Item_Wheat  Item_Yams  \n",
              "0          False                False       False      False  \n",
              "1          False                False       False      False  \n",
              "2          False                False       False      False  \n",
              "3          False                False       False      False  \n",
              "4           True                False       False      False  \n",
              "\n",
              "[5 rows x 115 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-33ae2dc6-755e-47f7-abff-ea2fe6351eda\" class=\"colab-df-container\">\n",
              "    <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>hg/ha_yield</th>\n",
              "      <th>average_rain_fall_mm_per_year</th>\n",
              "      <th>pesticides_tonnes</th>\n",
              "      <th>avg_temp</th>\n",
              "      <th>Country_Albania</th>\n",
              "      <th>Country_Algeria</th>\n",
              "      <th>Country_Angola</th>\n",
              "      <th>Country_Argentina</th>\n",
              "      <th>Country_Armenia</th>\n",
              "      <th>Country_Australia</th>\n",
              "      <th>...</th>\n",
              "      <th>Item_Cassava</th>\n",
              "      <th>Item_Maize</th>\n",
              "      <th>Item_Plantains and others</th>\n",
              "      <th>Item_Potatoes</th>\n",
              "      <th>Item_Rice, paddy</th>\n",
              "      <th>Item_Sorghum</th>\n",
              "      <th>Item_Soybeans</th>\n",
              "      <th>Item_Sweet potatoes</th>\n",
              "      <th>Item_Wheat</th>\n",
              "      <th>Item_Yams</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>36613</td>\n",
              "      <td>1485.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>16.37</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>66667</td>\n",
              "      <td>1485.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>16.37</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>23333</td>\n",
              "      <td>1485.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>16.37</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12500</td>\n",
              "      <td>1485.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>16.37</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7000</td>\n",
              "      <td>1485.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>16.37</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 115 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-33ae2dc6-755e-47f7-abff-ea2fe6351eda')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-33ae2dc6-755e-47f7-abff-ea2fe6351eda button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-33ae2dc6-755e-47f7-abff-ea2fe6351eda');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-a35a8e91-586a-4afb-9e47-fbdca46bf4b2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a35a8e91-586a-4afb-9e47-fbdca46bf4b2')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\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",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a35a8e91-586a-4afb-9e47-fbdca46bf4b2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "yeild_df_onehot"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# setting test data to columns from dataframe and excluding 'hg/ha_yeild' values where ml model be predicting\n",
        "test_df=pd.DataFrame(x_test,columns=yeild_df_onehot.loc[:, yeild_df_onehot.columns != 'hg/ha_yield'].columns)\n",
        "\n",
        "# using stack function to return a reshaped DataFrame by pivoting the columns of the current dataframe\n",
        "cntry=test_df[[col for col in test_df.columns if 'Country' in col]].stack()[test_df[[col for col in test_df.columns if 'Country' in col]].stack()>0]\n",
        "cntrylist=list(pd.DataFrame(cntry).index.get_level_values(1))\n",
        "countries=[i.split(\"_\")[1] for i in cntrylist]\n",
        "itm=test_df[[col for col in test_df.columns if 'Item' in col]].stack()[test_df[[col for col in test_df.columns if 'Item' in col]].stack()>0]\n",
        "itmlist=list(pd.DataFrame(itm).index.get_level_values(1))\n",
        "items=[i.split(\"_\")[1] for i in itmlist]"
      ],
      "metadata": {
        "id": "rRAXf-TASTHh"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The given code snippet appears to set up test data for the model's prediction in the **crop yield prediction project**, specifically aiming to handle the one-hot encoded **countries** and **items** (crops) for the test set (`x_test`). Here's a detailed explanation of each part:\n",
        "\n",
        "### 1. **Setting up the Test Data for Prediction**\n",
        "\n",
        "```python\n",
        "test_df = pd.DataFrame(x_test, columns=yeild_df_onehot.loc[:, yeild_df_onehot.columns != 'hg/ha_yield'].columns)\n",
        "```\n",
        "\n",
        "- **What this does**:\n",
        "  - The test data (`x_test`) is turned into a `DataFrame`, with the same columns as the original data (`yeild_df_onehot`), except for the target variable (`hg/ha_yield`).\n",
        "  - `yeild_df_onehot.loc[:, yeild_df_onehot.columns != 'hg/ha_yield'].columns` fetches all column names except for the target (which you want to predict).\n",
        "\n",
        "- **Why it's done**:\n",
        "  - This step ensures that the test data is structured similarly to the training data, with all features properly aligned. Since the machine learning model will be making predictions on this data, it's important that the columns match those used during training.\n",
        "\n",
        "### 2. **Extracting Country Information from One-Hot Encoded Columns**\n",
        "\n",
        "```python\n",
        "cntry = test_df[[col for col in test_df.columns if 'Country' in col]].stack()[test_df[[col for col in test_df.columns if 'Country' in col]].stack() > 0]\n",
        "```\n",
        "\n",
        "- **What this does**:\n",
        "  - **`[col for col in test_df.columns if 'Country' in col]`**: This selects all columns from `test_df` that correspond to countries (since these columns were created via one-hot encoding).\n",
        "  - **`.stack()`**: This reshapes the `DataFrame` by stacking the one-hot encoded country columns. It transforms the wide-form columns (one column for each country) into a long-form, where each row represents a single value for the country.\n",
        "  - **`.stack() > 0`**: Filters the stacked DataFrame, only keeping the rows where the value is greater than 0 (i.e., `True` values for the one-hot encoding, which represent the countries in each row).\n",
        "  - **`cntry`**: This now holds the countries for each row in the test set where the country column is `1` (or `True`).\n",
        "\n",
        "- **Why it's done**:\n",
        "  - In a one-hot encoded format, the country information is spread across multiple columns. This step gathers that information back into a more usable format, identifying which country each row in the test set corresponds to.\n",
        "\n",
        "### 3. **Getting Country List**\n",
        "\n",
        "```python\n",
        "cntrylist = list(pd.DataFrame(cntry).index.get_level_values(1))\n",
        "countries = [i.split(\"_\")[1] for i in cntrylist]\n",
        "```\n",
        "\n",
        "- **What this does**:\n",
        "  - **`pd.DataFrame(cntry).index.get_level_values(1)`**: Extracts the second level of the index (the column names) from the stacked DataFrame. This gives you a list of the country column names for each row.\n",
        "  - **`[i.split(\"_\")[1] for i in cntrylist]`**: Splits each country column name by the underscore (e.g., `Country_Albania` becomes `['Country', 'Albania']`) and extracts the actual country name (e.g., `Albania`).\n",
        "\n",
        "- **Why it's done**:\n",
        "  - This converts the one-hot encoded country information back into a human-readable list of country names. For each row in the test set, you now know which country it corresponds to.\n",
        "\n",
        "### 4. **Extracting Item (Crop) Information from One-Hot Encoded Columns**\n",
        "\n",
        "```python\n",
        "itm = test_df[[col for col in test_df.columns if 'Item' in col]].stack()[test_df[[col for col in test_df.columns if 'Item' in col]].stack() > 0]\n",
        "```\n",
        "\n",
        "- **What this does**:\n",
        "  - Similar to how the country information was extracted, this code handles the crop (or \"Item\") information.\n",
        "  - **`[col for col in test_df.columns if 'Item' in col]`**: This selects all columns related to crop items (e.g., `Item_Maize`, `Item_Potatoes`).\n",
        "  - **`.stack()`**: Stacks the one-hot encoded columns.\n",
        "  - **`.stack() > 0`**: Filters to keep only the rows where the value is greater than `0` (or `True`), representing the specific crop for each row.\n",
        "\n",
        "- **Why it's done**:\n",
        "  - Since the crop items are one-hot encoded, this step consolidates that information, making it easier to identify which crop each row in the test set corresponds to.\n",
        "\n",
        "### 5. **Getting Item (Crop) List**\n",
        "\n",
        "```python\n",
        "itmlist = list(pd.DataFrame(itm).index.get_level_values(1))\n",
        "items = [i.split(\"_\")[1] for i in itmlist]\n",
        "```\n",
        "\n",
        "- **What this does**:\n",
        "  - **`pd.DataFrame(itm).index.get_level_values(1)`**: Extracts the second level of the index (the column names) for crop items from the stacked DataFrame.\n",
        "  - **`[i.split(\"_\")[1] for i in itmlist]`**: Splits the column names (e.g., `Item_Maize` becomes `['Item', 'Maize']`) and extracts the crop name (e.g., `Maize`).\n",
        "\n",
        "- **Why it's done**:\n",
        "  - This step creates a list of crop names that correspond to each row in the test set. Now, you know which crop (e.g., Maize, Potatoes, etc.) is associated with each row of the test data.\n",
        "\n",
        "---\n",
        "\n",
        "### **Summary of What This Code Does**\n",
        "- The code is working with the test data (`x_test`) to extract the **country** and **crop** (item) information that was one-hot encoded during preprocessing.\n",
        "- The goal is to create lists of country names (`countries`) and crop names (`items`) for each row in the test set, which can then be used to interpret the predictions made by the machine learning models.\n",
        "- By using `.stack()`, the one-hot encoded columns are transformed back into a more understandable format, allowing the identification of the country and crop associated with each data point in the test set.\n",
        "\n",
        "### **Why This Matters for Model Prediction**\n",
        "When the model makes predictions (i.e., predicts the crop yield), you need to know the **context** for each prediction—i.e., which **country** and **crop** that prediction corresponds to. This code helps you extract that context from the one-hot encoded features, making the predictions more interpretable and useful in real-world applications."
      ],
      "metadata": {
        "id": "EDNksM2563Po"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "test_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "CvftRO_qXXe1",
        "outputId": "b6cb36ed-1360-47ea-faec-160ac425e77b"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   average_rain_fall_mm_per_year  pesticides_tonnes  avg_temp  \\\n",
              "0                       0.529006           0.008521  0.545486   \n",
              "1                       0.507056           0.214084  0.480409   \n",
              "2                       0.138915           0.016812  0.871210   \n",
              "3                       0.536218           0.938137  0.784327   \n",
              "4                       0.048918           0.001920  0.628279   \n",
              "\n",
              "   Country_Albania  Country_Algeria  Country_Angola  Country_Argentina  \\\n",
              "0              0.0              0.0             0.0                0.0   \n",
              "1              0.0              0.0             0.0                0.0   \n",
              "2              0.0              0.0             0.0                0.0   \n",
              "3              0.0              0.0             0.0                0.0   \n",
              "4              0.0              0.0             0.0                0.0   \n",
              "\n",
              "   Country_Armenia  Country_Australia  Country_Austria  ...  Item_Cassava  \\\n",
              "0              0.0                0.0              0.0  ...           0.0   \n",
              "1              0.0                0.0              0.0  ...           0.0   \n",
              "2              0.0                0.0              0.0  ...           0.0   \n",
              "3              0.0                0.0              0.0  ...           0.0   \n",
              "4              0.0                0.0              0.0  ...           0.0   \n",
              "\n",
              "   Item_Maize  Item_Plantains and others  Item_Potatoes  Item_Rice, paddy  \\\n",
              "0         0.0                        0.0            0.0               0.0   \n",
              "1         0.0                        0.0            0.0               0.0   \n",
              "2         0.0                        0.0            1.0               0.0   \n",
              "3         0.0                        0.0            0.0               0.0   \n",
              "4         0.0                        0.0            0.0               0.0   \n",
              "\n",
              "   Item_Sorghum  Item_Soybeans  Item_Sweet potatoes  Item_Wheat  Item_Yams  \n",
              "0           0.0            0.0                  1.0         0.0        0.0  \n",
              "1           0.0            0.0                  0.0         1.0        0.0  \n",
              "2           0.0            0.0                  0.0         0.0        0.0  \n",
              "3           1.0            0.0                  0.0         0.0        0.0  \n",
              "4           1.0            0.0                  0.0         0.0        0.0  \n",
              "\n",
              "[5 rows x 114 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0a0a474f-d36a-4516-824e-a8a9464dca21\" class=\"colab-df-container\">\n",
              "    <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>average_rain_fall_mm_per_year</th>\n",
              "      <th>pesticides_tonnes</th>\n",
              "      <th>avg_temp</th>\n",
              "      <th>Country_Albania</th>\n",
              "      <th>Country_Algeria</th>\n",
              "      <th>Country_Angola</th>\n",
              "      <th>Country_Argentina</th>\n",
              "      <th>Country_Armenia</th>\n",
              "      <th>Country_Australia</th>\n",
              "      <th>Country_Austria</th>\n",
              "      <th>...</th>\n",
              "      <th>Item_Cassava</th>\n",
              "      <th>Item_Maize</th>\n",
              "      <th>Item_Plantains and others</th>\n",
              "      <th>Item_Potatoes</th>\n",
              "      <th>Item_Rice, paddy</th>\n",
              "      <th>Item_Sorghum</th>\n",
              "      <th>Item_Soybeans</th>\n",
              "      <th>Item_Sweet potatoes</th>\n",
              "      <th>Item_Wheat</th>\n",
              "      <th>Item_Yams</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.529006</td>\n",
              "      <td>0.008521</td>\n",
              "      <td>0.545486</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.507056</td>\n",
              "      <td>0.214084</td>\n",
              "      <td>0.480409</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.138915</td>\n",
              "      <td>0.016812</td>\n",
              "      <td>0.871210</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.536218</td>\n",
              "      <td>0.938137</td>\n",
              "      <td>0.784327</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.048918</td>\n",
              "      <td>0.001920</td>\n",
              "      <td>0.628279</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 114 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0a0a474f-d36a-4516-824e-a8a9464dca21')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-0a0a474f-d36a-4516-824e-a8a9464dca21 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-0a0a474f-d36a-4516-824e-a8a9464dca21');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-111b1b9c-9509-4577-b4aa-77c4360e0af4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-111b1b9c-9509-4577-b4aa-77c4360e0af4')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\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",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-111b1b9c-9509-4577-b4aa-77c4360e0af4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "test_df"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_df.drop([col for col in test_df.columns if 'Item' in col],axis=1,inplace=True)\n",
        "test_df.drop([col for col in test_df.columns if 'Country' in col],axis=1,inplace=True)\n",
        "test_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Zr0DdpHXXbeO",
        "outputId": "c783000f-a595-46b8-cda8-729833d187e6"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   average_rain_fall_mm_per_year  pesticides_tonnes  avg_temp\n",
              "0                       0.529006           0.008521  0.545486\n",
              "1                       0.507056           0.214084  0.480409\n",
              "2                       0.138915           0.016812  0.871210\n",
              "3                       0.536218           0.938137  0.784327\n",
              "4                       0.048918           0.001920  0.628279"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9c4a2ff0-7ea8-4d35-ac2f-c48c774eedb1\" class=\"colab-df-container\">\n",
              "    <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>average_rain_fall_mm_per_year</th>\n",
              "      <th>pesticides_tonnes</th>\n",
              "      <th>avg_temp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.529006</td>\n",
              "      <td>0.008521</td>\n",
              "      <td>0.545486</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.507056</td>\n",
              "      <td>0.214084</td>\n",
              "      <td>0.480409</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.138915</td>\n",
              "      <td>0.016812</td>\n",
              "      <td>0.871210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.536218</td>\n",
              "      <td>0.938137</td>\n",
              "      <td>0.784327</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.048918</td>\n",
              "      <td>0.001920</td>\n",
              "      <td>0.628279</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9c4a2ff0-7ea8-4d35-ac2f-c48c774eedb1')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9c4a2ff0-7ea8-4d35-ac2f-c48c774eedb1 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-9c4a2ff0-7ea8-4d35-ac2f-c48c774eedb1');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-d1dd6b31-9946-4733-85b2-01460d780821\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d1dd6b31-9946-4733-85b2-01460d780821')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\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",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d1dd6b31-9946-4733-85b2-01460d780821 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "test_df",
              "summary": "{\n  \"name\": \"test_df\",\n  \"rows\": 5649,\n  \"fields\": [\n    {\n      \"column\": \"average_rain_fall_mm_per_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.22118577488233318,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          0.011915961116337408,\n          0.603637503919724,\n          0.4261523988711195\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pesticides_tonnes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.16880874289098577,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 1293,\n        \"samples\": [\n          0.06608324218232109,\n          0.08479290058599487,\n          0.00010593348225652239\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_temp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.21442406702840394,\n        \"min\": 0.011243611584327087,\n        \"max\": 1.0,\n        \"num_unique_values\": 1524,\n        \"samples\": [\n          0.8732538330494038,\n          0.6637137989778537,\n          0.6531516183986372\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_df['Country']=countries\n",
        "test_df['Item']=items\n",
        "test_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "pT97kl2lXhnF",
        "outputId": "5eb96042-96fd-4cee-adb9-099fa6574e98"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   average_rain_fall_mm_per_year  pesticides_tonnes  avg_temp   Country  \\\n",
              "0                       0.529006           0.008521  0.545486      Peru   \n",
              "1                       0.507056           0.214084  0.480409     Japan   \n",
              "2                       0.138915           0.016812  0.871210  Pakistan   \n",
              "3                       0.536218           0.938137  0.784327    Brazil   \n",
              "4                       0.048918           0.001920  0.628279   Tunisia   \n",
              "\n",
              "             Item  \n",
              "0  Sweet potatoes  \n",
              "1           Wheat  \n",
              "2        Potatoes  \n",
              "3         Sorghum  \n",
              "4         Sorghum  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b8d67cc0-0057-452b-959c-4b71ce08605b\" class=\"colab-df-container\">\n",
              "    <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>average_rain_fall_mm_per_year</th>\n",
              "      <th>pesticides_tonnes</th>\n",
              "      <th>avg_temp</th>\n",
              "      <th>Country</th>\n",
              "      <th>Item</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.529006</td>\n",
              "      <td>0.008521</td>\n",
              "      <td>0.545486</td>\n",
              "      <td>Peru</td>\n",
              "      <td>Sweet potatoes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.507056</td>\n",
              "      <td>0.214084</td>\n",
              "      <td>0.480409</td>\n",
              "      <td>Japan</td>\n",
              "      <td>Wheat</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.138915</td>\n",
              "      <td>0.016812</td>\n",
              "      <td>0.871210</td>\n",
              "      <td>Pakistan</td>\n",
              "      <td>Potatoes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.536218</td>\n",
              "      <td>0.938137</td>\n",
              "      <td>0.784327</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>Sorghum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.048918</td>\n",
              "      <td>0.001920</td>\n",
              "      <td>0.628279</td>\n",
              "      <td>Tunisia</td>\n",
              "      <td>Sorghum</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b8d67cc0-0057-452b-959c-4b71ce08605b')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b8d67cc0-0057-452b-959c-4b71ce08605b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b8d67cc0-0057-452b-959c-4b71ce08605b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-23dbb663-b9df-4ae3-b6de-8a4b406883ca\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-23dbb663-b9df-4ae3-b6de-8a4b406883ca')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\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",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-23dbb663-b9df-4ae3-b6de-8a4b406883ca button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "test_df",
              "summary": "{\n  \"name\": \"test_df\",\n  \"rows\": 5649,\n  \"fields\": [\n    {\n      \"column\": \"average_rain_fall_mm_per_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.22118577488233318,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          0.011915961116337408,\n          0.603637503919724,\n          0.4261523988711195\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pesticides_tonnes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.16880874289098577,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 1293,\n        \"samples\": [\n          0.06608324218232109,\n          0.08479290058599487,\n          0.00010593348225652239\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_temp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.21442406702840394,\n        \"min\": 0.011243611584327087,\n        \"max\": 1.0,\n        \"num_unique_values\": 1524,\n        \"samples\": [\n          0.8732538330494038,\n          0.6637137989778537,\n          0.6531516183986372\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 101,\n        \"samples\": [\n          \"Algeria\",\n          \"Germany\",\n          \"Tajikistan\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Item\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Cassava\",\n          \"Wheat\",\n          \"Soybeans\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf=DecisionTreeRegressor()\n",
        "model=clf.fit(x_train,y_train)\n",
        "\n",
        "test_df[\"yield_predicted\"]= model.predict(x_test)\n",
        "test_df[\"yield_actual\"]=pd.DataFrame(y_test)[\"hg/ha_yield\"].tolist()\n",
        "test_group=test_df.groupby(\"Item\")\n",
        "test_group.apply(lambda x: r2_score(x.yield_actual,x.yield_predicted))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "NkRtL6GxXp7f",
        "outputId": "641070f4-ea51-4fa0-ccdb-a6526dc9e50a"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Item\n",
              "Cassava                 0.930399\n",
              "Maize                   0.899483\n",
              "Plantains and others    0.832473\n",
              "Potatoes                0.913435\n",
              "Rice, paddy             0.907787\n",
              "Sorghum                 0.783900\n",
              "Soybeans                0.845929\n",
              "Sweet potatoes          0.883172\n",
              "Wheat                   0.946742\n",
              "Yams                    0.924401\n",
              "dtype: float64"
            ],
            "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>0</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Item</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Cassava</th>\n",
              "      <td>0.930399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Maize</th>\n",
              "      <td>0.899483</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Plantains and others</th>\n",
              "      <td>0.832473</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Potatoes</th>\n",
              "      <td>0.913435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rice, paddy</th>\n",
              "      <td>0.907787</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sorghum</th>\n",
              "      <td>0.783900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Soybeans</th>\n",
              "      <td>0.845929</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sweet potatoes</th>\n",
              "      <td>0.883172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wheat</th>\n",
              "      <td>0.946742</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yams</th>\n",
              "      <td>0.924401</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " following steps for a crop yield prediction task using a `DecisionTreeRegressor`. Here's a breakdown:\n",
        "\n",
        "1. **Model Training**:\n",
        "   ```python\n",
        "   clf = DecisionTreeRegressor()\n",
        "   model = clf.fit(x_train, y_train)\n",
        "   ```\n",
        "   This initializes a `DecisionTreeRegressor` and fits it to the training data (`x_train` and `y_train`).\n",
        "\n",
        "2. **Making Predictions**:\n",
        "   ```python\n",
        "   test_df[\"yield_predicted\"] = model.predict(x_test)\n",
        "   ```\n",
        "   Here, the model predicts the yields for the test dataset (`x_test`) and stores the results in a new column in `test_df`.\n",
        "\n",
        "3. **Storing Actual Yields**:\n",
        "   ```python\n",
        "   test_df[\"yield_actual\"] = pd.DataFrame(y_test)[\"hg/ha_yield\"].tolist()\n",
        "   ```\n",
        "   This creates a new column for the actual yields using the values from `y_test`.\n",
        "\n",
        "4. **Grouping and Evaluating**:\n",
        "   ```python\n",
        "   test_group = test_df.groupby(\"Item\")\n",
        "   test_group.apply(lambda x: r2_score(x.yield_actual, x.yield_predicted))\n",
        "   ```\n",
        "   Finally, the code groups the test data by the \"Item\" column and applies the `r2_score` function to calculate the R² score for each group, which indicates the model's performance.\n",
        "\n"
      ],
      "metadata": {
        "id": "heTKi8CxkVVY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# So let's run the model actual values against the predicted ones\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "ax.scatter(test_df[\"yield_actual\"], test_df[\"yield_predicted\"],edgecolors=(0, 0, 0))\n",
        "\n",
        "ax.set_xlabel('Actual')\n",
        "ax.set_ylabel('Predicted')\n",
        "ax.set_title(\"Actual vs Predicted\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "T6wKmNQPYOT7",
        "outputId": "44c5f5d9-5cb4-4769-cf0f-eeb5349b1601"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It can be seen that R Square score is excellent. This means that we have found a good fitting model to predict the crops yield value for a certain country. Adding more features, like climate data; wind and pollution, the economical situation of a given country and so on will probably enhance the model’s predictions."
      ],
      "metadata": {
        "id": "c1AbN3lNYUOo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def adjusted_r_squared(y,yhat,x):\n",
        "    score=1- (((1-(r2_score(y,yhat)))*(len(y)-1))/(len(y)-x.shape[1]-2))\n",
        "    return score\n",
        "\n",
        "test_group.apply(lambda x: adjusted_r_squared(x.yield_actual,x.yield_predicted,x))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "18NcM0eQYbDF",
        "outputId": "fb05faf5-1fee-4e92-e758-fdb964783fa0"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Item\n",
              "Cassava                 0.928982\n",
              "Maize                   0.898468\n",
              "Plantains and others    0.822002\n",
              "Potatoes                0.912645\n",
              "Rice, paddy             0.906630\n",
              "Sorghum                 0.781161\n",
              "Soybeans                0.843994\n",
              "Sweet potatoes          0.881435\n",
              "Wheat                   0.946180\n",
              "Yams                    0.920801\n",
              "dtype: float64"
            ],
            "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>0</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Item</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Cassava</th>\n",
              "      <td>0.928982</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Maize</th>\n",
              "      <td>0.898468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Plantains and others</th>\n",
              "      <td>0.822002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Potatoes</th>\n",
              "      <td>0.912645</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rice, paddy</th>\n",
              "      <td>0.906630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sorghum</th>\n",
              "      <td>0.781161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Soybeans</th>\n",
              "      <td>0.843994</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sweet potatoes</th>\n",
              "      <td>0.881435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wheat</th>\n",
              "      <td>0.946180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yams</th>\n",
              "      <td>0.920801</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Model Results & Conclusions**"
      ],
      "metadata": {
        "id": "fr6gDojPYg_m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "varimp= {'importances':model.feature_importances_,'names':yeild_df_onehot.columns[yeild_df_onehot.columns!=\"hg/ha_yield\"]}"
      ],
      "metadata": {
        "id": "_XlgfZwQYliu"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "creating a dictionary to store the feature importances of the `DecisionTreeRegressor` model along with the corresponding feature names. Here’s a breakdown:\n",
        "\n",
        "1. **Feature Importances**:\n",
        "   ```python\n",
        "   model.feature_importances_\n",
        "   ```\n",
        "   This retrieves the importance scores for each feature used in the model. These scores indicate how much each feature contributes to the model's predictions.\n",
        "\n",
        "2. **Feature Names**:\n",
        "   ```python\n",
        "   yeild_df_onehot.columns[yeild_df_onehot.columns != \"hg/ha_yield\"]\n",
        "   ```\n",
        "   This selects the column names from `yeild_df_onehot`, excluding the target variable \"hg/ha_yield\".\n",
        "\n",
        "3. **Creating the Dictionary**:\n",
        "   ```python\n",
        "   varimp = {\n",
        "       'importances': model.feature_importances_,\n",
        "       'names': yeild_df_onehot.columns[yeild_df_onehot.columns != \"hg/ha_yield\"]\n",
        "   }\n",
        "   ```\n",
        "\n"
      ],
      "metadata": {
        "id": "4ee3KgbSltDD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a4_dims = (10,30)\n",
        "\n",
        "fig, ax = plt.subplots(figsize=a4_dims)\n",
        "df=pd.DataFrame.from_dict(varimp)\n",
        "df.sort_values(ascending=False,by=[\"importances\"],inplace=True)\n",
        "df=df.dropna()\n",
        "sns.barplot(x=\"importances\",y=\"names\",palette=\"vlag\",data=df,orient=\"h\",ax=ax);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Yr8DueGDZIPh",
        "outputId": "769c9f67-f31d-4d47-f956-3e31fc8898fd"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-37-3b87af8195ea>:7: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x=\"importances\",y=\"names\",palette=\"vlag\",data=df,orient=\"h\",ax=ax);\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x3000 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Your code snippet is set up to create a horizontal bar plot of feature importances using `seaborn`. Here’s a breakdown of each part and some suggestions for improvement:\n",
        "\n",
        "1. **Figure Dimensions**:\n",
        "   ```python\n",
        "   a4_dims = (10, 30)\n",
        "   fig, ax = plt.subplots(figsize=a4_dims)\n",
        "   ```\n",
        "   This initializes a figure with specified dimensions suitable for displaying a horizontal bar plot.\n",
        "\n",
        "2. **Creating DataFrame from Dictionary**:\n",
        "   ```python\n",
        "   df = pd.DataFrame.from_dict(varimp)\n",
        "   ```\n",
        "   This creates a DataFrame from the `varimp` dictionary you created earlier, which contains the feature importances and their names.\n",
        "\n",
        "3. **Sorting Values**:\n",
        "   ```python\n",
        "   df.sort_values(ascending=False, by=[\"importances\"], inplace=True)\n",
        "   df = df.dropna()\n",
        "   ```\n",
        "   This sorts the DataFrame by importances in descending order and drops any rows with NaN values.\n",
        "\n",
        "4. **Plotting with Seaborn**:\n",
        "   ```python\n",
        "   sns.barplot(x=\"importances\", y=\"names\", palette=\"vlag\", data=df, orient=\"h\", ax=ax);\n",
        "   ```\n",
        "\n",
        "### Suggestions\n",
        "- Make sure you have imported the necessary libraries:\n",
        "  ```python\n",
        "  import matplotlib.pyplot as plt\n",
        "  import seaborn as sns\n",
        "  import pandas as pd\n",
        "  ```\n",
        "\n",
        "- **Setting the Palette**: Ensure that the `palette` you selected (`\"vlag\"`) fits your visualization needs. You could also choose other palettes like `\"viridis\"` or `\"coolwarm\"` for different color schemes.\n",
        "\n",
        "- **Displaying the Plot**: To display the plot, you might want to add:\n",
        "  ```python\n",
        "  plt.title('Feature Importances')\n",
        "  plt.xlabel('Importance Score')\n",
        "  plt.ylabel('Feature Names')\n",
        "  plt.show()\n",
        "  ```\n",
        "\n",
        "- **Handling Large Data**: If you have many features, consider adjusting the figure size or the font size for better readability.\n",
        "\n"
      ],
      "metadata": {
        "id": "jHxs9ORPmHdG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Getting only top 7 of features importance in the model:"
      ],
      "metadata": {
        "id": "ofSIpdeSZPqC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#7 most important factors that affect crops\n",
        "a4_dims = (16.7, 8.27)\n",
        "\n",
        "fig, ax = plt.subplots(figsize=a4_dims)\n",
        "df=pd.DataFrame.from_dict(varimp)\n",
        "df.sort_values(ascending=False,by=[\"importances\"],inplace=True)\n",
        "df=df.dropna()\n",
        "df=df.nlargest(7, 'importances')\n",
        "sns.barplot(x=\"importances\",y=\"names\",palette=\"vlag\",data=df,orient=\"h\",ax=ax);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 793
        },
        "id": "m8Mu6dG5ZRRm",
        "outputId": "71a1df13-4e2a-4509-8d00-08513e874dda"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-38-f07bf187ec5a>:9: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x=\"importances\",y=\"names\",palette=\"vlag\",data=df,orient=\"h\",ax=ax);\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1670x827 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The crop being potatoes has the highest importance in the decision making for the model, where it's the highest crops in the dataset. Cassava too, then as expected we see the effect of pesticides, where its the third most important feature, and then if the crop is sweet potatoes, we see some of the highest crops in features importance in dataset.\n",
        "\n",
        "If the crop is grown in India, makes sense since India has the largest crops sum in the dataset. Then comes rainfall and temperature. Thr first assumption about these features were correct, where they all significantly impact the expected crops yield in the model."
      ],
      "metadata": {
        "id": "Ud7QkUWVZaQN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This code snippet visualizes the **top 7 most important factors** that affect crop yield in your machine learning model. Let’s break it down step by step:\n",
        "\n",
        "### 1. **Setting the Figure Size**\n",
        "\n",
        "```python\n",
        "a4_dims = (16.7, 8.27)\n",
        "fig, ax = plt.subplots(figsize=a4_dims)\n",
        "```\n",
        "\n",
        "- **What this does**:\n",
        "  - Sets the size of the figure that will display the barplot. The dimensions are given in inches to create a wide, landscape-oriented figure.\n",
        "  \n",
        "- **Why it’s done**:\n",
        "  - This ensures that the plot has enough space to display the labels and bars clearly without crowding, especially if there are longer names or values.\n",
        "\n",
        "### 2. **Converting the Variable Importance Data to a DataFrame**\n",
        "\n",
        "```python\n",
        "df = pd.DataFrame.from_dict(varimp)\n",
        "```\n",
        "\n",
        "- **What this does**:\n",
        "  - `varimp` is likely a dictionary that contains the feature importances (in this case, the importance of factors affecting crop yield). This line converts it into a `DataFrame` so it can be manipulated and visualized more easily.\n",
        "\n",
        "- **Why it’s done**:\n",
        "  - Working with a `DataFrame` makes it easier to sort, filter, and plot the data, which are the next steps in the code.\n",
        "\n",
        "### 3. **Sorting and Cleaning the Data**\n",
        "\n",
        "```python\n",
        "df.sort_values(ascending=False, by=[\"importances\"], inplace=True)\n",
        "df = df.dropna()\n",
        "df = df.nlargest(7, 'importances')\n",
        "```\n",
        "\n",
        "- **What this does**:\n",
        "  - **Sorting**: The `sort_values()` function sorts the DataFrame in descending order based on the `importances` column, so that the most important features are at the top.\n",
        "  - **Removing `NaN` values**: `df.dropna()` removes any rows with missing values to ensure clean data for plotting.\n",
        "  - **Selecting Top 7**: The `nlargest(7, 'importances')` function selects the top 7 rows based on the `importances` column. These represent the 7 most important factors.\n",
        "\n",
        "- **Why it’s done**:\n",
        "  - You want to display the top 7 most important features and ensure the data is clean and sorted for visualization.\n",
        "\n",
        "### 4. **Plotting the Bar Chart**\n",
        "\n",
        "```python\n",
        "sns.barplot(x=\"importances\", y=\"names\", palette=\"vlag\", data=df, orient=\"h\", ax=ax);\n",
        "```\n",
        "\n",
        "- **What this does**:\n",
        "  - **`sns.barplot()`**: Creates a barplot using Seaborn. The `x=\"importances\"` sets the feature importances on the x-axis, while `y=\"names\"` sets the corresponding feature names on the y-axis.\n",
        "  - **`palette=\"vlag\"`**: Applies a color palette for the bars (you can adjust this to your preference).\n",
        "  - **`orient=\"h\"`**: Ensures the bars are horizontal, which makes it easier to read the feature names on the y-axis.\n",
        "  - **`ax=ax`**: Plots the bar chart on the figure you initialized earlier.\n",
        "\n",
        "- **Why it’s done**:\n",
        "  - Bar plots are great for comparing the relative importance of different features. Horizontal bars are especially useful when you have longer names (like country or crop names).\n",
        "\n",
        "### **Summary of What This Code Does:**\n",
        "\n",
        "- It visualizes the **top 7 most important features** (factors) that influence crop yield in your prediction model. The importance of these features is calculated by the model, likely during the training process, and is stored in `varimp`.\n",
        "- This kind of visualization helps you understand **which factors the model deems most significant**, making it easier to interpret the results and focus on key variables (like **rainfall, temperature, pesticide use**, etc.).\n",
        "\n",
        "### **Why Feature Importance Matters:**\n",
        "In your **crop yield prediction project**, knowing which factors are most important helps in:\n",
        "1. **Improving Model Interpretability**: You can see why certain predictions are made by understanding which factors influence the outcome.\n",
        "2. **Decision-Making**: Farmers, policymakers, and agricultural planners can focus on the most important factors (e.g., optimizing rainfall, managing pesticides) to increase yield.\n",
        "3. **Feature Selection**: It helps you remove less important features to reduce the model's complexity while retaining accuracy.\n",
        "\n",
        "This plot will give you a clear understanding of the **top contributors** to crop yield in your machine learning model."
      ],
      "metadata": {
        "id": "hzpgs0EG2Qsm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Boxplot that shows yield for each item\n",
        "a4_dims = (16, 8.)\n",
        "\n",
        "fig, ax = plt.subplots(figsize=a4_dims)\n",
        "sns.boxplot(x=\"Item\",y=\"hg/ha_yield\",palette=\"vlag\",data=yeild_df,ax=ax);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 787
        },
        "id": "bkisBRdhZZaJ",
        "outputId": "6c955420-6f8f-43bd-b491-7a45ee3ee78d"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-39-7fa74b186812>:5: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.boxplot(x=\"Item\",y=\"hg/ha_yield\",palette=\"vlag\",data=yeild_df,ax=ax);\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "GraphViz Library is used for graph generation of the Decision Tree Regressor."
      ],
      "metadata": {
        "id": "TmZSSe3XZpVR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install graphviz"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dYSleHWnZryw",
        "outputId": "bc948a0c-2c7f-420b-e2ae-0280a2beaff6"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.10/dist-packages (0.20.3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.environ[\"PATH\"] += os.pathsep + '/content/sample_data'"
      ],
      "metadata": {
        "id": "aJzG6tcfZ28r"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Install six\n",
        "!pip install six\n",
        "\n",
        "#Import StringIO from six\n",
        "from six import StringIO\n",
        "from IPython.display import Image\n",
        "from sklearn.tree import export_graphviz\n",
        "import pydotplus\n",
        "import collections\n",
        "dot_data = export_graphviz(model, out_file=None, max_depth=5,\n",
        "                                feature_names=yeild_df_onehot.columns[yeild_df_onehot.columns!=\"hg/ha_yield\"])\n",
        "\n",
        "# Draw graph\n",
        "graph = pydotplus.graph_from_dot_data(dot_data)\n",
        "\n",
        "\n",
        "# Show graph\n",
        "graph.write_pdf(\"model_depth.pdf\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VuWQ48Ntolyf",
        "outputId": "39d28135-023e-425f-9c3e-87fe3ea53410"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (1.16.0)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_max=yeild_df_onehot.loc[:, yeild_df_onehot.columns != 'hg/ha_yield'].max()\n",
        "data_min=yeild_df_onehot.loc[:, yeild_df_onehot.columns != 'hg/ha_yield'].min()\n",
        "#saving the model using pickle\n",
        "import pickle\n",
        "filename = 'yield_model.sav'\n",
        "pickle.dump(model, open(filename, 'wb'))\n",
        "\n",
        "pickle.dump(data_max, open('max.data', 'wb'))\n",
        "pickle.dump(data_min, open('min.data', 'wb'))"
      ],
      "metadata": {
        "id": "RtlfPa90qJGK"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "\n",
        "# Load the model from the file\n",
        "with open('/content/yield_model.sav', 'rb') as file:\n",
        "    loaded_model = pickle.load(file)\n",
        "\n",
        "# Make predictions with the loaded model\n",
        "predictions = loaded_model.predict(x_test)\n",
        "\n",
        "print(predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "plOQwMzXs6Fh",
        "outputId": "2c554471-29f4-4cc9-dc7a-f37e1216d0a2"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[171233.  31797. 104452. ...   8602.  61563.  14042.]\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}