6.10-GradientBoost.ipynb

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      "name": "6.10-GradientBoost.ipynb",
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    {
      "cell_type": "code",
      "metadata": {
        "id": "-nui_PbH1Irm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import r2_score\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "from sklearn.metrics import r2_score\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xsbDZ3eg1WZD",
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        "outputId": "33722af1-4695-445f-ef5b-0a1df852063d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 271
        }
      },
      "source": [
        "url='https://raw.githubusercontent.com/FazlyRabbiBD/Data-Science-Book/master/data-fuel-consumption.csv'\n",
        "df = pd.read_csv(url)\n",
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MODELYEAR</th>\n",
              "      <th>MAKE</th>\n",
              "      <th>MODEL</th>\n",
              "      <th>VEHICLECLASS</th>\n",
              "      <th>ENGINESIZE</th>\n",
              "      <th>CYLINDERS</th>\n",
              "      <th>TRANSMISSION</th>\n",
              "      <th>FUELTYPE</th>\n",
              "      <th>FUELCONSUMPTION_CITY</th>\n",
              "      <th>FUELCONSUMPTION_HWY</th>\n",
              "      <th>FUELCONSUMPTION_COMB</th>\n",
              "      <th>FUELCONSUMPTION_COMB_MPG</th>\n",
              "      <th>CO2EMISSIONS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2014</td>\n",
              "      <td>ACURA</td>\n",
              "      <td>ILX</td>\n",
              "      <td>COMPACT</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4</td>\n",
              "      <td>AS5</td>\n",
              "      <td>Z</td>\n",
              "      <td>9.9</td>\n",
              "      <td>6.7</td>\n",
              "      <td>8.5</td>\n",
              "      <td>33</td>\n",
              "      <td>196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2014</td>\n",
              "      <td>ACURA</td>\n",
              "      <td>ILX</td>\n",
              "      <td>COMPACT</td>\n",
              "      <td>2.4</td>\n",
              "      <td>4</td>\n",
              "      <td>M6</td>\n",
              "      <td>Z</td>\n",
              "      <td>11.2</td>\n",
              "      <td>7.7</td>\n",
              "      <td>9.6</td>\n",
              "      <td>29</td>\n",
              "      <td>221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2014</td>\n",
              "      <td>ACURA</td>\n",
              "      <td>ILX HYBRID</td>\n",
              "      <td>COMPACT</td>\n",
              "      <td>1.5</td>\n",
              "      <td>4</td>\n",
              "      <td>AV7</td>\n",
              "      <td>Z</td>\n",
              "      <td>6.0</td>\n",
              "      <td>5.8</td>\n",
              "      <td>5.9</td>\n",
              "      <td>48</td>\n",
              "      <td>136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2014</td>\n",
              "      <td>ACURA</td>\n",
              "      <td>MDX 4WD</td>\n",
              "      <td>SUV - SMALL</td>\n",
              "      <td>3.5</td>\n",
              "      <td>6</td>\n",
              "      <td>AS6</td>\n",
              "      <td>Z</td>\n",
              "      <td>12.7</td>\n",
              "      <td>9.1</td>\n",
              "      <td>11.1</td>\n",
              "      <td>25</td>\n",
              "      <td>255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014</td>\n",
              "      <td>ACURA</td>\n",
              "      <td>RDX AWD</td>\n",
              "      <td>SUV - SMALL</td>\n",
              "      <td>3.5</td>\n",
              "      <td>6</td>\n",
              "      <td>AS6</td>\n",
              "      <td>Z</td>\n",
              "      <td>12.1</td>\n",
              "      <td>8.7</td>\n",
              "      <td>10.6</td>\n",
              "      <td>27</td>\n",
              "      <td>244</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   MODELYEAR   MAKE  ... FUELCONSUMPTION_COMB_MPG CO2EMISSIONS\n",
              "0       2014  ACURA  ...                       33          196\n",
              "1       2014  ACURA  ...                       29          221\n",
              "2       2014  ACURA  ...                       48          136\n",
              "3       2014  ACURA  ...                       25          255\n",
              "4       2014  ACURA  ...                       27          244\n",
              "\n",
              "[5 rows x 13 columns]"
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          "metadata": {
            "tags": []
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          "execution_count": 19
        }
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      "metadata": {
        "id": "K55VlmK_1mZE",
        "colab_type": "code",
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      "source": [
        "X= df[[ 'ENGINESIZE', 'CYLINDERS', 'FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY', 'FUELCONSUMPTION_COMB', 'FUELCONSUMPTION_COMB_MPG']]\n",
        "y = df['CO2EMISSIONS']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s_bTVifLGeev",
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      "source": [
        "X"
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        {
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          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ENGINESIZE</th>\n",
              "      <th>CYLINDERS</th>\n",
              "      <th>FUELCONSUMPTION_CITY</th>\n",
              "      <th>FUELCONSUMPTION_HWY</th>\n",
              "      <th>FUELCONSUMPTION_COMB</th>\n",
              "      <th>FUELCONSUMPTION_COMB_MPG</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.0</td>\n",
              "      <td>4</td>\n",
              "      <td>9.9</td>\n",
              "      <td>6.7</td>\n",
              "      <td>8.5</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.4</td>\n",
              "      <td>4</td>\n",
              "      <td>11.2</td>\n",
              "      <td>7.7</td>\n",
              "      <td>9.6</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.5</td>\n",
              "      <td>4</td>\n",
              "      <td>6.0</td>\n",
              "      <td>5.8</td>\n",
              "      <td>5.9</td>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.5</td>\n",
              "      <td>6</td>\n",
              "      <td>12.7</td>\n",
              "      <td>9.1</td>\n",
              "      <td>11.1</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.5</td>\n",
              "      <td>6</td>\n",
              "      <td>12.1</td>\n",
              "      <td>8.7</td>\n",
              "      <td>10.6</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1062</th>\n",
              "      <td>3.0</td>\n",
              "      <td>6</td>\n",
              "      <td>13.4</td>\n",
              "      <td>9.8</td>\n",
              "      <td>11.8</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1063</th>\n",
              "      <td>3.2</td>\n",
              "      <td>6</td>\n",
              "      <td>13.2</td>\n",
              "      <td>9.5</td>\n",
              "      <td>11.5</td>\n",
              "      <td>25</td>\n",
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              "    <tr>\n",
              "      <th>1064</th>\n",
              "      <td>3.0</td>\n",
              "      <td>6</td>\n",
              "      <td>13.4</td>\n",
              "      <td>9.8</td>\n",
              "      <td>11.8</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1065</th>\n",
              "      <td>3.2</td>\n",
              "      <td>6</td>\n",
              "      <td>12.9</td>\n",
              "      <td>9.3</td>\n",
              "      <td>11.3</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1066</th>\n",
              "      <td>3.2</td>\n",
              "      <td>6</td>\n",
              "      <td>14.9</td>\n",
              "      <td>10.2</td>\n",
              "      <td>12.8</td>\n",
              "      <td>22</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>1067 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      ENGINESIZE  CYLINDERS  ...  FUELCONSUMPTION_COMB  FUELCONSUMPTION_COMB_MPG\n",
              "0            2.0          4  ...                   8.5                        33\n",
              "1            2.4          4  ...                   9.6                        29\n",
              "2            1.5          4  ...                   5.9                        48\n",
              "3            3.5          6  ...                  11.1                        25\n",
              "4            3.5          6  ...                  10.6                        27\n",
              "...          ...        ...  ...                   ...                       ...\n",
              "1062         3.0          6  ...                  11.8                        24\n",
              "1063         3.2          6  ...                  11.5                        25\n",
              "1064         3.0          6  ...                  11.8                        24\n",
              "1065         3.2          6  ...                  11.3                        25\n",
              "1066         3.2          6  ...                  12.8                        22\n",
              "\n",
              "[1067 rows x 6 columns]"
            ]
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          "metadata": {
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          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Foa9iY97Cn6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_7r-tUt77rKU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "gradientregressor = GradientBoostingRegressor(n_estimators=100,learning_rate=1.0)\n",
        "model = gradientregressor.fit(X_train, y_train)\n",
        "predictions = model.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PRJ1CaGs7wxB",
        "colab_type": "code",
        "outputId": "b0daa20e-10af-4eb7-e899-af8939dcc67a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        }
      },
      "source": [
        "print('MAE:', metrics.mean_absolute_error(y_test, predictions))\n",
        "print('MSE:', metrics.mean_squared_error(y_test, predictions))\n",
        "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, predictions)))\n",
        "accuracy = model.score(X_test,y_test)\n",
        "print('Accuracy:',accuracy*100,'%')\n",
        "print('R2:', r2_score(y_test, predictions))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MAE: 6.351983219847304\n",
            "MSE: 283.176678564719\n",
            "RMSE: 16.82785424719144\n",
            "Accuracy: 93.50150686212176 %\n",
            "R2: 0.9350150686212176\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0LuZRPmo8Qrh",
        "colab_type": "code",
        "outputId": "5c8d21d3-a270-48ff-a312-90e8e306ff5e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "# Plot feature importance\n",
        "feature_importance = model.feature_importances_\n",
        "\n",
        "# make importances relative to max importance\n",
        "feature_importance = 100.0 * (feature_importance / feature_importance.max())\n",
        "sorted_idx = np.argsort(feature_importance)\n",
        "pos = np.arange(sorted_idx.shape[0]) + .5\n",
        "plt.barh(pos, feature_importance[sorted_idx], align='center')\n",
        "plt.yticks(pos,X.columns[sorted_idx])\n",
        "plt.xlabel('Relative Importance')\n",
        "plt.title('Variable Importance')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lp1oo06r9JQw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}