fuelPolynomial81_69.ipynb

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      "name": "fuelPolynomial81.69.ipynb",
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      "metadata": {
        "id": "j6Pw2cR7Mz7P",
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      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
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        "id": "K7w_x2bgM2YJ",
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      "source": [
        "df=pd.read_csv('/content/FuelConsumptionCo2.csv')"
      ],
      "execution_count": 0,
      "outputs": []
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      },
      "source": [
        "df.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1067 entries, 0 to 1066\n",
            "Data columns (total 13 columns):\n",
            " #   Column                    Non-Null Count  Dtype  \n",
            "---  ------                    --------------  -----  \n",
            " 0   MODELYEAR                 1067 non-null   int64  \n",
            " 1   MAKE                      1067 non-null   object \n",
            " 2   MODEL                     1067 non-null   object \n",
            " 3   VEHICLECLASS              1067 non-null   object \n",
            " 4   ENGINESIZE                1067 non-null   float64\n",
            " 5   CYLINDERS                 1067 non-null   int64  \n",
            " 6   TRANSMISSION              1067 non-null   object \n",
            " 7   FUELTYPE                  1067 non-null   object \n",
            " 8   FUELCONSUMPTION_CITY      1067 non-null   float64\n",
            " 9   FUELCONSUMPTION_HWY       1067 non-null   float64\n",
            " 10  FUELCONSUMPTION_COMB      1067 non-null   float64\n",
            " 11  FUELCONSUMPTION_COMB_MPG  1067 non-null   int64  \n",
            " 12  CO2EMISSIONS              1067 non-null   int64  \n",
            "dtypes: float64(4), int64(4), object(5)\n",
            "memory usage: 108.5+ KB\n"
          ],
          "name": "stdout"
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        "df.describe(include='all')"
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              "      <th>FUELCONSUMPTION_COMB_MPG</th>\n",
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              "      <th>freq</th>\n",
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              "      <th>mean</th>\n",
              "      <td>2014.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.346298</td>\n",
              "      <td>5.794752</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13.296532</td>\n",
              "      <td>9.474602</td>\n",
              "      <td>11.580881</td>\n",
              "      <td>26.441425</td>\n",
              "      <td>256.228679</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.415895</td>\n",
              "      <td>1.797447</td>\n",
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              "      <td>4.101253</td>\n",
              "      <td>2.794510</td>\n",
              "      <td>3.485595</td>\n",
              "      <td>7.468702</td>\n",
              "      <td>63.372304</td>\n",
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              "      <th>min</th>\n",
              "      <td>2014.0</td>\n",
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              "      <td>3.000000</td>\n",
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              "      <td>2014.0</td>\n",
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              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
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              "      <td>10.250000</td>\n",
              "      <td>7.500000</td>\n",
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              "      <td>21.000000</td>\n",
              "      <td>207.000000</td>\n",
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            "text/plain": [
              "        MODELYEAR  MAKE  ... FUELCONSUMPTION_COMB_MPG CO2EMISSIONS\n",
              "count      1067.0  1067  ...              1067.000000  1067.000000\n",
              "unique        NaN    39  ...                      NaN          NaN\n",
              "top           NaN  FORD  ...                      NaN          NaN\n",
              "freq          NaN    90  ...                      NaN          NaN\n",
              "mean       2014.0   NaN  ...                26.441425   256.228679\n",
              "std           0.0   NaN  ...                 7.468702    63.372304\n",
              "min        2014.0   NaN  ...                11.000000   108.000000\n",
              "25%        2014.0   NaN  ...                21.000000   207.000000\n",
              "50%        2014.0   NaN  ...                26.000000   251.000000\n",
              "75%        2014.0   NaN  ...                31.000000   294.000000\n",
              "max        2014.0   NaN  ...                60.000000   488.000000\n",
              "\n",
              "[11 rows x 13 columns]"
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              "(1067, 13)"
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      "source": [
        "df.drop(['MODELYEAR','MAKE','MODEL'],axis=1,inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
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      "cell_type": "code",
      "metadata": {
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      "source": [
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>VEHICLECLASS</th>\n",
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              "      <th>TRANSMISSION</th>\n",
              "      <th>FUELTYPE</th>\n",
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              "      <th>FUELCONSUMPTION_HWY</th>\n",
              "      <th>FUELCONSUMPTION_COMB</th>\n",
              "      <th>FUELCONSUMPTION_COMB_MPG</th>\n",
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              "      <td>196</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>COMPACT</td>\n",
              "      <td>2.4</td>\n",
              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>COMPACT</td>\n",
              "      <td>1.5</td>\n",
              "      <td>4</td>\n",
              "      <td>AV7</td>\n",
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              "      <td>6.0</td>\n",
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              "      <td>136</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>SUV - SMALL</td>\n",
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              "      <td>9.1</td>\n",
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              "      <td>255</td>\n",
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              "      <td>10.6</td>\n",
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              "      <td>244</td>\n",
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            ],
            "text/plain": [
              "  VEHICLECLASS  ENGINESIZE  ...  FUELCONSUMPTION_COMB_MPG CO2EMISSIONS\n",
              "0      COMPACT         2.0  ...                        33          196\n",
              "1      COMPACT         2.4  ...                        29          221\n",
              "2      COMPACT         1.5  ...                        48          136\n",
              "3  SUV - SMALL         3.5  ...                        25          255\n",
              "4  SUV - SMALL         3.5  ...                        27          244\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
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          "execution_count": 66
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        "colab": {
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      },
      "source": [
        "plt.scatter(df['ENGINESIZE'],df['CO2EMISSIONS'],color='red');\n",
        "plt.title('Polynomial Regression Graph')\n",
        "plt.xlabel('Engine Size')\n",
        "plt.ylabel('CO2 Emission')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ElDi4jTQoAah",
        "colab_type": "code",
        "outputId": "958ea1f4-1fc7-4a75-9bac-0d6c7aa856f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.scatter(df['CYLINDERS'],df['CO2EMISSIONS'],color='red');\n",
        "plt.title('Polynomial Regression Graph')\n",
        "plt.xlabel('Cylinders')\n",
        "plt.ylabel('CO2 Emission')\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": "7KbptYJZoOo5",
        "colab_type": "code",
        "outputId": "3e370dbf-ea86-4356-b03b-da498672d6a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "plt.scatter(df['FUELCONSUMPTION_CITY'],df['CO2EMISSIONS'],color='red');\n",
        "plt.title('Polynomial Regression Graph')\n",
        "plt.xlabel('FUELCONSUMPTION_CITY')\n",
        "plt.ylabel('CO2 Emission')\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": "wfuoVrSsoaLj",
        "colab_type": "code",
        "outputId": "2abf7dac-b910-469e-c07b-bc2eea9eda97",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "plt.scatter(df['FUELCONSUMPTION_HWY'],df['CO2EMISSIONS'],color='red');\n",
        "plt.title('Polynomial Regression Graph')\n",
        "plt.xlabel('FUELCONSUMPTION_HWY')\n",
        "plt.ylabel('CO2 Emission')\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": "RQ5OmpZhohWv",
        "colab_type": "code",
        "outputId": "c216f46e-af38-4b30-f41b-aed411109e29",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "plt.scatter(df['FUELCONSUMPTION_COMB'],df['CO2EMISSIONS'],color='red');\n",
        "plt.title('Polynomial Regression Graph')\n",
        "plt.xlabel('FUELCONSUMPTION_COMB')\n",
        "plt.ylabel('CO2 Emission')\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": "y-JhkF7bosj-",
        "colab_type": "code",
        "outputId": "9c9c0a8b-0c23-4b90-b830-46c1a688eb51",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "plt.scatter(df['FUELCONSUMPTION_COMB_MPG'],df['CO2EMISSIONS'],color='red');\n",
        "plt.title('Polynomial Regression Graph')\n",
        "plt.xlabel('FUELCONSUMPTION_COMB_MPG')\n",
        "plt.ylabel('CO2 Emission')\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": "4njZ76G_PS07",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dummies=pd.get_dummies(df['VEHICLECLASS'])\n",
        "df.drop('VEHICLECLASS',axis=1,inplace=True)\n",
        "df=pd.concat([dummies,df],axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dvF4JfzbP1cN",
        "colab_type": "code",
        "outputId": "13daa4b0-80c2-4dc8-f500-062dc411c272",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>COMPACT</th>\n",
              "      <th>FULL-SIZE</th>\n",
              "      <th>MID-SIZE</th>\n",
              "      <th>MINICOMPACT</th>\n",
              "      <th>MINIVAN</th>\n",
              "      <th>PICKUP TRUCK - SMALL</th>\n",
              "      <th>PICKUP TRUCK - STANDARD</th>\n",
              "      <th>SPECIAL PURPOSE VEHICLE</th>\n",
              "      <th>STATION WAGON - MID-SIZE</th>\n",
              "      <th>STATION WAGON - SMALL</th>\n",
              "      <th>SUBCOMPACT</th>\n",
              "      <th>SUV - SMALL</th>\n",
              "      <th>SUV - STANDARD</th>\n",
              "      <th>TWO-SEATER</th>\n",
              "      <th>VAN - CARGO</th>\n",
              "      <th>VAN - PASSENGER</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>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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": [
              "   COMPACT  FULL-SIZE  ...  FUELCONSUMPTION_COMB_MPG  CO2EMISSIONS\n",
              "0        1          0  ...                        33           196\n",
              "1        1          0  ...                        29           221\n",
              "2        1          0  ...                        48           136\n",
              "3        0          0  ...                        25           255\n",
              "4        0          0  ...                        27           244\n",
              "\n",
              "[5 rows x 25 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jfO81tKsP2Xz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dummies=pd.get_dummies(df['TRANSMISSION'])\n",
        "df.drop('TRANSMISSION',axis=1,inplace=True)\n",
        "df=pd.concat([dummies,df],axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pAE3XF-yQINz",
        "colab_type": "code",
        "outputId": "dfa5c4db-6540-4458-8099-0d60cd924ebd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
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              "   A4  A5  A6  ...  FUELCONSUMPTION_COMB  FUELCONSUMPTION_COMB_MPG  CO2EMISSIONS\n",
              "0   0   0   0  ...                   8.5                        33           196\n",
              "1   0   0   0  ...                   9.6                        29           221\n",
              "2   0   0   0  ...                   5.9                        48           136\n",
              "3   0   0   0  ...                  11.1                        25           255\n",
              "4   0   0   0  ...                  10.6                        27           244\n",
              "\n",
              "[5 rows x 46 columns]"
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          "metadata": {
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    {
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        "id": "U0mhjOEaQJl6",
        "colab_type": "code",
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      "source": [
        "dummies=pd.get_dummies(df['FUELTYPE'])\n",
        "df.drop('FUELTYPE',axis=1,inplace=True)\n",
        "df=pd.concat([dummies,df],axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sE8MO-W8QWqf",
        "colab_type": "code",
        "outputId": "4997c57b-accb-41dc-ac46-4a74df5a8eb3",
        "colab": {
          "base_uri": "https://localhost:8080/",
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      "source": [
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      "execution_count": 0,
      "outputs": [
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\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",
              "      <td>136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\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",
              "      <td>255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\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",
              "      <td>244</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   D  E  X  ...  FUELCONSUMPTION_COMB  FUELCONSUMPTION_COMB_MPG  CO2EMISSIONS\n",
              "0  0  0  0  ...                   8.5                        33           196\n",
              "1  0  0  0  ...                   9.6                        29           221\n",
              "2  0  0  0  ...                   5.9                        48           136\n",
              "3  0  0  0  ...                  11.1                        25           255\n",
              "4  0  0  0  ...                  10.6                        27           244\n",
              "\n",
              "[5 rows x 49 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H6nUUZ0wQYED",
        "colab_type": "code",
        "outputId": "f6f206eb-0f22-46af-d453-fe70c16b9c80",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 969
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1067 entries, 0 to 1066\n",
            "Data columns (total 49 columns):\n",
            " #   Column                    Non-Null Count  Dtype  \n",
            "---  ------                    --------------  -----  \n",
            " 0   D                         1067 non-null   uint8  \n",
            " 1   E                         1067 non-null   uint8  \n",
            " 2   X                         1067 non-null   uint8  \n",
            " 3   Z                         1067 non-null   uint8  \n",
            " 4   A4                        1067 non-null   uint8  \n",
            " 5   A5                        1067 non-null   uint8  \n",
            " 6   A6                        1067 non-null   uint8  \n",
            " 7   A7                        1067 non-null   uint8  \n",
            " 8   A8                        1067 non-null   uint8  \n",
            " 9   A9                        1067 non-null   uint8  \n",
            " 10  AM5                       1067 non-null   uint8  \n",
            " 11  AM6                       1067 non-null   uint8  \n",
            " 12  AM7                       1067 non-null   uint8  \n",
            " 13  AS4                       1067 non-null   uint8  \n",
            " 14  AS5                       1067 non-null   uint8  \n",
            " 15  AS6                       1067 non-null   uint8  \n",
            " 16  AS7                       1067 non-null   uint8  \n",
            " 17  AS8                       1067 non-null   uint8  \n",
            " 18  AS9                       1067 non-null   uint8  \n",
            " 19  AV                        1067 non-null   uint8  \n",
            " 20  AV6                       1067 non-null   uint8  \n",
            " 21  AV7                       1067 non-null   uint8  \n",
            " 22  AV8                       1067 non-null   uint8  \n",
            " 23  M5                        1067 non-null   uint8  \n",
            " 24  M6                        1067 non-null   uint8  \n",
            " 25  M7                        1067 non-null   uint8  \n",
            " 26  COMPACT                   1067 non-null   uint8  \n",
            " 27  FULL-SIZE                 1067 non-null   uint8  \n",
            " 28  MID-SIZE                  1067 non-null   uint8  \n",
            " 29  MINICOMPACT               1067 non-null   uint8  \n",
            " 30  MINIVAN                   1067 non-null   uint8  \n",
            " 31  PICKUP TRUCK - SMALL      1067 non-null   uint8  \n",
            " 32  PICKUP TRUCK - STANDARD   1067 non-null   uint8  \n",
            " 33  SPECIAL PURPOSE VEHICLE   1067 non-null   uint8  \n",
            " 34  STATION WAGON - MID-SIZE  1067 non-null   uint8  \n",
            " 35  STATION WAGON - SMALL     1067 non-null   uint8  \n",
            " 36  SUBCOMPACT                1067 non-null   uint8  \n",
            " 37  SUV - SMALL               1067 non-null   uint8  \n",
            " 38  SUV - STANDARD            1067 non-null   uint8  \n",
            " 39  TWO-SEATER                1067 non-null   uint8  \n",
            " 40  VAN - CARGO               1067 non-null   uint8  \n",
            " 41  VAN - PASSENGER           1067 non-null   uint8  \n",
            " 42  ENGINESIZE                1067 non-null   float64\n",
            " 43  CYLINDERS                 1067 non-null   int64  \n",
            " 44  FUELCONSUMPTION_CITY      1067 non-null   float64\n",
            " 45  FUELCONSUMPTION_HWY       1067 non-null   float64\n",
            " 46  FUELCONSUMPTION_COMB      1067 non-null   float64\n",
            " 47  FUELCONSUMPTION_COMB_MPG  1067 non-null   int64  \n",
            " 48  CO2EMISSIONS              1067 non-null   int64  \n",
            "dtypes: float64(4), int64(3), uint8(42)\n",
            "memory usage: 102.2 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c0eFzzE3QcMX",
        "colab_type": "code",
        "outputId": "33e321b8-ebc7-49f3-ac19-581ebe2d7866",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1067, 49)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yrackBb6Qg-r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x=df.iloc[:,:-1].values\n",
        "y=df.iloc[:,-1].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4-41tf-6Qsfn",
        "colab_type": "code",
        "outputId": "c853a6bd-a312-4bbf-c418-3a4dd5ba9248",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "x=x[:,1:]\n",
        "x"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0. ,  0. ,  1. , ...,  6.7,  8.5, 33. ],\n",
              "       [ 0. ,  0. ,  1. , ...,  7.7,  9.6, 29. ],\n",
              "       [ 0. ,  0. ,  1. , ...,  5.8,  5.9, 48. ],\n",
              "       ...,\n",
              "       [ 0. ,  1. ,  0. , ...,  9.8, 11.8, 24. ],\n",
              "       [ 0. ,  1. ,  0. , ...,  9.3, 11.3, 25. ],\n",
              "       [ 0. ,  1. ,  0. , ..., 10.2, 12.8, 22. ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i9k1Upi-RAPp",
        "colab_type": "code",
        "outputId": "24b2e630-b456-4bff-a397-8debc5a862c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "y"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([196, 221, 136, ..., 271, 260, 294])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JPqgClt_RBTl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#backward elimination\n",
        "import statsmodels.regression.linear_model as sm\n",
        "#prep\n",
        "x=np.append(arr=np.ones((1067,1)).astype(int),values=x,axis=1)\n",
        "\n",
        "\n",
        "#function for backward elimination\n",
        "def backwardElimination(x, sl):\n",
        "    numVars = len(x[0])\n",
        "    for i in range(0, numVars):\n",
        "        regressor_OLS = sm.OLS(y, x).fit()\n",
        "        maxVar = max(regressor_OLS.pvalues).astype(float)\n",
        "        if maxVar > sl:\n",
        "            for j in range(0, numVars - i):\n",
        "                if (regressor_OLS.pvalues[j].astype(float) == maxVar):\n",
        "                    x = np.delete(x, j, 1)\n",
        "    regressor_OLS.summary()\n",
        "    return x\n",
        " \n",
        "SL = 0.05\n",
        "x = backwardElimination(x, SL)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9lgYaGGXS8bZ",
        "colab_type": "code",
        "outputId": "657c1c75-2885-4cdf-fe80-6d861876227c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "x.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1067, 24)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vBIReYpCRjwa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SsslP9D2R8EC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "poly_reg=PolynomialFeatures(degree=4)\n",
        "x_poly=poly_reg.fit_transform(x)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nm3jfLtYSOs6",
        "colab_type": "code",
        "outputId": "3aa845f9-f059-4961-911b-e8c3c1951851",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "regressor=LinearRegression()\n",
        "regressor.fit(x_poly,y)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "4kCbn9UFVLpf",
        "colab": {}
      },
      "source": [
        "y_pred=regressor.predict(poly_reg.fit_transform(x_test))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0tk7SV-8Unxf",
        "colab_type": "code",
        "outputId": "76edf936-4715-4234-8595-6503cc5d8ec0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "print('Intercept: \\n', regressor.intercept_)\n",
        "print('Coefficients: \\n', regressor.coef_)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Intercept: \n",
            " -66003.63766233968\n",
            "Coefficients: \n",
            " [ 3.53025394e-01 -5.54124096e-01  1.66405975e+01 ... -1.35451788e-01\n",
            " -3.83387302e-02 -3.88459276e-03]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4h4NOe_cbb9o",
        "colab_type": "code",
        "outputId": "b91df7c7-202e-48df-dfa2-2cdc5f549746",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn import metrics\n",
        "print(np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.1831235525483166\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jeukmphtcSeW",
        "colab_type": "code",
        "outputId": "46bd440c-e74d-4835-8e89-387fed48ae96",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(\"Manual Test Accuracy: {:.2f}%\".format((100 - np.sqrt(metrics.mean_squared_error(y_pred,y_test))*100)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Manual Test Accuracy: 81.69%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Aq9SDTbBqNY7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}