1) Regression Models/Polynomial Regression/Polynomical_Regression.ipynb

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            "--2024-12-10 14:59:44--  https://media.githubusercontent.com/media/pradeep-016/ML_Course/refs/heads/main/1)%20Regression%20Models/Polynomial%20Regression/car%20data.csv\n",
            "Resolving media.githubusercontent.com (media.githubusercontent.com)... 185.199.110.133, 185.199.108.133, 185.199.109.133, ...\n",
            "Connecting to media.githubusercontent.com (media.githubusercontent.com)|185.199.110.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 16908 (17K) [text/plain]\n",
            "Saving to: ‘car data.csv’\n",
            "\n",
            "car data.csv        100%[===================>]  16.51K  --.-KB/s    in 0s      \n",
            "\n",
            "2024-12-10 14:59:44 (45.8 MB/s) - ‘car data.csv’ saved [16908/16908]\n",
            "\n"
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      "source": [
        "!wget \"https://media.githubusercontent.com/media/pradeep-016/ML_Course/refs/heads/main/1)%20Regression%20Models/Polynomial%20Regression/car%20data.csv\""
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "7auqsWywHyYI"
      },
      "execution_count": 2,
      "outputs": []
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      "source": [
        "df = pd.read_csv('/content/car data.csv')"
      ],
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      "execution_count": 3,
      "outputs": []
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        "df.head()"
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      "execution_count": 4,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Car_Name  Year  Selling_Price  Present_Price  Driven_kms Fuel_Type  \\\n",
              "0     ritz  2014           3.35           5.59       27000    Petrol   \n",
              "1      sx4  2013           4.75           9.54       43000    Diesel   \n",
              "2     ciaz  2017           7.25           9.85        6900    Petrol   \n",
              "3  wagon r  2011           2.85           4.15        5200    Petrol   \n",
              "4    swift  2014           4.60           6.87       42450    Diesel   \n",
              "\n",
              "  Selling_type Transmission  Owner  \n",
              "0       Dealer       Manual      0  \n",
              "1       Dealer       Manual      0  \n",
              "2       Dealer       Manual      0  \n",
              "3       Dealer       Manual      0  \n",
              "4       Dealer       Manual      0  "
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              "      <td>5200</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 301,\n  \"fields\": [\n    {\n      \"column\": \"Car_Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 98,\n        \"samples\": [\n          \"Hero Passion Pro\",\n          \"Honda CB Hornet 160R\",\n          \"city\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 2003,\n        \"max\": 2018,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          2014,\n          2013,\n          2015\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Selling_Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.082811556177804,\n        \"min\": 0.1,\n        \"max\": 35.0,\n        \"num_unique_values\": 156,\n        \"samples\": [\n          0.4,\n          7.05,\n          0.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Present_Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.64258435982547,\n        \"min\": 0.32,\n        \"max\": 92.6,\n        \"num_unique_values\": 148,\n        \"samples\": [\n          8.4,\n          36.23,\n          11.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Driven_kms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38886,\n        \"min\": 500,\n        \"max\": 500000,\n        \"num_unique_values\": 206,\n        \"samples\": [\n          41678,\n          42367,\n          60076\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fuel_Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Petrol\",\n          \"Diesel\",\n          \"CNG\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Selling_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Individual\",\n          \"Dealer\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Transmission\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Automatic\",\n          \"Manual\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Owner\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
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    {
      "cell_type": "code",
      "source": [
        "df['Car_Age'] = 2024-df['Year']"
      ],
      "metadata": {
        "id": "_b3sllVZI44t"
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      "execution_count": 5,
      "outputs": []
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        "df = df.drop(['Year','Car_Name'], axis = 1)"
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        "id": "m-wE0wjEOyRz"
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      "execution_count": 6,
      "outputs": []
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      "source": [
        "df.info()"
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      "execution_count": 7,
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 301 entries, 0 to 300\n",
            "Data columns (total 8 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   Selling_Price  301 non-null    float64\n",
            " 1   Present_Price  301 non-null    float64\n",
            " 2   Driven_kms     301 non-null    int64  \n",
            " 3   Fuel_Type      301 non-null    object \n",
            " 4   Selling_type   301 non-null    object \n",
            " 5   Transmission   301 non-null    object \n",
            " 6   Owner          301 non-null    int64  \n",
            " 7   Car_Age        301 non-null    int64  \n",
            "dtypes: float64(2), int64(3), object(3)\n",
            "memory usage: 18.9+ KB\n"
          ]
        }
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      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
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        "id": "ObYR9NJgJUts",
        "outputId": "0a838492-076c-4933-e97a-0c161e3ab4cd"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Selling_Price  Present_Price     Driven_kms       Owner     Car_Age\n",
              "count     301.000000     301.000000     301.000000  301.000000  301.000000\n",
              "mean        4.661296       7.628472   36947.205980    0.043189   10.372093\n",
              "std         5.082812       8.642584   38886.883882    0.247915    2.891554\n",
              "min         0.100000       0.320000     500.000000    0.000000    6.000000\n",
              "25%         0.900000       1.200000   15000.000000    0.000000    8.000000\n",
              "50%         3.600000       6.400000   32000.000000    0.000000   10.000000\n",
              "75%         6.000000       9.900000   48767.000000    0.000000   12.000000\n",
              "max        35.000000      92.600000  500000.000000    3.000000   21.000000"
            ],
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Selling_Price</th>\n",
              "      <th>Present_Price</th>\n",
              "      <th>Driven_kms</th>\n",
              "      <th>Owner</th>\n",
              "      <th>Car_Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>301.000000</td>\n",
              "      <td>301.000000</td>\n",
              "      <td>301.000000</td>\n",
              "      <td>301.000000</td>\n",
              "      <td>301.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.661296</td>\n",
              "      <td>7.628472</td>\n",
              "      <td>36947.205980</td>\n",
              "      <td>0.043189</td>\n",
              "      <td>10.372093</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.082812</td>\n",
              "      <td>8.642584</td>\n",
              "      <td>38886.883882</td>\n",
              "      <td>0.247915</td>\n",
              "      <td>2.891554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.320000</td>\n",
              "      <td>500.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.900000</td>\n",
              "      <td>1.200000</td>\n",
              "      <td>15000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.600000</td>\n",
              "      <td>6.400000</td>\n",
              "      <td>32000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>9.900000</td>\n",
              "      <td>48767.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>12.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>35.000000</td>\n",
              "      <td>92.600000</td>\n",
              "      <td>500000.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>21.000000</td>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
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              "      border-color: transparent;\n",
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              "      const quickchartButtonEl =\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Selling_Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 104.23236562425842,\n        \"min\": 0.1,\n        \"max\": 301.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          4.661295681063123,\n          3.6,\n          301.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Present_Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 104.59732672773872,\n        \"min\": 0.32,\n        \"max\": 301.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          7.628471760797343,\n          6.4,\n          301.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Driven_kms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 169026.31889375782,\n        \"min\": 301.0,\n        \"max\": 500000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          36947.20598006644,\n          32000.0,\n          301.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Owner\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 106.25839718786119,\n        \"min\": 0.0,\n        \"max\": 301.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.04318936877076412,\n          3.0,\n          0.2479148926949208\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Car_Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 103.00648692264097,\n        \"min\": 2.8915541273366836,\n        \"max\": 301.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          10.372093023255815,\n          10.0,\n          301.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "categories = df.columns[df.dtypes == 'object']"
      ],
      "metadata": {
        "id": "aJOG7gngKrd9"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in categories:\n",
        "  print(i)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T_IZEQjdLjiQ",
        "outputId": "6959890b-00ca-49b3-b062-0941eaa0909e"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fuel_Type\n",
            "Selling_type\n",
            "Transmission\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "encoder = LabelEncoder()\n",
        "\n",
        "for col in categories:\n",
        "  df[col] = encoder.fit_transform(df[col])"
      ],
      "metadata": {
        "id": "rHwS1rR2LpN4"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(df['Selling_Price'], bins = 20, edgecolor = 'black', alpha = 0.27)\n",
        "plt.title('Selling Price Distribution')\n",
        "plt.xlabel('Selling Price')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "xRvJwxKpJl61",
        "outputId": "b1bd28c1-d708-474f-dac5-9ff13ea21b73"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Selling_Price'].skew()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "flDmA-F6J9qI",
        "outputId": "d1756fc7-2816-4bba-e683-33d786e56065"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.493422417797524"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "correlation_matrix = df.corr()"
      ],
      "metadata": {
        "id": "QmVLIlWEKkTs"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 5))\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')\n",
        "plt.title('Correlation Heatmap')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        },
        "id": "r_FwC2vcKNGg",
        "outputId": "d3ca1083-692d-47ea-c7f4-2d7ddbb5f546"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Selling_Price'] = np.log1p(df['Selling_Price'])"
      ],
      "metadata": {
        "id": "7kjdK3f4PZM7"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error, r2_score"
      ],
      "metadata": {
        "id": "NPjPVdcYKciE"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = df['Selling_Price']\n",
        "X = df.drop(['Selling_Price'], axis = 1)"
      ],
      "metadata": {
        "id": "KhqS3CtgMo53"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"
      ],
      "metadata": {
        "id": "xSiDk4rnNE9r"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "v7q10GJiNPJc"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "poly = PolynomialFeatures(degree=2)\n",
        "X_train_poly = poly.fit_transform(X_train_scaled)\n",
        "X_test_poly = poly.transform(X_test_scaled)"
      ],
      "metadata": {
        "id": "n-htfun_NU1F"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = LinearRegression()\n",
        "model.fit(X_train_poly, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "N7KlfFcuNYeh",
        "outputId": "68bcc19a-cebf-46c6-bf62-2e45b5a3df46"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test_poly)"
      ],
      "metadata": {
        "id": "F2N79kkxNaZu"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mse = mean_squared_error(y_test, y_pred)\n",
        "r2 = r2_score(y_test, y_pred)"
      ],
      "metadata": {
        "id": "6-7YKSYaNc9c"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Mean Squared Error: {mse}\")\n",
        "print(f\"R2 Score: {r2}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DZn4tLq2Ne4N",
        "outputId": "b079f4d3-39ae-404b-88ad-712b3f418ed9"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error: 0.01538540705664156\n",
            "R2 Score: 0.9748043031582908\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(y_test, y_pred, alpha=0.7, edgecolor='k')\n",
        "plt.xlabel('Actual Selling Price')\n",
        "plt.ylabel('Predicted Selling Price')\n",
        "plt.title('Actual vs Predicted Prices')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "KbJmD9tSNg6m",
        "outputId": "c6f0baa5-d646-4cc0-be52-51eca61ff937"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "residuals = y_test - y_pred\n",
        "plt.hist(residuals, bins=20, edgecolor='black', alpha=0.7)\n",
        "plt.title('Distribution of Residuals')\n",
        "plt.xlabel('Residual')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "iYA9_-a6NnBb",
        "outputId": "52c3fce9-261b-4839-b83c-51024a4b9f3d"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yHNS18PSNpiE"
      },
      "execution_count": 27,
      "outputs": []
    }
  ]
}