car_price_prediction (1).ipynb

{
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      "cell_type": "markdown",
      "id": "436f0fb7-973f-49a1-8e16-277c82f2d6a4",
      "metadata": {
        "id": "436f0fb7-973f-49a1-8e16-277c82f2d6a4"
      },
      "source": [
        "# Importing the Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f067bc40-718d-44eb-9d2a-e41083c1029a",
      "metadata": {
        "id": "f067bc40-718d-44eb-9d2a-e41083c1029a"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn import metrics"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "aa4220f9-a99a-4560-aa90-fabe3d01f7fc",
      "metadata": {
        "id": "aa4220f9-a99a-4560-aa90-fabe3d01f7fc"
      },
      "source": [
        "# Data collection and Processing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "13a29ea3-6216-4560-b326-65f8f0598e21",
      "metadata": {
        "id": "13a29ea3-6216-4560-b326-65f8f0598e21"
      },
      "outputs": [],
      "source": [
        "# Loading the data from CSV file to pandas Dataframe\n",
        "car_data=pd.read_csv(\"CarPrice_Assignment.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dcba8601-ce9e-42f4-8d02-fb248324cd16",
      "metadata": {
        "id": "dcba8601-ce9e-42f4-8d02-fb248324cd16",
        "outputId": "463eaa7f-230f-4361-8468-ef1c341b4c25"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>car_ID</th>\n",
              "      <th>symboling</th>\n",
              "      <th>CarName</th>\n",
              "      <th>fueltype</th>\n",
              "      <th>aspiration</th>\n",
              "      <th>doornumber</th>\n",
              "      <th>carbody</th>\n",
              "      <th>drivewheel</th>\n",
              "      <th>enginelocation</th>\n",
              "      <th>wheelbase</th>\n",
              "      <th>...</th>\n",
              "      <th>enginesize</th>\n",
              "      <th>fuelsystem</th>\n",
              "      <th>boreratio</th>\n",
              "      <th>stroke</th>\n",
              "      <th>compressionratio</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>peakrpm</th>\n",
              "      <th>citympg</th>\n",
              "      <th>highwaympg</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>alfa-romero giulia</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>convertible</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>88.6</td>\n",
              "      <td>...</td>\n",
              "      <td>130</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.47</td>\n",
              "      <td>2.68</td>\n",
              "      <td>9.0</td>\n",
              "      <td>111</td>\n",
              "      <td>5000</td>\n",
              "      <td>21</td>\n",
              "      <td>27</td>\n",
              "      <td>13495.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>alfa-romero stelvio</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>convertible</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>88.6</td>\n",
              "      <td>...</td>\n",
              "      <td>130</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.47</td>\n",
              "      <td>2.68</td>\n",
              "      <td>9.0</td>\n",
              "      <td>111</td>\n",
              "      <td>5000</td>\n",
              "      <td>21</td>\n",
              "      <td>27</td>\n",
              "      <td>16500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>alfa-romero Quadrifoglio</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>94.5</td>\n",
              "      <td>...</td>\n",
              "      <td>152</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>2.68</td>\n",
              "      <td>3.47</td>\n",
              "      <td>9.0</td>\n",
              "      <td>154</td>\n",
              "      <td>5000</td>\n",
              "      <td>19</td>\n",
              "      <td>26</td>\n",
              "      <td>16500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>audi 100 ls</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>99.8</td>\n",
              "      <td>...</td>\n",
              "      <td>109</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.19</td>\n",
              "      <td>3.40</td>\n",
              "      <td>10.0</td>\n",
              "      <td>102</td>\n",
              "      <td>5500</td>\n",
              "      <td>24</td>\n",
              "      <td>30</td>\n",
              "      <td>13950.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>audi 100ls</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>4wd</td>\n",
              "      <td>front</td>\n",
              "      <td>99.4</td>\n",
              "      <td>...</td>\n",
              "      <td>136</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.19</td>\n",
              "      <td>3.40</td>\n",
              "      <td>8.0</td>\n",
              "      <td>115</td>\n",
              "      <td>5500</td>\n",
              "      <td>18</td>\n",
              "      <td>22</td>\n",
              "      <td>17450.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 26 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   car_ID  symboling                   CarName fueltype aspiration doornumber  \\\n",
              "0       1          3        alfa-romero giulia      gas        std        two   \n",
              "1       2          3       alfa-romero stelvio      gas        std        two   \n",
              "2       3          1  alfa-romero Quadrifoglio      gas        std        two   \n",
              "3       4          2               audi 100 ls      gas        std       four   \n",
              "4       5          2                audi 100ls      gas        std       four   \n",
              "\n",
              "       carbody drivewheel enginelocation  wheelbase  ...  enginesize  \\\n",
              "0  convertible        rwd          front       88.6  ...         130   \n",
              "1  convertible        rwd          front       88.6  ...         130   \n",
              "2    hatchback        rwd          front       94.5  ...         152   \n",
              "3        sedan        fwd          front       99.8  ...         109   \n",
              "4        sedan        4wd          front       99.4  ...         136   \n",
              "\n",
              "   fuelsystem  boreratio  stroke compressionratio horsepower  peakrpm citympg  \\\n",
              "0        mpfi       3.47    2.68              9.0        111     5000      21   \n",
              "1        mpfi       3.47    2.68              9.0        111     5000      21   \n",
              "2        mpfi       2.68    3.47              9.0        154     5000      19   \n",
              "3        mpfi       3.19    3.40             10.0        102     5500      24   \n",
              "4        mpfi       3.19    3.40              8.0        115     5500      18   \n",
              "\n",
              "   highwaympg    price  \n",
              "0          27  13495.0  \n",
              "1          27  16500.0  \n",
              "2          26  16500.0  \n",
              "3          30  13950.0  \n",
              "4          22  17450.0  \n",
              "\n",
              "[5 rows x 26 columns]"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Inspecting the first ive rows of the Dataframe\n",
        "car_data.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e9397c07-6887-4083-8a4f-9f7d01a53947",
      "metadata": {
        "id": "e9397c07-6887-4083-8a4f-9f7d01a53947",
        "outputId": "f0c8dd9f-6d5f-4f84-8d70-a6bc8d386367"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(205, 26)"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Checking the number of rows and columns\n",
        "car_data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8f26d5f7-ffab-4a05-b23f-e9f265e7db06",
      "metadata": {
        "id": "8f26d5f7-ffab-4a05-b23f-e9f265e7db06",
        "outputId": "ec0055f0-a311-4788-8260-5b3e005c6e08"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "car_ID              0\n",
              "symboling           0\n",
              "CarName             0\n",
              "fueltype            0\n",
              "aspiration          0\n",
              "doornumber          0\n",
              "carbody             0\n",
              "drivewheel          0\n",
              "enginelocation      0\n",
              "wheelbase           0\n",
              "carlength           0\n",
              "carwidth            0\n",
              "carheight           0\n",
              "curbweight          0\n",
              "enginetype          0\n",
              "cylindernumber      0\n",
              "enginesize          0\n",
              "fuelsystem          0\n",
              "boreratio           0\n",
              "stroke              0\n",
              "compressionratio    0\n",
              "horsepower          0\n",
              "peakrpm             0\n",
              "citympg             0\n",
              "highwaympg          0\n",
              "price               0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Checking the number of missing values\n",
        "car_data.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b04cfbf7-a688-4ccf-9b84-0e93a6a92a74",
      "metadata": {
        "id": "b04cfbf7-a688-4ccf-9b84-0e93a6a92a74",
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      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<bound method DataFrame.info of      car_ID  symboling                   CarName fueltype aspiration  \\\n",
              "0         1          3        alfa-romero giulia      gas        std   \n",
              "1         2          3       alfa-romero stelvio      gas        std   \n",
              "2         3          1  alfa-romero Quadrifoglio      gas        std   \n",
              "3         4          2               audi 100 ls      gas        std   \n",
              "4         5          2                audi 100ls      gas        std   \n",
              "..      ...        ...                       ...      ...        ...   \n",
              "200     201         -1           volvo 145e (sw)      gas        std   \n",
              "201     202         -1               volvo 144ea      gas      turbo   \n",
              "202     203         -1               volvo 244dl      gas        std   \n",
              "203     204         -1                 volvo 246   diesel      turbo   \n",
              "204     205         -1               volvo 264gl      gas      turbo   \n",
              "\n",
              "    doornumber      carbody drivewheel enginelocation  wheelbase  ...  \\\n",
              "0          two  convertible        rwd          front       88.6  ...   \n",
              "1          two  convertible        rwd          front       88.6  ...   \n",
              "2          two    hatchback        rwd          front       94.5  ...   \n",
              "3         four        sedan        fwd          front       99.8  ...   \n",
              "4         four        sedan        4wd          front       99.4  ...   \n",
              "..         ...          ...        ...            ...        ...  ...   \n",
              "200       four        sedan        rwd          front      109.1  ...   \n",
              "201       four        sedan        rwd          front      109.1  ...   \n",
              "202       four        sedan        rwd          front      109.1  ...   \n",
              "203       four        sedan        rwd          front      109.1  ...   \n",
              "204       four        sedan        rwd          front      109.1  ...   \n",
              "\n",
              "     enginesize  fuelsystem  boreratio  stroke compressionratio horsepower  \\\n",
              "0           130        mpfi       3.47    2.68              9.0        111   \n",
              "1           130        mpfi       3.47    2.68              9.0        111   \n",
              "2           152        mpfi       2.68    3.47              9.0        154   \n",
              "3           109        mpfi       3.19    3.40             10.0        102   \n",
              "4           136        mpfi       3.19    3.40              8.0        115   \n",
              "..          ...         ...        ...     ...              ...        ...   \n",
              "200         141        mpfi       3.78    3.15              9.5        114   \n",
              "201         141        mpfi       3.78    3.15              8.7        160   \n",
              "202         173        mpfi       3.58    2.87              8.8        134   \n",
              "203         145         idi       3.01    3.40             23.0        106   \n",
              "204         141        mpfi       3.78    3.15              9.5        114   \n",
              "\n",
              "     peakrpm citympg  highwaympg    price  \n",
              "0       5000      21          27  13495.0  \n",
              "1       5000      21          27  16500.0  \n",
              "2       5000      19          26  16500.0  \n",
              "3       5500      24          30  13950.0  \n",
              "4       5500      18          22  17450.0  \n",
              "..       ...     ...         ...      ...  \n",
              "200     5400      23          28  16845.0  \n",
              "201     5300      19          25  19045.0  \n",
              "202     5500      18          23  21485.0  \n",
              "203     4800      26          27  22470.0  \n",
              "204     5400      19          25  22625.0  \n",
              "\n",
              "[205 rows x 26 columns]>"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Getting some information about the dataset\n",
        "car_data.info"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9df6906f-9861-4401-abd2-d51754791f1f",
      "metadata": {
        "id": "9df6906f-9861-4401-abd2-d51754791f1f",
        "outputId": "4494d7d8-a56d-4bcb-c834-6926ba38f997"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['car_ID', 'symboling', 'CarName', 'fueltype', 'aspiration',\n",
              "       'doornumber', 'carbody', 'drivewheel', 'enginelocation', 'wheelbase',\n",
              "       'carlength', 'carwidth', 'carheight', 'curbweight', 'enginetype',\n",
              "       'cylindernumber', 'enginesize', 'fuelsystem', 'boreratio', 'stroke',\n",
              "       'compressionratio', 'horsepower', 'peakrpm', 'citympg', 'highwaympg',\n",
              "       'price'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Extracting the columns\n",
        "car_data.columns"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "95699b70-668e-4003-8f42-4a2983286248",
      "metadata": {
        "id": "95699b70-668e-4003-8f42-4a2983286248"
      },
      "source": [
        "# Checking the distribution of Categorical data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ad877544-17d7-4949-8484-f8c89f5a52cb",
      "metadata": {
        "id": "ad877544-17d7-4949-8484-f8c89f5a52cb",
        "outputId": "006c1ce5-3da7-481c-ea0c-e34f91694a63"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "gas       185\n",
            "diesel     20\n",
            "Name: fueltype, dtype: int64\n",
            "sedan          96\n",
            "hatchback      70\n",
            "wagon          25\n",
            "hardtop         8\n",
            "convertible     6\n",
            "Name: carbody, dtype: int64\n",
            "four      159\n",
            "six        24\n",
            "five       11\n",
            "eight       5\n",
            "two         4\n",
            "three       1\n",
            "twelve      1\n",
            "Name: cylindernumber, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "print(car_data.fueltype.value_counts())\n",
        "print(car_data.carbody.value_counts())\n",
        "print(car_data.cylindernumber.value_counts())"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "729fb909-e55b-40c8-b002-ab9f891d08af",
      "metadata": {
        "id": "729fb909-e55b-40c8-b002-ab9f891d08af"
      },
      "source": [
        "# Encoding the Categorical data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e8a1bae9-02ad-448f-9693-d9c0833b0dce",
      "metadata": {
        "id": "e8a1bae9-02ad-448f-9693-d9c0833b0dce"
      },
      "outputs": [],
      "source": [
        "car_data.replace({\"fueltype\":{\"gas\":0,\"diesel\":1}},inplace=True)\n",
        "car_data.replace({\"carbody\":{\"sedan\":0,\"hatchback\":1,\"wagon\":2,\"hardtop\":3,\"convertible\":4}},inplace=True)\n",
        "car_data.replace({\"doornumber\":{\"two\":0,\"four\":1}},inplace=True)\n",
        "car_data.replace({\"cylindernumber\":{\"four\":0,\"six\":1,\"five\":2,\"eight\":3,\"two\":4,\"three\":5,\"twelve\":6}},inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "eb3538ed-fae2-440a-bb31-0d0020ae1022",
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        "outputId": "1ced5e50-2f78-44de-cfe5-304b4d91e1d8"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\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>car_ID</th>\n",
              "      <th>symboling</th>\n",
              "      <th>CarName</th>\n",
              "      <th>fueltype</th>\n",
              "      <th>aspiration</th>\n",
              "      <th>doornumber</th>\n",
              "      <th>carbody</th>\n",
              "      <th>drivewheel</th>\n",
              "      <th>enginelocation</th>\n",
              "      <th>wheelbase</th>\n",
              "      <th>...</th>\n",
              "      <th>enginesize</th>\n",
              "      <th>fuelsystem</th>\n",
              "      <th>boreratio</th>\n",
              "      <th>stroke</th>\n",
              "      <th>compressionratio</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>peakrpm</th>\n",
              "      <th>citympg</th>\n",
              "      <th>highwaympg</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>alfa-romero giulia</td>\n",
              "      <td>0</td>\n",
              "      <td>std</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>88.6</td>\n",
              "      <td>...</td>\n",
              "      <td>130</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.47</td>\n",
              "      <td>2.68</td>\n",
              "      <td>9.0</td>\n",
              "      <td>111</td>\n",
              "      <td>5000</td>\n",
              "      <td>21</td>\n",
              "      <td>27</td>\n",
              "      <td>13495.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>alfa-romero stelvio</td>\n",
              "      <td>0</td>\n",
              "      <td>std</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>88.6</td>\n",
              "      <td>...</td>\n",
              "      <td>130</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.47</td>\n",
              "      <td>2.68</td>\n",
              "      <td>9.0</td>\n",
              "      <td>111</td>\n",
              "      <td>5000</td>\n",
              "      <td>21</td>\n",
              "      <td>27</td>\n",
              "      <td>16500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>alfa-romero Quadrifoglio</td>\n",
              "      <td>0</td>\n",
              "      <td>std</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>94.5</td>\n",
              "      <td>...</td>\n",
              "      <td>152</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>2.68</td>\n",
              "      <td>3.47</td>\n",
              "      <td>9.0</td>\n",
              "      <td>154</td>\n",
              "      <td>5000</td>\n",
              "      <td>19</td>\n",
              "      <td>26</td>\n",
              "      <td>16500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>audi 100 ls</td>\n",
              "      <td>0</td>\n",
              "      <td>std</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>99.8</td>\n",
              "      <td>...</td>\n",
              "      <td>109</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.19</td>\n",
              "      <td>3.40</td>\n",
              "      <td>10.0</td>\n",
              "      <td>102</td>\n",
              "      <td>5500</td>\n",
              "      <td>24</td>\n",
              "      <td>30</td>\n",
              "      <td>13950.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>audi 100ls</td>\n",
              "      <td>0</td>\n",
              "      <td>std</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4wd</td>\n",
              "      <td>front</td>\n",
              "      <td>99.4</td>\n",
              "      <td>...</td>\n",
              "      <td>136</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.19</td>\n",
              "      <td>3.40</td>\n",
              "      <td>8.0</td>\n",
              "      <td>115</td>\n",
              "      <td>5500</td>\n",
              "      <td>18</td>\n",
              "      <td>22</td>\n",
              "      <td>17450.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 26 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   car_ID  symboling                   CarName  fueltype aspiration  \\\n",
              "0       1          3        alfa-romero giulia         0        std   \n",
              "1       2          3       alfa-romero stelvio         0        std   \n",
              "2       3          1  alfa-romero Quadrifoglio         0        std   \n",
              "3       4          2               audi 100 ls         0        std   \n",
              "4       5          2                audi 100ls         0        std   \n",
              "\n",
              "   doornumber  carbody drivewheel enginelocation  wheelbase  ...  enginesize  \\\n",
              "0           0        4        rwd          front       88.6  ...         130   \n",
              "1           0        4        rwd          front       88.6  ...         130   \n",
              "2           0        1        rwd          front       94.5  ...         152   \n",
              "3           1        0        fwd          front       99.8  ...         109   \n",
              "4           1        0        4wd          front       99.4  ...         136   \n",
              "\n",
              "   fuelsystem  boreratio  stroke compressionratio  horsepower  peakrpm  \\\n",
              "0        mpfi       3.47    2.68              9.0         111     5000   \n",
              "1        mpfi       3.47    2.68              9.0         111     5000   \n",
              "2        mpfi       2.68    3.47              9.0         154     5000   \n",
              "3        mpfi       3.19    3.40             10.0         102     5500   \n",
              "4        mpfi       3.19    3.40              8.0         115     5500   \n",
              "\n",
              "  citympg  highwaympg    price  \n",
              "0      21          27  13495.0  \n",
              "1      21          27  16500.0  \n",
              "2      19          26  16500.0  \n",
              "3      24          30  13950.0  \n",
              "4      18          22  17450.0  \n",
              "\n",
              "[5 rows x 26 columns]"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "car_data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d09782e9-5435-4332-ad47-624286595c41",
      "metadata": {
        "id": "d09782e9-5435-4332-ad47-624286595c41"
      },
      "source": [
        "# Splitting the data and Taget"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1319ca05-8554-4271-926a-a9399700c834",
      "metadata": {
        "id": "1319ca05-8554-4271-926a-a9399700c834"
      },
      "outputs": [],
      "source": [
        "X=car_data.drop([\"CarName\",\"aspiration\",\"drivewheel\",\"enginelocation\",\"fuelsystem\",\"price\",\"enginetype\"],axis=1)\n",
        "Y=car_data[\"price\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "560a3f27-1ad3-4039-b832-3674f228d7db",
      "metadata": {
        "id": "560a3f27-1ad3-4039-b832-3674f228d7db",
        "outputId": "c57e8017-2067-42cb-c1cd-53877e41036c"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>car_ID</th>\n",
              "      <th>symboling</th>\n",
              "      <th>fueltype</th>\n",
              "      <th>doornumber</th>\n",
              "      <th>carbody</th>\n",
              "      <th>wheelbase</th>\n",
              "      <th>carlength</th>\n",
              "      <th>carwidth</th>\n",
              "      <th>carheight</th>\n",
              "      <th>curbweight</th>\n",
              "      <th>cylindernumber</th>\n",
              "      <th>enginesize</th>\n",
              "      <th>boreratio</th>\n",
              "      <th>stroke</th>\n",
              "      <th>compressionratio</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>peakrpm</th>\n",
              "      <th>citympg</th>\n",
              "      <th>highwaympg</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>88.6</td>\n",
              "      <td>168.8</td>\n",
              "      <td>64.1</td>\n",
              "      <td>48.8</td>\n",
              "      <td>2548</td>\n",
              "      <td>0</td>\n",
              "      <td>130</td>\n",
              "      <td>3.47</td>\n",
              "      <td>2.68</td>\n",
              "      <td>9.0</td>\n",
              "      <td>111</td>\n",
              "      <td>5000</td>\n",
              "      <td>21</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>88.6</td>\n",
              "      <td>168.8</td>\n",
              "      <td>64.1</td>\n",
              "      <td>48.8</td>\n",
              "      <td>2548</td>\n",
              "      <td>0</td>\n",
              "      <td>130</td>\n",
              "      <td>3.47</td>\n",
              "      <td>2.68</td>\n",
              "      <td>9.0</td>\n",
              "      <td>111</td>\n",
              "      <td>5000</td>\n",
              "      <td>21</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>94.5</td>\n",
              "      <td>171.2</td>\n",
              "      <td>65.5</td>\n",
              "      <td>52.4</td>\n",
              "      <td>2823</td>\n",
              "      <td>1</td>\n",
              "      <td>152</td>\n",
              "      <td>2.68</td>\n",
              "      <td>3.47</td>\n",
              "      <td>9.0</td>\n",
              "      <td>154</td>\n",
              "      <td>5000</td>\n",
              "      <td>19</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>99.8</td>\n",
              "      <td>176.6</td>\n",
              "      <td>66.2</td>\n",
              "      <td>54.3</td>\n",
              "      <td>2337</td>\n",
              "      <td>0</td>\n",
              "      <td>109</td>\n",
              "      <td>3.19</td>\n",
              "      <td>3.40</td>\n",
              "      <td>10.0</td>\n",
              "      <td>102</td>\n",
              "      <td>5500</td>\n",
              "      <td>24</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>99.4</td>\n",
              "      <td>176.6</td>\n",
              "      <td>66.4</td>\n",
              "      <td>54.3</td>\n",
              "      <td>2824</td>\n",
              "      <td>2</td>\n",
              "      <td>136</td>\n",
              "      <td>3.19</td>\n",
              "      <td>3.40</td>\n",
              "      <td>8.0</td>\n",
              "      <td>115</td>\n",
              "      <td>5500</td>\n",
              "      <td>18</td>\n",
              "      <td>22</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>200</th>\n",
              "      <td>201</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>109.1</td>\n",
              "      <td>188.8</td>\n",
              "      <td>68.9</td>\n",
              "      <td>55.5</td>\n",
              "      <td>2952</td>\n",
              "      <td>0</td>\n",
              "      <td>141</td>\n",
              "      <td>3.78</td>\n",
              "      <td>3.15</td>\n",
              "      <td>9.5</td>\n",
              "      <td>114</td>\n",
              "      <td>5400</td>\n",
              "      <td>23</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>201</th>\n",
              "      <td>202</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>109.1</td>\n",
              "      <td>188.8</td>\n",
              "      <td>68.8</td>\n",
              "      <td>55.5</td>\n",
              "      <td>3049</td>\n",
              "      <td>0</td>\n",
              "      <td>141</td>\n",
              "      <td>3.78</td>\n",
              "      <td>3.15</td>\n",
              "      <td>8.7</td>\n",
              "      <td>160</td>\n",
              "      <td>5300</td>\n",
              "      <td>19</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>202</th>\n",
              "      <td>203</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>109.1</td>\n",
              "      <td>188.8</td>\n",
              "      <td>68.9</td>\n",
              "      <td>55.5</td>\n",
              "      <td>3012</td>\n",
              "      <td>1</td>\n",
              "      <td>173</td>\n",
              "      <td>3.58</td>\n",
              "      <td>2.87</td>\n",
              "      <td>8.8</td>\n",
              "      <td>134</td>\n",
              "      <td>5500</td>\n",
              "      <td>18</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>203</th>\n",
              "      <td>204</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>109.1</td>\n",
              "      <td>188.8</td>\n",
              "      <td>68.9</td>\n",
              "      <td>55.5</td>\n",
              "      <td>3217</td>\n",
              "      <td>1</td>\n",
              "      <td>145</td>\n",
              "      <td>3.01</td>\n",
              "      <td>3.40</td>\n",
              "      <td>23.0</td>\n",
              "      <td>106</td>\n",
              "      <td>4800</td>\n",
              "      <td>26</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>204</th>\n",
              "      <td>205</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>109.1</td>\n",
              "      <td>188.8</td>\n",
              "      <td>68.9</td>\n",
              "      <td>55.5</td>\n",
              "      <td>3062</td>\n",
              "      <td>0</td>\n",
              "      <td>141</td>\n",
              "      <td>3.78</td>\n",
              "      <td>3.15</td>\n",
              "      <td>9.5</td>\n",
              "      <td>114</td>\n",
              "      <td>5400</td>\n",
              "      <td>19</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>205 rows × 19 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     car_ID  symboling  fueltype  doornumber  carbody  wheelbase  carlength  \\\n",
              "0         1          3         0           0        4       88.6      168.8   \n",
              "1         2          3         0           0        4       88.6      168.8   \n",
              "2         3          1         0           0        1       94.5      171.2   \n",
              "3         4          2         0           1        0       99.8      176.6   \n",
              "4         5          2         0           1        0       99.4      176.6   \n",
              "..      ...        ...       ...         ...      ...        ...        ...   \n",
              "200     201         -1         0           1        0      109.1      188.8   \n",
              "201     202         -1         0           1        0      109.1      188.8   \n",
              "202     203         -1         0           1        0      109.1      188.8   \n",
              "203     204         -1         1           1        0      109.1      188.8   \n",
              "204     205         -1         0           1        0      109.1      188.8   \n",
              "\n",
              "     carwidth  carheight  curbweight  cylindernumber  enginesize  boreratio  \\\n",
              "0        64.1       48.8        2548               0         130       3.47   \n",
              "1        64.1       48.8        2548               0         130       3.47   \n",
              "2        65.5       52.4        2823               1         152       2.68   \n",
              "3        66.2       54.3        2337               0         109       3.19   \n",
              "4        66.4       54.3        2824               2         136       3.19   \n",
              "..        ...        ...         ...             ...         ...        ...   \n",
              "200      68.9       55.5        2952               0         141       3.78   \n",
              "201      68.8       55.5        3049               0         141       3.78   \n",
              "202      68.9       55.5        3012               1         173       3.58   \n",
              "203      68.9       55.5        3217               1         145       3.01   \n",
              "204      68.9       55.5        3062               0         141       3.78   \n",
              "\n",
              "     stroke  compressionratio  horsepower  peakrpm  citympg  highwaympg  \n",
              "0      2.68               9.0         111     5000       21          27  \n",
              "1      2.68               9.0         111     5000       21          27  \n",
              "2      3.47               9.0         154     5000       19          26  \n",
              "3      3.40              10.0         102     5500       24          30  \n",
              "4      3.40               8.0         115     5500       18          22  \n",
              "..      ...               ...         ...      ...      ...         ...  \n",
              "200    3.15               9.5         114     5400       23          28  \n",
              "201    3.15               8.7         160     5300       19          25  \n",
              "202    2.87               8.8         134     5500       18          23  \n",
              "203    3.40              23.0         106     4800       26          27  \n",
              "204    3.15               9.5         114     5400       19          25  \n",
              "\n",
              "[205 rows x 19 columns]"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fd9d1620-8988-4d57-8161-6e79d60005f1",
      "metadata": {
        "id": "fd9d1620-8988-4d57-8161-6e79d60005f1",
        "outputId": "7fca9075-5dee-4697-c85e-de1bd0391eec"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0      13495.0\n",
              "1      16500.0\n",
              "2      16500.0\n",
              "3      13950.0\n",
              "4      17450.0\n",
              "        ...   \n",
              "200    16845.0\n",
              "201    19045.0\n",
              "202    21485.0\n",
              "203    22470.0\n",
              "204    22625.0\n",
              "Name: price, Length: 205, dtype: float64"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Y"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "89da453c-dff1-40e2-95a8-0eb59e047655",
      "metadata": {
        "id": "89da453c-dff1-40e2-95a8-0eb59e047655"
      },
      "source": [
        "# Splitting Training data and Test data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dbc1db25-e32f-4270-b20c-3d42497b7a01",
      "metadata": {
        "id": "dbc1db25-e32f-4270-b20c-3d42497b7a01"
      },
      "outputs": [],
      "source": [
        "X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.1,random_state=2)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b4a54c87-654f-423a-8954-e22f2eccf6b7",
      "metadata": {
        "id": "b4a54c87-654f-423a-8954-e22f2eccf6b7"
      },
      "source": [
        "# Model Training ( Linear Regression)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b574605d-12ff-4733-8690-ce894842c251",
      "metadata": {
        "id": "b574605d-12ff-4733-8690-ce894842c251",
        "outputId": "25b4a600-02cb-474e-8139-0ab531e48042"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" 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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Loading the Linear Regression model\n",
        "lin_reg=LinearRegression()\n",
        "lin_reg.fit(X_train,Y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "945566a6-4649-4c43-b152-0fa1736407b7",
      "metadata": {
        "id": "945566a6-4649-4c43-b152-0fa1736407b7"
      },
      "source": [
        "# Model Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ac90b6e9-b808-4e05-bd0f-b611f1a72e28",
      "metadata": {
        "id": "ac90b6e9-b808-4e05-bd0f-b611f1a72e28"
      },
      "outputs": [],
      "source": [
        "# Prediction on Training data\n",
        "training_data_prediction=lin_reg.predict(X_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e7f1e3ed-51f5-4964-8489-0fe2eb3d9163",
      "metadata": {
        "id": "e7f1e3ed-51f5-4964-8489-0fe2eb3d9163",
        "outputId": "c13ced11-4040-4ef0-cebc-0cf0692c4fd8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "R squared error for training data :  0.8724092127919401\n"
          ]
        }
      ],
      "source": [
        "# R squared Error\n",
        "error_score=metrics.r2_score(Y_train,training_data_prediction)\n",
        "print(\"R squared error for training data : \",error_score)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9a6d9fc9-82d1-4a23-9d91-fde7d7cd09f9",
      "metadata": {
        "id": "9a6d9fc9-82d1-4a23-9d91-fde7d7cd09f9"
      },
      "source": [
        "# Visualize the Actual Prices and Predicted prices"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "03c91114-afa9-494b-84f1-9b78ae9c04c1",
      "metadata": {
        "id": "03c91114-afa9-494b-84f1-9b78ae9c04c1",
        "outputId": "ed762d5a-bd10-45c5-ff91-05e08bf443db"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.scatter(Y_train,training_data_prediction)\n",
        "plt.xlabel(\"Actual Price\")\n",
        "plt.ylabel(\"Predicted Price\")\n",
        "plt.title(\"Actual Price vs Predicted Price\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3cc327c3-654a-4c9f-86b6-434472fe4910",
      "metadata": {
        "id": "3cc327c3-654a-4c9f-86b6-434472fe4910"
      },
      "outputs": [],
      "source": [
        "# Prediction on Test data\n",
        "test_data_prediction=lin_reg.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "072c3ac4-0f13-45b1-a939-e4e1fa3acd0b",
      "metadata": {
        "id": "072c3ac4-0f13-45b1-a939-e4e1fa3acd0b",
        "outputId": "446a5849-6001-4110-b114-421574b75a4f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "R squared error for test data :  0.8378142169328674\n"
          ]
        }
      ],
      "source": [
        "#R squared Error\n",
        "error_score=metrics.r2_score(Y_test,test_data_prediction)\n",
        "print(\"R squared error for test data : \",error_score)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3db26874-ec85-442b-a765-4472ac4fe04d",
      "metadata": {
        "id": "3db26874-ec85-442b-a765-4472ac4fe04d",
        "outputId": "f9ebb7c3-a223-4d9a-a9c0-15e11ca42de4"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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CzN13322OHDnifs8HH3xgrr76auPj41Purq+3337bdOnSxfj5+ZnIyEjz4YcfVnj34vz5803nzp2N3W43V1xxhZk9e7aZN2+ex51dxlT97sULkWQeeuihStedewebN/1x/Phx85e//MV07tzZ+Pr6GofDYbp27Woefvhhd79eyOOPP24kmQ4dOpS7Ey05Odn89re/NeHh4cZut5vWrVubvn37ms8+++yC262JfklPTzdjxowx7dq1Mz4+PqZt27amd+/eZtasWR5127dvN7fddpvx8/MzQUFB5oEHHjCffvrpBe9eNObMHax///vfTVRUlLsPY2JizJIlS9w1+/btMwMHDjSBgYFGksc2vP0OcnNzzZgxY8xll11mmjdvbm677Tazc+fOKt29+Pzzz5+3ruwOxXPvpjx73dnHuDHGLF261PTt29cEBASY5s2bm8jISPPss8961GzZssXcfffdJjg42Pj4+Bin02n69+/vvisZqCk2Y2pwxkQAAABUiDFdAAAAFiB0AQAAWIDQBQAAYAFCFwAAgAUIXQAAABYgdAEAAFiAyVFrUGlpqQ4fPqzAwECvH4cCAADqljFG+fn5CgsLU5MmtXc+itBVgw4fPqwOHTrUdTMAAEA1HDx48LwPSb9YhK4aVPaMroMHD5Z7PAsAAKif8vLy1KFDh1p/1iahqwaVXVJs2bIloQsAgAamtocGMZAeAADAAoQuAAAACxC6AAAALEDoAgAAsAChCwAAwAKELgAAAAsQugAAACxA6AIAALAAoQsAAMACzEgPAADqtZJSo5T0Y8rKL1BwoJ9ujAhS0ya1O3t8bSB0AQCAeispLUMzl2xXhqvAvSzU4afpQyM1OCq0DltWdVxeBAAA9VJSWobGL9zsEbgkKdNVoPELNyspLaOOWlY9hC4AAFDvlJQazVyyXaaCdWXLZi7ZrpLSiirqJ0IXAACod1LSj5U7w3U2IynDVaCU9GPWNeoiEboAAEC9k5VfeeCqTl19QOgCAAD1TnCgX43W1QeELgAAUO/cGBGkUIefKpsYwqYzdzHeGBFkZbMuCqELAADUO02b2DR9aKQklQteZX9PHxrZoObrInQBAIB6aXBUqN6473o5HZ6XEJ0OP71x3/UNbp4uJkcFAAD11uCoUN0W6WRGegAAgNrWtIlNMZ1a13UzLhqXFwEAACxA6AIAALAAoQsAAMAChC4AAAALELoAAAAsQOgCAACwAKELAADAAoQuAAAACxC6AAAALFCnoWv27Nm64YYbFBgYqODgYA0fPly7du3yqBk9erRsNpvHKzo62qOmsLBQEydOVJs2bRQQEKBhw4bp559/9qjJyclRfHy8HA6HHA6H4uPjlZub61Fz4MABDR06VAEBAWrTpo0mTZqkoqKiWtl3AABwaanT0LVmzRo99NBDWr9+vVasWKHTp09r4MCBOnHihEfd4MGDlZGR4X4tXbrUY/3kyZO1ePFiJSYmau3atTp+/Lji4uJUUlLirhkxYoRSU1OVlJSkpKQkpaamKj4+3r2+pKREsbGxOnHihNauXavExEQtWrRIU6ZMqd1OAAAAlwSbMcbUdSPKHD16VMHBwVqzZo369Okj6cyZrtzcXH3yyScVvsflcqlt27Z69913dc8990iSDh8+rA4dOmjp0qUaNGiQduzYocjISK1fv169evWSJK1fv14xMTHauXOnOnfurGXLlikuLk4HDx5UWFiYJCkxMVGjR49WVlaWWrZsecH25+XlyeFwyOVyeVUPAADqnlW/3/VqTJfL5ZIkBQUFeSxfvXq1goODddVVV2ns2LHKyspyr9u0aZOKi4s1cOBA97KwsDBFRUVp3bp1kqTk5GQ5HA534JKk6OhoORwOj5qoqCh34JKkQYMGqbCwUJs2baqwvYWFhcrLy/N4AQAAVKTehC5jjB555BHddNNNioqKci8fMmSI3nvvPa1cuVIvvPCCNm7cqP79+6uwsFCSlJmZKV9fX7Vq1cpjeyEhIcrMzHTXBAcHl/vM4OBgj5qQkBCP9a1atZKvr6+75lyzZ892jxFzOBzq0KFD9TsAAAA0as3qugFlEhIS9MMPP2jt2rUey8suGUpSVFSUevbsqfDwcH3xxRe64447Kt2eMUY2m83999n/vpias02bNk2PPPKI+++8vDyCFwAAqFC9ONM1ceJEffbZZ1q1apXat29/3trQ0FCFh4dr9+7dkiSn06mioiLl5OR41GVlZbnPXDmdTh05cqTcto4ePepRc+4ZrZycHBUXF5c7A1bGbrerZcuWHi8AAICK1GnoMsYoISFBH3/8sVauXKmIiIgLvic7O1sHDx5UaGioJKlHjx7y8fHRihUr3DUZGRlKS0tT7969JUkxMTFyuVxKSUlx12zYsEEul8ujJi0tTRkZGe6a5cuXy263q0ePHjWyvwAA4NJVp3cvTpgwQe+//74+/fRTde7c2b3c4XDI399fx48f14wZM3TnnXcqNDRU+/bt0+OPP64DBw5ox44dCgwMlCSNHz9en3/+ud566y0FBQVp6tSpys7O1qZNm9S0aVNJZ8aGHT58WHPnzpUkjRs3TuHh4VqyZImkM1NGdO/eXSEhIXr++ed17NgxjR49WsOHD9err77q1f5w9yIAAA2PZb/fpg5JqvC1YMECY4wxJ0+eNAMHDjRt27Y1Pj4+pmPHjmbUqFHmwIEDHts5deqUSUhIMEFBQcbf39/ExcWVq8nOzjYjR440gYGBJjAw0IwcOdLk5OR41Ozfv9/ExsYaf39/ExQUZBISEkxBQYHX++NyuYwk43K5qtUfAADAelb9ftereboaOs50AQDQ8FyS83QBAAA0VoQuAAAACxC6AAAALEDoAgAAsAChCwAAwAKELgAAAAsQugAAACxA6AIAALAAoQsAAMAChC4AAAALELoAAAAsQOgCAACwAKELAADAAoQuAAAACxC6AAAALEDoAgAAsAChCwAAwAKELgAAAAsQugAAACxA6AIAALAAoQsAAMAChC4AAAALELoAAAAsQOgCAACwAKELAADAAoQuAAAACxC6AAAALEDoAgAAsAChCwAAwAKELgAAAAsQugAAACxA6AIAALAAoQsAAMAChC4AAAALELoAAAAsQOgCAACwAKELAADAAoQuAAAACxC6AAAALEDoAgAAsAChCwAAwAKELgAAAAsQugAAACxA6AIAALAAoQsAAMAChC4AAAALELoAAAAsQOgCAACwAKELAADAAoQuAAAACxC6AAAALEDoAgAAsAChCwAAwAKELgAAAAsQugAAACxA6AIAALAAoQsAAMAChC4AAAALELoAAAAsQOgCAACwAKELAADAAoQuAAAACxC6AAAALEDoAgAAsAChCwAAwAKELgAAAAsQugAAACxA6AIAALAAoQsAAMACdRq6Zs+erRtuuEGBgYEKDg7W8OHDtWvXLo8aY4xmzJihsLAw+fv7q1+/ftq2bZtHTWFhoSZOnKg2bdooICBAw4YN088//+xRk5OTo/j4eDkcDjkcDsXHxys3N9ej5sCBAxo6dKgCAgLUpk0bTZo0SUVFRbWy7wAA4NJSp6FrzZo1euihh7R+/XqtWLFCp0+f1sCBA3XixAl3zXPPPacXX3xRr732mjZu3Cin06nbbrtN+fn57prJkydr8eLFSkxM1Nq1a3X8+HHFxcWppKTEXTNixAilpqYqKSlJSUlJSk1NVXx8vHt9SUmJYmNjdeLECa1du1aJiYlatGiRpkyZYk1nAACAxs3UI1lZWUaSWbNmjTHGmNLSUuN0Os0zzzzjrikoKDAOh8PMmTPHGGNMbm6u8fHxMYmJie6aQ4cOmSZNmpikpCRjjDHbt283ksz69evdNcnJyUaS2blzpzHGmKVLl5omTZqYQ4cOuWs++OADY7fbjcvl8qr9LpfLSPK6HgAA1D2rfr/r1Zgul8slSQoKCpIkpaenKzMzUwMHDnTX2O129e3bV+vWrZMkbdq0ScXFxR41YWFhioqKctckJyfL4XCoV69e7pro6Gg5HA6PmqioKIWFhblrBg0apMLCQm3atKnC9hYWFiovL8/jBQAAUJF6E7qMMXrkkUd00003KSoqSpKUmZkpSQoJCfGoDQkJca/LzMyUr6+vWrVqdd6a4ODgcp8ZHBzsUXPu57Rq1Uq+vr7umnPNnj3bPUbM4XCoQ4cOVd1tAABwiag3oSshIUE//PCDPvjgg3LrbDabx9/GmHLLznVuTUX11ak527Rp0+RyudyvgwcPnrdNAADg0lUvQtfEiRP12WefadWqVWrfvr17udPplKRyZ5qysrLcZ6WcTqeKioqUk5Nz3pojR46U+9yjR4961Jz7OTk5OSouLi53BqyM3W5Xy5YtPV4AAAAVqdPQZYxRQkKCPv74Y61cuVIREREe6yMiIuR0OrVixQr3sqKiIq1Zs0a9e/eWJPXo0UM+Pj4eNRkZGUpLS3PXxMTEyOVyKSUlxV2zYcMGuVwuj5q0tDRlZGS4a5YvXy673a4ePXrU/M4DAIBLis0YY+rqwydMmKD3339fn376qTp37uxe7nA45O/vL0l69tlnNXv2bC1YsEBXXnmlnn76aa1evVq7du1SYGCgJGn8+PH6/PPP9dZbbykoKEhTp05Vdna2Nm3apKZNm0qShgwZosOHD2vu3LmSpHHjxik8PFxLliyRdGbKiO7duyskJETPP/+8jh07ptGjR2v48OF69dVXvdqfvLw8ORwOuVwuznoBANBAWPb7Xav3Rl6ApApfCxYscNeUlpaa6dOnG6fTaex2u+nTp4/ZunWrx3ZOnTplEhISTFBQkPH39zdxcXHmwIEDHjXZ2dlm5MiRJjAw0AQGBpqRI0eanJwcj5r9+/eb2NhY4+/vb4KCgkxCQoIpKCjwen+YMgIAgIbHqt/vOj3T1dhwpgsAgIbHqt/vejGQHgAAoLEjdAEAAFiA0AUAAGABQhcAAIAFCF0AAAAWIHQBAABYgNAFAABgAUIXAACABQhdAAAAFiB0AQAAWKDaoauoqEi7du3S6dOna7I9AAAAjVKVQ9fJkyf1wAMPqHnz5rrmmmt04MABSdKkSZP0zDPP1HgDAQAAGoMqh65p06Zpy5YtWr16tfz8/NzLBwwYoA8//LBGGwcAANBYNKvqGz755BN9+OGHio6Ols1mcy+PjIzUTz/9VKONAwAAaCyqfKbr6NGjCg4OLrf8xIkTHiEMAAAA/1Xl0HXDDTfoiy++cP9dFrTefPNNxcTE1FzLAAAAGpEqX16cPXu2Bg8erO3bt+v06dN6+eWXtW3bNiUnJ2vNmjW10UYAAIAGr8pnunr37q1vv/1WJ0+eVKdOnbR8+XKFhIQoOTlZPXr0qI02AgAANHg2Y4yp60Y0Fnl5eXI4HHK5XGrZsmVdNwcAAHjBqt/vKp/pWrp0qf7zn/+UW/6f//xHy5Ytq5FGAQAANDZVDl2PPfaYSkpKyi03xuixxx6rkUYBAAA0NlUOXbt371ZkZGS55VdffbX27NlTI40CAABobKocuhwOh/bu3Vtu+Z49exQQEFAjjQIAAGhsqhy6hg0bpsmTJ3vMPr9nzx5NmTJFw4YNq9HGAQAANBZVDl3PP/+8AgICdPXVVysiIkIRERHq0qWLWrdurf/7v/+rjTYCAAA0eFWeHNXhcGjdunVasWKFtmzZIn9/f1177bXq06dPbbQPAACgUWCerhrEPF0AADQ8Vv1+e3Wm65VXXtG4cePk5+enV1555by1kyZNqpGGAQAANCZenemKiIjQd999p9atWysiIqLyjdlsFd7ZeKngTBcAAA1PvTrTlZ6eXuG/AQAA4J0q3b1YXFysK664Qtu3b6+t9gAAADRKVQpdPj4+KiwslM1mq632AAAANEpVnqdr4sSJevbZZ3X69OnaaA8AAECjVOV5ujZs2KCvvvpKy5cvV9euXcs9+ufjjz+uscYBAAA0FlUOXZdddpnuvPPO2mgLAABAo1Xl0LVgwYLaaAcAAECj5vWYrtLSUj3//PP69a9/rRtvvFGPP/64CgoKarNtAAAAjYbXoevZZ5/VY489poCAAIWGhurFF19k9nmgHispNUr+KVufph5S8k/ZKinliV91ge8BQBmvn73YuXNn/elPf9KECRMkSUlJSRo+fLhOnTrFFBL/P2akR32RlJahmUu2K8P137PRoQ4/TR8aqcFRoXXYsksL3wPQMFj1++116PLz89OPP/6ojh07SpKMMfLz89PevXvVrl27WmtgQ0LoQn2QlJah8Qs369z/YZf9p9Eb913PD74F+B6AhsOq32+vLy8WFRXJ39/f/bfNZpOvr68KCwtrpWEAqq6k1Gjmku3lfugluZfNXLKdS1y1jO8BQEWqdPfiX//6VzVv3tz9d1FRkf72t7/J4XC4l7344os11zoAVZKSfszjUta5jKQMV4FS0o8pplNr6xp2ieF7AFARr0NXnz59tGvXLo9lvXv31t69e91/M7YLqFtZ+d7dUextHaqH7wFARbwOXatXr67FZgCoCcGBfjVah+rhewBQkSo/exFA/XVjRJBCHX6q7JyzTWfunrsxIsjKZl1y+B4AVITQBVisNudtatrEpulDIyWp3A9+2d/Th0aqaROGAtQmvgcAFfF6yghcGFNG4EKsmreJ+aHqB74HoGGod/N04cIIXTgfq+dtKik1Skk/pqz8AgUHnrmUxZkV6/E9APWfVb/fVX7gNYCqu9C8TTadmbfptkhnjf0gN21iYzqCeoDvAUAZr0LXDz/84PUGr7322mo3BmismLepZnDWCEBD5lXo6t69u2w2m4wxF5yLq6SkpEYaBjQmzNt08RgfBaCh8+ruxfT0dO3du1fp6elatGiRIiIi9Prrr+v777/X999/r9dff12dOnXSokWLaru9QIPEvE0Xp2w83LlnCzNdBRq/cLOS0jLqqGUA4D2vznSFh4e7//273/1Or7zyim6//Xb3smuvvVYdOnTQX//6Vw0fPrzGGwk0dGXzNmW6Cioc12WT5GTepgrVxXg4AKgNVZ6na+vWrYqIiCi3PCIiQtu3b6+RRgENWUXzcDFvU/VVZTwcANRnVb57sUuXLpo1a5bmzZsnP78zl0IKCws1a9YsdenSpcYbCDQkFxp39MZ915db72Rc0nkxHg5AY1Hl0DVnzhwNHTpUHTp0ULdu3SRJW7Zskc1m0+eff17jDQQaisrm4Sobd1Q2D9dtkU7uwKsCxsMBaCyqHLpuvPFGpaena+HChdq5c6eMMbrnnns0YsQIBQQE1EYbgXqvquOOmBbCe4yHA9BYVGty1ObNm2vcuHE13RagwWIertpTNh5u/MLNskkewYvxcAAakmo98Prdd9/VTTfdpLCwMO3fv1+S9Pe//12ffvppjTYOaCgYd1S7ysbDOR2elxCdDr8af3wSANSWKp/peuONN/Tkk09q8uTJmjVrlnsy1FatWumll17Sb37zmxpvJFDfMe6o9jEeDkBDV+UzXa+++qrefPNNPfHEE2rW7L+ZrWfPntq6dWuNNg5oKMrGHVX282/TmbsYGXd0ccrGw/2mezvFdGpN4ALQoFQ5dKWnp+u6664rt9xut+vEiRM10iigobF6Hq6K5gIDANRvVb68GBERodTUVI9Z6iVp2bJlioyMrLGGAQ2NVfNw8QxCAGiYqhy6/t//+3966KGHVFBQIGOMUlJS9MEHH2j27Nn65z//WRttBBqM2h535O1cYACA+qfKoev+++/X6dOn9eijj+rkyZMaMWKE2rVrp5dffln33ntvbbQRaFBqax4unkEIAA1btebpGjt2rMaOHatffvlFpaWlCg4Orul2ATgHc4EBQMNW5YH0/fv3V25uriSpTZs27sCVl5en/v3712jjAPwXc4EBQMNW5dC1evVqFRUVlVteUFCgb775pkYaBaA85gIDgIbN68uLP/zwg/vf27dvV2ZmpvvvkpISJSUlqV27djXbOgBuPIMQABo2r890de/eXdddd51sNpv69++v7t27u189evTQrFmz9OSTT1bpw7/++msNHTpUYWFhstls+uSTTzzWjx49WjabzeMVHR3tUVNYWKiJEyeqTZs2CggI0LBhw/Tzzz971OTk5Cg+Pl4Oh0MOh0Px8fHuS6RlDhw4oKFDhyogIEBt2rTRpEmTKjyjB9QVq+cCAwDULK9DV3p6un766Sf3NBHp6enu16FDh5SXl6cxY8ZU6cNPnDihbt266bXXXqu0ZvDgwcrIyHC/li5d6rF+8uTJWrx4sRITE7V27VodP35ccXFx7scTSdKIESOUmpqqpKQkJSUlKTU1VfHx8e71JSUlio2N1YkTJ7R27VolJiZq0aJFmjJlSpX2B6htPIMQABoumzGmXkxlbbPZtHjxYg0fPty9bPTo0crNzS13BqyMy+VS27Zt9e677+qee+6RJB0+fFgdOnTQ0qVLNWjQIO3YsUORkZFav369evXqJUlav369YmJitHPnTnXu3FnLli1TXFycDh48qLCwMElSYmKiRo8eraysLLVs2dKrfcjLy5PD4ZDL5fL6PUB1lJQankEIADXEqt/vKg+knz17tubPn19u+fz58/Xss8/WSKPOtnr1agUHB+uqq67S2LFjlZWV5V63adMmFRcXa+DAge5lYWFhioqK0rp16yRJycnJcjgc7sAlSdHR0XI4HB41UVFR7sAlSYMGDVJhYaE2bdpU4/sEXCyeQQgADU+VQ9fcuXN19dVXl1t+zTXXaM6cOTXSqDJDhgzRe++9p5UrV+qFF17Qxo0b1b9/fxUWFkqSMjMz5evrq1atWnm8LyQkxD3QPzMzs8J5xIKDgz1qQkJCPNa3atVKvr6+HjcMnKuwsFB5eXkeLwAAgIpUeXLUzMxMhYaWHzfStm1bZWRk1EijypRdMpSkqKgo9ezZU+Hh4friiy90xx13VPo+Y4xstv/+l//Z/76YmnPNnj1bM2fOvOB+AAAAVPlMV4cOHfTtt9+WW/7tt996XJ6rDaGhoQoPD9fu3bslSU6nU0VFRcrJyfGoy8rKcp+5cjqdOnLkSLltHT161KPm3DNaOTk5Ki4uLncG7GzTpk2Ty+Vyvw4ePHhR+wcAABqvKoeuBx98UJMnT9aCBQu0f/9+7d+/X/Pnz9fDDz+ssWPH1kYb3bKzs3Xw4EH3mbYePXrIx8dHK1ascNdkZGQoLS1NvXv3liTFxMTI5XIpJSXFXbNhwwa5XC6PmrS0NI8zdcuXL5fdblePHj0qbY/dblfLli09XgAAABWp8uXFRx99VMeOHdOECRPc81j5+fnpz3/+s6ZNm1albR0/flx79uxx/52enq7U1FQFBQUpKChIM2bM0J133qnQ0FDt27dPjz/+uNq0aaPf/va3kiSHw6EHHnhAU6ZMUevWrRUUFKSpU6eqa9euGjBggCSpS5cuGjx4sMaOHau5c+dKksaNG6e4uDh17txZkjRw4EBFRkYqPj5ezz//vI4dO6apU6dq7NixBCkAAFAzTDXl5+eblJQUs3XrVlNQUFCtbaxatcrozHN6PV6jRo0yJ0+eNAMHDjRt27Y1Pj4+pmPHjmbUqFHmwIEDHts4deqUSUhIMEFBQcbf39/ExcWVq8nOzjYjR440gYGBJjAw0IwcOdLk5OR41Ozfv9/ExsYaf39/ExQUZBISEqq8Xy6Xy0gyLperWv0BNBSnS0rNuj2/mE++/9ms2/OLOV1SWtdNAoBqs+r3u97M09UYME8XLgVJaRmauWS7Mlz/fbB2qMNP04dGMjkrgAbJqt9vry4v3nHHHXrrrbfUsmXL8941KEkff/xxjTQMQP2TlJah8Qs3l3v2Y6arQOMXbmZWfAA4D69Cl8PhcE+d4HA4arVBAOqnklKjmUu2V/iwbaMzz3+cuWS7bot0MlkrAFTAq9C1YMGCCv8N4NKRkn7M45LiuYykDFeBUtKPKaZTa+saBgANRJWnjABwacrKrzxwVacOAC41Xp3puu666847M/vZNm/efFENAlA/BQf61WgdAFxqvApdw4cPd/+7oKBAr7/+uiIjIxUTEyNJWr9+vbZt26YJEybUSiMB1L0bI4IU6vBTpqugwnFdNklOh59ujAiyumkA0CB4FbqmT5/u/veDDz6oSZMm6X//93/L1fAYHKDxatrEpulDIzV+4WbZJI/gVXYefPrQSAbRA0AlqjxPl8Ph0Hfffacrr7zSY/nu3bvVs2dPuVyuGm1gQ8I8XbgU1PU8XSWlRinpx5SVX6DgwDNn1gh6AC5GvZqn62z+/v5au3ZtudC1du1a+fkxlgNo7AZHheq2SGedBJ+6DnwAcDGqHLomT56s8ePHa9OmTYqOjpZ0ZkzX/Pnz9eSTT9Z4AwHUP02b2CyfFoKJWQE0dFUOXY899piuuOIKvfzyy3r//fclnXmo9FtvvaW77767xhsIAEzMCqAxqHLokqS7776bgAXAMkzMCqAxqNbkqLm5ufrnP/+pxx9/XMeOHZN0Zn6uQ4cO1WjjAEBiYlYAjUOVz3T98MMPGjBggBwOh/bt26cHH3xQQUFBWrx4sfbv36933nmnNtoJ4BLGxKwAGoMqn+l65JFHNHr0aO3evdvjbsUhQ4bo66+/rtHGAYD034lZKxutZdOZuxiZmBVAfVbl0LVx40b94Q9/KLe8Xbt2yszMrJFGAcDZyiZmlVQueDExK4CGosqhy8/PT3l5eeWW79q1S23btq2RRgHAuQZHheqN+66X0+F5CdHp8GO6CAANQpXHdP3mN7/RU089pY8++kiSZLPZdODAAT322GO68847a7yBAFCmLidmBYCLVeXHAOXl5en222/Xtm3blJ+fr7CwMGVmZiomJkZLly5VQEBAbbW13uMxQAAANDz19jFALVu21Nq1a7Vy5Upt3rxZpaWluv766zVgwIDaaB8AAECjUKXQdfr0afn5+Sk1NVX9+/dX//79a6tdAAAAjUqVBtI3a9ZM4eHhKikpqa32AAAANEpVvnvxL3/5i6ZNm+aeiR4AAAAXVuUxXa+88or27NmjsLAwhYeHlxs4v3nz5hprHAAAQGNRrSkjbDZuzwYAAKiKKk8ZgcoxZQQAAA2PVb/fXo/pOnnypB566CG1a9dOwcHBGjFihH755ZdaaxgAAEBj4nXomj59ut566y3Fxsbq3nvv1YoVKzR+/PjabBsAAECj4fWYro8//ljz5s3TvffeK0m677779Otf/1olJSVq2rRprTUQAACgMfD6TNfBgwd18803u/++8cYb1axZMx0+fLhWGgYAANCYeB26SkpK5Ovr67GsWbNmOn36dI03CgAAoLHx+vKiMUajR4+W3W53LysoKNAf//hHj7m6Pv7445ptIQAAQCPgdegaNWpUuWX33XdfjTYGAACgsfI6dC1YsKA22wEAANCoVfnZiwAAAKg6QhcAAIAFCF0AAAAWIHQBAABYgNAFAABgAUIXAACABQhdAAAAFiB0AQAAWIDQBQAAYAFCFwAAgAUIXQAAABYgdAEAAFiA0AUAAGABQhcAAIAFCF0AAAAWIHQBAABYgNAFAABgAUIXAACABQhdAAAAFiB0AQAAWIDQBQAAYAFCFwAAgAUIXQAAABYgdAEAAFiA0AUAAGCBZnXdAFyaSkqNUtKPKSu/QMGBfroxIkhNm9jqulkAANQaQhcsl5SWoZlLtivDVeBeFurw0/ShkRocFVqHLQMAoPZweRGWSkrL0PiFmz0ClyRlugo0fuFmJaVl1FHLAACoXYQuWKak1Gjmku0yFawrWzZzyXaVlFZUAQBAw0bogmVS0o+VO8N1NiMpw1WglPRj1jUKAACLELpgmaz8ygNXdeoAAGhICF2wTHCgX43WAQDQkBC6YJkbI4IU6vBTZRND2HTmLsYbI4KsbBYAAJYgdMEyTZvYNH1opCSVC15lf08fGsl8XQCARonQBUsNjgrVG/ddL6fD8xKi0+GnN+67nnm6AACNFpOjwnKDo0J1W6STGekBAJeUOj3T9fXXX2vo0KEKCwuTzWbTJ5984rHeGKMZM2YoLCxM/v7+6tevn7Zt2+ZRU1hYqIkTJ6pNmzYKCAjQsGHD9PPPP3vU5OTkKD4+Xg6HQw6HQ/Hx8crNzfWoOXDggIYOHaqAgAC1adNGkyZNUlFRUW3sNnTmUmNMp9b6Tfd2iunUmsAFAGj06jR0nThxQt26ddNrr71W4frnnntOL774ol577TVt3LhRTqdTt912m/Lz8901kydP1uLFi5WYmKi1a9fq+PHjiouLU0lJibtmxIgRSk1NVVJSkpKSkpSamqr4+Hj3+pKSEsXGxurEiRNau3atEhMTtWjRIk2ZMqX2dh4AAFxaTD0hySxevNj9d2lpqXE6neaZZ55xLysoKDAOh8PMmTPHGGNMbm6u8fHxMYmJie6aQ4cOmSZNmpikpCRjjDHbt283ksz69evdNcnJyUaS2blzpzHGmKVLl5omTZqYQ4cOuWs++OADY7fbjcvl8nofXC6XkVSl9wAAgLpl1e93vR1In56erszMTA0cONC9zG63q2/fvlq3bp0kadOmTSouLvaoCQsLU1RUlLsmOTlZDodDvXr1ctdER0fL4XB41ERFRSksLMxdM2jQIBUWFmrTpk2VtrGwsFB5eXkeLwAAgIrU29CVmZkpSQoJCfFYHhIS4l6XmZkpX19ftWrV6rw1wcHB5bYfHBzsUXPu57Rq1Uq+vr7umorMnj3bPU7M4XCoQ4cOVdxLAABwqai3oauMzeY5wNoYU27Zuc6tqai+OjXnmjZtmlwul/t18ODB87YLAABcuupt6HI6nZJU7kxTVlaW+6yU0+lUUVGRcnJyzltz5MiRcts/evSoR825n5OTk6Pi4uJyZ8DOZrfb1bJlS48XAABARept6IqIiJDT6dSKFSvcy4qKirRmzRr17t1bktSjRw/5+Ph41GRkZCgtLc1dExMTI5fLpZSUFHfNhg0b5HK5PGrS0tKUkZHhrlm+fLnsdrt69OhRq/sJAAAuDXU6Oerx48e1Z88e99/p6elKTU1VUFCQOnbsqMmTJ+vpp5/WlVdeqSuvvFJPP/20mjdvrhEjRkiSHA6HHnjgAU2ZMkWtW7dWUFCQpk6dqq5du2rAgAGSpC5dumjw4MEaO3as5s6dK0kaN26c4uLi1LlzZ0nSwIEDFRkZqfj4eD3//PM6duyYpk6dqrFjx3L2CgAA1IxavTfyAlatWmUklXuNGjXKGHNm2ojp06cbp9Np7Ha76dOnj9m6davHNk6dOmUSEhJMUFCQ8ff3N3FxcebAgQMeNdnZ2WbkyJEmMDDQBAYGmpEjR5qcnByPmv3795vY2Fjj7+9vgoKCTEJCgikoKKjS/jBlBAAADY9Vv982Y4ypw8zXqOTl5cnhcMjlcnGGDACABsKq3+96O6YLAACgMSF0AQAAWIDQBQAAYAFCFwAAgAUIXQAAABYgdAEAAFiA0AUAAGCBOp2RHvVDSalRSvoxZeUXqE2AXbJJvxwvVHCgn26MCFLTJud/wDgAALgwQtclLiktQzOXbFeGq6DC9aEOP00fGqnBUaEWtwwAgMaFy4uXsKS0DI1fuLnSwCVJma4CjV+4WUlpGZXWAACACyN0XaJKSo1mLtmuCz0Dqmz9zCXbVVLKE6MAAKguQtclKiX92HnPcJ3NSMpwFSgl/VjtNgoAgEaM0HWJysr3LnBd7HsAAMAZhK5LVHCgnyXvAQAAZxC6LlE3RgQp1OGnqkwGkXOisNbaAwBAY0foukQ1bWLT9KGRkuR18PrfL3YwmB4AgGoidF0CSkqNkn/K1qeph5T8U7Y7OA2OCtUb910vp8O7y4YMpgcAoPqYHLWRq2jy07MnPB0cFarbIp36+4of9dqqPRfcHoPpAQCoHs50NWKVTX567oSnTZvY9OtftfFqmwymBwCgeghdjdT5Jj+taMLTCw2st+nMGbIbI4JqobUAADR+hK5G6kKTn5474en5BtaX/T19aCQPvwYAoJoIXY2Ut2Ovzq6rbGC90+GnN+67nodeAwBwERhI30h5O/bq3LqygfUp6ceUlV+g4MAzlxQ5wwUAwMUhdDVSZWO0Ml0FFY7rsunMGayKxmg1bWJTTKfWtd5GAAAuJVxebKQYowUAQP1C6GrEGuoYrcomcwUAoCHj8mIj19DGaF1oMlcAABoqmzGG0wg1JC8vTw6HQy6XSy1btqzr5jQ4ZZO5nntAlsXD+nx2DgDQcFn1+83lRdQLVZ3MFQCAhobQhXqhqpO5AgDQ0BC6UC9UZzJXAAAaEkIX6oXqTuYKAEBDQehCvcADtwEAjR2hC/UCk7kCABo7QhfqjYY6mSsAAN5gctR6rqTUNJiJTWtCQ5vMFQAAbxG66rFLdXZ2HrgNAGiMuLxYT5XNzn7u3FWZrgKNX7hZSWkZddQyAABQHYSueojZ2QEAaHwIXfUQs7MDAND4MKarHqrK7OxnD7RvE2CXbNIvxwsZgA4AQD1D6KqHvJ11fd8vJ3TTsysrPSt2KQy6BwCgoeDyYj3kzezsrZr76O9f7j7vZUgG3QMAUH8Quuohb2Zn92YIPYPuAQCoPwhd9dT5ZmefPOAq5Z4s9mo7DLoHAKB+YExXPVbZ7Oyf/3C4ytvydnA+AACoHYSueq6i2dm9HWh/se8BAAA1h8uLDdCNEUG6rLmPV7U2nbmL8caIoNptFAAAOC9CVyNWNuh++tBI5usCAKCOEboaoJT0Y14NpA8K8NUb913PPF0AANQDjOlqgLwdFP+X2C7uwHX2zPXMVg8AgPUIXQ2Qt4PinQ5/SVJSWoZmLtnuMZEqs9UDAGAtLi82QN7MWF82eD4pLUPjF24uN3M9s9UDAGAtQlcD5M2M9WXrZy7ZXuHs9cxWDwCAtQhdDdT5ZqwvGzyfkn7svM9mZLZ6AACsw5iuBqyyGevLBsh7O+Ce2eoBAKh9hK4GrqIZ68t4O+Ce2eoBAKh9XF5sxKoy4B4AANQuQlcj5u2Ae+brAgCg9hG6GjlvBtwDAIDax5iuS8CFBtwDAIDaR+i6RJxvwD0AAKh9XF4EAACwAGe6cEE8LBsAgItH6MJ58bBsAABqBpcXUSkelg0AQM0hdKFCJaWGh2UDAFCDCF2oEA/LBgCgZhG6UCEelg0AQM2q16FrxowZstlsHi+n0+leb4zRjBkzFBYWJn9/f/Xr10/btm3z2EZhYaEmTpyoNm3aKCAgQMOGDdPPP//sUZOTk6P4+Hg5HA45HA7Fx8crNzfXil2st3hYNgAANatehy5Juuaaa5SRkeF+bd261b3uueee04svvqjXXntNGzdulNPp1G233ab8/Hx3zeTJk7V48WIlJiZq7dq1On78uOLi4lRSUuKuGTFihFJTU5WUlKSkpCSlpqYqPj7e0v2sb3hYNgAANaveTxnRrFkzj7NbZYwxeumll/TEE0/ojjvukCS9/fbbCgkJ0fvvv68//OEPcrlcmjdvnt59910NGDBAkrRw4UJ16NBBX375pQYNGqQdO3YoKSlJ69evV69evSRJb775pmJiYrRr1y517tzZup2tR8oelj1+4WbZJI8B9TwsGwCAqqv3Z7p2796tsLAwRURE6N5779XevXslSenp6crMzNTAgQPdtXa7XX379tW6deskSZs2bVJxcbFHTVhYmKKiotw1ycnJcjgc7sAlSdHR0XI4HO6ayhQWFiovL8/j1ZjwsGwAAGpOvT7T1atXL73zzju66qqrdOTIEc2aNUu9e/fWtm3blJmZKUkKCQnxeE9ISIj2798vScrMzJSvr69atWpVrqbs/ZmZmQoODi732cHBwe6aysyePVszZ86s9v41BDwsGwCAmlGvQ9eQIUPc/+7atatiYmLUqVMnvf3224qOjpYk2WyeP/7GmHLLznVuTUX13mxn2rRpeuSRR9x/5+XlqUOHDud9T0PEw7IBALh49f7y4tkCAgLUtWtX7d692z3O69yzUVlZWe6zX06nU0VFRcrJyTlvzZEjR8p91tGjR8udRTuX3W5Xy5YtPV4AAAAVaVChq7CwUDt27FBoaKgiIiLkdDq1YsUK9/qioiKtWbNGvXv3liT16NFDPj4+HjUZGRlKS0tz18TExMjlciklJcVds2HDBrlcLncNAADAxarXlxenTp2qoUOHqmPHjsrKytKsWbOUl5enUaNGyWazafLkyXr66ad15ZVX6sorr9TTTz+t5s2ba8SIEZIkh8OhBx54QFOmTFHr1q0VFBSkqVOnqmvXru67Gbt06aLBgwdr7Nixmjt3riRp3LhxiouLu2TvXAQAADWvXoeun3/+Wb///e/1yy+/qG3btoqOjtb69esVHh4uSXr00Ud16tQpTZgwQTk5OerVq5eWL1+uwMBA9zb+/ve/q1mzZrr77rt16tQp3XrrrXrrrbfUtGlTd817772nSZMmue9yHDZsmF577TVrdxYAADRqNmMMTyyuIXl5eXI4HHK5XIzvAgCggbDq97tBjekCAABoqAhdAAAAFiB0AQAAWIDQBQAAYIF6ffdiQ1N2T0JjewYjAACNWdnvdm3fW0joqkH5+fmS1CgfBQQAQGOXn58vh8NRa9tnyogaVFpaqsOHD8sYo44dO+rgwYNMHVFNZc+xpA+rjz6sGfTjxaMPLx59ePHO14fGGOXn5yssLExNmtTeyCvOdNWgJk2aqH379u7TlDyP8eLRhxePPqwZ9OPFow8vHn148Srrw9o8w1WGgfQAAAAWIHQBAABYgNBVC+x2u6ZPny673V7XTWmw6MOLRx/WDPrx4tGHF48+vHj1oQ8ZSA8AAGABznQBAABYgNAFAABgAUIXAACABQhdAAAAFiB0VWLGjBmy2WweL6fT6V5vjNGMGTMUFhYmf39/9evXT9u2bfPYRmFhoSZOnKg2bdooICBAw4YN088//+xRk5OTo/j4eDkcDjkcDsXHxys3N9eKXaxxX3/9tYYOHaqwsDDZbDZ98sknHuut7LMDBw5o6NChCggIUJs2bTRp0iQVFRXVxm7XqAv14ejRo8sdl9HR0R41l3Ifzp49WzfccIMCAwMVHBys4cOHa9euXR41HIfn500fchxe2BtvvKFrr73WPRFnTEyMli1b5l7PcXhhF+rDBnkcGlRo+vTp5pprrjEZGRnuV1ZWlnv9M888YwIDA82iRYvM1q1bzT333GNCQ0NNXl6eu+aPf/yjadeunVmxYoXZvHmzueWWW0y3bt3M6dOn3TWDBw82UVFRZt26dWbdunUmKirKxMXFWbqvNWXp0qXmiSeeMIsWLTKSzOLFiz3WW9Vnp0+fNlFRUeaWW24xmzdvNitWrDBhYWEmISGh1vvgYl2oD0eNGmUGDx7scVxmZ2d71FzKfTho0CCzYMECk5aWZlJTU01sbKzp2LGjOX78uLuG4/D8vOlDjsML++yzz8wXX3xhdu3aZXbt2mUef/xx4+PjY9LS0owxHIfeuFAfNsTjkNBVienTp5tu3bpVuK60tNQ4nU7zzDPPuJcVFBQYh8Nh5syZY4wxJjc31/j4+JjExER3zaFDh0yTJk1MUlKSMcaY7du3G0lm/fr17prk5GQjyezcubMW9so65wYGK/ts6dKlpkmTJubQoUPumg8++MDY7XbjcrlqZX9rQ2Wh6ze/+U2l76EPPWVlZRlJZs2aNcYYjsPqOLcPjeE4rK5WrVqZf/7znxyHF6GsD41pmMchlxfPY/fu3QoLC1NERITuvfde7d27V5KUnp6uzMxMDRw40F1rt9vVt29frVu3TpK0adMmFRcXe9SEhYUpKirKXZOcnCyHw6FevXq5a6Kjo+VwONw1jYWVfZacnKyoqCiFhYW5awYNGqTCwkJt2rSpVvfTCqtXr1ZwcLCuuuoqjR07VllZWe519KEnl8slSQoKCpLEcVgd5/ZhGY5D75WUlCgxMVEnTpxQTEwMx2E1nNuHZRraccgDryvRq1cvvfPOO7rqqqt05MgRzZo1S71799a2bduUmZkpSQoJCfF4T0hIiPbv3y9JyszMlK+vr1q1alWupuz9mZmZCg4OLvfZwcHB7prGwso+y8zMLPc5rVq1kq+vb4Pv1yFDhuh3v/udwsPDlZ6err/+9a/q37+/Nm3aJLvdTh+exRijRx55RDfddJOioqIkcRxWVUV9KHEcemvr1q2KiYlRQUGBWrRoocWLFysyMtL9Y85xeGGV9aHUMI9DQlclhgwZ4v53165dFRMTo06dOuntt992D9Sz2Wwe7zHGlFt2rnNrKqr3ZjsNlVV91lj79Z577nH/OyoqSj179lR4eLi++OIL3XHHHZW+71Lsw4SEBP3www9au3ZtuXUch96prA85Dr3TuXNnpaamKjc3V4sWLdKoUaO0Zs0a93qOwwurrA8jIyMb5HHI5UUvBQQEqGvXrtq9e7f7LsZzE25WVpY7DTudThUVFSknJ+e8NUeOHCn3WUePHi2Xqhs6K/vM6XSW+5ycnBwVFxc3un4NDQ1VeHi4du/eLYk+LDNx4kR99tlnWrVqldq3b+9eznHovcr6sCIchxXz9fXVr371K/Xs2VOzZ89Wt27d9PLLL3McVkFlfViRhnAcErq8VFhYqB07dig0NFQRERFyOp1asWKFe31RUZHWrFmj3r17S5J69OghHx8fj5qMjAylpaW5a2JiYuRyuZSSkuKu2bBhg1wul7umsbCyz2JiYpSWlqaMjAx3zfLly2W329WjR49a3U+rZWdn6+DBgwoNDZVEHxpjlJCQoI8//lgrV65URESEx3qOwwu7UB9WhOPQO8YYFRYWchxehLI+rEiDOA6rNOz+EjJlyhSzevVqs3fvXrN+/XoTFxdnAgMDzb59+4wxZ273dTgc5uOPPzZbt241v//97yu83bd9+/bmyy+/NJs3bzb9+/ev8FbVa6+91iQnJ5vk5GTTtWvXBjtlRH5+vvn+++/N999/bySZF1980Xz//fdm//79xhjr+qzs9t5bb73VbN682Xz55Zemffv2DeIW6fP1YX5+vpkyZYpZt26dSU9PN6tWrTIxMTGmXbt29OH/b/z48cbhcJjVq1d73EZ+8uRJdw3H4fldqA85Dr0zbdo08/XXX5v09HTzww8/mMcff9w0adLELF++3BjDceiN8/VhQz0OCV2VKJszxcfHx4SFhZk77rjDbNu2zb2+tLTUTJ8+3TidTmO3202fPn3M1q1bPbZx6tQpk5CQYIKCgoy/v7+Ji4szBw4c8KjJzs42I0eONIGBgSYwMNCMHDnS5OTkWLGLNW7VqlVGUrnXqFGjjDHW9tn+/ftNbGys8ff3N0FBQSYhIcEUFBTU5u7XiPP14cmTJ83AgQNN27ZtjY+Pj+nYsaMZNWpUuf65lPuwor6TZBYsWOCu4Tg8vwv1Icehd8aMGWPCw8ONr6+vadu2rbn11lvdgcsYjkNvnK8PG+pxaDPGmKqdGwMAAEBVMaYLAADAAoQuAAAACxC6AAAALEDoAgAAsAChCwAAwAKELgAAAAsQugAAACxA6AIAL9lsNn3yySc1vt1+/fpp8uTJNb5dAPULoQtAvbNu3To1bdpUgwcPrvJ7L7/8cr300ks13ygvjB49WjabTTabTT4+Prriiis0depUnThx4rzv+/jjj/W///u/FrUSQF0hdAGod+bPn6+JEydq7dq1OnDgQF03p0oGDx6sjIwM7d27V7NmzdLrr7+uqVOnVlhbXFwsSQoKClJgYKCVzQRQBwhdAOqVEydO6KOPPtL48eMVFxent956q1zNZ599pp49e8rPz09t2rTRHXfcIenMZbr9+/fr4Ycfdp9xkqQZM2aoe/fuHtt46aWXdPnll7v/3rhxo2677Ta1adNGDodDffv21ebNm6vcfrvdLqfTqQ4dOmjEiBEaOXKk+5JkWTvmz5+vK664Qna7XcaYcpcXCwsL9eijj6pDhw6y2+268sorNW/ePPf67du36/bbb1eLFi0UEhKi+Ph4/fLLL1VuKwBrEboA1CsffvihOnfurM6dO+u+++7TggULdPYjYr/44gvdcccdio2N1ffff6+vvvpKPXv2lHTmMl379u311FNPKSMjQxkZGV5/bn5+vkaNGqVvvvlG69ev15VXXqnbb79d+fn5F7U//v7+7jNakrRnzx599NFHWrRokVJTUyt8z//8z/8oMTFRr7zyinbs2KE5c+aoRYsWkqSMjAz17dtX3bt313fffaekpCQdOXJEd99990W1E0Dta1bXDQCAs82bN0/33XefpDOX6o4fP66vvvpKAwYMkCT97W9/07333quZM2e639OtWzdJZy7TNW3aVIGBgXI6nVX63P79+3v8PXfuXLVq1Upr1qxRXFxctfYlJSVF77//vm699Vb3sqKiIr377rtq27Zthe/58ccf9dFHH2nFihXufb7iiivc69944w1df/31evrpp93L5s+frw4dOujHH3/UVVddVa22Aqh9nOkCUG/s2rVLKSkpuvfeeyVJzZo10z333KP58+e7a1JTUz1CTE3JysrSH//4R1111VVyOBxyOBw6fvx4lceUff7552rRooX8/PwUExOjPn366NVXX3WvDw8PrzRwSWf2r2nTpurbt2+F6zdt2qRVq1apRYsW7tfVV18tSfrpp5+q1FYA1uJMF4B6Y968eTp9+rTatWvnXmaMkY+Pj3JyctSqVSv5+/tXebtNmjTxuEQpyeOSn3TmzsOjR4/qpZdeUnh4uOx2u2JiYlRUVFSlz7rlllv0xhtvyMfHR2FhYfLx8fFYHxAQcN73X2j/SktLNXToUD377LPl1oWGhlaprQCsxZkuAPXC6dOn9c477+iFF15Qamqq+7VlyxaFh4frvffekyRde+21+uqrryrdjq+vr0pKSjyWtW3bVpmZmR7B69zxVN98840mTZqk22+/Xddcc43sdnu1BqcHBAToV7/6lcLDw8sFLm907dpVpaWlWrNmTYXrr7/+em3btk2XX365fvWrX3m8LhToANQtQheAeuHzzz9XTk6OHnjgAUVFRXm87rrrLvfde9OnT9cHH3yg6dOna8eOHdq6dauee+4593Yuv/xyff311zp06JA7NPXr109Hjx7Vc889p59++kn/+Mc/tGzZMo/P/9WvfqV3331XO3bs0IYNGzRy5MhqnVW7WJdffrlGjRqlMWPG6JNPPlF6erpWr16tjz76SJL00EMP6dixY/r973+vlJQU7d27V8uXL9eYMWPKhU0A9QuhC0C9MG/ePA0YMEAOh6PcujvvvFOpqanavHmz+vXrp3/961/67LPP1L17d/Xv318bNmxw1z711FPat2+fOnXq5B471aVLF73++uv6xz/+oW7duiklJaXc3Fnz589XTk6OrrvuOsXHx2vSpEkKDg6u3Z2uxBtvvKG77rpLEyZM0NVXX62xY8e6J1gNCwvTt99+q5KSEg0aNEhRUVH605/+JIfDoSZN+L90oD6zmXMHOgAAAKDG8Z9FAAAAFiB0AQAAWIDQBQAAYAFCFwAAgAUIXQAAABYgdAEAAFiA0AUAAGABQhcAAIAFCF0AAAAWIHQBAABYgNAFAABgAUIXAACABf4/9kWDH7wz3wIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.scatter(Y_test,test_data_prediction)\n",
        "plt.xlabel(\"Actual Price\")\n",
        "plt.ylabel(\"Predicted Price\")\n",
        "plt.title(\"Actual Price vs Predicted Price\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cdde490a-6e2a-41bd-8eda-eb724d203984",
      "metadata": {
        "id": "cdde490a-6e2a-41bd-8eda-eb724d203984"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.8"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}