Car Price Prediction.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "##Importing Dependencies\n"
   ]
  },
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   "metadata": {
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_bsuXGNhrwYF"
   },
   "source": [
    "##Data Collection"
   ]
  },
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   "execution_count": 2,
   "metadata": {
    "id": "6MET7Zjn17GZ"
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   "outputs": [],
   "source": [
    "#reading the data\n",
    "car_data = pd.read_csv(\"C://Users//rhush//Notebook//iris_flowers_classification//car_data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hXiD1xCurzrl"
   },
   "source": [
    "##Data Preprocessing"
   ]
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       "      <td>27000</td>\n",
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      "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  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "#displaying first 5 rows of dataset\n",
    "car_data.head()"
   ]
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       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-65b527e2-acdf-4e9e-b69b-bd7d997e5ce7');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "\n",
       "<div id=\"df-bd26b39f-fd14-4b46-8f84-6ede7d2e4ef2\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bd26b39f-fd14-4b46-8f84-6ede7d2e4ef2')\"\n",
       "            title=\"Suggest charts\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "     width=\"24px\">\n",
       "    <g>\n",
       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-bd26b39f-fd14-4b46-8f84-6ede7d2e4ef2 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "    Car_Name  Year  Selling_Price  Present_Price  Driven_kms Fuel_Type  \\\n",
       "296     city  2016           9.50           11.6       33988    Diesel   \n",
       "297     brio  2015           4.00            5.9       60000    Petrol   \n",
       "298     city  2009           3.35           11.0       87934    Petrol   \n",
       "299     city  2017          11.50           12.5        9000    Diesel   \n",
       "300     brio  2016           5.30            5.9        5464    Petrol   \n",
       "\n",
       "    Selling_type Transmission  Owner  \n",
       "296       Dealer       Manual      0  \n",
       "297       Dealer       Manual      0  \n",
       "298       Dealer       Manual      0  \n",
       "299       Dealer       Manual      0  \n",
       "300       Dealer       Manual      0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#displaying last 5 rows of dataset\n",
    "car_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "L4-zmvyO2eUb",
    "outputId": "90ba34d2-facb-44f0-ef1b-8654e4b61e1d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(301, 9)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#displaying the number of rows and columns present in the data\n",
    "car_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5sSwO5wJ2hpO",
    "outputId": "96b1018e-3b41-46f1-bd15-dfda024c7fc9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 301 entries, 0 to 300\n",
      "Data columns (total 9 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Car_Name       301 non-null    object \n",
      " 1   Year           301 non-null    int64  \n",
      " 2   Selling_Price  301 non-null    float64\n",
      " 3   Present_Price  301 non-null    float64\n",
      " 4   Driven_kms     301 non-null    int64  \n",
      " 5   Fuel_Type      301 non-null    object \n",
      " 6   Selling_type   301 non-null    object \n",
      " 7   Transmission   301 non-null    object \n",
      " 8   Owner          301 non-null    int64  \n",
      "dtypes: float64(2), int64(3), object(4)\n",
      "memory usage: 21.3+ KB\n"
     ]
    }
   ],
   "source": [
    "car_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QRXTp2EZr47N"
   },
   "source": [
    "##Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "5paLkhh42moj",
    "outputId": "0b1c78d8-03d3-4c7e-f931-d38409c54bba"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-fe340571-5324-4a50-ba6e-d59bbd0e253e\" class=\"colab-df-container\">\n",
       "    <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Driven_kms</th>\n",
       "      <th>Owner</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>2013.627907</td>\n",
       "      <td>4.661296</td>\n",
       "      <td>7.628472</td>\n",
       "      <td>36947.205980</td>\n",
       "      <td>0.043189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.891554</td>\n",
       "      <td>5.082812</td>\n",
       "      <td>8.642584</td>\n",
       "      <td>38886.883882</td>\n",
       "      <td>0.247915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2003.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>15000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2014.000000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>32000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.900000</td>\n",
       "      <td>48767.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2018.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>92.600000</td>\n",
       "      <td>500000.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fe340571-5324-4a50-ba6e-d59bbd0e253e')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
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       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
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       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-fe340571-5324-4a50-ba6e-d59bbd0e253e button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-fe340571-5324-4a50-ba6e-d59bbd0e253e');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
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       "\n",
       "\n",
       "<div id=\"df-7e417ec4-4211-49ef-92fb-999e909e3b84\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7e417ec4-4211-49ef-92fb-999e909e3b84')\"\n",
       "            title=\"Suggest charts\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
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       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-7e417ec4-4211-49ef-92fb-999e909e3b84 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "              Year  Selling_Price  Present_Price     Driven_kms       Owner\n",
       "count   301.000000     301.000000     301.000000     301.000000  301.000000\n",
       "mean   2013.627907       4.661296       7.628472   36947.205980    0.043189\n",
       "std       2.891554       5.082812       8.642584   38886.883882    0.247915\n",
       "min    2003.000000       0.100000       0.320000     500.000000    0.000000\n",
       "25%    2012.000000       0.900000       1.200000   15000.000000    0.000000\n",
       "50%    2014.000000       3.600000       6.400000   32000.000000    0.000000\n",
       "75%    2016.000000       6.000000       9.900000   48767.000000    0.000000\n",
       "max    2018.000000      35.000000      92.600000  500000.000000    3.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zzBQqDLGr7rD"
   },
   "source": [
    "##Evaluation Of Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dOKhXLJD2ubR",
    "outputId": "37abbbcc-775d-4156-b54b-8cf7673b0910"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car_Name         0\n",
       "Year             0\n",
       "Selling_Price    0\n",
       "Present_Price    0\n",
       "Driven_kms       0\n",
       "Fuel_Type        0\n",
       "Selling_type     0\n",
       "Transmission     0\n",
       "Owner            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for missing value\n",
    "car_data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VFE1vmCUr_9N"
   },
   "source": [
    "##Data Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 454
    },
    "id": "Tv-M700D3ThS",
    "outputId": "45c4b1a8-31ed-42b3-b4bd-4f78c88f8eb0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#creating countplot for 'Fuel_Type' column\n",
    "sns.countplot(x = 'Fuel_Type', data = car_data);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 449
    },
    "id": "DldzuL553efh",
    "outputId": "91b0aebc-e15f-402d-f35a-bea88bc711ff"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#creating countplot for 'Transmission' column\n",
    "sns.countplot(x = 'Transmission', data = car_data);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 449
    },
    "id": "l0Ev8juI3rDO",
    "outputId": "1d7efaa7-a2c1-49d7-98e7-70d05aa2197a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#creating countplot for 'Owner' column\n",
    "sns.countplot(x = 'Owner', data = car_data);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 564
    },
    "id": "kNn64qEe53Nn",
    "outputId": "15ebc933-743b-492e-8f90-b6faa19ab601"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "car = pd.concat([car_data['Car_Name'],car_data['Present_Price']])\n",
    "car_count = car.value_counts().reset_index()\n",
    "car_count.columns = ['Car Name', 'Price']\n",
    "plt.figure(figsize = (12,6))\n",
    "sns.barplot(x = 'Price', y = 'Car Name', data = car_count.head(15), palette = 'viridis')\n",
    "plt.xlabel(\"Price\")\n",
    "plt.ylabel(\"Car Name\")\n",
    "plt.title(\"Most Expensive Cars\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MPGUvF7IsL-P"
   },
   "source": [
    "##Checking For Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 445
    },
    "id": "q6DBMnx1-i-Q",
    "outputId": "701aef54-cea9-444a-9a34-6208738a02cf"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,5))\n",
    "sns.boxplot(car_data);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Xks7cUd_skyB"
   },
   "source": [
    "##Handling Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IJUIRYObBj36",
    "outputId": "75668125-cde7-4353-cfef-112aac61affa"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-48-719fa239bed6>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  car_data['Driven_kms'].loc[car_data['Driven_kms'] > 650000] = 50000\n"
     ]
    }
   ],
   "source": [
    "car_data['Driven_kms'].loc[car_data['Driven_kms'] > 650000] = 50000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 445
    },
    "id": "Yv7ykn2TC-LW",
    "outputId": "9036f8f4-5927-400c-8e68-e862dc1a3aca"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,5))\n",
    "sns.boxplot(car_data);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cvnVHd49vXXe"
   },
   "source": [
    "Performing Encoding\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YkmMR1BDqks9",
    "outputId": "e2fc8155-cf3c-4ad6-edae-f2c3e3e9aea3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 301 entries, 0 to 300\n",
      "Data columns (total 9 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Car_Name       301 non-null    object \n",
      " 1   Year           301 non-null    int64  \n",
      " 2   Selling_Price  301 non-null    float64\n",
      " 3   Present_Price  301 non-null    float64\n",
      " 4   Driven_kms     301 non-null    int64  \n",
      " 5   Fuel_Type      301 non-null    object \n",
      " 6   Selling_type   301 non-null    object \n",
      " 7   Transmission   301 non-null    object \n",
      " 8   Owner          301 non-null    int64  \n",
      "dtypes: float64(2), int64(3), object(4)\n",
      "memory usage: 21.3+ KB\n"
     ]
    }
   ],
   "source": [
    "car_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9VkzYYnhva8V",
    "outputId": "e7d76afa-e8b6-457c-a614-1b4f3a674d43"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Petrol' 'Diesel' 'CNG']\n",
      "['Dealer' 'Individual']\n",
      "['Manual' 'Automatic']\n"
     ]
    }
   ],
   "source": [
    "print(car_data['Fuel_Type'].unique())\n",
    "print(car_data['Selling_type'].unique())\n",
    "print(car_data['Transmission'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "Siay3-uvv3q1"
   },
   "outputs": [],
   "source": [
    "car_data['Fuel_Type'].replace(to_replace = 'Petrol', value = 0, inplace = True)\n",
    "car_data['Fuel_Type'].replace(to_replace = 'Diesel', value = 1, inplace = True)\n",
    "car_data['Fuel_Type'].replace(to_replace = 'CNG', value = 2, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UCD_eQT3wgXF",
    "outputId": "99c4855e-c89f-4cf8-80f0-149eeee305e1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2]\n"
     ]
    }
   ],
   "source": [
    "print(car_data['Fuel_Type'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Chu4yFcBwkkP",
    "outputId": "b598a652-b673-4b0e-b8f4-22a6bce2c7e0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data['Selling_type'].replace(to_replace = 'Dealer', value = 0, inplace = True)\n",
    "car_data['Selling_type'].replace(to_replace = 'Individual', value = 1, inplace = True)\n",
    "car_data['Selling_type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "I0Vs5zGFw0Ht",
    "outputId": "02920157-59a5-4be6-80a4-53f55a981ea0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data['Transmission'].replace(to_replace = 'Manual', value = 0, inplace = True)\n",
    "car_data['Transmission'].replace(to_replace = 'Automatic', value = 1, inplace = True)\n",
    "car_data['Transmission'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "K19zS2utw_v4",
    "outputId": "e0a88314-aed6-4016-d57b-2434c51d2b97"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 301 entries, 0 to 300\n",
      "Data columns (total 9 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Car_Name       301 non-null    object \n",
      " 1   Year           301 non-null    int64  \n",
      " 2   Selling_Price  301 non-null    float64\n",
      " 3   Present_Price  301 non-null    float64\n",
      " 4   Driven_kms     301 non-null    int64  \n",
      " 5   Fuel_Type      301 non-null    int64  \n",
      " 6   Selling_type   301 non-null    int64  \n",
      " 7   Transmission   301 non-null    int64  \n",
      " 8   Owner          301 non-null    int64  \n",
      "dtypes: float64(2), int64(6), object(1)\n",
      "memory usage: 21.3+ KB\n"
     ]
    }
   ],
   "source": [
    "car_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lkBEIIeoxJzo"
   },
   "source": [
    "##Splitting The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "uS0oifCRxLzc"
   },
   "outputs": [],
   "source": [
    "#importing dependencies\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "KIBpmLxpxVWy"
   },
   "outputs": [],
   "source": [
    "#initialising the values of x and y\n",
    "y = car_data['Selling_Price']\n",
    "x = car_data.drop(['Car_Name', 'Selling_Price'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "id": "TpbShzV_xleD",
    "outputId": "1dea7e56-a89b-48d8-b214-14e85dc00fe4"
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "    <div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Driven_kms</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Selling_type</th>\n",
       "      <th>Transmission</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>2016</td>\n",
       "      <td>11.60</td>\n",
       "      <td>33988</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>2015</td>\n",
       "      <td>5.90</td>\n",
       "      <td>60000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>2009</td>\n",
       "      <td>11.00</td>\n",
       "      <td>87934</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>2017</td>\n",
       "      <td>12.50</td>\n",
       "      <td>9000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>2016</td>\n",
       "      <td>5.90</td>\n",
       "      <td>5464</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "<p>301 rows × 7 columns</p>\n",
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       "\n",
       "  @keyframes spin {\n",
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       "      border-right-color: var(--fill-color);\n",
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       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
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       "      border-bottom-color: var(--fill-color);\n",
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       "  }\n",
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       "\n",
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       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
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       "        console.error('Error during call to suggestCharts:', error);\n",
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       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "     Year  Present_Price  Driven_kms  Fuel_Type  Selling_type  Transmission  \\\n",
       "0    2014           5.59       27000          0             0             0   \n",
       "1    2013           9.54       43000          1             0             0   \n",
       "2    2017           9.85        6900          0             0             0   \n",
       "3    2011           4.15        5200          0             0             0   \n",
       "4    2014           6.87       42450          1             0             0   \n",
       "..    ...            ...         ...        ...           ...           ...   \n",
       "296  2016          11.60       33988          1             0             0   \n",
       "297  2015           5.90       60000          0             0             0   \n",
       "298  2009          11.00       87934          0             0             0   \n",
       "299  2017          12.50        9000          1             0             0   \n",
       "300  2016           5.90        5464          0             0             0   \n",
       "\n",
       "     Owner  \n",
       "0        0  \n",
       "1        0  \n",
       "2        0  \n",
       "3        0  \n",
       "4        0  \n",
       "..     ...  \n",
       "296      0  \n",
       "297      0  \n",
       "298      0  \n",
       "299      0  \n",
       "300      0  \n",
       "\n",
       "[301 rows x 7 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#displaying x\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yogu13sfx1uD",
    "outputId": "0ee3bfdd-fca4-46e2-b1d4-ca695df7e1fd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       3.35\n",
       "1       4.75\n",
       "2       7.25\n",
       "3       2.85\n",
       "4       4.60\n",
       "       ...  \n",
       "296     9.50\n",
       "297     4.00\n",
       "298     3.35\n",
       "299    11.50\n",
       "300     5.30\n",
       "Name: Selling_Price, Length: 301, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#displaying y\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "61KGMFZtx3D6"
   },
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3, random_state = 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bRi0D87tyDHL"
   },
   "source": [
    "##Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "id": "VimEbjbbyFTC"
   },
   "outputs": [],
   "source": [
    "#importing LinearRegression module\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "SvfbUonVyK7Z"
   },
   "outputs": [],
   "source": [
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 74
    },
    "id": "C0Eg_gzMyNzy",
    "outputId": "2779d30d-cd77-4a4a-fdb4-4cc077f50dce"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</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 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 sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Lj8c6z7tyd_9"
   },
   "source": [
    "##r2 score and mse check\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "id": "845r-u6qyiEg"
   },
   "outputs": [],
   "source": [
    "#importing dependencies\n",
    "from sklearn.metrics import r2_score, mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "ULWvRi9lzPp1"
   },
   "outputs": [],
   "source": [
    "y_train_pred = model.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Dz0HT_OYzYaD",
    "outputId": "89175cff-0c85-43fd-ee0c-fc433416174b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8821824587147267"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#displaying r2_score for 'training' data\n",
    "train_score = r2_score(y_train, y_train_pred)\n",
    "train_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "Al5WccqkzyDi",
    "outputId": "368c72e8-490a-40ed-cd0f-91fccd7652a3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting graph for actual v/s predicted price for 'training  data'\n",
    "plt.scatter(y_train, y_train_pred)\n",
    "plt.xlabel(\"Actual Price\")\n",
    "plt.ylabel(\"Predicted Price\")\n",
    "plt.title(\" Actual Prices vs Predicted Prices\")\n",
    "plt.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)], linestyle='--', color='red', linewidth=2, label='Regression Line')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6FG_Q70js7rV",
    "outputId": "41c61590-830d-4abf-c7f5-1e6bb4be2a3e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.8915530515522323"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#displaying mean_squared_error for 'training' data\n",
    "train_mse = mean_squared_error(y_train, y_train_pred)\n",
    "train_mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "id": "n48V4Cr3yVLW"
   },
   "outputs": [],
   "source": [
    "y_test_pred = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mArMLJlZypgJ",
    "outputId": "0113537d-e4f2-4cac-94e1-4f90bc37c273"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8873117818457653"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#displaying r2_score for 'testing' data\n",
    "test_score = r2_score(y_test, y_test_pred)\n",
    "test_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "fkIiE8NZzNDO",
    "outputId": "05afff09-71ca-4d03-9fab-e358f97a0800"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting graph for actual v/s predicted price for 'testing  data'\n",
    "plt.scatter(y_test, y_test_pred)\n",
    "plt.xlabel(\"Actual Price\")\n",
    "plt.ylabel(\"Predicted Price\")\n",
    "plt.title(\" Actual Prices vs Predicted Prices\")\n",
    "plt.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)], linestyle='--', color='red', linewidth=2, label='Regression Line')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show();train_mse = mean_squared_error(y_train, y_train_pred)\n",
    "train_mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Yp8xnev5tMuO",
    "outputId": "247b86b6-0ded-4507-fe8a-a5b9830fe993"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.2087069266859234"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#displaying mean_squared_error for 'testing' data\n",
    "test_mse = mean_squared_error(y_test, y_test_pred)\n",
    "test_mse"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyPWv3mpoLexph2UMFEieP2e",
   "include_colab_link": true,
   "provenance": []
  },
  "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}