Price_Prediction.ipynb

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        "<a href=\"https://colab.research.google.com/github/KhamessiTaha/CarVisionProject/blob/main/Price_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "price_table = pd.read_csv(\"/content/drive/MyDrive/Price Prediction/Price_table.csv\")\n",
        "ad_table = pd.read_csv(\"/content/drive/MyDrive/Price Prediction/Ad_table.csv\")"
      ],
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        "outputId": "ad431a00-3194-4448-d490-097675cb663c"
      },
      "execution_count": 2,
      "outputs": [
        {
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          "text": [
            "<ipython-input-2-3dbe8385a7a2>:6: DtypeWarning: Columns (13) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  ad_table = pd.read_csv(\"/content/drive/MyDrive/Price Prediction/Ad_table.csv\")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "price_table.head()"
      ],
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      "execution_count": 3,
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              "    Maker    Genmodel Genmodel_ID  Year  Entry_price\n",
              "0  Abarth  124 Spider         2_1  2016        29365\n",
              "1  Abarth  124 Spider         2_1  2017        26665\n",
              "2  Abarth  124 Spider         2_1  2018        26665\n",
              "3  Abarth  124 Spider         2_1  2019        29515\n",
              "4  Abarth         500         2_2  2009        13400"
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              "variable_name": "price_table",
              "summary": "{\n  \"name\": \"price_table\",\n  \"rows\": 6333,\n  \"fields\": [\n    {\n      \"column\": \"Maker\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 60,\n        \"samples\": [\n          \"Abarth\",\n          \"BMW\",\n          \"MG\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Genmodel\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 632,\n        \"samples\": [\n          \"Tribute\",\n          \"Q30\",\n          \"Crossland X\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Genmodel_ID\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 647,\n        \"samples\": [\n          \"96_14\",\n          \"34_2\",\n          \"64_5\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 1998,\n        \"max\": 2021,\n        \"num_unique_values\": 24,\n        \"samples\": [\n          2013,\n          2005,\n          2016\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Entry_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39314,\n        \"min\": 4499,\n        \"max\": 320120,\n        \"num_unique_values\": 3402,\n        \"samples\": [\n          136000,\n          23812,\n          27342\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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              "     Maker Genmodel Genmodel_ID   Adv_ID  Adv_year  Adv_month   Color  \\\n",
              "0  Bentley   Arnage        10_1  10_1$$1      2018          4  Silver   \n",
              "1  Bentley   Arnage        10_1  10_1$$2      2018          6    Grey   \n",
              "2  Bentley   Arnage        10_1  10_1$$3      2017         11    Blue   \n",
              "3  Bentley   Arnage        10_1  10_1$$4      2018          4   Green   \n",
              "4  Bentley   Arnage        10_1  10_1$$5      2017         11    Grey   \n",
              "\n",
              "   Reg_year Bodytype Runned_Miles Engin_size    Gearbox Fuel_type  Price  \\\n",
              "0    2000.0   Saloon        60000       6.8L  Automatic    Petrol  21500   \n",
              "1    2002.0   Saloon        44000       6.8L  Automatic    Petrol  28750   \n",
              "2    2002.0   Saloon        55000       6.8L  Automatic    Petrol  29999   \n",
              "3    2003.0   Saloon        14000       6.8L  Automatic    Petrol  34948   \n",
              "4    2003.0   Saloon        61652       6.8L  Automatic    Petrol  26555   \n",
              "\n",
              "   Seat_num  Door_num  \n",
              "0       5.0       4.0  \n",
              "1       5.0       4.0  \n",
              "2       5.0       4.0  \n",
              "3       5.0       4.0  \n",
              "4       5.0       4.0  "
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              "      <td>Bentley</td>\n",
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              "      <td>2018</td>\n",
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              "      <td>Petrol</td>\n",
              "      <td>21500</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Bentley</td>\n",
              "      <td>Arnage</td>\n",
              "      <td>10_1</td>\n",
              "      <td>10_1$$2</td>\n",
              "      <td>2018</td>\n",
              "      <td>6</td>\n",
              "      <td>Grey</td>\n",
              "      <td>2002.0</td>\n",
              "      <td>Saloon</td>\n",
              "      <td>44000</td>\n",
              "      <td>6.8L</td>\n",
              "      <td>Automatic</td>\n",
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              "      <td>28750</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Bentley</td>\n",
              "      <td>Arnage</td>\n",
              "      <td>10_1</td>\n",
              "      <td>10_1$$3</td>\n",
              "      <td>2017</td>\n",
              "      <td>11</td>\n",
              "      <td>Blue</td>\n",
              "      <td>2002.0</td>\n",
              "      <td>Saloon</td>\n",
              "      <td>55000</td>\n",
              "      <td>6.8L</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>29999</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Bentley</td>\n",
              "      <td>Arnage</td>\n",
              "      <td>10_1</td>\n",
              "      <td>10_1$$4</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "      <td>Green</td>\n",
              "      <td>2003.0</td>\n",
              "      <td>Saloon</td>\n",
              "      <td>14000</td>\n",
              "      <td>6.8L</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>34948</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bentley</td>\n",
              "      <td>Arnage</td>\n",
              "      <td>10_1</td>\n",
              "      <td>10_1$$5</td>\n",
              "      <td>2017</td>\n",
              "      <td>11</td>\n",
              "      <td>Grey</td>\n",
              "      <td>2003.0</td>\n",
              "      <td>Saloon</td>\n",
              "      <td>61652</td>\n",
              "      <td>6.8L</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>26555</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</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-188b3139-1235-4a6f-a561-5fed14713dbf')\"\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",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-188b3139-1235-4a6f-a561-5fed14713dbf 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-188b3139-1235-4a6f-a561-5fed14713dbf');\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-7f842726-66f3-430c-862a-e1137903ffd6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7f842726-66f3-430c-862a-e1137903ffd6')\"\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",
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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",
              "    }\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-7f842726-66f3-430c-862a-e1137903ffd6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "ad_table"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "median_entry_price = price_table['Entry_price'].median()\n",
        "price_table_cleaned=price_table['Entry_price'].fillna(median_entry_price, inplace=True)"
      ],
      "metadata": {
        "id": "fL8btceuX-8v"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ad_table_cleaned = ad_table.drop(columns=['Seat_num','Door_num','Runned_Miles', 'Engin_size', 'Gearbox', 'Fuel_type', 'Adv_ID', 'Adv_month', 'Color', 'Reg_year', 'Bodytype'])\n",
        "ad_table_cleaned.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "FoDe1RsNgmZH",
        "outputId": "59495ee6-a07a-47b9-be5f-dfaff3da2157"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Maker Genmodel Genmodel_ID  Adv_year  Price\n",
              "0  Bentley   Arnage        10_1      2018  21500\n",
              "1  Bentley   Arnage        10_1      2018  28750\n",
              "2  Bentley   Arnage        10_1      2017  29999\n",
              "3  Bentley   Arnage        10_1      2018  34948\n",
              "4  Bentley   Arnage        10_1      2017  26555"
            ],
            "text/html": [
              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "\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",
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              "\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3b1b84af-bc25-4a7c-abb1-90f79616c928');\n",
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              "          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",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "    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",
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              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
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    {
      "cell_type": "code",
      "source": [
        "ad_table_cleaned['Price'] = pd.to_numeric(ad_table_cleaned['Price'], errors='coerce')\n",
        "median_price = ad_table_cleaned['Price'].median()\n",
        "ad_table_cleaned['Price'].fillna(median_price, inplace=True)"
      ],
      "metadata": {
        "id": "hPTwk7EckPZv"
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      "execution_count": 7,
      "outputs": []
    },
    {
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      "source": [
        "merged_table = pd.merge(price_table, ad_table_cleaned, on=['Maker', 'Genmodel'], how='inner')\n",
        "new_table = merged_table[['Maker', 'Genmodel', 'Year', 'Entry_price', 'Price','Adv_year']]\n",
        "\n",
        "new_table.head(10)"
      ],
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        "colab": {
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        "id": "87v2o2vRlk0E",
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      "execution_count": 8,
      "outputs": [
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          "data": {
            "text/plain": [
              "    Maker    Genmodel  Year  Entry_price    Price  Adv_year\n",
              "0  Abarth  124 Spider  2016        29365  21800.0      2018\n",
              "1  Abarth  124 Spider  2016        29365  25499.0      2018\n",
              "2  Abarth  124 Spider  2016        29365  26995.0      2018\n",
              "3  Abarth  124 Spider  2016        29365  21480.0      2018\n",
              "4  Abarth  124 Spider  2016        29365  22995.0      2017\n",
              "5  Abarth  124 Spider  2016        29365  24995.0      2017\n",
              "6  Abarth  124 Spider  2016        29365  24000.0      2017\n",
              "7  Abarth  124 Spider  2016        29365  22470.0      2018\n",
              "8  Abarth  124 Spider  2016        29365  19998.0      2018\n",
              "9  Abarth  124 Spider  2016        29365  23897.0      2018"
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    {
      "cell_type": "markdown",
      "source": [
        "Calculate the mean of the prices from all advertisements on the same year"
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      "metadata": {
        "id": "pDhpMliv0YDj"
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    },
    {
      "cell_type": "code",
      "source": [
        "mean_prices = new_table.groupby(['Maker', 'Genmodel', 'Year', 'Entry_price', 'Adv_year'])['Price'].mean().reset_index()\n",
        "mean_prices['Price'] = mean_prices['Price'].round(2)\n",
        "mean_prices.head(20)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        },
        "id": "VYPOS7m_pewU",
        "outputId": "07ea31cb-ae43-4f06-d13c-60c6c1caa3c0"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Maker    Genmodel  Year  Entry_price  Adv_year     Price\n",
              "0   Abarth  124 Spider  2016        29365      2017  24267.14\n",
              "1   Abarth  124 Spider  2016        29365      2018  23450.56\n",
              "2   Abarth  124 Spider  2016        29365      2021  20770.62\n",
              "3   Abarth  124 Spider  2017        26665      2017  24267.14\n",
              "4   Abarth  124 Spider  2017        26665      2018  23450.56\n",
              "5   Abarth  124 Spider  2017        26665      2021  20770.62\n",
              "6   Abarth  124 Spider  2018        26665      2017  24267.14\n",
              "7   Abarth  124 Spider  2018        26665      2018  23450.56\n",
              "8   Abarth  124 Spider  2018        26665      2021  20770.62\n",
              "9   Abarth  124 Spider  2019        29515      2017  24267.14\n",
              "10  Abarth  124 Spider  2019        29515      2018  23450.56\n",
              "11  Abarth  124 Spider  2019        29515      2021  20770.62\n",
              "12  Abarth         595  2012        17500      2016  11492.50\n",
              "13  Abarth         595  2012        17500      2017  11500.00\n",
              "14  Abarth         595  2012        17500      2018  12448.89\n",
              "15  Abarth         595  2012        17500      2019  12395.00\n",
              "16  Abarth         595  2012        17500      2020  14815.00\n",
              "17  Abarth         595  2012        17500      2021  14746.22\n",
              "18  Abarth         595  2013        17675      2016  11492.50\n",
              "19  Abarth         595  2013        17675      2017  11500.00"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Maker</th>\n",
              "      <th>Genmodel</th>\n",
              "      <th>Year</th>\n",
              "      <th>Entry_price</th>\n",
              "      <th>Adv_year</th>\n",
              "      <th>Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Abarth</td>\n",
              "      <td>124 Spider</td>\n",
              "      <td>2016</td>\n",
              "      <td>29365</td>\n",
              "      <td>2017</td>\n",
              "      <td>24267.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Abarth</td>\n",
              "      <td>124 Spider</td>\n",
              "      <td>2016</td>\n",
              "      <td>29365</td>\n",
              "      <td>2018</td>\n",
              "      <td>23450.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Abarth</td>\n",
              "      <td>124 Spider</td>\n",
              "      <td>2016</td>\n",
              "      <td>29365</td>\n",
              "      <td>2021</td>\n",
              "      <td>20770.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Abarth</td>\n",
              "      <td>124 Spider</td>\n",
              "      <td>2017</td>\n",
              "      <td>26665</td>\n",
              "      <td>2017</td>\n",
              "      <td>24267.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Abarth</td>\n",
              "      <td>124 Spider</td>\n",
              "      <td>2017</td>\n",
              "      <td>26665</td>\n",
              "      <td>2018</td>\n",
              "      <td>23450.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Abarth</td>\n",
              "      <td>124 Spider</td>\n",
              "      <td>2017</td>\n",
              "      <td>26665</td>\n",
              "      <td>2021</td>\n",
              "      <td>20770.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Abarth</td>\n",
              "      <td>124 Spider</td>\n",
              "      <td>2018</td>\n",
              "      <td>26665</td>\n",
              "      <td>2017</td>\n",
              "      <td>24267.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Abarth</td>\n",
              "      <td>124 Spider</td>\n",
              "      <td>2018</td>\n",
              "      <td>26665</td>\n",
              "      <td>2018</td>\n",
              "      <td>23450.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Abarth</td>\n",
              "      <td>124 Spider</td>\n",
              "      <td>2018</td>\n",
              "      <td>26665</td>\n",
              "      <td>2021</td>\n",
              "      <td>20770.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Abarth</td>\n",
              "      <td>124 Spider</td>\n",
              "      <td>2019</td>\n",
              "      <td>29515</td>\n",
              "      <td>2017</td>\n",
              "      <td>24267.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Abarth</td>\n",
              "      <td>124 Spider</td>\n",
              "      <td>2019</td>\n",
              "      <td>29515</td>\n",
              "      <td>2018</td>\n",
              "      <td>23450.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Abarth</td>\n",
              "      <td>124 Spider</td>\n",
              "      <td>2019</td>\n",
              "      <td>29515</td>\n",
              "      <td>2021</td>\n",
              "      <td>20770.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Abarth</td>\n",
              "      <td>595</td>\n",
              "      <td>2012</td>\n",
              "      <td>17500</td>\n",
              "      <td>2016</td>\n",
              "      <td>11492.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Abarth</td>\n",
              "      <td>595</td>\n",
              "      <td>2012</td>\n",
              "      <td>17500</td>\n",
              "      <td>2017</td>\n",
              "      <td>11500.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Abarth</td>\n",
              "      <td>595</td>\n",
              "      <td>2012</td>\n",
              "      <td>17500</td>\n",
              "      <td>2018</td>\n",
              "      <td>12448.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Abarth</td>\n",
              "      <td>595</td>\n",
              "      <td>2012</td>\n",
              "      <td>17500</td>\n",
              "      <td>2019</td>\n",
              "      <td>12395.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Abarth</td>\n",
              "      <td>595</td>\n",
              "      <td>2012</td>\n",
              "      <td>17500</td>\n",
              "      <td>2020</td>\n",
              "      <td>14815.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Abarth</td>\n",
              "      <td>595</td>\n",
              "      <td>2012</td>\n",
              "      <td>17500</td>\n",
              "      <td>2021</td>\n",
              "      <td>14746.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Abarth</td>\n",
              "      <td>595</td>\n",
              "      <td>2013</td>\n",
              "      <td>17675</td>\n",
              "      <td>2016</td>\n",
              "      <td>11492.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Abarth</td>\n",
              "      <td>595</td>\n",
              "      <td>2013</td>\n",
              "      <td>17675</td>\n",
              "      <td>2017</td>\n",
              "      <td>11500.00</td>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "mean_prices",
              "summary": "{\n  \"name\": \"mean_prices\",\n  \"rows\": 20485,\n  \"fields\": [\n    {\n      \"column\": \"Maker\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 59,\n        \"samples\": [\n          \"Abarth\",\n          \"Bentley\",\n          \"Maybach\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Genmodel\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 586,\n        \"samples\": [\n          \"Previa\",\n          \"Clubman\",\n          \"Celica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 1998,\n        \"max\": 2021,\n        \"num_unique_values\": 24,\n        \"samples\": [\n          2006,\n          1998,\n          2016\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Entry_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38587,\n        \"min\": 4499,\n        \"max\": 320120,\n        \"num_unique_values\": 3262,\n        \"samples\": [\n          8805,\n          32945,\n          33940\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adv_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2012,\n        \"max\": 2021,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          2014,\n          2018,\n          2020\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 36461.85353143896,\n        \"min\": 450.0,\n        \"max\": 499990.0,\n        \"num_unique_values\": 1711,\n        \"samples\": [\n          8797.0,\n          780.68,\n          4868.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_prices['Brand'] = mean_prices['Maker'] + '_' + mean_prices['Genmodel'] + '_' + mean_prices['Year'].astype(str)\n",
        "mean_prices_final = mean_prices[['Brand', 'Maker', 'Genmodel', 'Year', 'Entry_price', 'Adv_year', 'Price']]\n",
        "mean_prices_final.drop(['Maker', 'Genmodel', 'Year'], axis=1, inplace=True)\n",
        "mean_prices_final.head(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 763
        },
        "id": "JCD-o565Bpym",
        "outputId": "0831d21a-e92d-4efb-e141-49528798f574"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-12-1ef6c2b2f3b1>:3: 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",
            "  mean_prices_final.drop(['Maker', 'Genmodel', 'Year'], axis=1, inplace=True)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     Brand  Entry_price  Adv_year     Price\n",
              "0   Abarth_124 Spider_2016        29365      2017  24267.14\n",
              "1   Abarth_124 Spider_2016        29365      2018  23450.56\n",
              "2   Abarth_124 Spider_2016        29365      2021  20770.62\n",
              "3   Abarth_124 Spider_2017        26665      2017  24267.14\n",
              "4   Abarth_124 Spider_2017        26665      2018  23450.56\n",
              "5   Abarth_124 Spider_2017        26665      2021  20770.62\n",
              "6   Abarth_124 Spider_2018        26665      2017  24267.14\n",
              "7   Abarth_124 Spider_2018        26665      2018  23450.56\n",
              "8   Abarth_124 Spider_2018        26665      2021  20770.62\n",
              "9   Abarth_124 Spider_2019        29515      2017  24267.14\n",
              "10  Abarth_124 Spider_2019        29515      2018  23450.56\n",
              "11  Abarth_124 Spider_2019        29515      2021  20770.62\n",
              "12         Abarth_595_2012        17500      2016  11492.50\n",
              "13         Abarth_595_2012        17500      2017  11500.00\n",
              "14         Abarth_595_2012        17500      2018  12448.89\n",
              "15         Abarth_595_2012        17500      2019  12395.00\n",
              "16         Abarth_595_2012        17500      2020  14815.00\n",
              "17         Abarth_595_2012        17500      2021  14746.22\n",
              "18         Abarth_595_2013        17675      2016  11492.50\n",
              "19         Abarth_595_2013        17675      2017  11500.00"
            ],
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              "      <th>Adv_year</th>\n",
              "      <th>Price</th>\n",
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              "      <th>0</th>\n",
              "      <td>Abarth_124 Spider_2016</td>\n",
              "      <td>29365</td>\n",
              "      <td>2017</td>\n",
              "      <td>24267.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Abarth_124 Spider_2016</td>\n",
              "      <td>29365</td>\n",
              "      <td>2018</td>\n",
              "      <td>23450.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Abarth_124 Spider_2016</td>\n",
              "      <td>29365</td>\n",
              "      <td>2021</td>\n",
              "      <td>20770.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Abarth_124 Spider_2017</td>\n",
              "      <td>26665</td>\n",
              "      <td>2017</td>\n",
              "      <td>24267.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Abarth_124 Spider_2017</td>\n",
              "      <td>26665</td>\n",
              "      <td>2018</td>\n",
              "      <td>23450.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Abarth_124 Spider_2017</td>\n",
              "      <td>26665</td>\n",
              "      <td>2021</td>\n",
              "      <td>20770.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Abarth_124 Spider_2018</td>\n",
              "      <td>26665</td>\n",
              "      <td>2017</td>\n",
              "      <td>24267.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Abarth_124 Spider_2018</td>\n",
              "      <td>26665</td>\n",
              "      <td>2018</td>\n",
              "      <td>23450.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Abarth_124 Spider_2018</td>\n",
              "      <td>26665</td>\n",
              "      <td>2021</td>\n",
              "      <td>20770.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Abarth_124 Spider_2019</td>\n",
              "      <td>29515</td>\n",
              "      <td>2017</td>\n",
              "      <td>24267.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Abarth_124 Spider_2019</td>\n",
              "      <td>29515</td>\n",
              "      <td>2018</td>\n",
              "      <td>23450.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Abarth_124 Spider_2019</td>\n",
              "      <td>29515</td>\n",
              "      <td>2021</td>\n",
              "      <td>20770.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Abarth_595_2012</td>\n",
              "      <td>17500</td>\n",
              "      <td>2016</td>\n",
              "      <td>11492.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Abarth_595_2012</td>\n",
              "      <td>17500</td>\n",
              "      <td>2017</td>\n",
              "      <td>11500.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Abarth_595_2012</td>\n",
              "      <td>17500</td>\n",
              "      <td>2018</td>\n",
              "      <td>12448.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Abarth_595_2012</td>\n",
              "      <td>17500</td>\n",
              "      <td>2019</td>\n",
              "      <td>12395.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Abarth_595_2012</td>\n",
              "      <td>17500</td>\n",
              "      <td>2020</td>\n",
              "      <td>14815.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Abarth_595_2012</td>\n",
              "      <td>17500</td>\n",
              "      <td>2021</td>\n",
              "      <td>14746.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Abarth_595_2013</td>\n",
              "      <td>17675</td>\n",
              "      <td>2016</td>\n",
              "      <td>11492.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Abarth_595_2013</td>\n",
              "      <td>17675</td>\n",
              "      <td>2017</td>\n",
              "      <td>11500.00</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "mean_prices_final",
              "summary": "{\n  \"name\": \"mean_prices_final\",\n  \"rows\": 20485,\n  \"fields\": [\n    {\n      \"column\": \"Brand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5981,\n        \"samples\": [\n          \"Proton_GEN-2_2010\",\n          \"Peugeot_RCZ_2012\",\n          \"Mercedes-Benz_B Class_2018\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Entry_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38587,\n        \"min\": 4499,\n        \"max\": 320120,\n        \"num_unique_values\": 3262,\n        \"samples\": [\n          8805,\n          32945,\n          33940\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adv_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2012,\n        \"max\": 2021,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          2014,\n          2018,\n          2020\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 36461.85353143896,\n        \"min\": 450.0,\n        \"max\": 499990.0,\n        \"num_unique_values\": 1711,\n        \"samples\": [\n          8797.0,\n          780.68,\n          4868.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_prices_final['Manifacture_Year'] = mean_prices_final['Brand'].str.extract(r'(\\d{4})')\n",
        "mean_prices_final['Manifacture_Year'] = mean_prices_final['Manifacture_Year'].astype(int)\n",
        "\n",
        "mean_prices_final.head(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 885
        },
        "id": "vBREtRHLVuAb",
        "outputId": "7e70f0ed-99ce-4f25-84a7-b29492580467"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-15-45a9e661e12b>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\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",
            "  mean_prices_final['Manifacture_Year'] = mean_prices_final['Brand'].str.extract(r'(\\d{4})')\n",
            "<ipython-input-15-45a9e661e12b>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\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",
            "  mean_prices_final['Manifacture_Year'] = mean_prices_final['Manifacture_Year'].astype(int)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     Brand  Entry_price  Adv_year     Price  Manifacture_Year\n",
              "0   Abarth_124 Spider_2016        29365      2017  24267.14              2016\n",
              "1   Abarth_124 Spider_2016        29365      2018  23450.56              2016\n",
              "2   Abarth_124 Spider_2016        29365      2021  20770.62              2016\n",
              "3   Abarth_124 Spider_2017        26665      2017  24267.14              2017\n",
              "4   Abarth_124 Spider_2017        26665      2018  23450.56              2017\n",
              "5   Abarth_124 Spider_2017        26665      2021  20770.62              2017\n",
              "6   Abarth_124 Spider_2018        26665      2017  24267.14              2018\n",
              "7   Abarth_124 Spider_2018        26665      2018  23450.56              2018\n",
              "8   Abarth_124 Spider_2018        26665      2021  20770.62              2018\n",
              "9   Abarth_124 Spider_2019        29515      2017  24267.14              2019\n",
              "10  Abarth_124 Spider_2019        29515      2018  23450.56              2019\n",
              "11  Abarth_124 Spider_2019        29515      2021  20770.62              2019\n",
              "12         Abarth_595_2012        17500      2016  11492.50              2012\n",
              "13         Abarth_595_2012        17500      2017  11500.00              2012\n",
              "14         Abarth_595_2012        17500      2018  12448.89              2012\n",
              "15         Abarth_595_2012        17500      2019  12395.00              2012\n",
              "16         Abarth_595_2012        17500      2020  14815.00              2012\n",
              "17         Abarth_595_2012        17500      2021  14746.22              2012\n",
              "18         Abarth_595_2013        17675      2016  11492.50              2013\n",
              "19         Abarth_595_2013        17675      2017  11500.00              2013"
            ],
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              "      <td>29365</td>\n",
              "      <td>2021</td>\n",
              "      <td>20770.62</td>\n",
              "      <td>2016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Abarth_124 Spider_2017</td>\n",
              "      <td>26665</td>\n",
              "      <td>2017</td>\n",
              "      <td>24267.14</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Abarth_124 Spider_2017</td>\n",
              "      <td>26665</td>\n",
              "      <td>2018</td>\n",
              "      <td>23450.56</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Abarth_124 Spider_2017</td>\n",
              "      <td>26665</td>\n",
              "      <td>2021</td>\n",
              "      <td>20770.62</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Abarth_124 Spider_2018</td>\n",
              "      <td>26665</td>\n",
              "      <td>2017</td>\n",
              "      <td>24267.14</td>\n",
              "      <td>2018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Abarth_124 Spider_2018</td>\n",
              "      <td>26665</td>\n",
              "      <td>2018</td>\n",
              "      <td>23450.56</td>\n",
              "      <td>2018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Abarth_124 Spider_2018</td>\n",
              "      <td>26665</td>\n",
              "      <td>2021</td>\n",
              "      <td>20770.62</td>\n",
              "      <td>2018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Abarth_124 Spider_2019</td>\n",
              "      <td>29515</td>\n",
              "      <td>2017</td>\n",
              "      <td>24267.14</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Abarth_124 Spider_2019</td>\n",
              "      <td>29515</td>\n",
              "      <td>2018</td>\n",
              "      <td>23450.56</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Abarth_124 Spider_2019</td>\n",
              "      <td>29515</td>\n",
              "      <td>2021</td>\n",
              "      <td>20770.62</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Abarth_595_2012</td>\n",
              "      <td>17500</td>\n",
              "      <td>2016</td>\n",
              "      <td>11492.50</td>\n",
              "      <td>2012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Abarth_595_2012</td>\n",
              "      <td>17500</td>\n",
              "      <td>2017</td>\n",
              "      <td>11500.00</td>\n",
              "      <td>2012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Abarth_595_2012</td>\n",
              "      <td>17500</td>\n",
              "      <td>2018</td>\n",
              "      <td>12448.89</td>\n",
              "      <td>2012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Abarth_595_2012</td>\n",
              "      <td>17500</td>\n",
              "      <td>2019</td>\n",
              "      <td>12395.00</td>\n",
              "      <td>2012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Abarth_595_2012</td>\n",
              "      <td>17500</td>\n",
              "      <td>2020</td>\n",
              "      <td>14815.00</td>\n",
              "      <td>2012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Abarth_595_2012</td>\n",
              "      <td>17500</td>\n",
              "      <td>2021</td>\n",
              "      <td>14746.22</td>\n",
              "      <td>2012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Abarth_595_2013</td>\n",
              "      <td>17675</td>\n",
              "      <td>2016</td>\n",
              "      <td>11492.50</td>\n",
              "      <td>2013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Abarth_595_2013</td>\n",
              "      <td>17675</td>\n",
              "      <td>2017</td>\n",
              "      <td>11500.00</td>\n",
              "      <td>2013</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "mean_prices_final",
              "summary": "{\n  \"name\": \"mean_prices_final\",\n  \"rows\": 20485,\n  \"fields\": [\n    {\n      \"column\": \"Brand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5981,\n        \"samples\": [\n          \"Proton_GEN-2_2010\",\n          \"Peugeot_RCZ_2012\",\n          \"Mercedes-Benz_B Class_2018\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Entry_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38587,\n        \"min\": 4499,\n        \"max\": 320120,\n        \"num_unique_values\": 3262,\n        \"samples\": [\n          8805,\n          32945,\n          33940\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adv_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2012,\n        \"max\": 2021,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          2014,\n          2018,\n          2020\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 36461.85353143896,\n        \"min\": 450.0,\n        \"max\": 499990.0,\n        \"num_unique_values\": 1711,\n        \"samples\": [\n          8797.0,\n          780.68,\n          4868.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Manifacture_Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 140,\n        \"min\": 1007,\n        \"max\": 5008,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          2007,\n          3008,\n          2006\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_prices_final['Car_Age_2024'] = 2024 - mean_prices_final['Manifacture_Year']\n",
        "mean_prices_final['Depreciation_Rate'] = (mean_prices_final['Entry_price'] - mean_prices_final['Price'])*100 / (mean_prices_final['Entry_price'])\n",
        "\n",
        "mean_prices_final.head(80)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "PG-ISo1kWpx4",
        "outputId": "437a1eee-e562-477b-a2ce-3eb759beb1ab"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-16-16be2d39cfa7>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\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",
            "  mean_prices_final['Car_Age_2024'] = 2024 - mean_prices_final['Manifacture_Year']\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     Brand  Entry_price  Adv_year     Price  Manifacture_Year  \\\n",
              "0   Abarth_124 Spider_2016        29365      2017  24267.14              2016   \n",
              "1   Abarth_124 Spider_2016        29365      2018  23450.56              2016   \n",
              "2   Abarth_124 Spider_2016        29365      2021  20770.62              2016   \n",
              "3   Abarth_124 Spider_2017        26665      2017  24267.14              2017   \n",
              "4   Abarth_124 Spider_2017        26665      2018  23450.56              2017   \n",
              "..                     ...          ...       ...       ...               ...   \n",
              "75      Alfa Romeo_4C_2018        52265      2018  43928.89              2018   \n",
              "76   Alfa Romeo_Brera_2006        22572      2017   3495.00              2006   \n",
              "77   Alfa Romeo_Brera_2006        22572      2018   4997.62              2006   \n",
              "78   Alfa Romeo_Brera_2007        22572      2017   3495.00              2007   \n",
              "79   Alfa Romeo_Brera_2007        22572      2018   4997.62              2007   \n",
              "\n",
              "    Car_Age_2024  Depreciation_Rate  \n",
              "0              8          17.360327  \n",
              "1              8          20.141120  \n",
              "2              8          29.267427  \n",
              "3              7           8.992537  \n",
              "4              7          12.054903  \n",
              "..           ...                ...  \n",
              "75             6          15.949699  \n",
              "76            18          84.516215  \n",
              "77            18          77.859206  \n",
              "78            17          84.516215  \n",
              "79            17          77.859206  \n",
              "\n",
              "[80 rows x 7 columns]"
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              "      <td>26665</td>\n",
              "      <td>2017</td>\n",
              "      <td>24267.14</td>\n",
              "      <td>2017</td>\n",
              "      <td>7</td>\n",
              "      <td>8.992537</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Abarth_124 Spider_2017</td>\n",
              "      <td>26665</td>\n",
              "      <td>2018</td>\n",
              "      <td>23450.56</td>\n",
              "      <td>2017</td>\n",
              "      <td>7</td>\n",
              "      <td>12.054903</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>75</th>\n",
              "      <td>Alfa Romeo_4C_2018</td>\n",
              "      <td>52265</td>\n",
              "      <td>2018</td>\n",
              "      <td>43928.89</td>\n",
              "      <td>2018</td>\n",
              "      <td>6</td>\n",
              "      <td>15.949699</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>Alfa Romeo_Brera_2006</td>\n",
              "      <td>22572</td>\n",
              "      <td>2017</td>\n",
              "      <td>3495.00</td>\n",
              "      <td>2006</td>\n",
              "      <td>18</td>\n",
              "      <td>84.516215</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>Alfa Romeo_Brera_2006</td>\n",
              "      <td>22572</td>\n",
              "      <td>2018</td>\n",
              "      <td>4997.62</td>\n",
              "      <td>2006</td>\n",
              "      <td>18</td>\n",
              "      <td>77.859206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>Alfa Romeo_Brera_2007</td>\n",
              "      <td>22572</td>\n",
              "      <td>2017</td>\n",
              "      <td>3495.00</td>\n",
              "      <td>2007</td>\n",
              "      <td>17</td>\n",
              "      <td>84.516215</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>Alfa Romeo_Brera_2007</td>\n",
              "      <td>22572</td>\n",
              "      <td>2018</td>\n",
              "      <td>4997.62</td>\n",
              "      <td>2007</td>\n",
              "      <td>17</td>\n",
              "      <td>77.859206</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "mean_prices_final",
              "summary": "{\n  \"name\": \"mean_prices_final\",\n  \"rows\": 20485,\n  \"fields\": [\n    {\n      \"column\": \"Brand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5981,\n        \"samples\": [\n          \"Proton_GEN-2_2010\",\n          \"Peugeot_RCZ_2012\",\n          \"Mercedes-Benz_B Class_2018\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Entry_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38587,\n        \"min\": 4499,\n        \"max\": 320120,\n        \"num_unique_values\": 3262,\n        \"samples\": [\n          8805,\n          32945,\n          33940\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adv_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2012,\n        \"max\": 2021,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          2014,\n          2018,\n          2020\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 36461.85353143896,\n        \"min\": 450.0,\n        \"max\": 499990.0,\n        \"num_unique_values\": 1711,\n        \"samples\": [\n          8797.0,\n          780.68,\n          4868.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Manifacture_Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 140,\n        \"min\": 1007,\n        \"max\": 5008,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          2007,\n          3008,\n          2006\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Car_Age_2024\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 140,\n        \"min\": -2984,\n        \"max\": 1017,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          17,\n          -984,\n          18\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Depreciation_Rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 46.285580010312366,\n        \"min\": -246.55339805825244,\n        \"max\": 97.55368306605055,\n        \"num_unique_values\": 14367,\n        \"samples\": [\n          63.12762305952102,\n          92.351410773177,\n          -33.317952035266785\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_prices_final = mean_prices_final[(mean_prices_final['Depreciation_Rate'] != float('inf')) & (mean_prices_final['Depreciation_Rate'] > 0)]\n",
        "mean_prices_final.reset_index(drop=True, inplace=True)\n",
        "mean_prices_final.head(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        },
        "id": "q_2xWz8EcOGH",
        "outputId": "f132632f-81df-47ee-d523-6a6e303cf6ce"
      },
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     Brand  Entry_price  Adv_year     Price  Manifacture_Year  \\\n",
              "0   Abarth_124 Spider_2016        29365      2017  24267.14              2016   \n",
              "1   Abarth_124 Spider_2016        29365      2018  23450.56              2016   \n",
              "2   Abarth_124 Spider_2016        29365      2021  20770.62              2016   \n",
              "3   Abarth_124 Spider_2017        26665      2018  23450.56              2017   \n",
              "4   Abarth_124 Spider_2017        26665      2021  20770.62              2017   \n",
              "5   Abarth_124 Spider_2018        26665      2021  20770.62              2018   \n",
              "6   Abarth_124 Spider_2019        29515      2021  20770.62              2019   \n",
              "7          Abarth_595_2012        17500      2016  11492.50              2012   \n",
              "8          Abarth_595_2012        17500      2017  11500.00              2012   \n",
              "9          Abarth_595_2012        17500      2018  12448.89              2012   \n",
              "10         Abarth_595_2012        17500      2019  12395.00              2012   \n",
              "11         Abarth_595_2012        17500      2020  14815.00              2012   \n",
              "12         Abarth_595_2012        17500      2021  14746.22              2012   \n",
              "13         Abarth_595_2013        17675      2016  11492.50              2013   \n",
              "14         Abarth_595_2013        17675      2017  11500.00              2013   \n",
              "15         Abarth_595_2013        17675      2018  12448.89              2013   \n",
              "16         Abarth_595_2013        17675      2019  12395.00              2013   \n",
              "17         Abarth_595_2013        17675      2020  14815.00              2013   \n",
              "18         Abarth_595_2013        17675      2021  14746.22              2013   \n",
              "19         Abarth_595_2014        14425      2016  11492.50              2014   \n",
              "\n",
              "    Car_Age_2024  Depreciation_Rate  \n",
              "0              8          17.360327  \n",
              "1              8          20.141120  \n",
              "2              8          29.267427  \n",
              "3              7          12.054903  \n",
              "4              7          22.105307  \n",
              "5              6          22.105307  \n",
              "6              5          29.626902  \n",
              "7             12          34.328571  \n",
              "8             12          34.285714  \n",
              "9             12          28.863486  \n",
              "10            12          29.171429  \n",
              "11            12          15.342857  \n",
              "12            12          15.735886  \n",
              "13            11          34.978784  \n",
              "14            11          34.936351  \n",
              "15            11          29.567808  \n",
              "16            11          29.872702  \n",
              "17            11          16.181047  \n",
              "18            11          16.570184  \n",
              "19            10          20.329289  "
            ],
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              "      <th></th>\n",
              "      <th>Brand</th>\n",
              "      <th>Entry_price</th>\n",
              "      <th>Adv_year</th>\n",
              "      <th>Price</th>\n",
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              "      <th>0</th>\n",
              "      <td>Abarth_124 Spider_2016</td>\n",
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              "      <td>Abarth_124 Spider_2016</td>\n",
              "      <td>29365</td>\n",
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              "      <td>Abarth_124 Spider_2016</td>\n",
              "      <td>29365</td>\n",
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              "      <td>20770.62</td>\n",
              "      <td>2016</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Abarth_124 Spider_2019</td>\n",
              "      <td>29515</td>\n",
              "      <td>2021</td>\n",
              "      <td>20770.62</td>\n",
              "      <td>2019</td>\n",
              "      <td>5</td>\n",
              "      <td>29.626902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Abarth_595_2012</td>\n",
              "      <td>17500</td>\n",
              "      <td>2016</td>\n",
              "      <td>11492.50</td>\n",
              "      <td>2012</td>\n",
              "      <td>12</td>\n",
              "      <td>34.328571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Abarth_595_2012</td>\n",
              "      <td>17500</td>\n",
              "      <td>2017</td>\n",
              "      <td>11500.00</td>\n",
              "      <td>2012</td>\n",
              "      <td>12</td>\n",
              "      <td>34.285714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Abarth_595_2012</td>\n",
              "      <td>17500</td>\n",
              "      <td>2018</td>\n",
              "      <td>12448.89</td>\n",
              "      <td>2012</td>\n",
              "      <td>12</td>\n",
              "      <td>28.863486</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Abarth_595_2012</td>\n",
              "      <td>17500</td>\n",
              "      <td>2019</td>\n",
              "      <td>12395.00</td>\n",
              "      <td>2012</td>\n",
              "      <td>12</td>\n",
              "      <td>29.171429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Abarth_595_2012</td>\n",
              "      <td>17500</td>\n",
              "      <td>2020</td>\n",
              "      <td>14815.00</td>\n",
              "      <td>2012</td>\n",
              "      <td>12</td>\n",
              "      <td>15.342857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Abarth_595_2012</td>\n",
              "      <td>17500</td>\n",
              "      <td>2021</td>\n",
              "      <td>14746.22</td>\n",
              "      <td>2012</td>\n",
              "      <td>12</td>\n",
              "      <td>15.735886</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Abarth_595_2013</td>\n",
              "      <td>17675</td>\n",
              "      <td>2016</td>\n",
              "      <td>11492.50</td>\n",
              "      <td>2013</td>\n",
              "      <td>11</td>\n",
              "      <td>34.978784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Abarth_595_2013</td>\n",
              "      <td>17675</td>\n",
              "      <td>2017</td>\n",
              "      <td>11500.00</td>\n",
              "      <td>2013</td>\n",
              "      <td>11</td>\n",
              "      <td>34.936351</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Abarth_595_2013</td>\n",
              "      <td>17675</td>\n",
              "      <td>2018</td>\n",
              "      <td>12448.89</td>\n",
              "      <td>2013</td>\n",
              "      <td>11</td>\n",
              "      <td>29.567808</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Abarth_595_2013</td>\n",
              "      <td>17675</td>\n",
              "      <td>2019</td>\n",
              "      <td>12395.00</td>\n",
              "      <td>2013</td>\n",
              "      <td>11</td>\n",
              "      <td>29.872702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Abarth_595_2013</td>\n",
              "      <td>17675</td>\n",
              "      <td>2020</td>\n",
              "      <td>14815.00</td>\n",
              "      <td>2013</td>\n",
              "      <td>11</td>\n",
              "      <td>16.181047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Abarth_595_2013</td>\n",
              "      <td>17675</td>\n",
              "      <td>2021</td>\n",
              "      <td>14746.22</td>\n",
              "      <td>2013</td>\n",
              "      <td>11</td>\n",
              "      <td>16.570184</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Abarth_595_2014</td>\n",
              "      <td>14425</td>\n",
              "      <td>2016</td>\n",
              "      <td>11492.50</td>\n",
              "      <td>2014</td>\n",
              "      <td>10</td>\n",
              "      <td>20.329289</td>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "mean_prices_final",
              "summary": "{\n  \"name\": \"mean_prices_final\",\n  \"rows\": 13449,\n  \"fields\": [\n    {\n      \"column\": \"Brand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5431,\n        \"samples\": [\n          \"Volvo_C70_1998\",\n          \"Vauxhall_Vectra_2005\",\n          \"Kia_Carens_2007\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Entry_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 37241,\n        \"min\": 4499,\n        \"max\": 320120,\n        \"num_unique_values\": 3012,\n        \"samples\": [\n          10970,\n          18973,\n          9770\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adv_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2012,\n        \"max\": 2021,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          2014,\n          2018,\n          2020\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24608.867678707644,\n        \"min\": 450.0,\n        \"max\": 299900.0,\n        \"num_unique_values\": 1433,\n        \"samples\": [\n          10746.67,\n          34000.0,\n          9114.06\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Manifacture_Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27,\n        \"min\": 1007,\n        \"max\": 2020,\n        \"num_unique_values\": 24,\n        \"samples\": [\n          2006,\n          1998,\n          2016\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Car_Age_2024\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27,\n        \"min\": 4,\n        \"max\": 1017,\n        \"num_unique_values\": 24,\n        \"samples\": [\n          18,\n          26,\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Depreciation_Rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27.394514717154145,\n        \"min\": 0.02297887836854082,\n        \"max\": 97.55368306605055,\n        \"num_unique_values\": 9542,\n        \"samples\": [\n          47.3469387755102,\n          89.7993879632778,\n          78.63551873198847\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 170
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "brand_depreciation = mean_prices_final.groupby('Brand')['Depreciation_Rate'].mean().reset_index()\n",
        "brand_depreciation.head(5)\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "zWH6fmyqcODy",
        "outputId": "c8104a47-db1d-43bd-caeb-d39dea47d63b"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    Brand  Depreciation_Rate\n",
              "0  Abarth_124 Spider_2016          22.256292\n",
              "1  Abarth_124 Spider_2017          14.384249\n",
              "2  Abarth_124 Spider_2018          14.384249\n",
              "3  Abarth_124 Spider_2019          22.651398\n",
              "4         Abarth_595_2012          26.287990"
            ],
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              "      background-color: #3B4455;\n",
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              "\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",
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              "\n",
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              "                                                    [key], {});\n",
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              "\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",
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              "      --bg-color: #E8F0FE;\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\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",
              "      } 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",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-33bd9e80-ae27-4799-baf6-755931ad9099 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "brand_depreciation",
              "summary": "{\n  \"name\": \"brand_depreciation\",\n  \"rows\": 5981,\n  \"fields\": [\n    {\n      \"column\": \"Brand\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5981,\n        \"samples\": [\n          \"Proton_GEN-2_2010\",\n          \"Peugeot_RCZ_2012\",\n          \"Mercedes-Benz_B Class_2018\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Depreciation_Rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38.01877503597276,\n        \"min\": -157.40081875697805,\n        \"max\": 96.7438494934877,\n        \"num_unique_values\": 4146,\n        \"samples\": [\n          48.81019456138355,\n          18.617861342604428,\n          -95.98639841314821\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ts=mean_prices_final.groupby('Brand')['Price'].mean().reset_index()\n",
        "ts.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "8Zsv80xR-Gsm",
        "outputId": "9241b4b1-18b0-4262-d44d-123296a775a1"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    Brand         Price\n",
              "0  Abarth_124 Spider_2016  22829.440000\n",
              "1  Abarth_124 Spider_2017  22829.440000\n",
              "2  Abarth_124 Spider_2018  22829.440000\n",
              "3  Abarth_124 Spider_2019  22829.440000\n",
              "4         Abarth_595_2012  12899.601667"
            ],
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              "      <td>22829.440000</td>\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
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              "    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",
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              "    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",
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              "    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",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      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-82141cf1-c21f-412b-8780-af966b8d607f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "ts",
              "summary": "{\n  \"name\": \"ts\",\n  \"rows\": 5981,\n  \"fields\": [\n    {\n      \"column\": \"Brand\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5981,\n        \"samples\": [\n          \"Proton_GEN-2_2010\",\n          \"Peugeot_RCZ_2012\",\n          \"Mercedes-Benz_B Class_2018\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31867.96818065641,\n        \"min\": 450.0,\n        \"max\": 317615.98285714287,\n        \"num_unique_values\": 594,\n        \"samples\": [\n          20376.4625,\n          79587.6625,\n          12455.745\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tq=mean_prices_final.groupby('Brand')['Car_Age_2024'].unique().reset_index()\n",
        "tq.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "On6XUT4s_LXX",
        "outputId": "70767e47-5226-46dc-c20f-0ae99edaf336"
      },
      "execution_count": 36,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "                    Brand Car_Age_2024\n",
              "0  Abarth_124 Spider_2016          [8]\n",
              "1  Abarth_124 Spider_2017          [7]\n",
              "2  Abarth_124 Spider_2018          [6]\n",
              "3  Abarth_124 Spider_2019          [5]\n",
              "4         Abarth_595_2012         [12]"
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              "        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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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "tq",
              "summary": "{\n  \"name\": \"tq\",\n  \"rows\": 5981,\n  \"fields\": [\n    {\n      \"column\": \"Brand\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5981,\n        \"samples\": [\n          \"Proton_GEN-2_2010\",\n          \"Peugeot_RCZ_2012\",\n          \"Mercedes-Benz_B Class_2018\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Car_Age_2024\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df = pd.merge(ts, tq , on='Brand', how='left')\n",
        "merged_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "e0y5em4v_5wa",
        "outputId": "0c90f11a-437b-4495-bf2f-78e3d8063bf1"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    Brand         Price Car_Age_2024\n",
              "0  Abarth_124 Spider_2016  22829.440000          [8]\n",
              "1  Abarth_124 Spider_2017  22829.440000          [7]\n",
              "2  Abarth_124 Spider_2018  22829.440000          [6]\n",
              "3  Abarth_124 Spider_2019  22829.440000          [5]\n",
              "4         Abarth_595_2012  12899.601667         [12]"
            ],
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          "metadata": {},
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    {
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        "tr= mean_prices_final.groupby('Brand')['Manifacture_Year'].unique().reset_index()\n",
        "tr.head()"
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              "                    Brand Manifacture_Year\n",
              "0  Abarth_124 Spider_2016           [2016]\n",
              "1  Abarth_124 Spider_2017           [2017]\n",
              "2  Abarth_124 Spider_2018           [2018]\n",
              "3  Abarth_124 Spider_2019           [2019]\n",
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              "summary": "{\n  \"name\": \"tr\",\n  \"rows\": 5981,\n  \"fields\": [\n    {\n      \"column\": \"Brand\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5981,\n        \"samples\": [\n          \"Proton_GEN-2_2010\",\n          \"Peugeot_RCZ_2012\",\n          \"Mercedes-Benz_B Class_2018\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Manifacture_Year\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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        "to= mean_prices_final.groupby('Brand')['Entry_price'].unique().reset_index()\n",
        "to.head()"
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      "execution_count": 41,
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        {
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          "data": {
            "text/plain": [
              "                    Brand Entry_price\n",
              "0  Abarth_124 Spider_2016     [29365]\n",
              "1  Abarth_124 Spider_2017     [26665]\n",
              "2  Abarth_124 Spider_2018     [26665]\n",
              "3  Abarth_124 Spider_2019     [29515]\n",
              "4         Abarth_595_2012     [17500]"
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              "        document.querySelector('#df-02d84c46-f041-453a-8625-01a82d479c20 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-02d84c46-f041-453a-8625-01a82d479c20');\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-3bead1eb-755d-4cda-b150-437b3de9c2b6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3bead1eb-755d-4cda-b150-437b3de9c2b6')\"\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-3bead1eb-755d-4cda-b150-437b3de9c2b6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "to",
              "summary": "{\n  \"name\": \"to\",\n  \"rows\": 5981,\n  \"fields\": [\n    {\n      \"column\": \"Brand\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5981,\n        \"samples\": [\n          \"Proton_GEN-2_2010\",\n          \"Peugeot_RCZ_2012\",\n          \"Mercedes-Benz_B Class_2018\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Entry_price\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df = pd.merge(merged_df, tr , on='Brand', how='right')\n",
        "merged_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Snowoa1yBse6",
        "outputId": "51179a6e-9481-4845-cffd-3fde13535ee0"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    Brand         Price Car_Age_2024 Entry_price_x  \\\n",
              "0  Abarth_124 Spider_2016  22829.440000          [8]       [29365]   \n",
              "1  Abarth_124 Spider_2017  22829.440000          [7]       [26665]   \n",
              "2  Abarth_124 Spider_2018  22829.440000          [6]       [26665]   \n",
              "3  Abarth_124 Spider_2019  22829.440000          [5]       [29515]   \n",
              "4         Abarth_595_2012  12899.601667         [12]       [17500]   \n",
              "\n",
              "  Entry_price_y Manifacture_Year  \n",
              "0       [29365]           [2016]  \n",
              "1       [26665]           [2017]  \n",
              "2       [26665]           [2018]  \n",
              "3       [29515]           [2019]  \n",
              "4       [17500]           [2012]  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-92c3a1b7-c1c0-4f53-bc6e-2e1c69ce6c3b\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "      <td>22829.440000</td>\n",
              "      <td>[8]</td>\n",
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              "      <td>[2016]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Abarth_124 Spider_2017</td>\n",
              "      <td>22829.440000</td>\n",
              "      <td>[7]</td>\n",
              "      <td>[26665]</td>\n",
              "      <td>[26665]</td>\n",
              "      <td>[2017]</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Abarth_124 Spider_2018</td>\n",
              "      <td>22829.440000</td>\n",
              "      <td>[6]</td>\n",
              "      <td>[26665]</td>\n",
              "      <td>[26665]</td>\n",
              "      <td>[2018]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Abarth_124 Spider_2019</td>\n",
              "      <td>22829.440000</td>\n",
              "      <td>[5]</td>\n",
              "      <td>[29515]</td>\n",
              "      <td>[29515]</td>\n",
              "      <td>[2019]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Abarth_595_2012</td>\n",
              "      <td>12899.601667</td>\n",
              "      <td>[12]</td>\n",
              "      <td>[17500]</td>\n",
              "      <td>[17500]</td>\n",
              "      <td>[2012]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\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",
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              "      padding: 0 0 0 0;\n",
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              "\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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "    [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",
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              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-92c3a1b7-c1c0-4f53-bc6e-2e1c69ce6c3b 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-92c3a1b7-c1c0-4f53-bc6e-2e1c69ce6c3b');\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",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
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              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "      --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",
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              "  }\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",
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              "      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-68b708d9-35bc-46c4-8367-f58501142d47 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
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              "summary": "{\n  \"name\": \"merged_df\",\n  \"rows\": 5981,\n  \"fields\": [\n    {\n      \"column\": \"Brand\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5981,\n        \"samples\": [\n          \"Proton_GEN-2_2010\",\n          \"Peugeot_RCZ_2012\",\n          \"Mercedes-Benz_B Class_2018\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31867.96818065641,\n        \"min\": 450.0,\n        \"max\": 317615.98285714287,\n        \"num_unique_values\": 594,\n        \"samples\": [\n          20376.4625,\n          79587.6625,\n          12455.745\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Car_Age_2024\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Entry_price_x\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Entry_price_y\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Manifacture_Year\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "brand_depreciation= pd.merge(brand_depreciation, merged_df , on='Brand', how='right')\n",
        "brand_depreciation.head()"
      ],
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      "execution_count": 46,
      "outputs": [
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          "data": {
            "text/plain": [
              "                    Brand  Depreciation_Rate         Price Car_Age_2024  \\\n",
              "0  Abarth_124 Spider_2016          22.256292  22829.440000          [8]   \n",
              "1  Abarth_124 Spider_2017          14.384249  22829.440000          [7]   \n",
              "2  Abarth_124 Spider_2018          14.384249  22829.440000          [6]   \n",
              "3  Abarth_124 Spider_2019          22.651398  22829.440000          [5]   \n",
              "4         Abarth_595_2012          26.287990  12899.601667         [12]   \n",
              "\n",
              "  Entry_price_x Entry_price_y Manifacture_Year  \n",
              "0       [29365]       [29365]           [2016]  \n",
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              "2       [26665]       [26665]           [2018]  \n",
              "3       [29515]       [29515]           [2019]  \n",
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              "type": "dataframe",
              "variable_name": "brand_depreciation",
              "summary": "{\n  \"name\": \"brand_depreciation\",\n  \"rows\": 5981,\n  \"fields\": [\n    {\n      \"column\": \"Brand\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5981,\n        \"samples\": [\n          \"Proton_GEN-2_2010\",\n          \"Peugeot_RCZ_2012\",\n          \"Mercedes-Benz_B Class_2018\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Depreciation_Rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38.01877503597276,\n        \"min\": -157.40081875697805,\n        \"max\": 96.7438494934877,\n        \"num_unique_values\": 4146,\n        \"samples\": [\n          48.81019456138355,\n          18.617861342604428,\n          -95.98639841314821\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31867.96818065641,\n        \"min\": 450.0,\n        \"max\": 317615.98285714287,\n        \"num_unique_values\": 594,\n        \"samples\": [\n          20376.4625,\n          79587.6625,\n          12455.745\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Car_Age_2024\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Entry_price_x\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Entry_price_y\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Manifacture_Year\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "brand_depreciation.drop(columns=['Entry_price_y'], inplace=True)\n",
        "\n",
        "\n",
        "brand_depreciation['Car_Age_2024'] = brand_depreciation['Car_Age_2024'].astype(int)\n",
        "brand_depreciation['Entry_price_x'] = brand_depreciation['Entry_price_x'].astype(int)\n",
        "brand_depreciation['Manifacture_Year'] = brand_depreciation['Manifacture_Year'].astype(int)\n",
        "\n",
        "\n",
        "brand_depreciation.rename(columns={'Entry_price_x': 'Entry_price'}, inplace=True)\n",
        "\n",
        "\n",
        "brand_depreciation.head()\n"
      ],
      "metadata": {
        "colab": {
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          "height": 206
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        "id": "rgHix2ZvBsZw",
        "outputId": "3cf43fd3-e242-4998-8586-18fabd2f92f3"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    Brand  Depreciation_Rate         Price  Car_Age_2024  \\\n",
              "0  Abarth_124 Spider_2016          22.256292  22829.440000             8   \n",
              "1  Abarth_124 Spider_2017          14.384249  22829.440000             7   \n",
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              "4         Abarth_595_2012          26.287990  12899.601667            12   \n",
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              "   Entry_price  Manifacture_Year  \n",
              "0        29365              2016  \n",
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              "      <td>Abarth_595_2012</td>\n",
              "      <td>26.287990</td>\n",
              "      <td>12899.601667</td>\n",
              "      <td>12</td>\n",
              "      <td>17500</td>\n",
              "      <td>2012</td>\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
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              "    }\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",
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              "        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",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "brand_depreciation",
              "summary": "{\n  \"name\": \"brand_depreciation\",\n  \"rows\": 5981,\n  \"fields\": [\n    {\n      \"column\": \"Brand\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5981,\n        \"samples\": [\n          \"Proton_GEN-2_2010\",\n          \"Peugeot_RCZ_2012\",\n          \"Mercedes-Benz_B Class_2018\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Depreciation_Rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38.01877503597276,\n        \"min\": -157.40081875697805,\n        \"max\": 96.7438494934877,\n        \"num_unique_values\": 4146,\n        \"samples\": [\n          48.81019456138355,\n          18.617861342604428,\n          -95.98639841314821\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31867.96818065641,\n        \"min\": 450.0,\n        \"max\": 317615.98285714287,\n        \"num_unique_values\": 594,\n        \"samples\": [\n          20376.4625,\n          79587.6625,\n          12455.745\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Car_Age_2024\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 141,\n        \"min\": -2984,\n        \"max\": 1017,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          17,\n          -984,\n          18\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Entry_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38980,\n        \"min\": 4499,\n        \"max\": 320120,\n        \"num_unique_values\": 3262,\n        \"samples\": [\n          8805,\n          32945,\n          33940\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Manifacture_Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 141,\n        \"min\": 1007,\n        \"max\": 5008,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          2007,\n          3008,\n          2006\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "file_path = \"/content/drive/MyDrive/Price Prediction/Final.csv\"\n",
        "\n",
        "brand_depreciation.to_csv(file_path, index=False)\n",
        "\n",
        "print(\"DataFrame has been saved to:\", file_path)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qUmsu8H1BsXI",
        "outputId": "f4f39838-e7f3-4440-dae7-300f6bc251b4"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DataFrame has been saved to: /content/drive/MyDrive/Price Prediction/Final.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "brand_depreciation['Price_2024'] = brand_depreciation['Price'] - (brand_depreciation['Depreciation_Rate'] * brand_depreciation['Car_Age_2024'])\n",
        "\n",
        "brand_depreciation.head(50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "1ie90-rWcOBl",
        "outputId": "8d744c32-dfe2-4eca-d8c1-72132adf7f48"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        Brand  Depreciation_Rate         Price  Car_Age_2024  \\\n",
              "0      Abarth_124 Spider_2016          22.256292  22829.440000             8   \n",
              "1      Abarth_124 Spider_2017          14.384249  22829.440000             7   \n",
              "2      Abarth_124 Spider_2018          14.384249  22829.440000             6   \n",
              "3      Abarth_124 Spider_2019          22.651398  22829.440000             5   \n",
              "4             Abarth_595_2012          26.287990  12899.601667            12   \n",
              "5             Abarth_595_2013          27.017812  12899.601667            11   \n",
              "6             Abarth_595_2014          10.574685  12899.601667            10   \n",
              "7             Abarth_595_2015          10.574685  12899.601667             9   \n",
              "8             Abarth_595_2016          10.574685  12899.601667             8   \n",
              "9             Abarth_595_2017          10.574685  12899.601667             7   \n",
              "10            Abarth_595_2018          15.440173  12899.601667             6   \n",
              "11        Alfa Romeo_159_2006          80.584019   3562.250000            18   \n",
              "12        Alfa Romeo_159_2007          80.571312   3562.250000            17   \n",
              "13        Alfa Romeo_159_2008          80.571312   3562.250000            16   \n",
              "14        Alfa Romeo_159_2009          80.502189   3562.250000            15   \n",
              "15        Alfa Romeo_159_2010          80.502189   3562.250000            14   \n",
              "16        Alfa Romeo_159_2011          82.369463   3562.250000            13   \n",
              "17        Alfa Romeo_159_2012          84.047246   3562.250000            12   \n",
              "18         Alfa Romeo_4C_2014           2.975993  43432.796667            10   \n",
              "19         Alfa Romeo_4C_2015           2.975993  43432.796667             9   \n",
              "20         Alfa Romeo_4C_2016          15.277876  43432.796667             8   \n",
              "21         Alfa Romeo_4C_2017          16.898887  43432.796667             7   \n",
              "22         Alfa Romeo_4C_2018          16.898887  43432.796667             6   \n",
              "23      Alfa Romeo_Brera_2006          81.187710   4246.310000            18   \n",
              "24      Alfa Romeo_Brera_2007          81.187710   4246.310000            17   \n",
              "25      Alfa Romeo_Brera_2008          81.187710   4246.310000            16   \n",
              "26      Alfa Romeo_Brera_2009          81.658993   4246.310000            15   \n",
              "27      Alfa Romeo_Brera_2010          82.825844   4246.310000            14   \n",
              "28      Alfa Romeo_Brera_2011          82.825844   4246.310000            13   \n",
              "29         Alfa Romeo_GT_2004          84.762475   3169.710000            20   \n",
              "30         Alfa Romeo_GT_2005          85.120130   3169.710000            19   \n",
              "31         Alfa Romeo_GT_2006          83.940264   3169.710000            18   \n",
              "32         Alfa Romeo_GT_2007          81.048072   3169.710000            17   \n",
              "33         Alfa Romeo_GT_2008          81.048072   3169.710000            16   \n",
              "34         Alfa Romeo_GT_2009          82.695256   3169.710000            15   \n",
              "35         Alfa Romeo_GT_2010          83.926420   3169.710000            14   \n",
              "36         Alfa Romeo_GT_2011          83.926420   3169.710000            13   \n",
              "37     Alfa Romeo_Giulia_2016          -2.274315  29654.437500             8   \n",
              "38     Alfa Romeo_Giulia_2017          -2.274315  29654.437500             7   \n",
              "39     Alfa Romeo_Giulia_2018          -0.116264  29654.437500             6   \n",
              "40  Alfa Romeo_Giulietta_2010          18.764627  13659.728000            14   \n",
              "41  Alfa Romeo_Giulietta_2011          18.764627  13659.728000            13   \n",
              "42  Alfa Romeo_Giulietta_2012          22.255390  13659.728000            12   \n",
              "43  Alfa Romeo_Giulietta_2013          22.255390  13659.728000            11   \n",
              "44  Alfa Romeo_Giulietta_2014          22.255390  13659.728000            10   \n",
              "45  Alfa Romeo_Giulietta_2015          24.280887  13659.728000             9   \n",
              "46  Alfa Romeo_Giulietta_2016          25.152175  13659.728000             8   \n",
              "47  Alfa Romeo_Giulietta_2017          26.163632  13659.728000             7   \n",
              "48  Alfa Romeo_Giulietta_2018          29.806125  13659.728000             6   \n",
              "49       Alfa Romeo_MiTo_2009          16.082340   8870.096667            15   \n",
              "\n",
              "    Entry_price  Manifacture_Year    Price_2024  \n",
              "0         29365              2016  22651.389668  \n",
              "1         26665              2017  22728.750257  \n",
              "2         26665              2018  22743.134506  \n",
              "3         29515              2019  22716.183012  \n",
              "4         17500              2012  12584.145781  \n",
              "5         17675              2013  12602.405731  \n",
              "6         14425              2014  12793.854815  \n",
              "7         14425              2015  12804.429500  \n",
              "8         14425              2016  12815.004185  \n",
              "9         14425              2017  12825.578871  \n",
              "10        15255              2018  12806.960631  \n",
              "11        18347              2006   2111.737655  \n",
              "12        18335              2007   2192.537701  \n",
              "13        18335              2008   2273.109013  \n",
              "14        18270              2009   2354.717159  \n",
              "15        18270              2010   2435.219349  \n",
              "16        20205              2011   2491.446981  \n",
              "17        22330              2012   2553.683050  \n",
              "18        44765              2014  43403.036735  \n",
              "19        44765              2015  43406.012728  \n",
              "20        51265              2016  43310.573656  \n",
              "21        52265              2017  43314.504457  \n",
              "22        52265              2018  43331.403344  \n",
              "23        22572              2006   2784.931212  \n",
              "24        22572              2007   2866.118923  \n",
              "25        22572              2008   2947.306633  \n",
              "26        23152              2009   3021.425109  \n",
              "27        24725              2010   3086.748180  \n",
              "28        24725              2011   3169.574024  \n",
              "29        20802              2004   1474.460505  \n",
              "30        21302              2005   1552.427538  \n",
              "31        19737              2006   1658.785239  \n",
              "32        16725              2007   1791.892780  \n",
              "33        16725              2008   1872.940852  \n",
              "34        18317              2009   1929.281163  \n",
              "35        19720              2010   1994.740122  \n",
              "36        19720              2011   2078.666542  \n",
              "37        28995              2016  29672.632016  \n",
              "38        28995              2017  29670.357702  \n",
              "39        29620              2018  29655.135086  \n",
              "40        16815              2010  13397.023225  \n",
              "41        16815              2011  13415.787851  \n",
              "42        17570              2012  13392.663322  \n",
              "43        17570              2013  13414.918711  \n",
              "44        17570              2014  13437.174101  \n",
              "45        18040              2015  13441.200018  \n",
              "46        18250              2016  13458.510597  \n",
              "47        18500              2017  13476.582573  \n",
              "48        19460              2018  13480.891248  \n",
              "49        10570              2009   8628.861567  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a1d808d7-6cb3-406e-be06-99bb641a9aff\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Brand</th>\n",
              "      <th>Depreciation_Rate</th>\n",
              "      <th>Price</th>\n",
              "      <th>Car_Age_2024</th>\n",
              "      <th>Entry_price</th>\n",
              "      <th>Manifacture_Year</th>\n",
              "      <th>Price_2024</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Abarth_124 Spider_2016</td>\n",
              "      <td>22.256292</td>\n",
              "      <td>22829.440000</td>\n",
              "      <td>8</td>\n",
              "      <td>29365</td>\n",
              "      <td>2016</td>\n",
              "      <td>22651.389668</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Abarth_124 Spider_2017</td>\n",
              "      <td>14.384249</td>\n",
              "      <td>22829.440000</td>\n",
              "      <td>7</td>\n",
              "      <td>26665</td>\n",
              "      <td>2017</td>\n",
              "      <td>22728.750257</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Abarth_124 Spider_2018</td>\n",
              "      <td>14.384249</td>\n",
              "      <td>22829.440000</td>\n",
              "      <td>6</td>\n",
              "      <td>26665</td>\n",
              "      <td>2018</td>\n",
              "      <td>22743.134506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Abarth_124 Spider_2019</td>\n",
              "      <td>22.651398</td>\n",
              "      <td>22829.440000</td>\n",
              "      <td>5</td>\n",
              "      <td>29515</td>\n",
              "      <td>2019</td>\n",
              "      <td>22716.183012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Abarth_595_2012</td>\n",
              "      <td>26.287990</td>\n",
              "      <td>12899.601667</td>\n",
              "      <td>12</td>\n",
              "      <td>17500</td>\n",
              "      <td>2012</td>\n",
              "      <td>12584.145781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Abarth_595_2013</td>\n",
              "      <td>27.017812</td>\n",
              "      <td>12899.601667</td>\n",
              "      <td>11</td>\n",
              "      <td>17675</td>\n",
              "      <td>2013</td>\n",
              "      <td>12602.405731</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Abarth_595_2014</td>\n",
              "      <td>10.574685</td>\n",
              "      <td>12899.601667</td>\n",
              "      <td>10</td>\n",
              "      <td>14425</td>\n",
              "      <td>2014</td>\n",
              "      <td>12793.854815</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Abarth_595_2015</td>\n",
              "      <td>10.574685</td>\n",
              "      <td>12899.601667</td>\n",
              "      <td>9</td>\n",
              "      <td>14425</td>\n",
              "      <td>2015</td>\n",
              "      <td>12804.429500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Abarth_595_2016</td>\n",
              "      <td>10.574685</td>\n",
              "      <td>12899.601667</td>\n",
              "      <td>8</td>\n",
              "      <td>14425</td>\n",
              "      <td>2016</td>\n",
              "      <td>12815.004185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Abarth_595_2017</td>\n",
              "      <td>10.574685</td>\n",
              "      <td>12899.601667</td>\n",
              "      <td>7</td>\n",
              "      <td>14425</td>\n",
              "      <td>2017</td>\n",
              "      <td>12825.578871</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Abarth_595_2018</td>\n",
              "      <td>15.440173</td>\n",
              "      <td>12899.601667</td>\n",
              "      <td>6</td>\n",
              "      <td>15255</td>\n",
              "      <td>2018</td>\n",
              "      <td>12806.960631</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Alfa Romeo_159_2006</td>\n",
              "      <td>80.584019</td>\n",
              "      <td>3562.250000</td>\n",
              "      <td>18</td>\n",
              "      <td>18347</td>\n",
              "      <td>2006</td>\n",
              "      <td>2111.737655</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Alfa Romeo_159_2007</td>\n",
              "      <td>80.571312</td>\n",
              "      <td>3562.250000</td>\n",
              "      <td>17</td>\n",
              "      <td>18335</td>\n",
              "      <td>2007</td>\n",
              "      <td>2192.537701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Alfa Romeo_159_2008</td>\n",
              "      <td>80.571312</td>\n",
              "      <td>3562.250000</td>\n",
              "      <td>16</td>\n",
              "      <td>18335</td>\n",
              "      <td>2008</td>\n",
              "      <td>2273.109013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Alfa Romeo_159_2009</td>\n",
              "      <td>80.502189</td>\n",
              "      <td>3562.250000</td>\n",
              "      <td>15</td>\n",
              "      <td>18270</td>\n",
              "      <td>2009</td>\n",
              "      <td>2354.717159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Alfa Romeo_159_2010</td>\n",
              "      <td>80.502189</td>\n",
              "      <td>3562.250000</td>\n",
              "      <td>14</td>\n",
              "      <td>18270</td>\n",
              "      <td>2010</td>\n",
              "      <td>2435.219349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Alfa Romeo_159_2011</td>\n",
              "      <td>82.369463</td>\n",
              "      <td>3562.250000</td>\n",
              "      <td>13</td>\n",
              "      <td>20205</td>\n",
              "      <td>2011</td>\n",
              "      <td>2491.446981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Alfa Romeo_159_2012</td>\n",
              "      <td>84.047246</td>\n",
              "      <td>3562.250000</td>\n",
              "      <td>12</td>\n",
              "      <td>22330</td>\n",
              "      <td>2012</td>\n",
              "      <td>2553.683050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Alfa Romeo_4C_2014</td>\n",
              "      <td>2.975993</td>\n",
              "      <td>43432.796667</td>\n",
              "      <td>10</td>\n",
              "      <td>44765</td>\n",
              "      <td>2014</td>\n",
              "      <td>43403.036735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Alfa Romeo_4C_2015</td>\n",
              "      <td>2.975993</td>\n",
              "      <td>43432.796667</td>\n",
              "      <td>9</td>\n",
              "      <td>44765</td>\n",
              "      <td>2015</td>\n",
              "      <td>43406.012728</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Alfa Romeo_4C_2016</td>\n",
              "      <td>15.277876</td>\n",
              "      <td>43432.796667</td>\n",
              "      <td>8</td>\n",
              "      <td>51265</td>\n",
              "      <td>2016</td>\n",
              "      <td>43310.573656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Alfa Romeo_4C_2017</td>\n",
              "      <td>16.898887</td>\n",
              "      <td>43432.796667</td>\n",
              "      <td>7</td>\n",
              "      <td>52265</td>\n",
              "      <td>2017</td>\n",
              "      <td>43314.504457</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Alfa Romeo_4C_2018</td>\n",
              "      <td>16.898887</td>\n",
              "      <td>43432.796667</td>\n",
              "      <td>6</td>\n",
              "      <td>52265</td>\n",
              "      <td>2018</td>\n",
              "      <td>43331.403344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Alfa Romeo_Brera_2006</td>\n",
              "      <td>81.187710</td>\n",
              "      <td>4246.310000</td>\n",
              "      <td>18</td>\n",
              "      <td>22572</td>\n",
              "      <td>2006</td>\n",
              "      <td>2784.931212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Alfa Romeo_Brera_2007</td>\n",
              "      <td>81.187710</td>\n",
              "      <td>4246.310000</td>\n",
              "      <td>17</td>\n",
              "      <td>22572</td>\n",
              "      <td>2007</td>\n",
              "      <td>2866.118923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Alfa Romeo_Brera_2008</td>\n",
              "      <td>81.187710</td>\n",
              "      <td>4246.310000</td>\n",
              "      <td>16</td>\n",
              "      <td>22572</td>\n",
              "      <td>2008</td>\n",
              "      <td>2947.306633</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Alfa Romeo_Brera_2009</td>\n",
              "      <td>81.658993</td>\n",
              "      <td>4246.310000</td>\n",
              "      <td>15</td>\n",
              "      <td>23152</td>\n",
              "      <td>2009</td>\n",
              "      <td>3021.425109</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Alfa Romeo_Brera_2010</td>\n",
              "      <td>82.825844</td>\n",
              "      <td>4246.310000</td>\n",
              "      <td>14</td>\n",
              "      <td>24725</td>\n",
              "      <td>2010</td>\n",
              "      <td>3086.748180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Alfa Romeo_Brera_2011</td>\n",
              "      <td>82.825844</td>\n",
              "      <td>4246.310000</td>\n",
              "      <td>13</td>\n",
              "      <td>24725</td>\n",
              "      <td>2011</td>\n",
              "      <td>3169.574024</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Alfa Romeo_GT_2004</td>\n",
              "      <td>84.762475</td>\n",
              "      <td>3169.710000</td>\n",
              "      <td>20</td>\n",
              "      <td>20802</td>\n",
              "      <td>2004</td>\n",
              "      <td>1474.460505</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Alfa Romeo_GT_2005</td>\n",
              "      <td>85.120130</td>\n",
              "      <td>3169.710000</td>\n",
              "      <td>19</td>\n",
              "      <td>21302</td>\n",
              "      <td>2005</td>\n",
              "      <td>1552.427538</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Alfa Romeo_GT_2006</td>\n",
              "      <td>83.940264</td>\n",
              "      <td>3169.710000</td>\n",
              "      <td>18</td>\n",
              "      <td>19737</td>\n",
              "      <td>2006</td>\n",
              "      <td>1658.785239</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Alfa Romeo_GT_2007</td>\n",
              "      <td>81.048072</td>\n",
              "      <td>3169.710000</td>\n",
              "      <td>17</td>\n",
              "      <td>16725</td>\n",
              "      <td>2007</td>\n",
              "      <td>1791.892780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Alfa Romeo_GT_2008</td>\n",
              "      <td>81.048072</td>\n",
              "      <td>3169.710000</td>\n",
              "      <td>16</td>\n",
              "      <td>16725</td>\n",
              "      <td>2008</td>\n",
              "      <td>1872.940852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Alfa Romeo_GT_2009</td>\n",
              "      <td>82.695256</td>\n",
              "      <td>3169.710000</td>\n",
              "      <td>15</td>\n",
              "      <td>18317</td>\n",
              "      <td>2009</td>\n",
              "      <td>1929.281163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Alfa Romeo_GT_2010</td>\n",
              "      <td>83.926420</td>\n",
              "      <td>3169.710000</td>\n",
              "      <td>14</td>\n",
              "      <td>19720</td>\n",
              "      <td>2010</td>\n",
              "      <td>1994.740122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Alfa Romeo_GT_2011</td>\n",
              "      <td>83.926420</td>\n",
              "      <td>3169.710000</td>\n",
              "      <td>13</td>\n",
              "      <td>19720</td>\n",
              "      <td>2011</td>\n",
              "      <td>2078.666542</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Alfa Romeo_Giulia_2016</td>\n",
              "      <td>-2.274315</td>\n",
              "      <td>29654.437500</td>\n",
              "      <td>8</td>\n",
              "      <td>28995</td>\n",
              "      <td>2016</td>\n",
              "      <td>29672.632016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Alfa Romeo_Giulia_2017</td>\n",
              "      <td>-2.274315</td>\n",
              "      <td>29654.437500</td>\n",
              "      <td>7</td>\n",
              "      <td>28995</td>\n",
              "      <td>2017</td>\n",
              "      <td>29670.357702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Alfa Romeo_Giulia_2018</td>\n",
              "      <td>-0.116264</td>\n",
              "      <td>29654.437500</td>\n",
              "      <td>6</td>\n",
              "      <td>29620</td>\n",
              "      <td>2018</td>\n",
              "      <td>29655.135086</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Alfa Romeo_Giulietta_2010</td>\n",
              "      <td>18.764627</td>\n",
              "      <td>13659.728000</td>\n",
              "      <td>14</td>\n",
              "      <td>16815</td>\n",
              "      <td>2010</td>\n",
              "      <td>13397.023225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Alfa Romeo_Giulietta_2011</td>\n",
              "      <td>18.764627</td>\n",
              "      <td>13659.728000</td>\n",
              "      <td>13</td>\n",
              "      <td>16815</td>\n",
              "      <td>2011</td>\n",
              "      <td>13415.787851</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>Alfa Romeo_Giulietta_2012</td>\n",
              "      <td>22.255390</td>\n",
              "      <td>13659.728000</td>\n",
              "      <td>12</td>\n",
              "      <td>17570</td>\n",
              "      <td>2012</td>\n",
              "      <td>13392.663322</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>Alfa Romeo_Giulietta_2013</td>\n",
              "      <td>22.255390</td>\n",
              "      <td>13659.728000</td>\n",
              "      <td>11</td>\n",
              "      <td>17570</td>\n",
              "      <td>2013</td>\n",
              "      <td>13414.918711</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>Alfa Romeo_Giulietta_2014</td>\n",
              "      <td>22.255390</td>\n",
              "      <td>13659.728000</td>\n",
              "      <td>10</td>\n",
              "      <td>17570</td>\n",
              "      <td>2014</td>\n",
              "      <td>13437.174101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>Alfa Romeo_Giulietta_2015</td>\n",
              "      <td>24.280887</td>\n",
              "      <td>13659.728000</td>\n",
              "      <td>9</td>\n",
              "      <td>18040</td>\n",
              "      <td>2015</td>\n",
              "      <td>13441.200018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>Alfa Romeo_Giulietta_2016</td>\n",
              "      <td>25.152175</td>\n",
              "      <td>13659.728000</td>\n",
              "      <td>8</td>\n",
              "      <td>18250</td>\n",
              "      <td>2016</td>\n",
              "      <td>13458.510597</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>Alfa Romeo_Giulietta_2017</td>\n",
              "      <td>26.163632</td>\n",
              "      <td>13659.728000</td>\n",
              "      <td>7</td>\n",
              "      <td>18500</td>\n",
              "      <td>2017</td>\n",
              "      <td>13476.582573</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>Alfa Romeo_Giulietta_2018</td>\n",
              "      <td>29.806125</td>\n",
              "      <td>13659.728000</td>\n",
              "      <td>6</td>\n",
              "      <td>19460</td>\n",
              "      <td>2018</td>\n",
              "      <td>13480.891248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>Alfa Romeo_MiTo_2009</td>\n",
              "      <td>16.082340</td>\n",
              "      <td>8870.096667</td>\n",
              "      <td>15</td>\n",
              "      <td>10570</td>\n",
              "      <td>2009</td>\n",
              "      <td>8628.861567</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "            style=\"display:none;\">\n",
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              "      gap: 12px;\n",
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              "    .colab-df-convert {\n",
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              "      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",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a1d808d7-6cb3-406e-be06-99bb641a9aff 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-a1d808d7-6cb3-406e-be06-99bb641a9aff');\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-082b9d69-1b93-4895-9d13-8746897216f8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-082b9d69-1b93-4895-9d13-8746897216f8')\"\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",
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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",
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              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --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",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    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-082b9d69-1b93-4895-9d13-8746897216f8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "brand_depreciation",
              "summary": "{\n  \"name\": \"brand_depreciation\",\n  \"rows\": 5981,\n  \"fields\": [\n    {\n      \"column\": \"Brand\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5981,\n        \"samples\": [\n          \"Proton_GEN-2_2010\",\n          \"Peugeot_RCZ_2012\",\n          \"Mercedes-Benz_B Class_2018\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Depreciation_Rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38.01877503597276,\n        \"min\": -157.40081875697805,\n        \"max\": 96.7438494934877,\n        \"num_unique_values\": 4146,\n        \"samples\": [\n          48.81019456138355,\n          18.617861342604428,\n          -95.98639841314821\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31867.96818065641,\n        \"min\": 450.0,\n        \"max\": 317615.98285714287,\n        \"num_unique_values\": 594,\n        \"samples\": [\n          20376.4625,\n          79587.6625,\n          12455.745\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Car_Age_2024\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 141,\n        \"min\": -2984,\n        \"max\": 1017,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          17,\n          -984,\n          18\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Entry_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38980,\n        \"min\": 4499,\n        \"max\": 320120,\n        \"num_unique_values\": 3262,\n        \"samples\": [\n          8805,\n          32945,\n          33940\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Manifacture_Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 141,\n        \"min\": 1007,\n        \"max\": 5008,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          2007,\n          3008,\n          2006\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price_2024\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32639.44831110086,\n        \"min\": -79927.68652809854,\n        \"max\": 317928.75052261545,\n        \"num_unique_values\": 5968,\n        \"samples\": [\n          -896.922252289759,\n          5528.671445727484,\n          -735.8121832144814\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final=brand_depreciation[['Brand','Manifacture_Year','Car_Age_2024','Entry_price','Price_2024']]\n",
        "final.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "kHw_gezgIWOe",
        "outputId": "1b53e33a-dd11-4d09-cf42-589159a2cc93"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    Brand  Manifacture_Year  Car_Age_2024  Entry_price  \\\n",
              "0  Abarth_124 Spider_2016              2016             8        29365   \n",
              "1  Abarth_124 Spider_2017              2017             7        26665   \n",
              "2  Abarth_124 Spider_2018              2018             6        26665   \n",
              "3  Abarth_124 Spider_2019              2019             5        29515   \n",
              "4         Abarth_595_2012              2012            12        17500   \n",
              "\n",
              "     Price_2024  \n",
              "0  22651.389668  \n",
              "1  22728.750257  \n",
              "2  22743.134506  \n",
              "3  22716.183012  \n",
              "4  12584.145781  "
            ],
            "text/html": [
              "\n",
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              "      <td>22728.750257</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Abarth_124 Spider_2018</td>\n",
              "      <td>2018</td>\n",
              "      <td>6</td>\n",
              "      <td>26665</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Abarth_124 Spider_2019</td>\n",
              "      <td>2019</td>\n",
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              "        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",
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              "\n",
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              "\n",
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              "\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    }\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",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
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              "  </div>\n"
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              "type": "dataframe",
              "variable_name": "final",
              "summary": "{\n  \"name\": \"final\",\n  \"rows\": 5981,\n  \"fields\": [\n    {\n      \"column\": \"Brand\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5981,\n        \"samples\": [\n          \"Proton_GEN-2_2010\",\n          \"Peugeot_RCZ_2012\",\n          \"Mercedes-Benz_B Class_2018\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Manifacture_Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 141,\n        \"min\": 1007,\n        \"max\": 5008,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          2007,\n          3008,\n          2006\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Car_Age_2024\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 141,\n        \"min\": -2984,\n        \"max\": 1017,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          17,\n          -984,\n          18\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Entry_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38980,\n        \"min\": 4499,\n        \"max\": 320120,\n        \"num_unique_values\": 3262,\n        \"samples\": [\n          8805,\n          32945,\n          33940\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price_2024\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32639.44831110086,\n        \"min\": -79927.68652809854,\n        \"max\": 317928.75052261545,\n        \"num_unique_values\": 5968,\n        \"samples\": [\n          -896.922252289759,\n          5528.671445727484,\n          -735.8121832144814\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Convert Pound to DT"
      ],
      "metadata": {
        "id": "jWMT2w3nJJZO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "final['Price_2024'] = final['Price_2024'].apply(lambda x: round(x * 3.92, 2))\n",
        "final.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "lny1vtEPJEKD",
        "outputId": "f910c58d-610c-44d6-96ee-49b16d2a58ea"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-56-082904e3a6c6>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\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",
            "  final['Price_2024'] = final['Price_2024'].apply(lambda x: round(x * 3.92, 2))\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    Brand  Manifacture_Year  Car_Age_2024  Entry_price  \\\n",
              "0  Abarth_124 Spider_2016              2016             8        29365   \n",
              "1  Abarth_124 Spider_2017              2017             7        26665   \n",
              "2  Abarth_124 Spider_2018              2018             6        26665   \n",
              "3  Abarth_124 Spider_2019              2019             5        29515   \n",
              "4         Abarth_595_2012              2012            12        17500   \n",
              "\n",
              "   Price_2024  \n",
              "0    88793.45  \n",
              "1    89096.70  \n",
              "2    89153.09  \n",
              "3    89047.44  \n",
              "4    49329.85  "
            ],
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "\n",
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              "    async function quickchart(key) {\n",
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              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "final",
              "summary": "{\n  \"name\": \"final\",\n  \"rows\": 5981,\n  \"fields\": [\n    {\n      \"column\": \"Brand\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5981,\n        \"samples\": [\n          \"Proton_GEN-2_2010\",\n          \"Peugeot_RCZ_2012\",\n          \"Mercedes-Benz_B Class_2018\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Manifacture_Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 141,\n        \"min\": 1007,\n        \"max\": 5008,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          2007,\n          3008,\n          2006\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Car_Age_2024\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 141,\n        \"min\": -2984,\n        \"max\": 1017,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          17,\n          -984,\n          18\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Entry_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38980,\n        \"min\": 4499,\n        \"max\": 320120,\n        \"num_unique_values\": 3262,\n        \"samples\": [\n          8805,\n          32945,\n          33940\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price_2024\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 127946.63737367703,\n        \"min\": -313316.53,\n        \"max\": 1246280.7,\n        \"num_unique_values\": 5965,\n        \"samples\": [\n          74801.36,\n          21672.39,\n          393412.26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final['Entry_price'] = final['Entry_price'].apply(lambda x: round(x * 3.92, 2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zzTkdVclKNvw",
        "outputId": "20b02829-a0b9-4ff5-fdbb-20ce947e26d9"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-57-44509cd0be7b>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\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",
            "  final['Entry_price'] = final['Entry_price'].apply(lambda x: round(x * 3.92, 2))\n"
          ]
        }
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    },
    {
      "cell_type": "code",
      "source": [
        "final.head(500)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "AbJwtH8bKWBs",
        "outputId": "0bf94033-1164-442d-b402-d58d42d1c301"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      Brand  Manifacture_Year  Car_Age_2024  Entry_price  \\\n",
              "0    Abarth_124 Spider_2016              2016             8     115110.8   \n",
              "1    Abarth_124 Spider_2017              2017             7     104526.8   \n",
              "2    Abarth_124 Spider_2018              2018             6     104526.8   \n",
              "3    Abarth_124 Spider_2019              2019             5     115698.8   \n",
              "4           Abarth_595_2012              2012            12      68600.0   \n",
              "..                      ...               ...           ...          ...   \n",
              "495             BMW_Z4_2014              2014            10     107819.6   \n",
              "496             BMW_Z4_2015              2015             9     107819.6   \n",
              "497             BMW_Z4_2016              2016             8     107819.6   \n",
              "498             BMW_Z4_2017              2017             7     115463.6   \n",
              "499             BMW_Z4_2018              2018             6     115463.6   \n",
              "\n",
              "     Price_2024  \n",
              "0      88793.45  \n",
              "1      89096.70  \n",
              "2      89153.09  \n",
              "3      89047.44  \n",
              "4      49329.85  \n",
              "..          ...  \n",
              "495    67785.30  \n",
              "496    67925.74  \n",
              "497    68066.19  \n",
              "498    68090.06  \n",
              "499    68247.16  \n",
              "\n",
              "[500 rows x 5 columns]"
            ],
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Brand</th>\n",
              "      <th>Manifacture_Year</th>\n",
              "      <th>Car_Age_2024</th>\n",
              "      <th>Entry_price</th>\n",
              "      <th>Price_2024</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Abarth_124 Spider_2016</td>\n",
              "      <td>2016</td>\n",
              "      <td>8</td>\n",
              "      <td>115110.8</td>\n",
              "      <td>88793.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Abarth_124 Spider_2017</td>\n",
              "      <td>2017</td>\n",
              "      <td>7</td>\n",
              "      <td>104526.8</td>\n",
              "      <td>89096.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Abarth_124 Spider_2018</td>\n",
              "      <td>2018</td>\n",
              "      <td>6</td>\n",
              "      <td>104526.8</td>\n",
              "      <td>89153.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Abarth_124 Spider_2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>5</td>\n",
              "      <td>115698.8</td>\n",
              "      <td>89047.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Abarth_595_2012</td>\n",
              "      <td>2012</td>\n",
              "      <td>12</td>\n",
              "      <td>68600.0</td>\n",
              "      <td>49329.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>495</th>\n",
              "      <td>BMW_Z4_2014</td>\n",
              "      <td>2014</td>\n",
              "      <td>10</td>\n",
              "      <td>107819.6</td>\n",
              "      <td>67785.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>BMW_Z4_2015</td>\n",
              "      <td>2015</td>\n",
              "      <td>9</td>\n",
              "      <td>107819.6</td>\n",
              "      <td>67925.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>497</th>\n",
              "      <td>BMW_Z4_2016</td>\n",
              "      <td>2016</td>\n",
              "      <td>8</td>\n",
              "      <td>107819.6</td>\n",
              "      <td>68066.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>BMW_Z4_2017</td>\n",
              "      <td>2017</td>\n",
              "      <td>7</td>\n",
              "      <td>115463.6</td>\n",
              "      <td>68090.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>BMW_Z4_2018</td>\n",
              "      <td>2018</td>\n",
              "      <td>6</td>\n",
              "      <td>115463.6</td>\n",
              "      <td>68247.16</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 5 columns</p>\n",
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              "\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "final",
              "summary": "{\n  \"name\": \"final\",\n  \"rows\": 5981,\n  \"fields\": [\n    {\n      \"column\": \"Brand\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5981,\n        \"samples\": [\n          \"Proton_GEN-2_2010\",\n          \"Peugeot_RCZ_2012\",\n          \"Mercedes-Benz_B Class_2018\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Manifacture_Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 141,\n        \"min\": 1007,\n        \"max\": 5008,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          2007,\n          3008,\n          2006\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Car_Age_2024\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 141,\n        \"min\": -2984,\n        \"max\": 1017,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          17,\n          -984,\n          18\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Entry_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 152804.43077156105,\n        \"min\": 17636.08,\n        \"max\": 1254870.4,\n        \"num_unique_values\": 3262,\n        \"samples\": [\n          34515.6,\n          129144.4,\n          133044.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price_2024\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 127946.63737367703,\n        \"min\": -313316.53,\n        \"max\": 1246280.7,\n        \"num_unique_values\": 5965,\n        \"samples\": [\n          74801.36,\n          21672.39,\n          393412.26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "file_path = \"/content/drive/MyDrive/Price Prediction/Final_Price_Prediction.csv\"\n",
        "\n",
        "final.to_csv(file_path, index=True)\n",
        "\n",
        "print(\"DataFrame has been saved to:\", file_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x8-1ttlnIs0t",
        "outputId": "bbce7fbb-1df2-48d0-9bfb-84c4c814e201"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DataFrame has been saved to: /content/drive/MyDrive/Price Prediction/Final_Price_Prediction.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final.info()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GV_CD2sVLUVw",
        "outputId": "186c899b-ac7f-4423-a69e-754cfa162856"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5981 entries, 0 to 5980\n",
            "Data columns (total 5 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   Brand             5981 non-null   object \n",
            " 1   Manifacture_Year  5981 non-null   int64  \n",
            " 2   Car_Age_2024      5981 non-null   int64  \n",
            " 3   Entry_price       5981 non-null   float64\n",
            " 4   Price_2024        5981 non-null   float64\n",
            "dtypes: float64(2), int64(2), object(1)\n",
            "memory usage: 233.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_prices_by_brand(brand_name, data):\n",
        "    brand_info = brand_name.split('_')\n",
        "    brand = brand_info[0]\n",
        "    manufacture_year = int(brand_info[-1])\n",
        "\n",
        "    filtered_data = data[(data['Brand'].str.contains(brand)) & (data['Manifacture_Year'] == manufacture_year)]\n",
        "\n",
        "    if filtered_data.empty:\n",
        "        return \"No data found for the given brand and year.\"\n",
        "\n",
        "\n",
        "    entry_price = filtered_data['Entry_price'].iloc[0]\n",
        "    price_2024 = filtered_data['Price_2024'].iloc[0]\n",
        "\n",
        "    return entry_price, price_2024\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "D2apvha7Ln-h"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "brand = 'BMW_Z4_2017'\n",
        "entry_price, price_2024 = get_prices_by_brand(brand, final)\n",
        "\n",
        "print(\"Entry Price for {}: {} DT. \".format(brand, entry_price))\n",
        "print(\"Price in 2024 for {}: {} DT. \".format(brand, price_2024))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gtWUx4ppLn76",
        "outputId": "5f478a90-cde3-44de-a12e-7bbecd9b866d"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Entry Price for BMW_Z4_2017: 83123.6 DT. \n",
            "Price in 2024 for BMW_Z4_2017: 54518.96 DT. \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "brand = 'Porsche_911_2015'\n",
        "entry_price, price_2024 = get_prices_by_brand(brand, final)\n",
        "\n",
        "print(\"Entry Price for {}: {} DT. \".format(brand, entry_price))\n",
        "print(\"Price in 2024 for {}: {} DT. \".format(brand, price_2024))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4A9bFSRvLn4_",
        "outputId": "fb603288-db39-465b-cc28-c6d9f8126eef"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Entry Price for Porsche_911_2015: 163616.88 DT. \n",
            "Price in 2024 for Porsche_911_2015: 222254.45 DT. \n"
          ]
        }
      ]
    }
  ]
}