Second_hand_Car_sale.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n"
      ]
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      "source": [
        "data=pd.read_csv('/content/1.04.+Real-life+example.csv')\n",
        "data.head()"
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              "           Brand    Price       Body  Mileage  EngineV Engine Type  \\\n",
              "0            BMW   4200.0      sedan      277      2.0      Petrol   \n",
              "1  Mercedes-Benz   7900.0        van      427      2.9      Diesel   \n",
              "2  Mercedes-Benz  13300.0      sedan      358      5.0         Gas   \n",
              "3           Audi  23000.0  crossover      240      4.2      Petrol   \n",
              "4         Toyota  18300.0  crossover      120      2.0      Petrol   \n",
              "\n",
              "  Registration  Year         Model  \n",
              "0          yes  1991           320  \n",
              "1          yes  1999  Sprinter 212  \n",
              "2          yes  2003         S 500  \n",
              "3          yes  2007            Q7  \n",
              "4          yes  2011         Rav 4  "
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      "cell_type": "markdown",
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        "Since null values are less than 5% of total values, directly drop the null values"
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        "data=data.dropna()\n",
        "data.isnull().sum()"
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              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Brand</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Price</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Body</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mileage</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EngineV</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Engine Type</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Registration</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Year</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Model</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W0uokFQPmUYm",
        "outputId": "337a2efd-766d-48d3-86e0-fbdb04e2ccd7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4025, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe(include='all')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "0LnRgjTrmXHN",
        "outputId": "fbf386f8-19a7-4a61-92dd-d20195ab05eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Brand          Price   Body      Mileage      EngineV  \\\n",
              "count         4025    4025.000000   4025  4025.000000  4025.000000   \n",
              "unique           7            NaN      6          NaN          NaN   \n",
              "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
              "freq           880            NaN   1534          NaN          NaN   \n",
              "mean           NaN   19552.308065    NaN   163.572174     2.764586   \n",
              "std            NaN   25815.734988    NaN   103.394703     4.935941   \n",
              "min            NaN     600.000000    NaN     0.000000     0.600000   \n",
              "25%            NaN    6999.000000    NaN    90.000000     1.800000   \n",
              "50%            NaN   11500.000000    NaN   158.000000     2.200000   \n",
              "75%            NaN   21900.000000    NaN   230.000000     3.000000   \n",
              "max            NaN  300000.000000    NaN   980.000000    99.990000   \n",
              "\n",
              "       Engine Type Registration         Year    Model  \n",
              "count         4025         4025  4025.000000     4025  \n",
              "unique           4            2          NaN      306  \n",
              "top         Diesel          yes          NaN  E-Class  \n",
              "freq          1861         3654          NaN      188  \n",
              "mean           NaN          NaN  2006.379627      NaN  \n",
              "std            NaN          NaN     6.695595      NaN  \n",
              "min            NaN          NaN  1969.000000      NaN  \n",
              "25%            NaN          NaN  2003.000000      NaN  \n",
              "50%            NaN          NaN  2007.000000      NaN  \n",
              "75%            NaN          NaN  2012.000000      NaN  \n",
              "max            NaN          NaN  2016.000000      NaN  "
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 11,\n  \"fields\": [\n    {\n      \"column\": \"Brand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          7,\n          \"880\",\n          \"4025\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 101893.08692361704,\n        \"min\": 600.0,\n        \"max\": 300000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          19552.308064596276,\n          11500.0,\n          4025.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Body\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          6,\n          \"1534\",\n          \"4025\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mileage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1370.696925729628,\n        \"min\": 0.0,\n        \"max\": 4025.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          163.57217391304349,\n          158.0,\n          4025.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EngineV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1417.6399774845904,\n        \"min\": 0.6,\n        \"max\": 4025.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2.7645863354037266,\n          2.2,\n          4025.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Engine Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          \"1861\",\n          \"4025\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Registration\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          \"3654\",\n          \"4025\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1074.0506224762385,\n        \"min\": 6.695595297055871,\n        \"max\": 4025.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2006.3796273291925,\n          2007.0,\n          4025.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          306,\n          \"188\",\n          \"4025\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dealing with Outliers"
      ],
      "metadata": {
        "id": "Mc0QtA6Wmbrv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(data['Price'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "id": "OcqZnQLFmeKT",
        "outputId": "27bb92a5-716d-45aa-aa32-dd64c2d75b26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Price', ylabel='Density'>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "q=data['Price'].quantile(0.99)\n",
        "data_1=data[data['Price']<q]\n",
        "data_1.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LnYfbnGVml7J",
        "outputId": "53744b94-bf99-4826-f59a-32e49a60ea72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3984, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(data_1['Price'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "id": "gT550mH8m29N",
        "outputId": "45e0d289-7aba-484c-cfeb-75f92d7f30ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Price', ylabel='Density'>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(data['Mileage'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "YqfVJ9KmnC_n",
        "outputId": "839013c9-0f21-4a86-81e8-5d4d5a90439c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Mileage', ylabel='Density'>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "q=data_1['Mileage'].quantile(0.99)\n",
        "data_2=data_1[data_1['Mileage']<q]\n",
        "data_2.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wNHoFMtsnMb0",
        "outputId": "c19d5518-d0c0-469b-df4c-4ed29e091061"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3944, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(data_2['Mileage'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "CZ7V3QaZnVEq",
        "outputId": "257a44eb-64af-494a-86a2-bdd2b211fc4a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Mileage', ylabel='Density'>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(data['EngineV'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "XCoMeqnLneZW",
        "outputId": "0dc33d69-2343-4a64-e7bb-c0ec3bafd1e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='EngineV', ylabel='Density'>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_3=data_2[data_2['EngineV']<6.5]\n",
        "data_3.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H1eK7NsEnzE4",
        "outputId": "76307568-21fd-4dd1-cd42-70b73af754c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3921, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(data_3['EngineV'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "yWxwWBK8n7NW",
        "outputId": "26114527-dd46-410b-8c0f-91196a732926"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='EngineV', ylabel='Density'>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(data['Year'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "Wbn5eyVgoBil",
        "outputId": "45238472-8687-41f2-dad1-f442d4048499"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Year', ylabel='Density'>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "q=data_3['Year'].quantile(0.01)\n",
        "data_4=data_3[data_3['Year']>q]\n",
        "data_4.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5yruC0vUoeJF",
        "outputId": "c1670c70-05c3-418d-c6f9-35bbddc90061"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3867, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(data_4['Year'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "_RzRns-zoj0u",
        "outputId": "1dcc30b9-cfa0-47f6-96bd-17bc50ac5bfa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Year', ylabel='Density'>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_cleaned=data_4.reset_index(drop=True)\n",
        "data_cleaned.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "6LSDQsQGoppa",
        "outputId": "19d6fb0f-8a9f-4ba2-d512-902d38f2ea67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Brand    Price       Body  Mileage  EngineV Engine Type  \\\n",
              "0            BMW   4200.0      sedan      277      2.0      Petrol   \n",
              "1  Mercedes-Benz   7900.0        van      427      2.9      Diesel   \n",
              "2  Mercedes-Benz  13300.0      sedan      358      5.0         Gas   \n",
              "3           Audi  23000.0  crossover      240      4.2      Petrol   \n",
              "4         Toyota  18300.0  crossover      120      2.0      Petrol   \n",
              "\n",
              "  Registration  Year         Model  \n",
              "0          yes  1991           320  \n",
              "1          yes  1999  Sprinter 212  \n",
              "2          yes  2003         S 500  \n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data_cleaned",
              "summary": "{\n  \"name\": \"data_cleaned\",\n  \"rows\": 3867,\n  \"fields\": [\n    {\n      \"column\": \"Brand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"BMW\",\n          \"Mercedes-Benz\",\n          \"Volkswagen\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19085.855165281162,\n        \"min\": 800.0,\n        \"max\": 129222.0,\n        \"num_unique_values\": 849,\n        \"samples\": [\n          2299.0,\n          37000.0,\n          22000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Body\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"sedan\",\n          \"van\",\n          \"hatch\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mileage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 95,\n        \"min\": 0,\n        \"max\": 435,\n        \"num_unique_values\": 377,\n        \"samples\": [\n          340,\n          55,\n          92\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EngineV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9493663654095749,\n        \"min\": 0.6,\n        \"max\": 6.3,\n        \"num_unique_values\": 55,\n        \"samples\": [\n          4.5,\n          1.5,\n          1.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Engine Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Diesel\",\n          \"Other\",\n          \"Petrol\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Registration\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 1988,\n        \"max\": 2016,\n        \"num_unique_values\": 29,\n        \"samples\": [\n          1997,\n          1996\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 291,\n        \"samples\": [\n          \"A4 Allroad\",\n          \"ML 400\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_cleaned.drop(['Model'],axis=1,inplace=True)\n",
        "data_cleaned.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Olp9qHhcpAHI",
        "outputId": "a4bfd86d-83de-4242-fa08-16474684577f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Brand    Price       Body  Mileage  EngineV Engine Type  \\\n",
              "0            BMW   4200.0      sedan      277      2.0      Petrol   \n",
              "1  Mercedes-Benz   7900.0        van      427      2.9      Diesel   \n",
              "2  Mercedes-Benz  13300.0      sedan      358      5.0         Gas   \n",
              "3           Audi  23000.0  crossover      240      4.2      Petrol   \n",
              "4         Toyota  18300.0  crossover      120      2.0      Petrol   \n",
              "\n",
              "  Registration  Year  \n",
              "0          yes  1991  \n",
              "1          yes  1999  \n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "        document.querySelector('#df-8912ac19-cb61-4a65-b031-6ac54b3ee7b6 button.colab-df-convert');\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8912ac19-cb61-4a65-b031-6ac54b3ee7b6');\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",
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              "\n",
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              "    border: none;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    border-color: transparent;\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\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",
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              "    (() => {\n",
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              "        document.querySelector('#df-aa83331b-cc3d-4548-9996-7fc811e9e0c8 button');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data_cleaned",
              "summary": "{\n  \"name\": \"data_cleaned\",\n  \"rows\": 3867,\n  \"fields\": [\n    {\n      \"column\": \"Brand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"BMW\",\n          \"Mercedes-Benz\",\n          \"Volkswagen\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19085.855165281162,\n        \"min\": 800.0,\n        \"max\": 129222.0,\n        \"num_unique_values\": 849,\n        \"samples\": [\n          2299.0,\n          37000.0,\n          22000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Body\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"sedan\",\n          \"van\",\n          \"hatch\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mileage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 95,\n        \"min\": 0,\n        \"max\": 435,\n        \"num_unique_values\": 377,\n        \"samples\": [\n          340,\n          55,\n          92\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EngineV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9493663654095749,\n        \"min\": 0.6,\n        \"max\": 6.3,\n        \"num_unique_values\": 55,\n        \"samples\": [\n          4.5,\n          1.5,\n          1.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Engine Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Diesel\",\n          \"Other\",\n          \"Petrol\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Registration\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 1988,\n        \"max\": 2016,\n        \"num_unique_values\": 29,\n        \"samples\": [\n          1997,\n          1996\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_cleaned.describe(include='all')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "GcHn4UNxo1hE",
        "outputId": "93349fb1-e0cf-4d43-ec29-df39ab07b01b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Brand          Price   Body      Mileage      EngineV  \\\n",
              "count         3867    3867.000000   3867  3867.000000  3867.000000   \n",
              "unique           7            NaN      6          NaN          NaN   \n",
              "top     Volkswagen            NaN  sedan          NaN          NaN   \n",
              "freq           848            NaN   1467          NaN          NaN   \n",
              "mean           NaN   18194.455679    NaN   160.542539     2.450440   \n",
              "std            NaN   19085.855165    NaN    95.633291     0.949366   \n",
              "min            NaN     800.000000    NaN     0.000000     0.600000   \n",
              "25%            NaN    7200.000000    NaN    91.000000     1.800000   \n",
              "50%            NaN   11700.000000    NaN   157.000000     2.200000   \n",
              "75%            NaN   21700.000000    NaN   225.000000     3.000000   \n",
              "max            NaN  129222.000000    NaN   435.000000     6.300000   \n",
              "\n",
              "       Engine Type Registration         Year  \n",
              "count         3867         3867  3867.000000  \n",
              "unique           4            2          NaN  \n",
              "top         Diesel          yes          NaN  \n",
              "freq          1807         3505          NaN  \n",
              "mean           NaN          NaN  2006.709853  \n",
              "std            NaN          NaN     6.103870  \n",
              "min            NaN          NaN  1988.000000  \n",
              "25%            NaN          NaN  2003.000000  \n",
              "50%            NaN          NaN  2008.000000  \n",
              "75%            NaN          NaN  2012.000000  \n",
              "max            NaN          NaN  2016.000000  "
            ],
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              "    <tr>\n",
              "      <th>count</th>\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-a93583a5-ea47-470c-b6f5-78145d8a9438');\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-10a337fa-d5f6-4ae0-8766-8ac178a8ea30\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-10a337fa-d5f6-4ae0-8766-8ac178a8ea30')\"\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-10a337fa-d5f6-4ae0-8766-8ac178a8ea30 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",
              "summary": "{\n  \"name\": \"data_cleaned\",\n  \"rows\": 11,\n  \"fields\": [\n    {\n      \"column\": \"Brand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          7,\n          \"848\",\n          \"3867\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 42195.23827386117,\n        \"min\": 800.0,\n        \"max\": 129222.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          18194.455678820792,\n          11700.0,\n          3867.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Body\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          6,\n          \"1467\",\n          \"3867\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mileage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1314.604631175874,\n        \"min\": 0.0,\n        \"max\": 3867.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          160.54253943625548,\n          157.0,\n          3867.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EngineV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1366.318301814153,\n        \"min\": 0.6,\n        \"max\": 3867.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2.4504396172743723,\n          2.2,\n          3867.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Engine Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          \"1807\",\n          \"3867\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Registration\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          \"3505\",\n          \"3867\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1032.395780865556,\n        \"min\": 6.103870156611222,\n        \"max\": 3867.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2006.7098525989138,\n          2008.0,\n          3867.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Checking the OLS assumptions**"
      ],
      "metadata": {
        "id": "3okHFxYBpHOJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "f,(ax1,ax2,ax3)=plt.subplots(1,3,sharey=True,figsize=(15,3))\n",
        "ax1.scatter(data_cleaned['Year'],data_cleaned['Price'])\n",
        "ax1.set_title('Price and Year')\n",
        "ax2.scatter(data_cleaned['EngineV'],data_cleaned['Price'])\n",
        "ax2.set_title('Price and EngineV')\n",
        "ax3.scatter(data_cleaned['Mileage'],data_cleaned['Price'])\n",
        "ax3.set_title('Price and Mileage')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "id": "PF-LxU66pGCK",
        "outputId": "f3143f2b-b74e-4ab6-877a-d989ec24d72e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x300 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "log_price=np.log(data_cleaned['Price'])\n",
        "data_cleaned['log_price']=log_price\n",
        "data_cleaned"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "C-Lay2lVpyIE",
        "outputId": "532edcf5-2df3-4b85-8711-ce8d0b7ed17f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Brand     Price       Body  Mileage  EngineV Engine Type  \\\n",
              "0               BMW    4200.0      sedan      277      2.0      Petrol   \n",
              "1     Mercedes-Benz    7900.0        van      427      2.9      Diesel   \n",
              "2     Mercedes-Benz   13300.0      sedan      358      5.0         Gas   \n",
              "3              Audi   23000.0  crossover      240      4.2      Petrol   \n",
              "4            Toyota   18300.0  crossover      120      2.0      Petrol   \n",
              "...             ...       ...        ...      ...      ...         ...   \n",
              "3862     Volkswagen   11500.0        van      163      2.5      Diesel   \n",
              "3863         Toyota   17900.0      sedan       35      1.6      Petrol   \n",
              "3864  Mercedes-Benz  125000.0      sedan        9      3.0      Diesel   \n",
              "3865            BMW    6500.0      sedan        1      3.5      Petrol   \n",
              "3866     Volkswagen   13500.0        van      124      2.0      Diesel   \n",
              "\n",
              "     Registration  Year  log_price  \n",
              "0             yes  1991   8.342840  \n",
              "1             yes  1999   8.974618  \n",
              "2             yes  2003   9.495519  \n",
              "3             yes  2007  10.043249  \n",
              "4             yes  2011   9.814656  \n",
              "...           ...   ...        ...  \n",
              "3862          yes  2008   9.350102  \n",
              "3863          yes  2014   9.792556  \n",
              "3864          yes  2014  11.736069  \n",
              "3865          yes  1999   8.779557  \n",
              "3866          yes  2013   9.510445  \n",
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              "[3867 rows x 9 columns]"
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              "      <td>10.043249</td>\n",
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              "      <td>Toyota</td>\n",
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              "      <td>2011</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <th>3862</th>\n",
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              "      <td>9.350102</td>\n",
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              "      <th>3865</th>\n",
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              "      <td>6500.0</td>\n",
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              "      <td>8.779557</td>\n",
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              "      <th>3866</th>\n",
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              "      <td>13500.0</td>\n",
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              "      <td>2013</td>\n",
              "      <td>9.510445</td>\n",
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              "<p>3867 rows × 9 columns</p>\n",
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              "type": "dataframe",
              "variable_name": "data_cleaned",
              "summary": "{\n  \"name\": \"data_cleaned\",\n  \"rows\": 3867,\n  \"fields\": [\n    {\n      \"column\": \"Brand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"BMW\",\n          \"Mercedes-Benz\",\n          \"Volkswagen\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19085.855165281162,\n        \"min\": 800.0,\n        \"max\": 129222.0,\n        \"num_unique_values\": 849,\n        \"samples\": [\n          2299.0,\n          37000.0,\n          22000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Body\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"sedan\",\n          \"van\",\n          \"hatch\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mileage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 95,\n        \"min\": 0,\n        \"max\": 435,\n        \"num_unique_values\": 377,\n        \"samples\": [\n          340,\n          55,\n          92\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EngineV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9493663654095749,\n        \"min\": 0.6,\n        \"max\": 6.3,\n        \"num_unique_values\": 55,\n        \"samples\": [\n          4.5,\n          1.5,\n          1.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Engine Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Diesel\",\n          \"Other\",\n          \"Petrol\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Registration\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 1988,\n        \"max\": 2016,\n        \"num_unique_values\": 29,\n        \"samples\": [\n          1997,\n          1996\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"log_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8766156343766975,\n        \"min\": 6.684611727667927,\n        \"max\": 11.769287134472648,\n        \"num_unique_values\": 849,\n        \"samples\": [\n          7.740229524763182,\n          10.51867319162636\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f,(ax1,ax2,ax3)=plt.subplots(1,3,sharey=True,figsize=(15,3))\n",
        "ax1.scatter(data_cleaned['Year'],data_cleaned['log_price'])\n",
        "ax1.set_title('log_price and Year')\n",
        "ax2.scatter(data_cleaned['EngineV'],data_cleaned['log_price'])\n",
        "ax2.set_title('log_price and EngineV')\n",
        "ax3.scatter(data_cleaned['Mileage'],data_cleaned['log_price'])\n",
        "ax3.set_title('log_price and Mileage')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227
        },
        "id": "EFgARZ5pp_ha",
        "outputId": "31d184f0-d527-47ad-ceef-5fb55afeedc2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x300 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_cleaned=data_cleaned.drop(['Price'],axis=1)"
      ],
      "metadata": {
        "id": "_OaDv-PuqPCg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Multicollinearity**"
      ],
      "metadata": {
        "id": "_wDQ4p1eqq52"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_cleaned.columns.values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7SyhkuswqvZY",
        "outputId": "0e09bae8-d443-4445-bbd0-c73023172589"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Brand', 'Body', 'Mileage', 'EngineV', 'Engine Type',\n",
              "       'Registration', 'Year', 'log_price'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
        "variables=data_cleaned[['Mileage','Year','EngineV']]\n",
        "vif=pd.DataFrame()\n",
        "vif['VIF']=[variance_inflation_factor(variables.values,i) for i in range(variables.shape[1])]\n",
        "vif['Features']=variables.columns\n",
        "vif"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "SfVEzHjYq3TJ",
        "outputId": "9269a252-b8af-424a-fb39-e2a1c8795b27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         VIF Features\n",
              "0   3.791584  Mileage\n",
              "1  10.354854     Year\n",
              "2   7.662068  EngineV"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4f1481f0-a740-4609-ad43-22eab820a0d1\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "      <th>0</th>\n",
              "      <td>3.791584</td>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "vif",
              "summary": "{\n  \"name\": \"vif\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"VIF\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.299198647161143,\n        \"min\": 3.7915836908224834,\n        \"max\": 10.35485444268896,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3.7915836908224834,\n          10.35485444268896,\n          7.662067874651502\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Features\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Mileage\",\n          \"Year\",\n          \"EngineV\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_no_multicollinearity=data_cleaned.drop(['Year'],axis=1)"
      ],
      "metadata": {
        "id": "ot-SD3JhrRBs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Create dummy variables**\n",
        "many variables together\n"
      ],
      "metadata": {
        "id": "tSTtsN6CrZRq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_with_dummies=pd.get_dummies(data_no_multicollinearity,drop_first=True)\n",
        "data_with_dummies.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "id": "uLV8ff7-rfbn",
        "outputId": "e8d8e386-bc78-4d42-a167-6bf050ec6197"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Mileage  EngineV  log_price  Brand_BMW  Brand_Mercedes-Benz  \\\n",
              "0      277      2.0   8.342840       True                False   \n",
              "1      427      2.9   8.974618      False                 True   \n",
              "2      358      5.0   9.495519      False                 True   \n",
              "3      240      4.2  10.043249      False                False   \n",
              "4      120      2.0   9.814656      False                False   \n",
              "\n",
              "   Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  \\\n",
              "0             False          False         False             False   \n",
              "1             False          False         False             False   \n",
              "2             False          False         False             False   \n",
              "3             False          False         False             False   \n",
              "4             False          False          True             False   \n",
              "\n",
              "   Body_hatch  Body_other  Body_sedan  Body_vagon  Body_van  Engine Type_Gas  \\\n",
              "0       False       False        True       False     False            False   \n",
              "1       False       False       False       False      True            False   \n",
              "2       False       False        True       False     False             True   \n",
              "3       False       False       False       False     False            False   \n",
              "4       False       False       False       False     False            False   \n",
              "\n",
              "   Engine Type_Other  Engine Type_Petrol  Registration_yes  \n",
              "0              False                True              True  \n",
              "1              False               False              True  \n",
              "2              False               False              True  \n",
              "3              False                True              True  \n",
              "4              False                True              True  "
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data_with_dummies",
              "summary": "{\n  \"name\": \"data_with_dummies\",\n  \"rows\": 3867,\n  \"fields\": [\n    {\n      \"column\": \"Mileage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 95,\n        \"min\": 0,\n        \"max\": 435,\n        \"num_unique_values\": 377,\n        \"samples\": [\n          340,\n          55,\n          92\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EngineV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9493663654095749,\n        \"min\": 0.6,\n        \"max\": 6.3,\n        \"num_unique_values\": 55,\n        \"samples\": [\n          4.5,\n          1.5,\n          1.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"log_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8766156343766975,\n        \"min\": 6.684611727667927,\n        \"max\": 11.769287134472648,\n        \"num_unique_values\": 849,\n        \"samples\": [\n          7.740229524763182,\n          10.51867319162636,\n          9.998797732340453\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Brand_BMW\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Brand_Mercedes-Benz\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Brand_Mitsubishi\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Brand_Renault\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Brand_Toyota\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Brand_Volkswagen\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Body_hatch\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Body_other\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Body_sedan\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Body_vagon\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Body_van\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Engine Type_Gas\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Engine Type_Other\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Engine Type_Petrol\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Registration_yes\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_with_dummies = data_with_dummies.replace({True: 1, False: 0})\n"
      ],
      "metadata": {
        "id": "ZWUYkN8GsCMq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_with_dummies.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "id": "ZAyjeDrfsOY0",
        "outputId": "9d43b3ec-620a-4937-af81-f38dc9d4bd77"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Mileage  EngineV  log_price  Brand_BMW  Brand_Mercedes-Benz  \\\n",
              "0      277      2.0   8.342840          1                    0   \n",
              "1      427      2.9   8.974618          0                    1   \n",
              "2      358      5.0   9.495519          0                    1   \n",
              "3      240      4.2  10.043249          0                    0   \n",
              "4      120      2.0   9.814656          0                    0   \n",
              "\n",
              "   Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  \\\n",
              "0                 0              0             0                 0   \n",
              "1                 0              0             0                 0   \n",
              "2                 0              0             0                 0   \n",
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              "4                 0              0             1                 0   \n",
              "\n",
              "   Body_hatch  Body_other  Body_sedan  Body_vagon  Body_van  Engine Type_Gas  \\\n",
              "0           0           0           1           0         0                0   \n",
              "1           0           0           0           0         1                0   \n",
              "2           0           0           1           0         0                1   \n",
              "3           0           0           0           0         0                0   \n",
              "4           0           0           0           0         0                0   \n",
              "\n",
              "   Engine Type_Other  Engine Type_Petrol  Registration_yes  \n",
              "0                  0                   1                 1  \n",
              "1                  0                   0                 1  \n",
              "2                  0                   0                 1  \n",
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              "4                  0                   1                 1  "
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "            'suggestCharts', [key], {});\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data_with_dummies",
              "summary": "{\n  \"name\": \"data_with_dummies\",\n  \"rows\": 3867,\n  \"fields\": [\n    {\n      \"column\": \"Mileage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 95,\n        \"min\": 0,\n        \"max\": 435,\n        \"num_unique_values\": 377,\n        \"samples\": [\n          340,\n          55,\n          92\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EngineV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9493663654095749,\n        \"min\": 0.6,\n        \"max\": 6.3,\n        \"num_unique_values\": 55,\n        \"samples\": [\n          4.5,\n          1.5,\n          1.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"log_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8766156343766975,\n        \"min\": 6.684611727667927,\n        \"max\": 11.769287134472648,\n        \"num_unique_values\": 849,\n        \"samples\": [\n          7.740229524763182,\n          10.51867319162636,\n          9.998797732340453\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Brand_BMW\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Brand_Mercedes-Benz\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Brand_Mitsubishi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Brand_Renault\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Brand_Toyota\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Brand_Volkswagen\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Body_hatch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Body_other\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Body_sedan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Body_vagon\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Body_van\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Engine Type_Gas\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Engine Type_Other\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Engine Type_Petrol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Registration_yes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_with_dummies.columns.values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nnkKW1fUrd9V",
        "outputId": "6886f404-d737-42ba-b07b-c1e11c0667ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Mileage', 'EngineV', 'log_price', 'Brand_BMW',\n",
              "       'Brand_Mercedes-Benz', 'Brand_Mitsubishi', 'Brand_Renault',\n",
              "       'Brand_Toyota', 'Brand_Volkswagen', 'Body_hatch', 'Body_other',\n",
              "       'Body_sedan', 'Body_vagon', 'Body_van', 'Engine Type_Gas',\n",
              "       'Engine Type_Other', 'Engine Type_Petrol', 'Registration_yes'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cols=['log_price', 'Mileage', 'EngineV', 'Brand_BMW',\n",
        "       'Brand_Mercedes-Benz', 'Brand_Mitsubishi', 'Brand_Renault',\n",
        "       'Brand_Toyota', 'Brand_Volkswagen','Body_hatch', 'Body_other',\n",
        "       'Body_sedan', 'Body_vagon', 'Body_van', 'Engine Type_Gas',\n",
        "       'Engine Type_Other', 'Engine Type_Petrol', 'Registration_yes']\n"
      ],
      "metadata": {
        "id": "lBFPehqHrvn9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_preprocessed=data_with_dummies[cols]\n",
        "data_preprocessed.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "id": "qJhgMRo9sDWx",
        "outputId": "d805b3fe-9e21-4dee-f6d5-56305d212be3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   log_price  Mileage  EngineV  Brand_BMW  Brand_Mercedes-Benz  \\\n",
              "0   8.342840      277      2.0          1                    0   \n",
              "1   8.974618      427      2.9          0                    1   \n",
              "2   9.495519      358      5.0          0                    1   \n",
              "3  10.043249      240      4.2          0                    0   \n",
              "4   9.814656      120      2.0          0                    0   \n",
              "\n",
              "   Brand_Mitsubishi  Brand_Renault  Brand_Toyota  Brand_Volkswagen  \\\n",
              "0                 0              0             0                 0   \n",
              "1                 0              0             0                 0   \n",
              "2                 0              0             0                 0   \n",
              "3                 0              0             0                 0   \n",
              "4                 0              0             1                 0   \n",
              "\n",
              "   Body_hatch  Body_other  Body_sedan  Body_vagon  Body_van  Engine Type_Gas  \\\n",
              "0           0           0           1           0         0                0   \n",
              "1           0           0           0           0         1                0   \n",
              "2           0           0           1           0         0                1   \n",
              "3           0           0           0           0         0                0   \n",
              "4           0           0           0           0         0                0   \n",
              "\n",
              "   Engine Type_Other  Engine Type_Petrol  Registration_yes  \n",
              "0                  0                   1                 1  \n",
              "1                  0                   0                 1  \n",
              "2                  0                   0                 1  \n",
              "3                  0                   1                 1  \n",
              "4                  0                   1                 1  "
            ],
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9.495519</td>\n",
              "      <td>358</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10.043249</td>\n",
              "      <td>240</td>\n",
              "      <td>4.2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9.814656</td>\n",
              "      <td>120</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</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-edbceb59-e5d3-438f-937a-33de8c0a31e2')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <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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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-edbceb59-e5d3-438f-937a-33de8c0a31e2 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-edbceb59-e5d3-438f-937a-33de8c0a31e2');\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-6bc8567b-a9df-4d7a-bc77-57bd56a7af86\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6bc8567b-a9df-4d7a-bc77-57bd56a7af86')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "  </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",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    display: none;\n",
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              "    height: 32px;\n",
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              "    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",
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              "    20% {\n",
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              "    30% {\n",
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              "      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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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    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-6bc8567b-a9df-4d7a-bc77-57bd56a7af86 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": "data_preprocessed",
              "summary": "{\n  \"name\": \"data_preprocessed\",\n  \"rows\": 3867,\n  \"fields\": [\n    {\n      \"column\": \"log_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8766156343766975,\n        \"min\": 6.684611727667927,\n        \"max\": 11.769287134472648,\n        \"num_unique_values\": 849,\n        \"samples\": [\n          7.740229524763182,\n          10.51867319162636,\n          9.998797732340453\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mileage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 95,\n        \"min\": 0,\n        \"max\": 435,\n        \"num_unique_values\": 377,\n        \"samples\": [\n          340,\n          55,\n          92\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EngineV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9493663654095749,\n        \"min\": 0.6,\n        \"max\": 6.3,\n        \"num_unique_values\": 55,\n        \"samples\": [\n          4.5,\n          1.5,\n          1.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Brand_BMW\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Brand_Mercedes-Benz\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Brand_Mitsubishi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Brand_Renault\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Brand_Toyota\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Brand_Volkswagen\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Body_hatch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Body_other\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Body_sedan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Body_vagon\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Body_van\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Engine Type_Gas\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Engine Type_Other\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Engine Type_Petrol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Registration_yes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Linear regression model\n",
        "Declare the inputs and the targets"
      ],
      "metadata": {
        "id": "iBYH5U1cqwoL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "targets=data_preprocessed['log_price']\n",
        "inputs=data_preprocessed.drop(['log_price'],axis=1)"
      ],
      "metadata": {
        "id": "nU7VxiTMquLY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Scale the data"
      ],
      "metadata": {
        "id": "SB2AJdQbsjao"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler=StandardScaler()\n",
        "scaler.fit(inputs)\n",
        "inputs_scaled=scaler.transform(inputs)"
      ],
      "metadata": {
        "id": "YUD5XkWrsmAH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inputs_scaled=scaler.transform(inputs)"
      ],
      "metadata": {
        "id": "hUjUGv25sv5m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train Test split"
      ],
      "metadata": {
        "id": "1YUMaVmztACl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test=train_test_split(inputs_scaled,targets,test_size=0.2,random_state=365)"
      ],
      "metadata": {
        "id": "mVBa4w9ftDMZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create the regression"
      ],
      "metadata": {
        "id": "mgCiuJrstVQo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression"
      ],
      "metadata": {
        "id": "CjjGzgyythNV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reg=LinearRegression()\n",
        "reg.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "emY6I_LmtU4N",
        "outputId": "1e9b81ec-4fd5-40b8-fd42-56b47bc5f85b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_hat= reg.predict(x_train)"
      ],
      "metadata": {
        "id": "3eVDHCYetsCj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(y_train,y_hat)\n",
        "plt.xlabel('Targets(y_train)',size=15)\n",
        "plt.ylabel('Predictions(y_hat)',size=15)\n",
        "plt.xlim(6,13)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "SRtMqb3Rt2Gc",
        "outputId": "76240e6b-10f6-4c20-8d44-7235a0edfc1f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6.0, 13.0)"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(y_train-y_hat)\n",
        "plt.title('Residuals PDF',size=18)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 494
        },
        "id": "eeM2alJnxCIQ",
        "outputId": "a7b94434-f35d-4a4b-f098-def21d7ea761"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Residuals PDF')"
            ]
          },
          "metadata": {},
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reg.score(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JadGe9_OxZi0",
        "outputId": "92daac7d-5bc4-4fb3-f694-915cba654f3a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.744996578792662"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finding the weights and bias"
      ],
      "metadata": {
        "id": "COhPHax2xoje"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "reg.intercept_"
      ],
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        },
        "id": "n9M1pMlFxw9e",
        "outputId": "c1cb860d-8c39-4f81-ffd7-2194683f81b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9.415239458021299"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reg.coef_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3q6gekOLx1Ir",
        "outputId": "d2ee24a0-f7ca-4569-89c3-741fb4e754f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-0.44871341,  0.20903483,  0.0142496 ,  0.01288174, -0.14055166,\n",
              "       -0.17990912, -0.06054988, -0.08992433, -0.1454692 , -0.10144383,\n",
              "       -0.20062984, -0.12988747, -0.16859669, -0.12149035, -0.03336798,\n",
              "       -0.14690868,  0.32047333])"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Weights interpretation\n",
        "A positive weight shows that as a feature increases in value, so the log_price and Price respectively. Bigger the Engine Volume  higher the **price**"
      ],
      "metadata": {
        "id": "FE9UNlroygBq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "reg.summary=pd.DataFrame(inputs.columns.values,columns=['Features'])\n",
        "reg.summary['Weights']=reg.coef_\n",
        "reg.summary"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "zJlBkAMNx7VN",
        "outputId": "0801202d-b354-43e2-9eed-865235d00b6e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Features   Weights\n",
              "0               Mileage -0.448713\n",
              "1               EngineV  0.209035\n",
              "2             Brand_BMW  0.014250\n",
              "3   Brand_Mercedes-Benz  0.012882\n",
              "4      Brand_Mitsubishi -0.140552\n",
              "5         Brand_Renault -0.179909\n",
              "6          Brand_Toyota -0.060550\n",
              "7      Brand_Volkswagen -0.089924\n",
              "8            Body_hatch -0.145469\n",
              "9            Body_other -0.101444\n",
              "10           Body_sedan -0.200630\n",
              "11           Body_vagon -0.129887\n",
              "12             Body_van -0.168597\n",
              "13      Engine Type_Gas -0.121490\n",
              "14    Engine Type_Other -0.033368\n",
              "15   Engine Type_Petrol -0.146909\n",
              "16     Registration_yes  0.320473"
            ],
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>EngineV</td>\n",
              "      <td>0.209035</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Brand_Mercedes-Benz</td>\n",
              "      <td>0.012882</td>\n",
              "    </tr>\n",
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              "      <th>4</th>\n",
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              "    </tr>\n",
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              "      <th>6</th>\n",
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              "      <td>-0.060550</td>\n",
              "    </tr>\n",
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              "      <th>7</th>\n",
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              "      <th>8</th>\n",
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              "      <th>9</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
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              "      <td>-0.200630</td>\n",
              "    </tr>\n",
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              "      <th>11</th>\n",
              "      <td>Body_vagon</td>\n",
              "      <td>-0.129887</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Body_van</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Engine Type_Gas</td>\n",
              "      <td>-0.121490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Engine Type_Other</td>\n",
              "      <td>-0.033368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Engine Type_Petrol</td>\n",
              "      <td>-0.146909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Registration_yes</td>\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"reg\",\n  \"rows\": 17,\n  \"fields\": [\n    {\n      \"column\": \"Features\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 17,\n        \"samples\": [\n          \"Mileage\",\n          \"EngineV\",\n          \"Brand_Renault\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weights\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.16758606508444693,\n        \"min\": -0.4487134056868019,\n        \"max\": 0.32047333181473925,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          -0.4487134056868019,\n          0.20903482814089283,\n          -0.1799091226799408\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_cleaned['Brand'].unique()"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "S9cvV5_OzVRn",
        "outputId": "dbd4ee6b-d341-49b2-96dc-8fe2ae55ab65"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['BMW', 'Mercedes-Benz', 'Audi', 'Toyota', 'Renault', 'Volkswagen',\n",
              "       'Mitsubishi'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
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      "cell_type": "markdown",
      "source": [
        "Testing"
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      "metadata": {
        "id": "vj86HN9h2FZO"
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      "source": [
        "y_hat_test=reg.predict(x_test)"
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      "metadata": {
        "id": "m8Ues0Xt2DAk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(y_test,y_hat_test,alpha=0.2)\n",
        "plt.xlabel('Targets(y_test)',size=15)\n",
        "plt.ylabel('Predictions(y_hat_test)',size=15)\n",
        "plt.xlim(6,13)\n",
        "plt.ylim(6,13)\n",
        "plt.show()"
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        "id": "3wTofww32owX",
        "outputId": "9eb2453c-858a-4fd1-bec1-36fee31a3297"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
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            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_pf=pd.DataFrame(np.exp(y_hat_test),columns=['Predictions'])\n",
        "df_pf.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "2eKxEp-h4laL",
        "outputId": "a04965cc-815c-4cf6-adef-928cfaf3fbbd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Predictions\n",
              "0  10685.501696\n",
              "1   3499.255242\n",
              "2   7553.285218\n",
              "3   7463.963017\n",
              "4  11353.490075"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-40dccd76-f2c0-4724-b010-0d845ac68c6b\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Predictions</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10685.501696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3499.255242</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7553.285218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7463.963017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11353.490075</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-40dccd76-f2c0-4724-b010-0d845ac68c6b')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-40dccd76-f2c0-4724-b010-0d845ac68c6b 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-40dccd76-f2c0-4724-b010-0d845ac68c6b');\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-53682d10-d108-440a-aea1-bc216c219484\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-53682d10-d108-440a-aea1-bc216c219484')\"\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-53682d10-d108-440a-aea1-bc216c219484 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": "df_pf",
              "summary": "{\n  \"name\": \"df_pf\",\n  \"rows\": 774,\n  \"fields\": [\n    {\n      \"column\": \"Predictions\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13133.197603974588,\n        \"min\": 1320.5627675088342,\n        \"max\": 77403.05522420812,\n        \"num_unique_values\": 749,\n        \"samples\": [\n          25576.575707904965,\n          4524.770205947631,\n          12096.18269054933\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_pf['Targets']=np.exp(y_test)\n",
        "df_pf.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "e_hX1YVC40uo",
        "outputId": "d93e3f2e-7045-48de-bbdd-3cd6248e7c36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Predictions  Targets\n",
              "0  10685.501696      NaN\n",
              "1   3499.255242   7900.0\n",
              "2   7553.285218      NaN\n",
              "3   7463.963017      NaN\n",
              "4  11353.490075      NaN"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bb52f0ad-6ceb-4da0-a4d7-e859bafef392\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Predictions</th>\n",
              "      <th>Targets</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>10685.501696</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3499.255242</td>\n",
              "      <td>7900.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7553.285218</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7463.963017</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11353.490075</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      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",
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              "\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-bb52f0ad-6ceb-4da0-a4d7-e859bafef392 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-bb52f0ad-6ceb-4da0-a4d7-e859bafef392');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
              "<div id=\"df-c3d11a68-d5b3-405a-9a86-5af8dc381e68\">\n",
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              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <g>\n",
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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",
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              "    90% {\n",
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              "      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",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "        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": "df_pf",
              "summary": "{\n  \"name\": \"df_pf\",\n  \"rows\": 774,\n  \"fields\": [\n    {\n      \"column\": \"Predictions\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13133.197603974588,\n        \"min\": 1320.5627675088342,\n        \"max\": 77403.05522420812,\n        \"num_unique_values\": 749,\n        \"samples\": [\n          25576.575707904965,\n          4524.770205947631,\n          12096.18269054933\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Targets\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14242.311606273874,\n        \"min\": 1399.9999999999998,\n        \"max\": 109999.00000000007,\n        \"num_unique_values\": 116,\n        \"samples\": [\n          18999.000000000004,\n          3499.9999999999995,\n          34900.00000000001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test=y_test.reset_index(drop=True)\n",
        "y_test.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "v2dkViqA4_4v",
        "outputId": "4beb244e-a847-4ddb-dd7f-c15d0c4a2edd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    7.740664\n",
              "1    7.937375\n",
              "2    7.824046\n",
              "3    8.764053\n",
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    {
      "cell_type": "code",
      "source": [
        "df_pf['Target']=np.exp(y_test)\n",
        "df_pf"
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          "base_uri": "https://localhost:8080/",
          "height": 423
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        "id": "P-zuGeMR5Ymu",
        "outputId": "330c448e-3852-4691-effa-57241ea64146"
      },
      "execution_count": null,
      "outputs": [
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          "data": {
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              "      Predictions  Targets   Target\n",
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              "        document.querySelector('#df-88e0c0b9-39d2-482e-9a05-f8a48a3bdaef button.colab-df-convert');\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    60% {\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
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              "\n",
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              "        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-40318dce-460e-44df-8001-f978d04ba3a3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate: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",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_pf')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "      (() => {\n",
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              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_pf');\n",
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          "metadata": {},
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        "df_pf=df_pf.drop(['Targets'],axis=1)\n",
        "df_pf"
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      "execution_count": null,
      "outputs": [
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            "text/plain": [
              "      Predictions   Target\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_pf",
              "summary": "{\n  \"name\": \"df_pf\",\n  \"rows\": 774,\n  \"fields\": [\n    {\n      \"column\": \"Predictions\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13133.197603974588,\n        \"min\": 1320.5627675088342,\n        \"max\": 77403.05522420812,\n        \"num_unique_values\": 749,\n        \"samples\": [\n          25576.575707904965,\n          4524.770205947631,\n          12096.18269054933\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19967.85890767613,\n        \"min\": 1200.0,\n        \"max\": 126000.00000000007,\n        \"num_unique_values\": 382,\n        \"samples\": [\n          18300.000000000015,\n          3998.9999999999995,\n          32999.99999999998\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          },
          "metadata": {},
          "execution_count": 56
        }
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    },
    {
      "cell_type": "code",
      "source": [
        "df_pf['Residual']=df_pf['Target']-df_pf['Predictions']\n",
        "df_pf"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 423
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        "id": "YaP8sUeJ6Fas",
        "outputId": "3fccd93e-f4f1-42bd-93ce-19e24f7103b7"
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      "execution_count": null,
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        {
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          "data": {
            "text/plain": [
              "      Predictions   Target      Residual\n",
              "0    10685.501696   2300.0  -8385.501696\n",
              "1     3499.255242   2800.0   -699.255242\n",
              "2     7553.285218   2500.0  -5053.285218\n",
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              "..            ...      ...           ...\n",
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              "773  13491.163043  10800.0  -2691.163043\n",
              "\n",
              "[774 rows x 3 columns]"
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7553.285218</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>-5053.285218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7463.963017</td>\n",
              "      <td>6400.0</td>\n",
              "      <td>-1063.963017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11353.490075</td>\n",
              "      <td>9150.0</td>\n",
              "      <td>-2203.490075</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>769</th>\n",
              "      <td>29651.726363</td>\n",
              "      <td>29500.0</td>\n",
              "      <td>-151.726363</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>770</th>\n",
              "      <td>10732.071179</td>\n",
              "      <td>9600.0</td>\n",
              "      <td>-1132.071179</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>771</th>\n",
              "      <td>13922.446953</td>\n",
              "      <td>18300.0</td>\n",
              "      <td>4377.553047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>772</th>\n",
              "      <td>27487.751303</td>\n",
              "      <td>68500.0</td>\n",
              "      <td>41012.248697</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>773</th>\n",
              "      <td>13491.163043</td>\n",
              "      <td>10800.0</td>\n",
              "      <td>-2691.163043</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } 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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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_pf",
              "summary": "{\n  \"name\": \"df_pf\",\n  \"rows\": 774,\n  \"fields\": [\n    {\n      \"column\": \"Predictions\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13133.197603974588,\n        \"min\": 1320.5627675088342,\n        \"max\": 77403.05522420812,\n        \"num_unique_values\": 749,\n        \"samples\": [\n          25576.575707904965,\n          4524.770205947631,\n          12096.18269054933\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19967.85890767613,\n        \"min\": 1200.0,\n        \"max\": 126000.00000000007,\n        \"num_unique_values\": 382,\n        \"samples\": [\n          18300.000000000015,\n          3998.9999999999995,\n          32999.99999999998\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Residual\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10871.218143083033,\n        \"min\": -29456.498330561455,\n        \"max\": 85106.16232895423,\n        \"num_unique_values\": 770,\n        \"samples\": [\n          -1179.3893167366177,\n          -1152.540951631659,\n          -6617.055625340013\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_pf['Difference%']=np.absolute(df_pf['Residual']/df_pf['Target']*100)\n",
        "df_pf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "YxkhUp_j6p0F",
        "outputId": "694621c4-c90b-41c1-e241-d94d9e994ac2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Predictions   Target      Residual  Difference%\n",
              "0    10685.501696   2300.0  -8385.501696   364.587030\n",
              "1     3499.255242   2800.0   -699.255242    24.973402\n",
              "2     7553.285218   2500.0  -5053.285218   202.131409\n",
              "3     7463.963017   6400.0  -1063.963017    16.624422\n",
              "4    11353.490075   9150.0  -2203.490075    24.081859\n",
              "..            ...      ...           ...          ...\n",
              "769  29651.726363  29500.0   -151.726363     0.514327\n",
              "770  10732.071179   9600.0  -1132.071179    11.792408\n",
              "771  13922.446953  18300.0   4377.553047    23.921055\n",
              "772  27487.751303  68500.0  41012.248697    59.871896\n",
              "773  13491.163043  10800.0  -2691.163043    24.918176\n",
              "\n",
              "[774 rows x 4 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Predictions</th>\n",
              "      <th>Target</th>\n",
              "      <th>Residual</th>\n",
              "      <th>Difference%</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>364.587030</td>\n",
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              "      <td>3499.255242</td>\n",
              "      <td>2800.0</td>\n",
              "      <td>-699.255242</td>\n",
              "      <td>24.973402</td>\n",
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              "      <th>2</th>\n",
              "      <td>7553.285218</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>-5053.285218</td>\n",
              "      <td>202.131409</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>7463.963017</td>\n",
              "      <td>6400.0</td>\n",
              "      <td>-1063.963017</td>\n",
              "      <td>16.624422</td>\n",
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              "      <th>4</th>\n",
              "      <td>11353.490075</td>\n",
              "      <td>9150.0</td>\n",
              "      <td>-2203.490075</td>\n",
              "      <td>24.081859</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <th>769</th>\n",
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              "      <td>0.514327</td>\n",
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              "    <tr>\n",
              "      <th>770</th>\n",
              "      <td>10732.071179</td>\n",
              "      <td>9600.0</td>\n",
              "      <td>-1132.071179</td>\n",
              "      <td>11.792408</td>\n",
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              "      <th>771</th>\n",
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              "      <td>59.871896</td>\n",
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              "    <tr>\n",
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              "</table>\n",
              "<p>774 rows × 4 columns</p>\n",
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              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c9c3c5b0-4eb3-4736-8a52-d7cff44337a0')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\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",
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              "      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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\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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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c9c3c5b0-4eb3-4736-8a52-d7cff44337a0 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-c9c3c5b0-4eb3-4736-8a52-d7cff44337a0');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-3bce7779-6100-4074-9e63-3905dd47a1ec\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3bce7779-6100-4074-9e63-3905dd47a1ec')\"\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",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
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              "\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",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      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-3bce7779-6100-4074-9e63-3905dd47a1ec button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_0453b380-5ca0-4962-b6d7-3f881db596b2\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_pf')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_pf');\n",
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              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_pf",
              "summary": "{\n  \"name\": \"df_pf\",\n  \"rows\": 774,\n  \"fields\": [\n    {\n      \"column\": \"Predictions\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13133.197603974588,\n        \"min\": 1320.5627675088342,\n        \"max\": 77403.05522420812,\n        \"num_unique_values\": 749,\n        \"samples\": [\n          25576.575707904965,\n          4524.770205947631,\n          12096.18269054933\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19967.85890767613,\n        \"min\": 1200.0,\n        \"max\": 126000.00000000007,\n        \"num_unique_values\": 382,\n        \"samples\": [\n          18300.000000000015,\n          3998.9999999999995,\n          32999.99999999998\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Residual\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10871.218143083033,\n        \"min\": -29456.498330561455,\n        \"max\": 85106.16232895423,\n        \"num_unique_values\": 770,\n        \"samples\": [\n          -1179.3893167366177,\n          -1152.540951631659,\n          -6617.055625340013\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Difference%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 55.066507245726655,\n        \"min\": 0.06279397460941667,\n        \"max\": 512.6880797823105,\n        \"num_unique_values\": 770,\n        \"samples\": [\n          26.208651483035943,\n          44.3284981396792,\n          206.7829882918752\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_pf.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "qDbamwDz6_DM",
        "outputId": "e94d858f-c1b1-4452-bc84-49d9e63101a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Predictions         Target      Residual  Difference%\n",
              "count    774.000000     774.000000    774.000000   774.000000\n",
              "mean   15946.760167   18165.817106   2219.056939    36.256693\n",
              "std    13133.197604   19967.858908  10871.218143    55.066507\n",
              "min     1320.562768    1200.000000 -29456.498331     0.062794\n",
              "25%     7413.644234    6900.000000  -2044.191251    12.108022\n",
              "50%    11568.168859   11600.000000    142.518577    23.467728\n",
              "75%    20162.408805   20500.000000   3147.343497    39.563570\n",
              "max    77403.055224  126000.000000  85106.162329   512.688080"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-668e9b8b-26a1-4ae2-a5ac-ac55eacb37dc\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Predictions</th>\n",
              "      <th>Target</th>\n",
              "      <th>Residual</th>\n",
              "      <th>Difference%</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>774.000000</td>\n",
              "      <td>774.000000</td>\n",
              "      <td>774.000000</td>\n",
              "      <td>774.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>15946.760167</td>\n",
              "      <td>18165.817106</td>\n",
              "      <td>2219.056939</td>\n",
              "      <td>36.256693</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>13133.197604</td>\n",
              "      <td>19967.858908</td>\n",
              "      <td>10871.218143</td>\n",
              "      <td>55.066507</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1320.562768</td>\n",
              "      <td>1200.000000</td>\n",
              "      <td>-29456.498331</td>\n",
              "      <td>0.062794</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>7413.644234</td>\n",
              "      <td>6900.000000</td>\n",
              "      <td>-2044.191251</td>\n",
              "      <td>12.108022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>11568.168859</td>\n",
              "      <td>11600.000000</td>\n",
              "      <td>142.518577</td>\n",
              "      <td>23.467728</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>20162.408805</td>\n",
              "      <td>20500.000000</td>\n",
              "      <td>3147.343497</td>\n",
              "      <td>39.563570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>77403.055224</td>\n",
              "      <td>126000.000000</td>\n",
              "      <td>85106.162329</td>\n",
              "      <td>512.688080</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-668e9b8b-26a1-4ae2-a5ac-ac55eacb37dc')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\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",
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              "      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",
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              "\n",
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              "      fill: #FFFFFF;\n",
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              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-668e9b8b-26a1-4ae2-a5ac-ac55eacb37dc 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-668e9b8b-26a1-4ae2-a5ac-ac55eacb37dc');\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-bd85fdc4-07e1-463e-b27b-00398d01a87c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bd85fdc4-07e1-463e-b27b-00398d01a87c')\"\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-bd85fdc4-07e1-463e-b27b-00398d01a87c 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",
              "summary": "{\n  \"name\": \"df_pf\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Predictions\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24750.027854460663,\n        \"min\": 774.0,\n        \"max\": 77403.05522420812,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          15946.760167164734,\n          11568.168858805191,\n          774.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 41318.495567135855,\n        \"min\": 774.0,\n        \"max\": 126000.00000000007,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          18165.817105943155,\n          11599.999999999993,\n          774.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Residual\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32999.32972300943,\n        \"min\": -29456.498330561455,\n        \"max\": 85106.16232895423,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2219.0569387784194,\n          142.5185767862672,\n          774.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Difference%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 293.8794475593993,\n        \"min\": 0.06279397460941667,\n        \"max\": 774.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          36.25669285671203,\n          23.467727538369033,\n          774.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xt2oEdKl8pRt"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}