carPriceDetection.ipynb

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        "import pandas as pd\n",
        "import numpy as np\n"
      ]
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      "source": [
        "df=pd.read_csv('/fiat500.csv')"
      ],
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      "outputs": []
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              "        ID   model  engine_power  age_in_days      km  previous_owners  \\\n",
              "0        1  lounge            51          882   25000                1   \n",
              "1        2     pop            51         1186   32500                1   \n",
              "2        3   sport            74         4658  142228                1   \n",
              "3        4  lounge            51         2739  160000                1   \n",
              "4        5     pop            73         3074  106880                1   \n",
              "...    ...     ...           ...          ...     ...              ...   \n",
              "1533  1534   sport            51         3712  115280                1   \n",
              "1534  1535  lounge            74         3835  112000                1   \n",
              "1535  1536     pop            51         2223   60457                1   \n",
              "1536  1537  lounge            51         2557   80750                1   \n",
              "1537  1538     pop            51         1766   54276                1   \n",
              "\n",
              "            lat        lon  price  \n",
              "0     44.907242   8.611560   8900  \n",
              "1     45.666359  12.241890   8800  \n",
              "2     45.503300  11.417840   4200  \n",
              "3     40.633171  17.634609   6000  \n",
              "4     41.903221  12.495650   5700  \n",
              "...         ...        ...    ...  \n",
              "1533  45.069679   7.704920   5200  \n",
              "1534  45.845692   8.666870   4600  \n",
              "1535  45.481541   9.413480   7500  \n",
              "1536  45.000702   7.682270   5990  \n",
              "1537  40.323410  17.568270   7900  \n",
              "\n",
              "[1538 rows x 9 columns]"
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              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1538,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 444,\n        \"min\": 1,\n        \"max\": 1538,\n        \"num_unique_values\": 1538,\n        \"samples\": [\n          482,\n          77,\n          1503\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"lounge\",\n          \"pop\",\n          \"sport\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"engine_power\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 51,\n        \"max\": 77,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          74,\n          66,\n          51\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age_in_days\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1289,\n        \"min\": 366,\n        \"max\": 4658,\n        \"num_unique_values\": 140,\n        \"samples\": [\n          2251,\n          1127,\n          640\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"km\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 40046,\n        \"min\": 1232,\n        \"max\": 235000,\n        \"num_unique_values\": 988,\n        \"samples\": [\n          20653,\n          117183,\n          14366\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"previous_owners\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          4,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1335178263566017,\n        \"min\": 36.8558387756348,\n        \"max\": 46.7956123352051,\n        \"num_unique_values\": 449,\n        \"samples\": [\n          45.0069007873535,\n          45.684211730957,\n          45.5459785461426\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lon\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.32818989939984,\n        \"min\": 7.24539995193481,\n        \"max\": 18.3655204772949,\n        \"num_unique_values\": 450,\n        \"samples\": [\n          7.64412021636963,\n          7.332200050354,\n          12.1605596542358\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1939,\n        \"min\": 2500,\n        \"max\": 11100,\n        \"num_unique_values\": 222,\n        \"samples\": [\n          7590,\n          5199,\n          5250\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6_kgl1Y_7J82",
        "outputId": "7e86f5c1-6364-4f52-a6f5-9b9a663825d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1538 entries, 0 to 1537\n",
            "Data columns (total 9 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   ID               1538 non-null   int64  \n",
            " 1   model            1538 non-null   object \n",
            " 2   engine_power     1538 non-null   int64  \n",
            " 3   age_in_days      1538 non-null   int64  \n",
            " 4   km               1538 non-null   int64  \n",
            " 5   previous_owners  1538 non-null   int64  \n",
            " 6   lat              1538 non-null   float64\n",
            " 7   lon              1538 non-null   float64\n",
            " 8   price            1538 non-null   int64  \n",
            "dtypes: float64(2), int64(6), object(1)\n",
            "memory usage: 108.3+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "asrV64H-6Trx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "oOTbuZ-t7NpP",
        "outputId": "ab2c4cfb-1e7e-44c5-d3d4-59c2d62a13f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                ID  engine_power  age_in_days             km  previous_owners  \\\n",
              "count  1538.000000   1538.000000  1538.000000    1538.000000      1538.000000   \n",
              "mean    769.500000     51.904421  1650.980494   53396.011704         1.123537   \n",
              "std     444.126671      3.988023  1289.522278   40046.830723         0.416423   \n",
              "min       1.000000     51.000000   366.000000    1232.000000         1.000000   \n",
              "25%     385.250000     51.000000   670.000000   20006.250000         1.000000   \n",
              "50%     769.500000     51.000000  1035.000000   39031.000000         1.000000   \n",
              "75%    1153.750000     51.000000  2616.000000   79667.750000         1.000000   \n",
              "max    1538.000000     77.000000  4658.000000  235000.000000         4.000000   \n",
              "\n",
              "               lat          lon         price  \n",
              "count  1538.000000  1538.000000   1538.000000  \n",
              "mean     43.541361    11.563428   8576.003901  \n",
              "std       2.133518     2.328190   1939.958641  \n",
              "min      36.855839     7.245400   2500.000000  \n",
              "25%      41.802990     9.505090   7122.500000  \n",
              "50%      44.394096    11.869260   9000.000000  \n",
              "75%      45.467960    12.769040  10000.000000  \n",
              "max      46.795612    18.365520  11100.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6976d80d-8a14-40ec-8c08-292f578c407d\" 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>ID</th>\n",
              "      <th>engine_power</th>\n",
              "      <th>age_in_days</th>\n",
              "      <th>km</th>\n",
              "      <th>previous_owners</th>\n",
              "      <th>lat</th>\n",
              "      <th>lon</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1538.000000</td>\n",
              "      <td>1538.000000</td>\n",
              "      <td>1538.000000</td>\n",
              "      <td>1538.000000</td>\n",
              "      <td>1538.000000</td>\n",
              "      <td>1538.000000</td>\n",
              "      <td>1538.000000</td>\n",
              "      <td>1538.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>769.500000</td>\n",
              "      <td>51.904421</td>\n",
              "      <td>1650.980494</td>\n",
              "      <td>53396.011704</td>\n",
              "      <td>1.123537</td>\n",
              "      <td>43.541361</td>\n",
              "      <td>11.563428</td>\n",
              "      <td>8576.003901</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>444.126671</td>\n",
              "      <td>3.988023</td>\n",
              "      <td>1289.522278</td>\n",
              "      <td>40046.830723</td>\n",
              "      <td>0.416423</td>\n",
              "      <td>2.133518</td>\n",
              "      <td>2.328190</td>\n",
              "      <td>1939.958641</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>366.000000</td>\n",
              "      <td>1232.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>36.855839</td>\n",
              "      <td>7.245400</td>\n",
              "      <td>2500.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>385.250000</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>670.000000</td>\n",
              "      <td>20006.250000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>41.802990</td>\n",
              "      <td>9.505090</td>\n",
              "      <td>7122.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>769.500000</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>1035.000000</td>\n",
              "      <td>39031.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>44.394096</td>\n",
              "      <td>11.869260</td>\n",
              "      <td>9000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1153.750000</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>2616.000000</td>\n",
              "      <td>79667.750000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>45.467960</td>\n",
              "      <td>12.769040</td>\n",
              "      <td>10000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1538.000000</td>\n",
              "      <td>77.000000</td>\n",
              "      <td>4658.000000</td>\n",
              "      <td>235000.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>46.795612</td>\n",
              "      <td>18.365520</td>\n",
              "      <td>11100.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
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              "      display:flex;\n",
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              "\n",
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              "        document.querySelector('#df-6976d80d-8a14-40ec-8c08-292f578c407d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-6976d80d-8a14-40ec-8c08-292f578c407d');\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",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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          "metadata": {},
          "execution_count": 7
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
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        "outputId": "ca3720f0-2c02-48be-f2bc-4d7f5fb20552"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1538, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
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          "height": 226
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        "id": "2m1t4cgb7kO6",
        "outputId": "0a0ee02f-3fe5-448f-b778-af98bb8a9c5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   ID   model  engine_power  age_in_days      km  previous_owners        lat  \\\n",
              "0   1  lounge            51          882   25000                1  44.907242   \n",
              "1   2     pop            51         1186   32500                1  45.666359   \n",
              "2   3   sport            74         4658  142228                1  45.503300   \n",
              "3   4  lounge            51         2739  160000                1  40.633171   \n",
              "4   5     pop            73         3074  106880                1  41.903221   \n",
              "\n",
              "         lon  price  \n",
              "0   8.611560   8900  \n",
              "1  12.241890   8800  \n",
              "2  11.417840   4200  \n",
              "3  17.634609   6000  \n",
              "4  12.495650   5700  "
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            "application/vnd.google.colaboratory.intrinsic+json": {
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1538,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 444,\n        \"min\": 1,\n        \"max\": 1538,\n        \"num_unique_values\": 1538,\n        \"samples\": [\n          482,\n          77,\n          1503\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"lounge\",\n          \"pop\",\n          \"sport\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"engine_power\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 51,\n        \"max\": 77,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          74,\n          66,\n          51\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age_in_days\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1289,\n        \"min\": 366,\n        \"max\": 4658,\n        \"num_unique_values\": 140,\n        \"samples\": [\n          2251,\n          1127,\n          640\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"km\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 40046,\n        \"min\": 1232,\n        \"max\": 235000,\n        \"num_unique_values\": 988,\n        \"samples\": [\n          20653,\n          117183,\n          14366\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"previous_owners\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          4,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1335178263566017,\n        \"min\": 36.8558387756348,\n        \"max\": 46.7956123352051,\n        \"num_unique_values\": 449,\n        \"samples\": [\n          45.0069007873535,\n          45.684211730957,\n          45.5459785461426\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lon\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.32818989939984,\n        \"min\": 7.24539995193481,\n        \"max\": 18.3655204772949,\n        \"num_unique_values\": 450,\n        \"samples\": [\n          7.64412021636963,\n          7.332200050354,\n          12.1605596542358\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1939,\n        \"min\": 2500,\n        \"max\": 11100,\n        \"num_unique_values\": 222,\n        \"samples\": [\n          7590,\n          5199,\n          5250\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.tail()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "5FUBkD0S7nQ8",
        "outputId": "0dda24f9-2e71-4f09-bc31-ea09acdc58aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        ID   model  engine_power  age_in_days      km  previous_owners  \\\n",
              "1533  1534   sport            51         3712  115280                1   \n",
              "1534  1535  lounge            74         3835  112000                1   \n",
              "1535  1536     pop            51         2223   60457                1   \n",
              "1536  1537  lounge            51         2557   80750                1   \n",
              "1537  1538     pop            51         1766   54276                1   \n",
              "\n",
              "            lat       lon  price  \n",
              "1533  45.069679   7.70492   5200  \n",
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              "['ID',\n",
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              " 'lat',\n",
              " 'lon',\n",
              " 'price']"
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            "text/plain": [
              "0    lounge\n",
              "1       pop\n",
              "2     sport\n",
              "3    lounge\n",
              "4       pop\n",
              "Name: model, dtype: object"
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          "execution_count": 13
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([51, 74, 73, 62, 63, 66, 77, 58])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
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        "df['engine_power'].count()"
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        "colab": {
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        "id": "0AO2UTTV8ArM",
        "outputId": "202937bf-c46d-4250-b72b-c4ca8fd53aec"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1538"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
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      "cell_type": "code",
      "source": [
        "df.groupby('model').count()"
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          "height": 195
        },
        "id": "J1xqepym8OQC",
        "outputId": "3f2d250a-8988-43ad-a18e-df870b8fb19b"
      },
      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
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              "          ID  engine_power  age_in_days    km  previous_owners   lat   lon  \\\n",
              "model                                                                        \n",
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              "\n",
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              "model          \n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"lounge\",\n          \"pop\",\n          \"sport\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 521,\n        \"min\": 86,\n        \"max\": 1094,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1094,\n          358,\n          86\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"engine_power\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 521,\n        \"min\": 86,\n        \"max\": 1094,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1094,\n          358,\n          86\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age_in_days\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 521,\n        \"min\": 86,\n        \"max\": 1094,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1094,\n          358,\n          86\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"km\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 521,\n        \"min\": 86,\n        \"max\": 1094,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1094,\n          358,\n          86\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"previous_owners\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 521,\n        \"min\": 86,\n        \"max\": 1094,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1094,\n          358,\n          86\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 521,\n        \"min\": 86,\n        \"max\": 1094,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1094,\n          358,\n          86\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lon\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 521,\n        \"min\": 86,\n        \"max\": 1094,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1094,\n          358,\n          86\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 521,\n        \"min\": 86,\n        \"max\": 1094,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1094,\n          358,\n          86\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "wVVgYd9r82qY",
        "outputId": "656f5789-3b41-4cd1-8fe3-f679b11e1985"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        ID   model  engine_power  age_in_days      km  previous_owners  \\\n",
              "0        1  lounge            51          882   25000                1   \n",
              "1        2     pop            51         1186   32500                1   \n",
              "2        3   sport            74         4658  142228                1   \n",
              "3        4  lounge            51         2739  160000                1   \n",
              "4        5     pop            73         3074  106880                1   \n",
              "...    ...     ...           ...          ...     ...              ...   \n",
              "1533  1534   sport            51         3712  115280                1   \n",
              "1534  1535  lounge            74         3835  112000                1   \n",
              "1535  1536     pop            51         2223   60457                1   \n",
              "1536  1537  lounge            51         2557   80750                1   \n",
              "1537  1538     pop            51         1766   54276                1   \n",
              "\n",
              "            lat        lon  price  \n",
              "0     44.907242   8.611560   8900  \n",
              "1     45.666359  12.241890   8800  \n",
              "2     45.503300  11.417840   4200  \n",
              "3     40.633171  17.634609   6000  \n",
              "4     41.903221  12.495650   5700  \n",
              "...         ...        ...    ...  \n",
              "1533  45.069679   7.704920   5200  \n",
              "1534  45.845692   8.666870   4600  \n",
              "1535  45.481541   9.413480   7500  \n",
              "1536  45.000702   7.682270   5990  \n",
              "1537  40.323410  17.568270   7900  \n",
              "\n",
              "[1538 rows x 9 columns]"
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              "      <th>ID</th>\n",
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              "      <th>...</th>\n",
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              "<p>1538 rows × 9 columns</p>\n",
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              "\n",
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              "\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1538,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 444,\n        \"min\": 1,\n        \"max\": 1538,\n        \"num_unique_values\": 1538,\n        \"samples\": [\n          482,\n          77,\n          1503\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"lounge\",\n          \"pop\",\n          \"sport\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"engine_power\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 51,\n        \"max\": 77,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          74,\n          66,\n          51\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age_in_days\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1289,\n        \"min\": 366,\n        \"max\": 4658,\n        \"num_unique_values\": 140,\n        \"samples\": [\n          2251,\n          1127,\n          640\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"km\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 40046,\n        \"min\": 1232,\n        \"max\": 235000,\n        \"num_unique_values\": 988,\n        \"samples\": [\n          20653,\n          117183,\n          14366\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"previous_owners\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          4,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1335178263566017,\n        \"min\": 36.8558387756348,\n        \"max\": 46.7956123352051,\n        \"num_unique_values\": 449,\n        \"samples\": [\n          45.0069007873535,\n          45.684211730957,\n          45.5459785461426\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lon\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.32818989939984,\n        \"min\": 7.24539995193481,\n        \"max\": 18.3655204772949,\n        \"num_unique_values\": 450,\n        \"samples\": [\n          7.64412021636963,\n          7.332200050354,\n          12.1605596542358\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1939,\n        \"min\": 2500,\n        \"max\": 11100,\n        \"num_unique_values\": 222,\n        \"samples\": [\n          7590,\n          5199,\n          5250\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "TXbp4pdV9UWT",
        "outputId": "5fbd6849-ebde-4111-cf63-7ccbc535006d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        ID  engine_power  age_in_days      km  previous_owners        lat  \\\n",
              "0        1            51          882   25000                1  44.907242   \n",
              "1        2            51         1186   32500                1  45.666359   \n",
              "2        3            74         4658  142228                1  45.503300   \n",
              "3        4            51         2739  160000                1  40.633171   \n",
              "4        5            73         3074  106880                1  41.903221   \n",
              "...    ...           ...          ...     ...              ...        ...   \n",
              "1533  1534            51         3712  115280                1  45.069679   \n",
              "1534  1535            74         3835  112000                1  45.845692   \n",
              "1535  1536            51         2223   60457                1  45.481541   \n",
              "1536  1537            51         2557   80750                1  45.000702   \n",
              "1537  1538            51         1766   54276                1  40.323410   \n",
              "\n",
              "            lon  price  model_lounge  model_pop  model_sport  \n",
              "0      8.611560   8900             1          0            0  \n",
              "1     12.241890   8800             0          1            0  \n",
              "2     11.417840   4200             0          0            1  \n",
              "3     17.634609   6000             1          0            0  \n",
              "4     12.495650   5700             0          1            0  \n",
              "...         ...    ...           ...        ...          ...  \n",
              "1533   7.704920   5200             0          0            1  \n",
              "1534   8.666870   4600             1          0            0  \n",
              "1535   9.413480   7500             0          1            0  \n",
              "1536   7.682270   5990             1          0            0  \n",
              "1537  17.568270   7900             0          1            0  \n",
              "\n",
              "[1538 rows x 11 columns]"
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              "      <td>4600</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1535</th>\n",
              "      <td>1536</td>\n",
              "      <td>51</td>\n",
              "      <td>2223</td>\n",
              "      <td>60457</td>\n",
              "      <td>1</td>\n",
              "      <td>45.481541</td>\n",
              "      <td>9.413480</td>\n",
              "      <td>7500</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1536</th>\n",
              "      <td>1537</td>\n",
              "      <td>51</td>\n",
              "      <td>2557</td>\n",
              "      <td>80750</td>\n",
              "      <td>1</td>\n",
              "      <td>45.000702</td>\n",
              "      <td>7.682270</td>\n",
              "      <td>5990</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1537</th>\n",
              "      <td>1538</td>\n",
              "      <td>51</td>\n",
              "      <td>1766</td>\n",
              "      <td>54276</td>\n",
              "      <td>1</td>\n",
              "      <td>40.323410</td>\n",
              "      <td>17.568270</td>\n",
              "      <td>7900</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1538 rows × 11 columns</p>\n",
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      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",
              "      .colab-df-generate:hover {\n",
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              "      [theme=dark] .colab-df-generate {\n",
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              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\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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              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_19ea056d-7006-40e4-a37f-d1cbd28c9b2f button.colab-df-generate');\n",
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              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1538,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 444,\n        \"min\": 1,\n        \"max\": 1538,\n        \"num_unique_values\": 1538,\n        \"samples\": [\n          482,\n          77,\n          1503\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"engine_power\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 51,\n        \"max\": 77,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          74,\n          66,\n          51\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age_in_days\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1289,\n        \"min\": 366,\n        \"max\": 4658,\n        \"num_unique_values\": 140,\n        \"samples\": [\n          2251,\n          1127,\n          640\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"km\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 40046,\n        \"min\": 1232,\n        \"max\": 235000,\n        \"num_unique_values\": 988,\n        \"samples\": [\n          20653,\n          117183,\n          14366\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"previous_owners\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          4,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1335178263566017,\n        \"min\": 36.8558387756348,\n        \"max\": 46.7956123352051,\n        \"num_unique_values\": 449,\n        \"samples\": [\n          45.0069007873535,\n          45.684211730957,\n          45.5459785461426\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lon\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.32818989939984,\n        \"min\": 7.24539995193481,\n        \"max\": 18.3655204772949,\n        \"num_unique_values\": 450,\n        \"samples\": [\n          7.64412021636963,\n          7.332200050354,\n          12.1605596542358\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1939,\n        \"min\": 2500,\n        \"max\": 11100,\n        \"num_unique_values\": 222,\n        \"samples\": [\n          7590,\n          5199,\n          5250\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model_lounge\",\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\": \"model_pop\",\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\": \"model_sport\",\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}"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "UUUo3oSH84M6",
        "outputId": "7cfb71d9-a381-4d3e-8ae9-e0b23808a3b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        ID  engine_power  age_in_days      km  previous_owners        lat  \\\n",
              "0        1            51          882   25000                1  44.907242   \n",
              "1        2            51         1186   32500                1  45.666359   \n",
              "2        3            74         4658  142228                1  45.503300   \n",
              "3        4            51         2739  160000                1  40.633171   \n",
              "4        5            73         3074  106880                1  41.903221   \n",
              "...    ...           ...          ...     ...              ...        ...   \n",
              "1533  1534            51         3712  115280                1  45.069679   \n",
              "1534  1535            74         3835  112000                1  45.845692   \n",
              "1535  1536            51         2223   60457                1  45.481541   \n",
              "1536  1537            51         2557   80750                1  45.000702   \n",
              "1537  1538            51         1766   54276                1  40.323410   \n",
              "\n",
              "            lon  price  model_lounge  model_pop  model_sport  \n",
              "0      8.611560   8900             1          0            0  \n",
              "1     12.241890   8800             0          1            0  \n",
              "2     11.417840   4200             0          0            1  \n",
              "3     17.634609   6000             1          0            0  \n",
              "4     12.495650   5700             0          1            0  \n",
              "...         ...    ...           ...        ...          ...  \n",
              "1533   7.704920   5200             0          0            1  \n",
              "1534   8.666870   4600             1          0            0  \n",
              "1535   9.413480   7500             0          1            0  \n",
              "1536   7.682270   5990             1          0            0  \n",
              "1537  17.568270   7900             0          1            0  \n",
              "\n",
              "[1538 rows x 11 columns]"
            ],
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              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>engine_power</th>\n",
              "      <th>age_in_days</th>\n",
              "      <th>km</th>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>51</td>\n",
              "      <td>1186</td>\n",
              "      <td>32500</td>\n",
              "      <td>1</td>\n",
              "      <td>45.666359</td>\n",
              "      <td>12.241890</td>\n",
              "      <td>8800</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>74</td>\n",
              "      <td>4658</td>\n",
              "      <td>142228</td>\n",
              "      <td>1</td>\n",
              "      <td>45.503300</td>\n",
              "      <td>11.417840</td>\n",
              "      <td>4200</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>51</td>\n",
              "      <td>2739</td>\n",
              "      <td>160000</td>\n",
              "      <td>1</td>\n",
              "      <td>40.633171</td>\n",
              "      <td>17.634609</td>\n",
              "      <td>6000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>73</td>\n",
              "      <td>3074</td>\n",
              "      <td>106880</td>\n",
              "      <td>1</td>\n",
              "      <td>41.903221</td>\n",
              "      <td>12.495650</td>\n",
              "      <td>5700</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1533</th>\n",
              "      <td>1534</td>\n",
              "      <td>51</td>\n",
              "      <td>3712</td>\n",
              "      <td>115280</td>\n",
              "      <td>1</td>\n",
              "      <td>45.069679</td>\n",
              "      <td>7.704920</td>\n",
              "      <td>5200</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1534</th>\n",
              "      <td>1535</td>\n",
              "      <td>74</td>\n",
              "      <td>3835</td>\n",
              "      <td>112000</td>\n",
              "      <td>1</td>\n",
              "      <td>45.845692</td>\n",
              "      <td>8.666870</td>\n",
              "      <td>4600</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1535</th>\n",
              "      <td>1536</td>\n",
              "      <td>51</td>\n",
              "      <td>2223</td>\n",
              "      <td>60457</td>\n",
              "      <td>1</td>\n",
              "      <td>45.481541</td>\n",
              "      <td>9.413480</td>\n",
              "      <td>7500</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1536</th>\n",
              "      <td>1537</td>\n",
              "      <td>51</td>\n",
              "      <td>2557</td>\n",
              "      <td>80750</td>\n",
              "      <td>1</td>\n",
              "      <td>45.000702</td>\n",
              "      <td>7.682270</td>\n",
              "      <td>5990</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1537</th>\n",
              "      <td>1538</td>\n",
              "      <td>51</td>\n",
              "      <td>1766</td>\n",
              "      <td>54276</td>\n",
              "      <td>1</td>\n",
              "      <td>40.323410</td>\n",
              "      <td>17.568270</td>\n",
              "      <td>7900</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1538 rows × 11 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6126630e-426c-4ce0-82d7-30413c4eceec')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\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",
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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",
              "    }\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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              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-6126630e-426c-4ce0-82d7-30413c4eceec 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-6126630e-426c-4ce0-82d7-30413c4eceec');\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",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --fill-color: #D2E3FC;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    display: none;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "  }\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",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\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",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-029aae49-db9d-4a9d-9eb2-865066c2c664 button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
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              "\n",
              "  <div id=\"id_d034dca6-d668-4a82-ac1d-4b5baf83f7ec\">\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",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "      }\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')\"\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",
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              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_d034dca6-d668-4a82-ac1d-4b5baf83f7ec button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1538,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 444,\n        \"min\": 1,\n        \"max\": 1538,\n        \"num_unique_values\": 1538,\n        \"samples\": [\n          482,\n          77,\n          1503\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"engine_power\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 51,\n        \"max\": 77,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          74,\n          66,\n          51\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age_in_days\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1289,\n        \"min\": 366,\n        \"max\": 4658,\n        \"num_unique_values\": 140,\n        \"samples\": [\n          2251,\n          1127,\n          640\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"km\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 40046,\n        \"min\": 1232,\n        \"max\": 235000,\n        \"num_unique_values\": 988,\n        \"samples\": [\n          20653,\n          117183,\n          14366\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"previous_owners\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          4,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1335178263566017,\n        \"min\": 36.8558387756348,\n        \"max\": 46.7956123352051,\n        \"num_unique_values\": 449,\n        \"samples\": [\n          45.0069007873535,\n          45.684211730957,\n          45.5459785461426\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lon\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.32818989939984,\n        \"min\": 7.24539995193481,\n        \"max\": 18.3655204772949,\n        \"num_unique_values\": 450,\n        \"samples\": [\n          7.64412021636963,\n          7.332200050354,\n          12.1605596542358\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1939,\n        \"min\": 2500,\n        \"max\": 11100,\n        \"num_unique_values\": 222,\n        \"samples\": [\n          7590,\n          5199,\n          5250\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model_lounge\",\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\": \"model_pop\",\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\": \"model_sport\",\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}"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 109
        },
        "id": "ZeVVZKRqAoCY",
        "outputId": "91648586-64f3-476c-8750-de409e307b00"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "Empty DataFrame\n",
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          },
          "metadata": {},
          "execution_count": 46
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              "\n",
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              "      background-color: #E8F0FE;\n",
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              "\n",
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              "        document.querySelector('#df-2790c3af-d29a-4aa6-8438-4ba49fa6d769 button.colab-df-convert');\n",
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              "                                                    [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",
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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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              "    (() => {\n",
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              "        document.querySelector('#df-3b69aa65-b083-4979-b61c-2829aaa9d0a2 button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
              "  <div id=\"id_be83d209-1ba6-43dd-b5fd-4d91bc3d24fa\">\n",
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              "      .colab-df-generate {\n",
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              "        border: none;\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "        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",
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              "      [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",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\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",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1538,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 444,\n        \"min\": 1,\n        \"max\": 1538,\n        \"num_unique_values\": 1538,\n        \"samples\": [\n          482,\n          77,\n          1503\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"engine_power\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 51,\n        \"max\": 77,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          74,\n          66,\n          51\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age_in_days\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1289,\n        \"min\": 366,\n        \"max\": 4658,\n        \"num_unique_values\": 140,\n        \"samples\": [\n          2251,\n          1127,\n          640\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"km\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 40046,\n        \"min\": 1232,\n        \"max\": 235000,\n        \"num_unique_values\": 988,\n        \"samples\": [\n          20653,\n          117183,\n          14366\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"previous_owners\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          4,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1335178263566017,\n        \"min\": 36.8558387756348,\n        \"max\": 46.7956123352051,\n        \"num_unique_values\": 449,\n        \"samples\": [\n          45.0069007873535,\n          45.684211730957,\n          45.5459785461426\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lon\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.32818989939984,\n        \"min\": 7.24539995193481,\n        \"max\": 18.3655204772949,\n        \"num_unique_values\": 450,\n        \"samples\": [\n          7.64412021636963,\n          7.332200050354,\n          12.1605596542358\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1939,\n        \"min\": 2500,\n        \"max\": 11100,\n        \"num_unique_values\": 222,\n        \"samples\": [\n          7590,\n          5199,\n          5250\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model_lounge\",\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\": \"model_pop\",\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\": \"model_sport\",\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}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.get_dummies(df,dtype='string')\n",
        "df"
      ],
      "metadata": {
        "id": "Tgbk__hzWfxU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 109
        },
        "id": "y6ZJc2fnAkzN",
        "outputId": "29f2282d-3de7-4170-b0a2-79b162a30e6c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [ID, engine_power, age_in_days, km, previous_owners, lat, lon, price, model_lounge, model_pop, model_sport]\n",
              "Index: []"
            ],
            "text/html": [
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              "      <th>ID</th>\n",
              "      <th>engine_power</th>\n",
              "      <th>age_in_days</th>\n",
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              "      <th>model_lounge</th>\n",
              "      <th>model_pop</th>\n",
              "      <th>model_sport</th>\n",
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              "        const element = document.querySelector('#df-b31968ce-60f1-4bb6-97bf-2966d31536c9');\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",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
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          "metadata": {},
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              "        ID   model  engine_power  age_in_days      km  previous_owners  \\\n",
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              "1535  45.481541   9.413480   7500  \n",
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              "      <td>11.417840</td>\n",
              "      <td>4200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>lounge</td>\n",
              "      <td>51</td>\n",
              "      <td>2739</td>\n",
              "      <td>160000</td>\n",
              "      <td>1</td>\n",
              "      <td>40.633171</td>\n",
              "      <td>17.634609</td>\n",
              "      <td>6000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>pop</td>\n",
              "      <td>73</td>\n",
              "      <td>3074</td>\n",
              "      <td>106880</td>\n",
              "      <td>1</td>\n",
              "      <td>41.903221</td>\n",
              "      <td>12.495650</td>\n",
              "      <td>5700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1533</th>\n",
              "      <td>1534</td>\n",
              "      <td>sport</td>\n",
              "      <td>51</td>\n",
              "      <td>3712</td>\n",
              "      <td>115280</td>\n",
              "      <td>1</td>\n",
              "      <td>45.069679</td>\n",
              "      <td>7.704920</td>\n",
              "      <td>5200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1534</th>\n",
              "      <td>1535</td>\n",
              "      <td>lounge</td>\n",
              "      <td>74</td>\n",
              "      <td>3835</td>\n",
              "      <td>112000</td>\n",
              "      <td>1</td>\n",
              "      <td>45.845692</td>\n",
              "      <td>8.666870</td>\n",
              "      <td>4600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1535</th>\n",
              "      <td>1536</td>\n",
              "      <td>pop</td>\n",
              "      <td>51</td>\n",
              "      <td>2223</td>\n",
              "      <td>60457</td>\n",
              "      <td>1</td>\n",
              "      <td>45.481541</td>\n",
              "      <td>9.413480</td>\n",
              "      <td>7500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1536</th>\n",
              "      <td>1537</td>\n",
              "      <td>lounge</td>\n",
              "      <td>51</td>\n",
              "      <td>2557</td>\n",
              "      <td>80750</td>\n",
              "      <td>1</td>\n",
              "      <td>45.000702</td>\n",
              "      <td>7.682270</td>\n",
              "      <td>5990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1537</th>\n",
              "      <td>1538</td>\n",
              "      <td>pop</td>\n",
              "      <td>51</td>\n",
              "      <td>1766</td>\n",
              "      <td>54276</td>\n",
              "      <td>1</td>\n",
              "      <td>40.323410</td>\n",
              "      <td>17.568270</td>\n",
              "      <td>7900</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1538 rows × 9 columns</p>\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1538,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 444,\n        \"min\": 1,\n        \"max\": 1538,\n        \"num_unique_values\": 1538,\n        \"samples\": [\n          482,\n          77,\n          1503\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"lounge\",\n          \"pop\",\n          \"sport\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"engine_power\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 51,\n        \"max\": 77,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          74,\n          66,\n          51\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age_in_days\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1289,\n        \"min\": 366,\n        \"max\": 4658,\n        \"num_unique_values\": 140,\n        \"samples\": [\n          2251,\n          1127,\n          640\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"km\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 40046,\n        \"min\": 1232,\n        \"max\": 235000,\n        \"num_unique_values\": 988,\n        \"samples\": [\n          20653,\n          117183,\n          14366\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"previous_owners\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          4,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1335178263566017,\n        \"min\": 36.8558387756348,\n        \"max\": 46.7956123352051,\n        \"num_unique_values\": 449,\n        \"samples\": [\n          45.0069007873535,\n          45.684211730957,\n          45.5459785461426\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lon\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.32818989939984,\n        \"min\": 7.24539995193481,\n        \"max\": 18.3655204772949,\n        \"num_unique_values\": 450,\n        \"samples\": [\n          7.64412021636963,\n          7.332200050354,\n          12.1605596542358\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1939,\n        \"min\": 2500,\n        \"max\": 11100,\n        \"num_unique_values\": 222,\n        \"samples\": [\n          7590,\n          5199,\n          5250\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.get_dummies(df,dtype='int')\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "6FD_GX05B3iD",
        "outputId": "489925b5-99f1-458b-ceb1-c39875cab45e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        ID  engine_power  age_in_days      km  previous_owners        lat  \\\n",
              "0        1            51          882   25000                1  44.907242   \n",
              "1        2            51         1186   32500                1  45.666359   \n",
              "2        3            74         4658  142228                1  45.503300   \n",
              "3        4            51         2739  160000                1  40.633171   \n",
              "4        5            73         3074  106880                1  41.903221   \n",
              "...    ...           ...          ...     ...              ...        ...   \n",
              "1533  1534            51         3712  115280                1  45.069679   \n",
              "1534  1535            74         3835  112000                1  45.845692   \n",
              "1535  1536            51         2223   60457                1  45.481541   \n",
              "1536  1537            51         2557   80750                1  45.000702   \n",
              "1537  1538            51         1766   54276                1  40.323410   \n",
              "\n",
              "            lon  price  model_lounge  model_pop  model_sport  \n",
              "0      8.611560   8900             1          0            0  \n",
              "1     12.241890   8800             0          1            0  \n",
              "2     11.417840   4200             0          0            1  \n",
              "3     17.634609   6000             1          0            0  \n",
              "4     12.495650   5700             0          1            0  \n",
              "...         ...    ...           ...        ...          ...  \n",
              "1533   7.704920   5200             0          0            1  \n",
              "1534   8.666870   4600             1          0            0  \n",
              "1535   9.413480   7500             0          1            0  \n",
              "1536   7.682270   5990             1          0            0  \n",
              "1537  17.568270   7900             0          1            0  \n",
              "\n",
              "[1538 rows x 11 columns]"
            ],
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              "\n",
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              "        text-align: right;\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>engine_power</th>\n",
              "      <th>age_in_days</th>\n",
              "      <th>km</th>\n",
              "      <th>previous_owners</th>\n",
              "      <th>lat</th>\n",
              "      <th>lon</th>\n",
              "      <th>price</th>\n",
              "      <th>model_lounge</th>\n",
              "      <th>model_pop</th>\n",
              "      <th>model_sport</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>51</td>\n",
              "      <td>882</td>\n",
              "      <td>25000</td>\n",
              "      <td>1</td>\n",
              "      <td>44.907242</td>\n",
              "      <td>8.611560</td>\n",
              "      <td>8900</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>51</td>\n",
              "      <td>1186</td>\n",
              "      <td>32500</td>\n",
              "      <td>1</td>\n",
              "      <td>45.666359</td>\n",
              "      <td>12.241890</td>\n",
              "      <td>8800</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>74</td>\n",
              "      <td>4658</td>\n",
              "      <td>142228</td>\n",
              "      <td>1</td>\n",
              "      <td>45.503300</td>\n",
              "      <td>11.417840</td>\n",
              "      <td>4200</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>51</td>\n",
              "      <td>2739</td>\n",
              "      <td>160000</td>\n",
              "      <td>1</td>\n",
              "      <td>40.633171</td>\n",
              "      <td>17.634609</td>\n",
              "      <td>6000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>73</td>\n",
              "      <td>3074</td>\n",
              "      <td>106880</td>\n",
              "      <td>1</td>\n",
              "      <td>41.903221</td>\n",
              "      <td>12.495650</td>\n",
              "      <td>5700</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1533</th>\n",
              "      <td>1534</td>\n",
              "      <td>51</td>\n",
              "      <td>3712</td>\n",
              "      <td>115280</td>\n",
              "      <td>1</td>\n",
              "      <td>45.069679</td>\n",
              "      <td>7.704920</td>\n",
              "      <td>5200</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1534</th>\n",
              "      <td>1535</td>\n",
              "      <td>74</td>\n",
              "      <td>3835</td>\n",
              "      <td>112000</td>\n",
              "      <td>1</td>\n",
              "      <td>45.845692</td>\n",
              "      <td>8.666870</td>\n",
              "      <td>4600</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1535</th>\n",
              "      <td>1536</td>\n",
              "      <td>51</td>\n",
              "      <td>2223</td>\n",
              "      <td>60457</td>\n",
              "      <td>1</td>\n",
              "      <td>45.481541</td>\n",
              "      <td>9.413480</td>\n",
              "      <td>7500</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1536</th>\n",
              "      <td>1537</td>\n",
              "      <td>51</td>\n",
              "      <td>2557</td>\n",
              "      <td>80750</td>\n",
              "      <td>1</td>\n",
              "      <td>45.000702</td>\n",
              "      <td>7.682270</td>\n",
              "      <td>5990</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1537</th>\n",
              "      <td>1538</td>\n",
              "      <td>51</td>\n",
              "      <td>1766</td>\n",
              "      <td>54276</td>\n",
              "      <td>1</td>\n",
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              "      <td>7900</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1538 rows × 11 columns</p>\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1538,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 444,\n        \"min\": 1,\n        \"max\": 1538,\n        \"num_unique_values\": 1538,\n        \"samples\": [\n          482,\n          77,\n          1503\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"engine_power\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 51,\n        \"max\": 77,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          74,\n          66,\n          51\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age_in_days\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1289,\n        \"min\": 366,\n        \"max\": 4658,\n        \"num_unique_values\": 140,\n        \"samples\": [\n          2251,\n          1127,\n          640\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"km\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 40046,\n        \"min\": 1232,\n        \"max\": 235000,\n        \"num_unique_values\": 988,\n        \"samples\": [\n          20653,\n          117183,\n          14366\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"previous_owners\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          4,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1335178263566017,\n        \"min\": 36.8558387756348,\n        \"max\": 46.7956123352051,\n        \"num_unique_values\": 449,\n        \"samples\": [\n          45.0069007873535,\n          45.684211730957,\n          45.5459785461426\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lon\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.32818989939984,\n        \"min\": 7.24539995193481,\n        \"max\": 18.3655204772949,\n        \"num_unique_values\": 450,\n        \"samples\": [\n          7.64412021636963,\n          7.332200050354,\n          12.1605596542358\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1939,\n        \"min\": 2500,\n        \"max\": 11100,\n        \"num_unique_values\": 222,\n        \"samples\": [\n          7590,\n          5199,\n          5250\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model_lounge\",\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\": \"model_pop\",\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\": \"model_sport\",\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}"
            }
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "sns.heatmap(cor,vmax=1,vmin=-1,annot=True,linewidth=5,cmap='bwr')"
      ],
      "metadata": {
        "id": "etsR4U8CCOIU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "PJEfKytCCX6T",
        "outputId": "f37f38fd-5f89-44fb-ca7c-f54f11cba47e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        ID  engine_power  age_in_days      km  previous_owners        lat  \\\n",
              "0        1            51          882   25000                1  44.907242   \n",
              "1        2            51         1186   32500                1  45.666359   \n",
              "2        3            74         4658  142228                1  45.503300   \n",
              "3        4            51         2739  160000                1  40.633171   \n",
              "4        5            73         3074  106880                1  41.903221   \n",
              "...    ...           ...          ...     ...              ...        ...   \n",
              "1533  1534            51         3712  115280                1  45.069679   \n",
              "1534  1535            74         3835  112000                1  45.845692   \n",
              "1535  1536            51         2223   60457                1  45.481541   \n",
              "1536  1537            51         2557   80750                1  45.000702   \n",
              "1537  1538            51         1766   54276                1  40.323410   \n",
              "\n",
              "            lon  price  model_lounge  model_pop  model_sport  \n",
              "0      8.611560   8900             1          0            0  \n",
              "1     12.241890   8800             0          1            0  \n",
              "2     11.417840   4200             0          0            1  \n",
              "3     17.634609   6000             1          0            0  \n",
              "4     12.495650   5700             0          1            0  \n",
              "...         ...    ...           ...        ...          ...  \n",
              "1533   7.704920   5200             0          0            1  \n",
              "1534   8.666870   4600             1          0            0  \n",
              "1535   9.413480   7500             0          1            0  \n",
              "1536   7.682270   5990             1          0            0  \n",
              "1537  17.568270   7900             0          1            0  \n",
              "\n",
              "[1538 rows x 11 columns]"
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
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              "            '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-619e453c-e05d-4743-b083-34f69640be4b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_7b3c4622-196b-4d84-8174-4fd7f7f0da6f\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-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')\"\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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              "        document.querySelector('#id_7b3c4622-196b-4d84-8174-4fd7f7f0da6f button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\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",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1538,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 444,\n        \"min\": 1,\n        \"max\": 1538,\n        \"num_unique_values\": 1538,\n        \"samples\": [\n          482,\n          77,\n          1503\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"engine_power\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 51,\n        \"max\": 77,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          74,\n          66,\n          51\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age_in_days\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1289,\n        \"min\": 366,\n        \"max\": 4658,\n        \"num_unique_values\": 140,\n        \"samples\": [\n          2251,\n          1127,\n          640\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"km\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 40046,\n        \"min\": 1232,\n        \"max\": 235000,\n        \"num_unique_values\": 988,\n        \"samples\": [\n          20653,\n          117183,\n          14366\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"previous_owners\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          4,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1335178263566017,\n        \"min\": 36.8558387756348,\n        \"max\": 46.7956123352051,\n        \"num_unique_values\": 449,\n        \"samples\": [\n          45.0069007873535,\n          45.684211730957,\n          45.5459785461426\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lon\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.32818989939984,\n        \"min\": 7.24539995193481,\n        \"max\": 18.3655204772949,\n        \"num_unique_values\": 450,\n        \"samples\": [\n          7.64412021636963,\n          7.332200050354,\n          12.1605596542358\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1939,\n        \"min\": 2500,\n        \"max\": 11100,\n        \"num_unique_values\": 222,\n        \"samples\": [\n          7590,\n          5199,\n          5250\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model_lounge\",\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\": \"model_pop\",\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\": \"model_sport\",\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}"
            }
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1=df.drop(['lat','lon'],axis=1)\n",
        "df1"
      ],
      "metadata": {
        "id": "LpjfQ5UsCccR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2=df1.drop(['model_lounge','model_pop','model_sport'],axis=1)\n",
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "oif4lU2JCq9x",
        "outputId": "3216c554-5bde-44c5-b30a-5801fba23bf6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        ID  engine_power  age_in_days      km  previous_owners  price\n",
              "0        1            51          882   25000                1   8900\n",
              "1        2            51         1186   32500                1   8800\n",
              "2        3            74         4658  142228                1   4200\n",
              "3        4            51         2739  160000                1   6000\n",
              "4        5            73         3074  106880                1   5700\n",
              "...    ...           ...          ...     ...              ...    ...\n",
              "1533  1534            51         3712  115280                1   5200\n",
              "1534  1535            74         3835  112000                1   4600\n",
              "1535  1536            51         2223   60457                1   7500\n",
              "1536  1537            51         2557   80750                1   5990\n",
              "1537  1538            51         1766   54276                1   7900\n",
              "\n",
              "[1538 rows x 6 columns]"
            ],
            "text/html": [
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>engine_power</th>\n",
              "      <th>age_in_days</th>\n",
              "      <th>km</th>\n",
              "      <th>previous_owners</th>\n",
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              "      <th>0</th>\n",
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              "      <td>51</td>\n",
              "      <td>882</td>\n",
              "      <td>25000</td>\n",
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              "      <td>8900</td>\n",
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              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>74</td>\n",
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              "      <td>142228</td>\n",
              "      <td>1</td>\n",
              "      <td>4200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>51</td>\n",
              "      <td>2739</td>\n",
              "      <td>160000</td>\n",
              "      <td>1</td>\n",
              "      <td>6000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>73</td>\n",
              "      <td>3074</td>\n",
              "      <td>106880</td>\n",
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              "      <td>5700</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>1533</th>\n",
              "      <td>1534</td>\n",
              "      <td>51</td>\n",
              "      <td>3712</td>\n",
              "      <td>115280</td>\n",
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              "      <th>1534</th>\n",
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              "      <td>74</td>\n",
              "      <td>3835</td>\n",
              "      <td>112000</td>\n",
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              "      <td>4600</td>\n",
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              "    <tr>\n",
              "      <th>1535</th>\n",
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              "      <td>7500</td>\n",
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              "    <tr>\n",
              "      <th>1536</th>\n",
              "      <td>1537</td>\n",
              "      <td>51</td>\n",
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              "      <td>80750</td>\n",
              "      <td>1</td>\n",
              "      <td>5990</td>\n",
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              "    <tr>\n",
              "      <th>1537</th>\n",
              "      <td>1538</td>\n",
              "      <td>51</td>\n",
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              "      <td>1</td>\n",
              "      <td>7900</td>\n",
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              "  </tbody>\n",
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              "<p>1538 rows × 6 columns</p>\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "    }\n",
              "  }\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",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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              "        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",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df2');\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df2",
              "summary": "{\n  \"name\": \"df2\",\n  \"rows\": 1538,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 444,\n        \"min\": 1,\n        \"max\": 1538,\n        \"num_unique_values\": 1538,\n        \"samples\": [\n          482,\n          77,\n          1503\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"engine_power\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 51,\n        \"max\": 77,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          74,\n          66,\n          51\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age_in_days\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1289,\n        \"min\": 366,\n        \"max\": 4658,\n        \"num_unique_values\": 140,\n        \"samples\": [\n          2251,\n          1127,\n          640\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"km\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 40046,\n        \"min\": 1232,\n        \"max\": 235000,\n        \"num_unique_values\": 988,\n        \"samples\": [\n          20653,\n          117183,\n          14366\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"previous_owners\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          4,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1939,\n        \"min\": 2500,\n        \"max\": 11100,\n        \"num_unique_values\": 222,\n        \"samples\": [\n          7590,\n          5199,\n          5250\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import linearRegration\n",
        "reg=linearRegration()\n",
        "reg.fit()"
      ],
      "metadata": {
        "id": "KkohRkZ0Hrsb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lBo1GKeuc9Rj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df=pd.read_csv('/winequality.csv')"
      ],
      "metadata": {
        "id": "-tiCrBFjc_l4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YHQ9JScxdGeO",
        "outputId": "3fcd3a89-a6b1-475c-fb9a-6c7baeaf0755"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4898 entries, 0 to 4897\n",
            "Data columns (total 1 columns):\n",
            " #   Column                                                                                                                                                                   Non-Null Count  Dtype \n",
            "---  ------                                                                                                                                                                   --------------  ----- \n",
            " 0   fixed acidity;\"volatile acidity\";\"citric acid\";\"residual sugar\";\"chlorides\";\"free sulfur dioxide\";\"total sulfur dioxide\";\"density\";\"pH\";\"sulphates\";\"alcohol\";\"quality\"  4898 non-null   object\n",
            "dtypes: object(1)\n",
            "memory usage: 38.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        },
        "id": "pEAi2PAddL8W",
        "outputId": "e2fba822-daad-46ba-c26a-85a909fb7a80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       fixed acidity;\"volatile acidity\";\"citric acid\";\"residual sugar\";\"chlorides\";\"free sulfur dioxide\";\"total sulfur dioxide\";\"density\";\"pH\";\"sulphates\";\"alcohol\";\"quality\"\n",
              "count                                                4898                                                                                                                     \n",
              "unique                                               3961                                                                                                                     \n",
              "top     7.3;0.19;0.27;13.9;0.057;45;155;0.99807;2.94;0...                                                                                                                     \n",
              "freq                                                    8                                                                                                                     "
            ],
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              "      <th>fixed acidity;\"volatile acidity\";\"citric acid\";\"residual sugar\";\"chlorides\";\"free sulfur dioxide\";\"total sulfur dioxide\";\"density\";\"pH\";\"sulphates\";\"alcohol\";\"quality\"</th>\n",
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              "      <th>count</th>\n",
              "      <td>4898</td>\n",
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              "      <td>3961</td>\n",
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              "      <th>freq</th>\n",
              "      <td>8</td>\n",
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              "        const element = document.querySelector('#df-862ffc76-45f7-44e8-a734-222a7bf9974f');\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",
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              "\n",
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              "\n",
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              "    border: 2px solid var(--fill-color);\n",
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              "    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",
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              "      border-color: transparent;\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",
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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",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"fixed acidity;\\\"volatile acidity\\\";\\\"citric acid\\\";\\\"residual sugar\\\";\\\"chlorides\\\";\\\"free sulfur dioxide\\\";\\\"total sulfur dioxide\\\";\\\"density\\\";\\\"pH\\\";\\\"sulphates\\\";\\\"alcohol\\\";\\\"quality\\\"\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3961,\n          \"8\",\n          \"4898\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "ycDehKwNdXP9",
        "outputId": "039ac45d-5345-46af-fe80-251f33037b10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     fixed acidity;\"volatile acidity\";\"citric acid\";\"residual sugar\";\"chlorides\";\"free sulfur dioxide\";\"total sulfur dioxide\";\"density\";\"pH\";\"sulphates\";\"alcohol\";\"quality\"\n",
              "0      7;0.27;0.36;20.7;0.045;45;170;1.001;3;0.45;8.8;6                                                                                                                     \n",
              "1     6.3;0.3;0.34;1.6;0.049;14;132;0.994;3.3;0.49;9...                                                                                                                     \n",
              "2     8.1;0.28;0.4;6.9;0.05;30;97;0.9951;3.26;0.44;1...                                                                                                                     \n",
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              "4     7.2;0.23;0.32;8.5;0.058;47;186;0.9956;3.19;0.4...                                                                                                                     \n",
              "...                                                 ...                                                                                                                     \n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 4898,\n  \"fields\": [\n    {\n      \"column\": \"fixed acidity;\\\"volatile acidity\\\";\\\"citric acid\\\";\\\"residual sugar\\\";\\\"chlorides\\\";\\\"free sulfur dioxide\\\";\\\"total sulfur dioxide\\\";\\\"density\\\";\\\"pH\\\";\\\"sulphates\\\";\\\"alcohol\\\";\\\"quality\\\"\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3961,\n        \"samples\": [\n          \"7.2;0.32;0.47;5.1;0.044;19;65;0.991;3.03;0.41;12.6;4\",\n          \"9.7;0.24;0.45;1.2;0.033;11;59;0.9926;2.74;0.47;10.8;6\",\n          \"7.2;0.27;0.74;12.5;0.037;47;156;0.9981;3.04;0.44;8.7;5\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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      "cell_type": "code",
      "source": [
        "df.shape\n"
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        },
        "id": "EmUK3p_6dZ98",
        "outputId": "daa07a15-d70c-4b9b-8bb7-b03b219a6687"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4898, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "q=df['quality']\n",
        "q"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "id": "GW-m0gJDdj0i",
        "outputId": "cbcd3366-7bc4-4189-c2c0-e4785d92b1ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "'quality'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3652\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3653\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3654\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'quality'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-8-9f3dd227d523>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'quality'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mq\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3759\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3760\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3761\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3762\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3763\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3653\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3654\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3655\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3656\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3657\u001b[0m             \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'quality'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# New Section"
      ],
      "metadata": {
        "id": "IHK1dH1_eKPt"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vsQ50KQ0eNzF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# New Section"
      ],
      "metadata": {
        "id": "tOqN3b2peQEc"
      }
    }
  ]
}