NYC.ipynb

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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"bhanushrichinta\",\"key\":\"ff51dcf48a3b86372d3894bf3c360293\"}'}"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "from google.colab import files\n",
        "files.upload()  # Choose the API key JSON file from your computer to upload\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install kaggle\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-E2ySs_dWd0d",
        "outputId": "e2b1eb91-f2a1-4ab1-c623-d46965a8508d"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.5.16)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.16.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from kaggle) (2023.7.22)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.31.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kaggle) (4.66.1)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle) (8.0.1)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.0.4)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle) (6.0.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle) (0.5.1)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Move the uploaded API key JSON file to the Kaggle directory\n",
        "!mkdir -p ~/.kaggle\n",
        "!mv kaggle.json ~/.kaggle/\n",
        "\n",
        "# Set permissions for the API key file\n",
        "!chmod 600 ~/.kaggle/kaggle.json\n"
      ],
      "metadata": {
        "id": "VB4I-N-jWeT9"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle competitions download -c new-york-city-taxi-fare-prediction"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LhF3KeCJWlHD",
        "outputId": "49986e28-03f9-4db1-ffb7-45133449490b"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading new-york-city-taxi-fare-prediction.zip to /content\n",
            "100% 1.56G/1.56G [00:57<00:00, 27.5MB/s]\n",
            "100% 1.56G/1.56G [00:57<00:00, 29.3MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import zipfile\n",
        "\n",
        "# Specify the path to the ZIP file you want to unzip\n",
        "zip_file_path = '/content/new-york-city-taxi-fare-prediction.zip'\n",
        "\n",
        "# Specify the directory where you want to extract the contents\n",
        "extract_to_directory = '/content'\n",
        "\n",
        "# Open the ZIP file\n",
        "with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:\n",
        "    # Extract all the contents of the ZIP file to the specified directory\n",
        "    zip_ref.extractall(extract_to_directory)\n",
        "\n",
        "print(f'Unzipped {zip_file_path} to {extract_to_directory}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kr2MVVbEXYxE",
        "outputId": "1dcf5720-6a59-44b0-a14f-6fac65405638"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unzipped /content/new-york-city-taxi-fare-prediction.zip to /content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import datetime as dt\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sb\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import KFold\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.linear_model import Ridge, Lasso\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.neighbors import KNeighborsRegressor"
      ],
      "metadata": {
        "id": "OgtlOgfaXJcM"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/train.csv', nrows=50000)\n",
        "#df= pd.read_csv('../input/new-york-city-taxi-fare-prediction/train.csv', skiprows=lambda x: x%2 == 1)\n",
        "df_test = pd.read_csv('/content/test.csv')\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "id": "UczHPtvXXNuk",
        "outputId": "3cbe7e8f-cd6d-4963-9c05-11ff39925717"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                             key  fare_amount          pickup_datetime  \\\n",
              "0    2009-06-15 17:26:21.0000001          4.5  2009-06-15 17:26:21 UTC   \n",
              "1    2010-01-05 16:52:16.0000002         16.9  2010-01-05 16:52:16 UTC   \n",
              "2   2011-08-18 00:35:00.00000049          5.7  2011-08-18 00:35:00 UTC   \n",
              "3    2012-04-21 04:30:42.0000001          7.7  2012-04-21 04:30:42 UTC   \n",
              "4  2010-03-09 07:51:00.000000135          5.3  2010-03-09 07:51:00 UTC   \n",
              "\n",
              "   pickup_longitude  pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
              "0        -73.844311        40.721319         -73.841610         40.712278   \n",
              "1        -74.016048        40.711303         -73.979268         40.782004   \n",
              "2        -73.982738        40.761270         -73.991242         40.750562   \n",
              "3        -73.987130        40.733143         -73.991567         40.758092   \n",
              "4        -73.968095        40.768008         -73.956655         40.783762   \n",
              "\n",
              "   passenger_count  \n",
              "0                1  \n",
              "1                1  \n",
              "2                2  \n",
              "3                1  \n",
              "4                1  "
            ],
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>key</th>\n",
              "      <th>fare_amount</th>\n",
              "      <th>pickup_datetime</th>\n",
              "      <th>pickup_longitude</th>\n",
              "      <th>pickup_latitude</th>\n",
              "      <th>dropoff_longitude</th>\n",
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              "      <td>2009-06-15 17:26:21.0000001</td>\n",
              "      <td>4.5</td>\n",
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              "      <th>1</th>\n",
              "      <td>2010-01-05 16:52:16.0000002</td>\n",
              "      <td>16.9</td>\n",
              "      <td>2010-01-05 16:52:16 UTC</td>\n",
              "      <td>-74.016048</td>\n",
              "      <td>40.711303</td>\n",
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              "      <td>40.782004</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-08-18 00:35:00.00000049</td>\n",
              "      <td>5.7</td>\n",
              "      <td>2011-08-18 00:35:00 UTC</td>\n",
              "      <td>-73.982738</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2012-04-21 04:30:42.0000001</td>\n",
              "      <td>7.7</td>\n",
              "      <td>2012-04-21 04:30:42 UTC</td>\n",
              "      <td>-73.987130</td>\n",
              "      <td>40.733143</td>\n",
              "      <td>-73.991567</td>\n",
              "      <td>40.758092</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2010-03-09 07:51:00.000000135</td>\n",
              "      <td>5.3</td>\n",
              "      <td>2010-03-09 07:51:00 UTC</td>\n",
              "      <td>-73.968095</td>\n",
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              "      <td>40.783762</td>\n",
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              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3559852e-8cb5-4418-aa74-cb8b7febc748 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-3559852e-8cb5-4418-aa74-cb8b7febc748');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
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              "\n",
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              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
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              "    border-radius: 50%;\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
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              "    width: 32px;\n",
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              "\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",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
              "  .colab-df-spinner {\n",
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              "    border-color: transparent;\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-815b0099-b5db-4393-b07c-2a039c492274 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df.shape,\"\\n\\n\", df.info(),\"\\n\\n\", df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kmzs_QUIX2Nt",
        "outputId": "56bcee63-9563-47c3-cf7a-7fd02d55be3c"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 50000 entries, 0 to 49999\n",
            "Data columns (total 8 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   key                50000 non-null  object \n",
            " 1   fare_amount        50000 non-null  float64\n",
            " 2   pickup_datetime    50000 non-null  object \n",
            " 3   pickup_longitude   50000 non-null  float64\n",
            " 4   pickup_latitude    50000 non-null  float64\n",
            " 5   dropoff_longitude  50000 non-null  float64\n",
            " 6   dropoff_latitude   50000 non-null  float64\n",
            " 7   passenger_count    50000 non-null  int64  \n",
            "dtypes: float64(5), int64(1), object(2)\n",
            "memory usage: 3.1+ MB\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((50000, 8),\n",
              " '\\n\\n',\n",
              " None,\n",
              " '\\n\\n',\n",
              "         fare_amount  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
              " count  50000.000000      50000.000000     50000.000000       50000.000000   \n",
              " mean      11.364171        -72.509756        39.933759         -72.504616   \n",
              " std        9.685557         10.393860         6.224857          10.407570   \n",
              " min       -5.000000        -75.423848       -74.006893         -84.654241   \n",
              " 25%        6.000000        -73.992062        40.734880         -73.991152   \n",
              " 50%        8.500000        -73.981840        40.752678         -73.980082   \n",
              " 75%       12.500000        -73.967148        40.767360         -73.963584   \n",
              " max      200.000000         40.783472       401.083332          40.851027   \n",
              " \n",
              "        dropoff_latitude  passenger_count  \n",
              " count      50000.000000     50000.000000  \n",
              " mean          39.926251         1.667840  \n",
              " std            6.014737         1.289195  \n",
              " min          -74.006377         0.000000  \n",
              " 25%           40.734372         1.000000  \n",
              " 50%           40.753372         1.000000  \n",
              " 75%           40.768167         2.000000  \n",
              " max           43.415190         6.000000  )"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fwgb_x_UX5TW",
        "outputId": "bf3a3266-15ca-4a30-9cdf-b7ebb737b09d"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "key                  0\n",
              "fare_amount          0\n",
              "pickup_datetime      0\n",
              "pickup_longitude     0\n",
              "pickup_latitude      0\n",
              "dropoff_longitude    0\n",
              "dropoff_latitude     0\n",
              "passenger_count      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZLCJjWgNX7im",
        "outputId": "e69209db-ffea-40c6-8607-6a7d9c746355"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dropna(axis=0, inplace=True)\n",
        "np.sum(pd.isnull(df))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oRxoKohGX-Aa",
        "outputId": "03f48637-6647-461b-ee40-a0f3993d98d3"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "key                  0\n",
              "fare_amount          0\n",
              "pickup_datetime      0\n",
              "pickup_longitude     0\n",
              "pickup_latitude      0\n",
              "dropoff_longitude    0\n",
              "dropoff_latitude     0\n",
              "passenger_count      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['fare_amount']<0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "NEbweLFoYAaD",
        "outputId": "414bea4b-532f-469f-ac05-c79067acdcb8"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               key  fare_amount          pickup_datetime  \\\n",
              "2039   2010-03-09 23:37:10.0000005         -2.9  2010-03-09 23:37:10 UTC   \n",
              "2486   2015-03-22 05:14:27.0000001         -2.5  2015-03-22 05:14:27 UTC   \n",
              "13032  2013-08-30 08:57:10.0000002         -3.0  2013-08-30 08:57:10 UTC   \n",
              "28839  2013-08-11 13:39:10.0000001         -2.5  2013-08-11 13:39:10 UTC   \n",
              "36722  2015-04-30 15:19:45.0000003         -2.5  2015-04-30 15:19:45 UTC   \n",
              "42337  2015-03-09 10:29:46.0000004         -5.0  2015-03-09 10:29:46 UTC   \n",
              "\n",
              "       pickup_longitude  pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
              "2039         -73.789450        40.643498         -73.788665         40.641952   \n",
              "2486         -74.000031        40.720631         -73.999809         40.720539   \n",
              "13032        -73.995062        40.740755         -73.995885         40.741357   \n",
              "28839        -73.785260        40.648442           0.000000          0.000000   \n",
              "36722        -73.952187        40.790112         -73.950043         40.792839   \n",
              "42337        -73.990974        40.755985         -73.980820         40.759869   \n",
              "\n",
              "       passenger_count  \n",
              "2039                 1  \n",
              "2486                 1  \n",
              "13032                4  \n",
              "28839                1  \n",
              "36722                1  \n",
              "42337                1  "
            ],
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              "      <th>28839</th>\n",
              "      <td>2013-08-11 13:39:10.0000001</td>\n",
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              "      <th>42337</th>\n",
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              "        const element = document.querySelector('#df-b950c803-4047-40fd-bc47-c6ca2e29f403');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Setting minimum fare amount to zero.\n",
        "df['fare_amount'][df['fare_amount']<0] = 0.1\n",
        "df[df['fare_amount']<0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "jH4IKPLRYEQH",
        "outputId": "e05dd381-41b4-4977-a763-d409d4f164b6"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [key, fare_amount, pickup_datetime, pickup_longitude, pickup_latitude, dropoff_longitude, dropoff_latitude, passenger_count]\n",
              "Index: []"
            ],
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              "      <th></th>\n",
              "      <th>key</th>\n",
              "      <th>fare_amount</th>\n",
              "      <th>pickup_datetime</th>\n",
              "      <th>pickup_longitude</th>\n",
              "      <th>pickup_latitude</th>\n",
              "      <th>dropoff_longitude</th>\n",
              "      <th>dropoff_latitude</th>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ecae195c-75ca-437a-a545-41f84f32c42b');\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",
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          "metadata": {},
          "execution_count": 13
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    },
    {
      "cell_type": "code",
      "source": [
        "df['pickup_datetime'] = pd.to_datetime(df.pickup_datetime)\n",
        "df_test['pickup_datetime'] = pd.to_datetime(df_test.pickup_datetime)"
      ],
      "metadata": {
        "id": "MHvIk0ZtYu-0"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[:, 'pickup_hour'] = df['pickup_datetime'].dt.hour\n",
        "df.loc[:, 'pickup_weekday'] = df['pickup_datetime'].dt.day_name()\n",
        "df.loc[:, 'pickup_date'] = df['pickup_datetime'].dt.day\n",
        "df.loc[:, 'pickup_month'] = df['pickup_datetime'].dt.month\n",
        "df.loc[:, 'pickup_day'] = df['pickup_datetime'].dt.dayofweek\n",
        "df_test.loc[:, 'pickup_hour'] = df_test['pickup_datetime'].dt.hour\n",
        "df_test.loc[:, 'pickup_weekday'] = df_test['pickup_datetime'].dt.day_name()\n",
        "df_test.loc[:, 'pickup_date'] = df_test['pickup_datetime'].dt.day\n",
        "df_test.loc[:, 'pickup_month'] = df_test['pickup_datetime'].dt.month\n",
        "df_test.loc[:, 'pickup_day'] = df_test['pickup_datetime'].dt.dayofweek"
      ],
      "metadata": {
        "id": "oTRgnA9nZC8Y"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
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        "id": "xgeiVtY7ZQB7",
        "outputId": "e0481803-94ac-4e3a-f374-33f4fc815d66",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        }
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                  key  fare_amount           pickup_datetime  \\\n",
              "0         2009-06-15 17:26:21.0000001          4.5 2009-06-15 17:26:21+00:00   \n",
              "1         2010-01-05 16:52:16.0000002         16.9 2010-01-05 16:52:16+00:00   \n",
              "2        2011-08-18 00:35:00.00000049          5.7 2011-08-18 00:35:00+00:00   \n",
              "3         2012-04-21 04:30:42.0000001          7.7 2012-04-21 04:30:42+00:00   \n",
              "4       2010-03-09 07:51:00.000000135          5.3 2010-03-09 07:51:00+00:00   \n",
              "...                               ...          ...                       ...   \n",
              "249995    2012-06-30 00:36:48.0000002          6.1 2012-06-30 00:36:48+00:00   \n",
              "249996  2013-03-10 21:24:00.000000123         11.5 2013-03-10 21:24:00+00:00   \n",
              "249997    2013-09-14 05:00:33.0000002          4.5 2013-09-14 05:00:33+00:00   \n",
              "249998    2014-09-09 15:22:23.0000002         10.5 2014-09-09 15:22:23+00:00   \n",
              "249999    2013-04-26 18:19:06.0000003          7.0 2013-04-26 18:19:06+00:00   \n",
              "\n",
              "        pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
              "0             -73.844311        40.721319         -73.841610   \n",
              "1             -74.016048        40.711303         -73.979268   \n",
              "2             -73.982738        40.761270         -73.991242   \n",
              "3             -73.987130        40.733143         -73.991567   \n",
              "4             -73.968095        40.768008         -73.956655   \n",
              "...                  ...              ...                ...   \n",
              "249995        -73.992554        40.758295         -74.002086   \n",
              "249996        -73.990002        40.689777         -73.953538   \n",
              "249997        -73.992934        40.719706         -73.986884   \n",
              "249998        -73.979372        40.761691         -73.987105   \n",
              "249999        -73.980853        40.776583         -73.967860   \n",
              "\n",
              "        dropoff_latitude  passenger_count  pickup_hour pickup_weekday  \\\n",
              "0              40.712278                1           17         Monday   \n",
              "1              40.782004                1           16        Tuesday   \n",
              "2              40.750562                2            0       Thursday   \n",
              "3              40.758092                1            4       Saturday   \n",
              "4              40.783762                1            7        Tuesday   \n",
              "...                  ...              ...          ...            ...   \n",
              "249995         40.739535                2            0       Saturday   \n",
              "249996         40.713095                1           21         Sunday   \n",
              "249997         40.722294                1            5       Saturday   \n",
              "249998         40.755775                1           15        Tuesday   \n",
              "249999         40.792093                1           18         Friday   \n",
              "\n",
              "        pickup_date  pickup_month  pickup_day  \n",
              "0                15             6           0  \n",
              "1                 5             1           1  \n",
              "2                18             8           3  \n",
              "3                21             4           5  \n",
              "4                 9             3           1  \n",
              "...             ...           ...         ...  \n",
              "249995           30             6           5  \n",
              "249996           10             3           6  \n",
              "249997           14             9           5  \n",
              "249998            9             9           1  \n",
              "249999           26             4           4  \n",
              "\n",
              "[249998 rows x 13 columns]"
            ],
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              "      <th></th>\n",
              "      <th>key</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-06-15 17:26:21.0000001</td>\n",
              "      <td>4.5</td>\n",
              "      <td>2009-06-15 17:26:21+00:00</td>\n",
              "      <td>-73.844311</td>\n",
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              "      <td>40.712278</td>\n",
              "      <td>1</td>\n",
              "      <td>17</td>\n",
              "      <td>Monday</td>\n",
              "      <td>15</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2010-01-05 16:52:16.0000002</td>\n",
              "      <td>16.9</td>\n",
              "      <td>2010-01-05 16:52:16+00:00</td>\n",
              "      <td>-74.016048</td>\n",
              "      <td>40.711303</td>\n",
              "      <td>-73.979268</td>\n",
              "      <td>40.782004</td>\n",
              "      <td>1</td>\n",
              "      <td>16</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-08-18 00:35:00.00000049</td>\n",
              "      <td>5.7</td>\n",
              "      <td>2011-08-18 00:35:00+00:00</td>\n",
              "      <td>-73.982738</td>\n",
              "      <td>40.761270</td>\n",
              "      <td>-73.991242</td>\n",
              "      <td>40.750562</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>18</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2012-04-21 04:30:42.0000001</td>\n",
              "      <td>7.7</td>\n",
              "      <td>2012-04-21 04:30:42+00:00</td>\n",
              "      <td>-73.987130</td>\n",
              "      <td>40.733143</td>\n",
              "      <td>-73.991567</td>\n",
              "      <td>40.758092</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>21</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2010-03-09 07:51:00.000000135</td>\n",
              "      <td>5.3</td>\n",
              "      <td>2010-03-09 07:51:00+00:00</td>\n",
              "      <td>-73.968095</td>\n",
              "      <td>40.768008</td>\n",
              "      <td>-73.956655</td>\n",
              "      <td>40.783762</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249995</th>\n",
              "      <td>2012-06-30 00:36:48.0000002</td>\n",
              "      <td>6.1</td>\n",
              "      <td>2012-06-30 00:36:48+00:00</td>\n",
              "      <td>-73.992554</td>\n",
              "      <td>40.758295</td>\n",
              "      <td>-74.002086</td>\n",
              "      <td>40.739535</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>30</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249996</th>\n",
              "      <td>2013-03-10 21:24:00.000000123</td>\n",
              "      <td>11.5</td>\n",
              "      <td>2013-03-10 21:24:00+00:00</td>\n",
              "      <td>-73.990002</td>\n",
              "      <td>40.689777</td>\n",
              "      <td>-73.953538</td>\n",
              "      <td>40.713095</td>\n",
              "      <td>1</td>\n",
              "      <td>21</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>10</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249997</th>\n",
              "      <td>2013-09-14 05:00:33.0000002</td>\n",
              "      <td>4.5</td>\n",
              "      <td>2013-09-14 05:00:33+00:00</td>\n",
              "      <td>-73.992934</td>\n",
              "      <td>40.719706</td>\n",
              "      <td>-73.986884</td>\n",
              "      <td>40.722294</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>14</td>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249998</th>\n",
              "      <td>2014-09-09 15:22:23.0000002</td>\n",
              "      <td>10.5</td>\n",
              "      <td>2014-09-09 15:22:23+00:00</td>\n",
              "      <td>-73.979372</td>\n",
              "      <td>40.761691</td>\n",
              "      <td>-73.987105</td>\n",
              "      <td>40.755775</td>\n",
              "      <td>1</td>\n",
              "      <td>15</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249999</th>\n",
              "      <td>2013-04-26 18:19:06.0000003</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2013-04-26 18:19:06+00:00</td>\n",
              "      <td>-73.980853</td>\n",
              "      <td>40.776583</td>\n",
              "      <td>-73.967860</td>\n",
              "      <td>40.792093</td>\n",
              "      <td>1</td>\n",
              "      <td>18</td>\n",
              "      <td>Friday</td>\n",
              "      <td>26</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>249998 rows × 13 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-768a04f8-ee2a-47b5-8a0a-c9c3546d3a87')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-768a04f8-ee2a-47b5-8a0a-c9c3546d3a87 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-768a04f8-ee2a-47b5-8a0a-c9c3546d3a87');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-1ee41ac3-9727-4d2b-9f00-0422a070d7b7\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1ee41ac3-9727-4d2b-9f00-0422a070d7b7')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1ee41ac3-9727-4d2b-9f00-0422a070d7b7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f, axes = plt.subplots(2,2,figsize=(10, 10), sharex=False, sharey = False)\n",
        "sb.despine(left=True)\n",
        "sb.distplot(df['pickup_latitude'].values, label = 'pickup_latitude',color=\"b\",bins = 100, ax=axes[0,0])\n",
        "sb.distplot(df['pickup_longitude'].values, label = 'pickup_longitude',color=\"r\",bins =100, ax=axes[1,0])\n",
        "sb.distplot(df['dropoff_latitude'].values, label = 'dropoff_latitude',color=\"b\",bins =100, ax=axes[0,1])\n",
        "sb.distplot(df['dropoff_longitude'].values, label = 'dropoff_longitude',color=\"r\",bins =100, ax=axes[1,1])\n",
        "axes[0, 0].set_xlim(-50,70)\n",
        "axes[0, 1].set_xlim(-50,70)\n",
        "axes[1, 0].set_xlim(-80,10)\n",
        "axes[1, 1].set_xlim(-80,10)\n",
        "plt.setp(axes, yticks=[])\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "b-N1Jt9LjOdX",
        "outputId": "3c62ddd1-f4b6-43e5-853e-715ef65cf1cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.loc[(df.pickup_latitude > 40.6) & (df.pickup_latitude < 40.9)]\n",
        "df = df.loc[(df.dropoff_latitude>40.6) & (df.dropoff_latitude < 40.9)]\n",
        "df = df.loc[(df.dropoff_longitude > -74.05) & (df.dropoff_longitude < -73.7)]\n",
        "df = df.loc[(df.pickup_longitude > -74.05) & (df.pickup_longitude < -73.7)]\n",
        "df_data_new = df.copy()\n",
        "sb.set(style=\"white\", palette=\"muted\", color_codes=True)\n",
        "f, axes = plt.subplots(2,2,figsize=(10, 10), sharex=False, sharey = False)#\n",
        "sb.despine(left=True)\n",
        "sb.distplot(df_data_new['pickup_latitude'].values, label = 'pickup_latitude',color=\"b\",bins = 100, ax=axes[0,0])\n",
        "sb.distplot(df_data_new['pickup_longitude'].values, label = 'pickup_longitude',color=\"r\",bins =100, ax=axes[0,1])\n",
        "sb.distplot(df_data_new['dropoff_latitude'].values, label = 'dropoff_latitude',color=\"b\",bins =100, ax=axes[1, 0])\n",
        "sb.distplot(df_data_new['dropoff_longitude'].values, label = 'dropoff_longitude',color=\"r\",bins =100, ax=axes[1, 1])\n",
        "plt.setp(axes, yticks=[])\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "vC6vts0GZ20-",
        "outputId": "92c98378-c67f-4e4a-d5f9-53a3940df3ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Distance\n"
      ],
      "metadata": {
        "id": "Q32xOEeonzlP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install geopy"
      ],
      "metadata": {
        "id": "0EuFWmYFjiji",
        "outputId": "b37e462c-31f9-4109-e03f-919f7caec1f5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: geopy in /usr/local/lib/python3.10/dist-packages (2.3.0)\n",
            "Requirement already satisfied: geographiclib<3,>=1.52 in /usr/local/lib/python3.10/dist-packages (from geopy) (2.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from geopy.geocoders import Nominatim\n",
        "\n",
        "# Create a geolocator object\n",
        "geolocator = Nominatim(user_agent=\"city_locator\")\n"
      ],
      "metadata": {
        "id": "UrB2CGmIpyfA"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_city_name(latitude, longitude):\n",
        "    try:\n",
        "        location = geolocator.reverse((latitude, longitude), exactly_one=True)\n",
        "        address = location.raw.get(\"address\", {})\n",
        "\n",
        "        # Extract the city name from the address dictionary\n",
        "        city = address.get(\"city\", \"\")\n",
        "\n",
        "        if not city:\n",
        "            # If city name is not available, try other alternatives like town or village\n",
        "            city = address.get(\"town\", \"\")\n",
        "            if not city:\n",
        "                city = address.get(\"village\", \"\")\n",
        "\n",
        "        return city\n",
        "\n",
        "    except Exception as e:\n",
        "        print(f\"Error: {e}\")\n",
        "        return None\n"
      ],
      "metadata": {
        "id": "PrkrlAXKp3at"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from patsy import dmatrices\n",
        "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
        "# the independent variables set\n",
        "X =df.drop(['key', 'pickup_datetime','pickup_weekday', 'fare_amount'], axis = 1)\n",
        "\n",
        "# VIF dataframe\n",
        "vif_data = pd.DataFrame()\n",
        "vif_data[\"feature\"] = X.columns\n",
        "\n",
        "# calculating VIF for each feature\n",
        "vif_data[\"VIF\"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
        "\n",
        "print(vif_data)"
      ],
      "metadata": {
        "id": "dIbHYYITzS9D",
        "outputId": "69a62bac-f136-4e3a-dbf8-1b820bbc8674",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "             feature           VIF\n",
            "0   pickup_longitude  4.159319e+06\n",
            "1    pickup_latitude  2.630434e+06\n",
            "2  dropoff_longitude  3.895089e+06\n",
            "3   dropoff_latitude  2.021469e+06\n",
            "4    passenger_count  2.663106e+00\n",
            "5        pickup_hour  5.379755e+00\n",
            "6        pickup_date  4.266828e+00\n",
            "7       pickup_month  4.321991e+00\n",
            "8         pickup_day  3.478447e+00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (12,6))\n",
        "sb.heatmap(df.drop(['key', 'pickup_datetime','pickup_weekday'], axis = 1).corr(),\n",
        "           cmap ='BuGn', annot = True);"
      ],
      "metadata": {
        "id": "d-Sd0Ye31dWm",
        "outputId": "cd2b41c7-6141-4121-ca2f-ac122725c9fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
        }
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[['pickup_latitude', 'pickup_longitude', 'dropoff_longitude', 'dropoff_latitude']].corr()\n",
        "\n"
      ],
      "metadata": {
        "id": "w2D4k75s18Fk",
        "outputId": "d83330bf-12da-4783-874e-a2a96e5d2c59",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   pickup_latitude  pickup_longitude  dropoff_longitude  \\\n",
              "pickup_latitude           1.000000         -0.027605           0.054257   \n",
              "pickup_longitude         -0.027605          1.000000           0.300852   \n",
              "dropoff_longitude         0.054257          0.300852           1.000000   \n",
              "dropoff_latitude          0.424762          0.046557           0.117761   \n",
              "\n",
              "                   dropoff_latitude  \n",
              "pickup_latitude            0.424762  \n",
              "pickup_longitude           0.046557  \n",
              "dropoff_longitude          0.117761  \n",
              "dropoff_latitude           1.000000  "
            ],
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              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9c23cc5f-98bd-4944-8e83-bb1a6ee82f90 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-9c23cc5f-98bd-4944-8e83-bb1a6ee82f90');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-39098bbd-1326-4aa4-8e95-d11b65ffe0f6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-39098bbd-1326-4aa4-8e95-d11b65ffe0f6')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-39098bbd-1326-4aa4-8e95-d11b65ffe0f6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def haversine_distance(lat1, lng1, lat2, lng2):\n",
        "    lat1, lng1, lat2, lng2 = map(np.radians, (lat1, lng1, lat2, lng2))\n",
        "    AVG_EARTH_RADIUS = 6371  # in km\n",
        "    lat = lat2 - lat1\n",
        "    lng = lng2 - lng1\n",
        "    d = np.sin(lat * 0.5) * 2 + np.cos(lat1) * np.cos(lat2) * np.sin(lng * 0.5) * 2\n",
        "    h = 2 * AVG_EARTH_RADIUS * np.arcsin(np.sqrt(d))\n",
        "    return h\n",
        "\n",
        "df['haversine_distance'] = haversine_distance(df['pickup_latitude'].values,\n",
        "                                                     df['pickup_longitude'].values,\n",
        "                                                     df['dropoff_latitude'].values,\n",
        "                                                     df['dropoff_longitude'].values)\n",
        "df_test['haversine_distance'] = haversine_distance(df_test['pickup_latitude'].values,\n",
        "                                                     df_test['pickup_longitude'].values,\n",
        "                                                     df_test['dropoff_latitude'].values,\n",
        "                                                     df_test['dropoff_longitude'].values)"
      ],
      "metadata": {
        "id": "a_VK_hmoqv40"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop(['key', 'pickup_datetime','pickup_weekday', 'fare_amount'],\n",
        "            axis = 1)\n",
        "y = df['fare_amount']"
      ],
      "metadata": {
        "id": "MxGldUrGDSQr"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import preprocessing\n",
        "X= preprocessing.StandardScaler().fit(X).transform(X)\n",
        "X[0:5]"
      ],
      "metadata": {
        "id": "0NtklTDgvwH1",
        "outputId": "4c78aa5e-bc33-4f4e-89ca-5bc0e58d3fd9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 3.86328449, -1.10815541,  4.00682446, -1.30276524, -0.51829393,\n",
              "         0.5398016 , -0.07619801, -0.07904686, -1.54979277,         nan],\n",
              "       [-1.19316328, -1.48290175, -0.15924119,  1.00945633, -0.51829393,\n",
              "         0.38606293, -1.2308139 , -1.52280759, -1.03861501,  2.016862  ],\n",
              "       [-0.21241786,  0.38660207, -0.52162093, -0.03320882,  0.257134  ,\n",
              "        -2.07375572,  0.27018675,  0.49845743, -0.0162595 ,         nan],\n",
              "       [-0.3417314 , -0.66576317, -0.53145669,  0.21649758, -0.51829393,\n",
              "        -1.45880106,  0.61657152, -0.65655115,  1.00609601,  0.02640386],\n",
              "       [ 0.21871558,  0.6387028 ,  0.525116  ,  1.06775432, -0.51829393,\n",
              "        -0.99758506, -0.76896755, -0.9453033 , -1.03861501,  0.02258963]])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n",
        "print(X_train.ndim)\n",
        "print(y_train.ndim)\n",
        "print(X_train.shape)\n",
        "print(y_train.shape)\n",
        "print(X_test.shape)\n",
        "print(y_test.shape)"
      ],
      "metadata": {
        "id": "pG-WHDnuxHGi",
        "outputId": "848767e9-10e6-4290-d6a0-bf5ac5e993a0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2\n",
            "1\n",
            "(34111, 10)\n",
            "(34111,)\n",
            "(14619, 10)\n",
            "(14619,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q --upgrade linear-tree"
      ],
      "metadata": {
        "id": "1LIU9bLfxKzv"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###"
      ],
      "metadata": {
        "id": "5U8joUIRzZC1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import RidgeCV\n",
        "ridge = RidgeCV(cv=5).fit(X_train, y_train)\n",
        "ridge.score(X_train, y_train)"
      ],
      "metadata": {
        "id": "ToIRdb2IzbRE",
        "outputId": "3abd14d0-ddba-478a-bbbe-9f849ddb0d5d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 810
        }
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-25-acbff28336c8>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear_model\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mRidgeCV\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mridge\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRidgeCV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\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      3\u001b[0m \u001b[0mridge\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\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/sklearn/linear_model/_ridge.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m   2358\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_params\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   2359\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2360\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\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   2361\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2362\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m   2182\u001b[0m                 \u001b[0mscoring\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscoring\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2183\u001b[0m             )\n\u001b[0;32m-> 2184\u001b[0;31m             \u001b[0mgs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\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   2185\u001b[0m             \u001b[0mestimator\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_estimator_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2186\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malpha_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_estimator_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malpha\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/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[1;32m    872\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    873\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 874\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluate_candidates\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    875\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    876\u001b[0m             \u001b[0;31m# multimetric is determined here because in the case of a callable\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/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[0;34m(self, evaluate_candidates)\u001b[0m\n\u001b[1;32m   1386\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevaluate_candidates\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   1387\u001b[0m         \u001b[0;34m\"\"\"Search all candidates in param_grid\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1388\u001b[0;31m         \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mParameterGrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparam_grid\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[0m\u001b[1;32m   1389\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1390\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[0;34m(candidate_params, cv, more_results)\u001b[0m\n\u001b[1;32m    849\u001b[0m                     )\n\u001b[1;32m    850\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 851\u001b[0;31m                 \u001b[0m_warn_or_raise_about_fit_failures\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merror_score\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    852\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    853\u001b[0m                 \u001b[0;31m# For callable self.scoring, the return type is only know after\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/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36m_warn_or_raise_about_fit_failures\u001b[0;34m(results, error_score)\u001b[0m\n\u001b[1;32m    365\u001b[0m                 \u001b[0;34mf\"Below are more details about the failures:\\n{fit_errors_summary}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    366\u001b[0m             )\n\u001b[0;32m--> 367\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_fits_failed_message\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    368\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    369\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: \nAll the 15 fits failed.\nIt is very likely that your model is misconfigured.\nYou can try to debug the error by setting error_score='raise'.\n\nBelow are more details about the failures:\n--------------------------------------------------------------------------------\n15 fits failed with the following error:\nTraceback (most recent call last):\n  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\n    estimator.fit(X_train, y_train, **fit_params)\n  File \"/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py\", line 1126, in fit\n    X, y = self._validate_data(\n  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 584, in _validate_data\n    X, y = check_X_y(X, y, **check_params)\n  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py\", line 1106, in check_X_y\n    X = check_array(\n  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py\", line 921, in check_array\n    _assert_all_finite(\n  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py\", line 161, in _assert_all_finite\n    raise ValueError(msg_err)\nValueError: Input X contains NaN.\nRidge does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "stzRBvHlzyT6"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}