NYC_Taxi_Fare_Prediction.ipynb

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    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMsa9vTz7J4vM0E68N3YPcv",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tanyasheru23/NYC_Taxi_fare_prediction/blob/main/NYC_Taxi_Fare_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install kaggle"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0iZS3MI5V4vu",
        "outputId": "5d1ec317-a794-41bf-c32d-99f5b9160f3e"
      },
      "execution_count": null,
      "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": [
        "from google.colab import files\n",
        "files.upload()  # Choose the API key JSON file from your computer to upload"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "id": "VT_QRXXuV3Ti",
        "outputId": "2112f95e-6cc9-4f8d-b383-4d1a346eb1f5"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
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              "\n",
              "     <input type=\"file\" id=\"files-5331d19f-c36b-413c-b1f1-ff7fa94d702c\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-5331d19f-c36b-413c-b1f1-ff7fa94d702c\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
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              "\n",
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              "      yield {\n",
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              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'---------------------------'}"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ]
    },
    {
      "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"
      ],
      "metadata": {
        "id": "1E_XheHqW0by",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "87bed169-6896-46cf-fd05-fd32f5ef4e13"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mv: cannot stat 'kaggle.json': No such file or directory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir -p ~/.kaggle\n",
        "!cp '/content/drive/MyDrive/path_to_your_api_key/kaggle.json' ~/.kaggle/\n",
        "!chmod 600 ~/.kaggle/kaggle.json"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tusLjL59W5lQ",
        "outputId": "d5f9a265-d944-4d7e-c9c5-8b61ec0ed15e"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cp: cannot stat '/content/drive/MyDrive/path_to_your_api_key/kaggle.json': No such file or directory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle competitions download -c new-york-city-taxi-fare-prediction"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D9TBqIUlWHTz",
        "outputId": "07dfbb99-1aae-4e60-d787-7a6604d3f677"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "new-york-city-taxi-fare-prediction.zip: Skipping, found more recently modified local copy (use --force to force download)\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}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JlF04qn_Xcfb",
        "outputId": "8b7a8bda-91ea-4091-ab95-81aec17374d6"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unzipped /content/new-york-city-taxi-fare-prediction.zip to /content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "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"
      ],
      "metadata": {
        "id": "YxPjYGWkWyyX"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('train.csv', nrows=100000)\n",
        "df_test = pd.read_csv('test.csv')\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "W9jhHbAxXG3M",
        "outputId": "8ce795c2-fbcb-40e3-f3b2-ddef6d80b40b"
      },
      "execution_count": 3,
      "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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              "  <div id=\"df-5336476a-5040-4f3d-91ac-3ebd963f0b91\" class=\"colab-df-container\">\n",
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              "      <th></th>\n",
              "      <th>key</th>\n",
              "      <th>fare_amount</th>\n",
              "      <th>pickup_datetime</th>\n",
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              "      <th>pickup_latitude</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",
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              "      <td>2011-08-18 00:35:00.00000049</td>\n",
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              "      <td>2012-04-21 04:30:42.0000001</td>\n",
              "      <td>7.7</td>\n",
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              "      <td>-73.987130</td>\n",
              "      <td>40.733143</td>\n",
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              "      <td>40.758092</td>\n",
              "      <td>1</td>\n",
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              "      <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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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
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              "\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-5336476a-5040-4f3d-91ac-3ebd963f0b91 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-5336476a-5040-4f3d-91ac-3ebd963f0b91');\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",
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              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
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              "      --disabled-bg-color: #3B4455;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
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              "      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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              "    }\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    }\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-e7eacf5e-197b-4661-a32e-2cb89f659329 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": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.shape,\"\\n\\n\", df.info(),\"\\n\\n\", df.describe())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ydWBO2kJXKF6",
        "outputId": "50058456-c529-4b14-b0cc-017bb7bf887f"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 100000 entries, 0 to 99999\n",
            "Data columns (total 8 columns):\n",
            " #   Column             Non-Null Count   Dtype  \n",
            "---  ------             --------------   -----  \n",
            " 0   key                100000 non-null  object \n",
            " 1   fare_amount        100000 non-null  float64\n",
            " 2   pickup_datetime    100000 non-null  object \n",
            " 3   pickup_longitude   100000 non-null  float64\n",
            " 4   pickup_latitude    100000 non-null  float64\n",
            " 5   dropoff_longitude  100000 non-null  float64\n",
            " 6   dropoff_latitude   100000 non-null  float64\n",
            " 7   passenger_count    100000 non-null  int64  \n",
            "dtypes: float64(5), int64(1), object(2)\n",
            "memory usage: 6.1+ MB\n",
            "(100000, 8) \n",
            "\n",
            " None \n",
            "\n",
            "          fare_amount  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
            "count  100000.000000     100000.000000    100000.000000      100000.000000   \n",
            "mean       11.354652        -72.494682        39.914481         -72.490967   \n",
            "std         9.716777         10.693934         6.225686          10.471386   \n",
            "min       -44.900000       -736.550000       -74.007670         -84.654241   \n",
            "25%         6.000000        -73.992041        40.734996         -73.991215   \n",
            "50%         8.500000        -73.981789        40.752765         -73.980000   \n",
            "75%        12.500000        -73.966982        40.767258         -73.963433   \n",
            "max       200.000000         40.787575       401.083332          40.851027   \n",
            "\n",
            "       dropoff_latitude  passenger_count  \n",
            "count     100000.000000    100000.000000  \n",
            "mean          39.919053         1.673820  \n",
            "std            6.213427         1.300171  \n",
            "min          -74.006377         0.000000  \n",
            "25%           40.734182         1.000000  \n",
            "50%           40.753243         1.000000  \n",
            "75%           40.768166         2.000000  \n",
            "max          404.616667         6.000000  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4GfBfpetX26X",
        "outputId": "dda327ac-ded2-4bcd-8ca2-e7f8099c9a42"
      },
      "execution_count": 5,
      "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": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DgLuvmGpX7Xu",
        "outputId": "a9ca7928-ea68-45cf-b0e7-04f42c2aacbe"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "key                  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": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AFzdiXx5X9Mc",
        "outputId": "b10ba979-57da-41ec-c2bc-093334fd673f"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dropna(axis=0, inplace=True)\n",
        "np.sum(pd.isnull(df))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dbTvJA_HYAX4",
        "outputId": "60e36081-ab71-4187-963b-2c3fd17dbd50"
      },
      "execution_count": 8,
      "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": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['fare_amount']<0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "bTQTMWIZYCxp",
        "outputId": "c99c8677-d2b1-430f-b334-276c74a91521"
      },
      "execution_count": 9,
      "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",
              "56748  2015-06-26 01:13:18.0000002         -5.0  2015-06-26 01:13:18 UTC   \n",
              "58937  2010-02-19 23:47:10.0000002        -44.9  2010-02-19 23:47:10 UTC   \n",
              "97838  2015-06-07 02:54:14.0000004         -3.0  2015-06-07 02:54:14 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",
              "56748        -73.979797        40.743240         -73.981216         40.737240   \n",
              "58937        -73.871120        40.773902         -73.676533         40.786890   \n",
              "97838        -73.913246        40.766212         -73.914963         40.764065   \n",
              "\n",
              "       passenger_count  \n",
              "2039                 1  \n",
              "2486                 1  \n",
              "13032                4  \n",
              "28839                1  \n",
              "36722                1  \n",
              "42337                1  \n",
              "56748                6  \n",
              "58937                1  \n",
              "97838                5  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-df2fd10a-0ab5-4a8e-929b-e4d0e6caee15\" 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",
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              "\n",
              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\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",
              "      <th>dropoff_latitude</th>\n",
              "      <th>passenger_count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2039</th>\n",
              "      <td>2010-03-09 23:37:10.0000005</td>\n",
              "      <td>-2.9</td>\n",
              "      <td>2010-03-09 23:37:10 UTC</td>\n",
              "      <td>-73.789450</td>\n",
              "      <td>40.643498</td>\n",
              "      <td>-73.788665</td>\n",
              "      <td>40.641952</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2486</th>\n",
              "      <td>2015-03-22 05:14:27.0000001</td>\n",
              "      <td>-2.5</td>\n",
              "      <td>2015-03-22 05:14:27 UTC</td>\n",
              "      <td>-74.000031</td>\n",
              "      <td>40.720631</td>\n",
              "      <td>-73.999809</td>\n",
              "      <td>40.720539</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>13032</th>\n",
              "      <td>2013-08-30 08:57:10.0000002</td>\n",
              "      <td>-3.0</td>\n",
              "      <td>2013-08-30 08:57:10 UTC</td>\n",
              "      <td>-73.995062</td>\n",
              "      <td>40.740755</td>\n",
              "      <td>-73.995885</td>\n",
              "      <td>40.741357</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28839</th>\n",
              "      <td>2013-08-11 13:39:10.0000001</td>\n",
              "      <td>-2.5</td>\n",
              "      <td>2013-08-11 13:39:10 UTC</td>\n",
              "      <td>-73.785260</td>\n",
              "      <td>40.648442</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>36722</th>\n",
              "      <td>2015-04-30 15:19:45.0000003</td>\n",
              "      <td>-2.5</td>\n",
              "      <td>2015-04-30 15:19:45 UTC</td>\n",
              "      <td>-73.952187</td>\n",
              "      <td>40.790112</td>\n",
              "      <td>-73.950043</td>\n",
              "      <td>40.792839</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42337</th>\n",
              "      <td>2015-03-09 10:29:46.0000004</td>\n",
              "      <td>-5.0</td>\n",
              "      <td>2015-03-09 10:29:46 UTC</td>\n",
              "      <td>-73.990974</td>\n",
              "      <td>40.755985</td>\n",
              "      <td>-73.980820</td>\n",
              "      <td>40.759869</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56748</th>\n",
              "      <td>2015-06-26 01:13:18.0000002</td>\n",
              "      <td>-5.0</td>\n",
              "      <td>2015-06-26 01:13:18 UTC</td>\n",
              "      <td>-73.979797</td>\n",
              "      <td>40.743240</td>\n",
              "      <td>-73.981216</td>\n",
              "      <td>40.737240</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58937</th>\n",
              "      <td>2010-02-19 23:47:10.0000002</td>\n",
              "      <td>-44.9</td>\n",
              "      <td>2010-02-19 23:47:10 UTC</td>\n",
              "      <td>-73.871120</td>\n",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "As there are few negative and zero values for the fare, let's set the minimum fare amount"
      ],
      "metadata": {
        "id": "dTfyu4InYEyi"
      }
    },
    {
      "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": "Xe-BW6CqYI8J",
        "outputId": "798c5d7c-5d50-477b-be5e-1d948dad9aed"
      },
      "execution_count": 10,
      "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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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <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",
              "      <th>passenger_count</th>\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<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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            ]
          },
          "metadata": {},
          "execution_count": 10
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### FEATURE EXTRACTION\n",
        "\n",
        "Using the pickup_datetime feature, various features were also extracted such as time, weekday, month etc. As time plays an important role in the fare prices, as they vary with busy traffic schedules."
      ],
      "metadata": {
        "id": "QnkFW3fgmFB7"
      }
    },
    {
      "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": "tJG2BtDRYK_H"
      },
      "execution_count": 11,
      "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",
        "\n",
        "\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": "taAPH0HKYM7y"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "BeSf_FlfYfQK",
        "outputId": "faca1df5-dd21-4a43-dea1-2e2d7f37b1c5"
      },
      "execution_count": 13,
      "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",
              "99995   2013-09-24 07:39:00.00000071          9.0 2013-09-24 07:39:00+00:00   \n",
              "99996    2014-05-15 12:15:45.0000001          6.0 2014-05-15 12:15:45+00:00   \n",
              "99997    2015-02-19 17:40:43.0000001          5.0 2015-02-19 17:40:43+00:00   \n",
              "99998  2009-10-10 23:35:00.000000165          6.9 2009-10-10 23:35:00+00:00   \n",
              "99999   2010-11-09 16:09:00.00000015          5.7 2010-11-09 16:09:00+00:00   \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",
              "99995        -73.947977        40.784792         -73.964262         40.792347   \n",
              "99996        -73.962918        40.799107         -73.974178         40.786487   \n",
              "99997        -73.996773        40.723549         -73.991974         40.724724   \n",
              "99998        -73.983652        40.756667         -73.982715         40.767067   \n",
              "99999        -73.975663        40.791653         -73.982267         40.774968   \n",
              "\n",
              "       passenger_count  pickup_hour pickup_weekday  pickup_date  pickup_month  \\\n",
              "0                    1           17         Monday           15             6   \n",
              "1                    1           16        Tuesday            5             1   \n",
              "2                    2            0       Thursday           18             8   \n",
              "3                    1            4       Saturday           21             4   \n",
              "4                    1            7        Tuesday            9             3   \n",
              "...                ...          ...            ...          ...           ...   \n",
              "99995                5            7        Tuesday           24             9   \n",
              "99996                1           12       Thursday           15             5   \n",
              "99997                1           17       Thursday           19             2   \n",
              "99998                4           23       Saturday           10            10   \n",
              "99999                1           16        Tuesday            9            11   \n",
              "\n",
              "       pickup_day  \n",
              "0               0  \n",
              "1               1  \n",
              "2               3  \n",
              "3               5  \n",
              "4               1  \n",
              "...           ...  \n",
              "99995           1  \n",
              "99996           3  \n",
              "99997           3  \n",
              "99998           5  \n",
              "99999           1  \n",
              "\n",
              "[100000 rows x 13 columns]"
            ],
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              "\n",
              "  <div id=\"df-297cc4cc-3dbe-41b8-aa08-596adf92fb57\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "        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>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",
              "      <th>passenger_count</th>\n",
              "      <th>pickup_hour</th>\n",
              "      <th>pickup_weekday</th>\n",
              "      <th>pickup_date</th>\n",
              "      <th>pickup_month</th>\n",
              "      <th>pickup_day</th>\n",
              "    </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",
              "      <td>40.721319</td>\n",
              "      <td>-73.841610</td>\n",
              "      <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>99995</th>\n",
              "      <td>2013-09-24 07:39:00.00000071</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2013-09-24 07:39:00+00:00</td>\n",
              "      <td>-73.947977</td>\n",
              "      <td>40.784792</td>\n",
              "      <td>-73.964262</td>\n",
              "      <td>40.792347</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>24</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99996</th>\n",
              "      <td>2014-05-15 12:15:45.0000001</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2014-05-15 12:15:45+00:00</td>\n",
              "      <td>-73.962918</td>\n",
              "      <td>40.799107</td>\n",
              "      <td>-73.974178</td>\n",
              "      <td>40.786487</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>15</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99997</th>\n",
              "      <td>2015-02-19 17:40:43.0000001</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2015-02-19 17:40:43+00:00</td>\n",
              "      <td>-73.996773</td>\n",
              "      <td>40.723549</td>\n",
              "      <td>-73.991974</td>\n",
              "      <td>40.724724</td>\n",
              "      <td>1</td>\n",
              "      <td>17</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>19</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99998</th>\n",
              "      <td>2009-10-10 23:35:00.000000165</td>\n",
              "      <td>6.9</td>\n",
              "      <td>2009-10-10 23:35:00+00:00</td>\n",
              "      <td>-73.983652</td>\n",
              "      <td>40.756667</td>\n",
              "      <td>-73.982715</td>\n",
              "      <td>40.767067</td>\n",
              "      <td>4</td>\n",
              "      <td>23</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99999</th>\n",
              "      <td>2010-11-09 16:09:00.00000015</td>\n",
              "      <td>5.7</td>\n",
              "      <td>2010-11-09 16:09:00+00:00</td>\n",
              "      <td>-73.975663</td>\n",
              "      <td>40.791653</td>\n",
              "      <td>-73.982267</td>\n",
              "      <td>40.774968</td>\n",
              "      <td>1</td>\n",
              "      <td>16</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>9</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100000 rows × 13 columns</p>\n",
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              "        document.querySelector('#df-297cc4cc-3dbe-41b8-aa08-596adf92fb57 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-297cc4cc-3dbe-41b8-aa08-596adf92fb57');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
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              "    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",
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              "    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",
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              "\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-top-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <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-471fcc6c-4766-4061-8f8c-8c0f75c3fc1a 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": 13
        }
      ]
    },
    {
      "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",
        "plt.setp(axes, yticks=[])\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Vv58ewcZrcco",
        "outputId": "b91ff686-1d65-4536-a565-4abb4fde0db6"
      },
      "execution_count": 14,
      "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": [
        "###Findings\n",
        " (Here, red represents pickup and dropoff Longitudes & blue represents pickup & dropoff lattitudes)\n",
        "\n",
        "1. From the plot above it is clear that pick and drop latitude are centered around 40 to 41, and longitude are situated around -74 to -73.\n",
        "2. Some extreme co-ordinates has squeezed the plot such that we see a spike here\n",
        "3. A good idea is to remove these outliers and look at the distribution more closely"
      ],
      "metadata": {
        "id": "Giui6mTCgJnT"
      }
    },
    {
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "CZpxGysLfvLm",
        "outputId": "ee04fd4f-5f9b-42b9-f2d3-749fd5808de4"
      },
      "execution_count": 15,
      "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": [
        "###Feature extraction\n",
        "\n",
        "Here various methods of distances were calculated viz. Manhattan, Euclidean and Haversine.\n",
        "\n",
        "But as the corrlation of these were near about = 0.99 hence, ended up removing the Manhattan and Euclidean distances as they add redundancy to the model."
      ],
      "metadata": {
        "id": "cDOvKRdvlTlR"
      }
    },
    {
      "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": "1N7m2iHgq7ip"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['haversine_distance'].median(), df['haversine_distance'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QGe4-SIIvi3O",
        "outputId": "f0ec95a8-d2b3-4f11-817e-0d8a719d5d6e"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2.152803685790151, 3.280894538507157)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.boxplot(df['haversine_distance'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "id": "2Mgvw_rgZwLb",
        "outputId": "d4016cc7-f666-47f2-ca4b-aae49f4a4eab"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " As there are few outliers let's remove them using the IQR and see the affect on number of data points.\n",
        "\n",
        " If there is no big difference in the number of data points let's remove them and use the new data."
      ],
      "metadata": {
        "id": "Z4VtEoT1nHsD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "IQR = df['haversine_distance'].quantile(0.75) - df['haversine_distance'].quantile(0.25)\n",
        "IQR"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lwIK8t_QceGc",
        "outputId": "dd835daa-9ca1-4cd8-d891-d1838c53b1e3"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.6445632516961988"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Q1 = df['haversine_distance'].quantile(0.25)\n",
        "Q3 = df['haversine_distance'].quantile(0.75)\n",
        "whisker_1 = Q1 - (1.5*IQR)\n",
        "whisker_2 = Q3 + (1.5*IQR)\n",
        "\n",
        "whisker_1, whisker_2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OCq_yThvm_A4",
        "outputId": "95ecb2a3-bb94-4200-d67c-30a026bb5584"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-2.7114900273961298, 7.866762979388666)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.loc[(df['haversine_distance']!=0) & (df['haversine_distance']<8)]\n",
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3IN-w7M2nDwc",
        "outputId": "25607bc6-5b74-484d-be20-3c0e6e88a52d"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(88627, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Check wether any variable or column is showing multicollinearity with other variables."
      ],
      "metadata": {
        "id": "IAuA-wRl1mG-"
      }
    },
    {
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PZMFEpka0Y8N",
        "outputId": "93d8a834-287f-497a-a860-af815a6b7003"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              feature           VIF\n",
            "0    pickup_longitude  1.801040e+07\n",
            "1     pickup_latitude  5.262471e+06\n",
            "2   dropoff_longitude  1.842213e+07\n",
            "3    dropoff_latitude  4.632861e+06\n",
            "4     passenger_count  2.660648e+00\n",
            "5         pickup_hour  5.450120e+00\n",
            "6         pickup_date  4.255785e+00\n",
            "7        pickup_month  4.281144e+00\n",
            "8          pickup_day  3.487932e+00\n",
            "9  haversine_distance  3.241042e+00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can see that pickup_longitude, pickup_latitude, dropoff_longitude and dropoff_latitude have values of (~10^6) i.e. >5\n",
        "\n",
        "We can drop any of the 2 of 4 atrributes which have high correlation"
      ],
      "metadata": {
        "id": "xJ8BK2dA16jm"
      }
    },
    {
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 666
        },
        "id": "GsnUj1Kmv1rc",
        "outputId": "66cf7332-3969-4cea-c14f-98fa3cfb97db"
      },
      "execution_count": 23,
      "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()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "rlCGMmNb04DC",
        "outputId": "29983ed1-4a11-45cc-ead0-200e2c24f535"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   pickup_latitude  pickup_longitude  dropoff_longitude  \\\n",
              "pickup_latitude           1.000000          0.395051           0.166610   \n",
              "pickup_longitude          0.395051          1.000000           0.593426   \n",
              "dropoff_longitude         0.166610          0.593426           1.000000   \n",
              "dropoff_latitude          0.591759          0.195972           0.357076   \n",
              "\n",
              "                   dropoff_latitude  \n",
              "pickup_latitude            0.591759  \n",
              "pickup_longitude           0.195972  \n",
              "dropoff_longitude          0.357076  \n",
              "dropoff_latitude           1.000000  "
            ],
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              "      <td>0.195972</td>\n",
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              "     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-8155410e-1df1-423f-81c6-4fd6493a05a0 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": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sxoKZm2no2uS"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nAM06MTkDo9W",
        "outputId": "1cd95804-54e6-4ca0-9ca5-05f677a1df65"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 88627 entries, 0 to 99999\n",
            "Data columns (total 14 columns):\n",
            " #   Column              Non-Null Count  Dtype              \n",
            "---  ------              --------------  -----              \n",
            " 0   key                 88627 non-null  object             \n",
            " 1   fare_amount         88627 non-null  float64            \n",
            " 2   pickup_datetime     88627 non-null  datetime64[ns, UTC]\n",
            " 3   pickup_longitude    88627 non-null  float64            \n",
            " 4   pickup_latitude     88627 non-null  float64            \n",
            " 5   dropoff_longitude   88627 non-null  float64            \n",
            " 6   dropoff_latitude    88627 non-null  float64            \n",
            " 7   passenger_count     88627 non-null  int64              \n",
            " 8   pickup_hour         88627 non-null  int64              \n",
            " 9   pickup_weekday      88627 non-null  object             \n",
            " 10  pickup_date         88627 non-null  int64              \n",
            " 11  pickup_month        88627 non-null  int64              \n",
            " 12  pickup_day          88627 non-null  int64              \n",
            " 13  haversine_distance  88627 non-null  float64            \n",
            "dtypes: datetime64[ns, UTC](1), float64(6), int64(5), object(2)\n",
            "memory usage: 10.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We need to remove few columns that are irrelevant for our model or are transformed into useful attributes"
      ],
      "metadata": {
        "id": "bugvKtOEkaax"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X=df.drop([\"key\",\"pickup_datetime\",\"pickup_weekday\",\"fare_amount\"],axis=1)\n",
        "y=df[\"fare_amount\"]\n",
        "X.shape, y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fT18zVAHja-B",
        "outputId": "e28fda94-0411-4733-cabe-49be625eaedb"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((88627, 10), (88627,))"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Standardizing the data into zero mean and unit variance"
      ],
      "metadata": {
        "id": "EN5G-zstklN1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import preprocessing\n",
        "X= preprocessing.StandardScaler().fit(X).transform(X)\n",
        "X[0:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2k0yMt0fkILO",
        "outputId": "a724f288-0067-4d1b-f3e8-8bd885375697"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 6.21493248, -1.31680653,  5.95968832, -1.56478694, -0.51688021,\n",
              "         0.53225828, -0.07600096, -0.07398561, -1.56325012, -0.86218178],\n",
              "       [-0.13179443,  0.38672213, -0.56637125, -0.06778567,  0.25266593,\n",
              "        -2.09734508,  0.26953958,  0.50488199, -0.02024791, -0.64952736],\n",
              "       [-0.33316283, -0.81262584, -0.58054582,  0.22665639, -0.51688021,\n",
              "        -1.47861488,  0.61508012, -0.65285321,  1.00842023,  0.18609377],\n",
              "       [ 0.53957115,  0.67403349,  0.94210837,  1.23041834, -0.51688021,\n",
              "        -1.01456722, -0.76708203, -0.94228701, -1.04891605, -0.28817051],\n",
              "       [-0.96743664, -0.87714084,  0.23394682,  0.23216984, -0.51688021,\n",
              "        -0.70520212, -1.11262257, -1.5211546 , -0.02024791,  0.77170864]])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Train-Test split"
      ],
      "metadata": {
        "id": "YF9tFmk3pOQu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_val, y_train, y_val = 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_val.shape)\n",
        "print(y_val.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X1GpxA7lkvC7",
        "outputId": "72d61bd9-b585-4fb8-c652-c0ba411bee31"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2\n",
            "1\n",
            "(62038, 10)\n",
            "(62038,)\n",
            "(26589, 10)\n",
            "(26589,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##LINEAR REGRESSION MODEL"
      ],
      "metadata": {
        "id": "T6jJNjIwpcwb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "model = LinearRegression()\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "y_pred_train = model.predict(X_train)\n",
        "y_pred_val = model.predict(X_val)\n",
        "\n",
        "rmse_train = np.sqrt(mean_squared_error(y_train, y_pred_train))\n",
        "rmse_val = np.sqrt(mean_squared_error(y_val, y_pred_val))\n",
        "\n",
        "print(\"Root Mean Square Error (RMSE) using Linear Regression on training data:\", rmse_train)\n",
        "print(\"Root Mean Square Error (RMSE) using Linear Regression on validation data:\", rmse_val)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5xhlkdMxk123",
        "outputId": "e28bf369-a8dc-4f97-ee2a-59f228ae1a99"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Root Mean Square Error (RMSE) using Linear Regression on training data: 3.7303103819307277\n",
            "Root Mean Square Error (RMSE) using Linear Regression on validation data: 3.5771865581892563\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "np.mean(cross_val_score(model, X_train, y_train, cv=5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bGZl5ZrGj58I",
        "outputId": "a5243823-d2c8-4c09-a443-4104fe468987"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4928455080201104"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### POLYNOMIAL REGRESSION MODEL\n",
        "Using different valus of degree"
      ],
      "metadata": {
        "id": "IecLEpHXphWL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "\n",
        "rmse_list=[]\n",
        "for p in range(2,5):\n",
        "  poly_features = PolynomialFeatures(p)\n",
        "  x_train_poly = poly_features.fit_transform(X_train)\n",
        "  x_valid_poly = poly_features.fit_transform(X_val)\n",
        "  regr = LinearRegression().fit(x_train_poly,y_train)\n",
        "\n",
        "  y_pred_train = regr.predict(x_train_poly)\n",
        "  y_pred_val = regr.predict(x_valid_poly)\n",
        "\n",
        "  rmse_train = np.sqrt(mean_squared_error(y_train, y_pred_train))\n",
        "  rmse_val = np.sqrt(mean_squared_error(y_val, y_pred_val))\n",
        "  print(f\"\\n The RMSE value of predicted and original Fare-Amount with polynomial degree = {p} on training data is {rmse_train}\")\n",
        "  print(f\"\\n The RMSE value of predicted and original Fare-Amount with polynomial degree = {p} on validation data is {rmse_val}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LPQ4asMimSN3",
        "outputId": "635bf2a5-b9bd-4c34-8169-c612f5e5b59b"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " The RMSE value of predicted and original Fare-Amount with polynomial degree = 2 on training data is 3.596202730959942\n",
            "\n",
            " The RMSE value of predicted and original Fare-Amount with polynomial degree = 2 on validation data is 3.463775074933503\n",
            "\n",
            " The RMSE value of predicted and original Fare-Amount with polynomial degree = 3 on training data is 3.503772001068168\n",
            "\n",
            " The RMSE value of predicted and original Fare-Amount with polynomial degree = 3 on validation data is 3.4271100067071143\n",
            "\n",
            " The RMSE value of predicted and original Fare-Amount with polynomial degree = 4 on training data is 3.3531475076015913\n",
            "\n",
            " The RMSE value of predicted and original Fare-Amount with polynomial degree = 4 on validation data is 3.6136749070069354\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### RIDGE REGRESSION MODEL"
      ],
      "metadata": {
        "id": "GoWirchCpsT1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "poly = PolynomialFeatures(degree=2)\n",
        "X_train_poly = poly.fit_transform(X_train)\n",
        "X_valid_poly = poly.transform(X_val)\n",
        "\n",
        "alpha = 0.001\n",
        "ridge_model = Ridge(alpha=alpha)\n",
        "ridge_model.fit(X_train_poly, y_train)\n",
        "\n",
        "y_pred_train = ridge_model.predict(X_train_poly)\n",
        "y_pred_val = ridge_model.predict(X_valid_poly)\n",
        "\n",
        "rmse_train = np.sqrt(mean_squared_error(y_train, y_pred_train))\n",
        "rmse_val = np.sqrt(mean_squared_error(y_val, y_pred_val))\n",
        "\n",
        "print(\"Root Mean Square Error (RMSE) using Ridge Regression on training data:\", rmse_train)\n",
        "print(\"Root Mean Square Error (RMSE) using Ridge Regression on validation data:\", rmse_val)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vi0TcygbvF7n",
        "outputId": "c272cf5d-ed1e-4e6f-8d50-ea80c293ec42"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Root Mean Square Error (RMSE) using Ridge Regression on training data: 3.597367153990055\n",
            "Root Mean Square Error (RMSE) using Ridge Regression on validation data: 3.466218005637418\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.mean(cross_val_score(ridge_model, X_train, y_train, cv=5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kSgJxTKznXNR",
        "outputId": "0edc5b5c-f0f4-4819-d3ca-47783d2cde8d"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.49284550804816796"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### LASSO REGRESSION MODEL"
      ],
      "metadata": {
        "id": "d4sr73SQqCh4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "poly = PolynomialFeatures(degree=2)\n",
        "X_train_poly = poly.fit_transform(X_train)\n",
        "X_valid_poly = poly.transform(X_val)\n",
        "\n",
        "alpha = 0.001\n",
        "lasso_model = Lasso(alpha=alpha)\n",
        "lasso_model.fit(X_train_poly, y_train)\n",
        "\n",
        "y_pred_train = lasso_model.predict(X_train_poly)\n",
        "y_pred_val = lasso_model.predict(X_valid_poly)\n",
        "\n",
        "rmse_train = np.sqrt(mean_squared_error(y_train, y_pred_train))\n",
        "rmse_val = np.sqrt(mean_squared_error(y_val, y_pred_val))\n",
        "\n",
        "print(\"Root Mean Square Error (RMSE) using Lasso Regression on training data:\", rmse_train)\n",
        "print(\"Root Mean Square Error (RMSE) using Lasso Regression on validation data:\", rmse_val)"
      ],
      "metadata": {
        "id": "z46AMFBex6sA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "64e9a86b-bcba-4a24-cfe4-b247ef454cb7"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Root Mean Square Error (RMSE) using Lasso Regression on training data: 3.601678685995427\n",
            "Root Mean Square Error (RMSE) using Lasso Regression on validation data: 3.472063495261603\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.mean(cross_val_score(lasso_model, X_train, y_train, cv=5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ScxThvHdlXHe",
        "outputId": "33f39356-1934-49b9-f78e-8779e652e481"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4928472246630653"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### CONCLUSION\n",
        "\n",
        "After testing with various regression models, the RMSE error on validation data is lowest for Lasso Model.\n",
        "\n",
        "We can say that lasso model is best fit for the used dataset and number of data points.\n",
        "\n",
        "Model can further be improvised by adding more features or using various kinds of regression model available.\n",
        "\n",
        "As for this model we have tried best our knowledge"
      ],
      "metadata": {
        "id": "xMGBBfLC7sIQ"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "J5u2WuI8tosX"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}