Taxi_Fare_Prediction.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOqUvCGky0mpu1+eh9gPBPH",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Ai-deen/MachineLearning/blob/main/Taxi_Fare_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HnwRLUehXBHJ"
      },
      "source": [
        "# New York City Taxi Fare Prediction\n",
        "\n",
        "Train Machine Learning models using training dataset of New York taxi fares and predict fares for test dataset.Take all possible parameters into consideration."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Cleaning and Visualization"
      ],
      "metadata": {
        "id": "pQXPlyGoHxTe"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UD4uZ_PrEe8O"
      },
      "outputs": [],
      "source": [
        "!pip install -q kaggle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b_nEeLb_cchh",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 91
        },
        "outputId": "ebbf8a68-38fc-4ab5-aaa8-87b51fc77de4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-06b9b097-b7ad-4aaa-b28e-f0b31ccc2b3a\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-06b9b097-b7ad-4aaa-b28e-f0b31ccc2b3a\">\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 src=\"/nbextensions/google.colab/files.js\"></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'{\"username\":\"keiraaideen\",\"key\":\"fbe008514cce5c79769f8bf40d7d56c4\"}'}"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-Fh_c0H6cfOB"
      },
      "outputs": [],
      "source": [
        "!mkdir ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "!chmod 600 ~/.kaggle/kaggle.json"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U-6c8rF7cmqP",
        "outputId": "1bc1cda4-9325-49e2-f96c-e23ddf15aca1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading new-york-city-taxi-fare-prediction.zip to /content\n",
            " 99% 1.54G/1.56G [00:07<00:00, 265MB/s]\n",
            "100% 1.56G/1.56G [00:08<00:00, 208MB/s]\n"
          ]
        }
      ],
      "source": [
        "!kaggle competitions download -c new-york-city-taxi-fare-prediction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l4gzylcpdQPL",
        "outputId": "0108bb6e-0d0c-4e80-f988-9f5ffc077237"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  new-york-city-taxi-fare-prediction.zip\n",
            "  inflating: GCP-Coupons-Instructions.rtf  \n",
            "  inflating: sample_submission.csv   \n",
            "  inflating: test.csv                \n",
            "  inflating: train.csv               \n"
          ]
        }
      ],
      "source": [
        "!unzip new-york-city-taxi-fare-prediction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-miARsGEdWzk"
      },
      "outputs": [],
      "source": [
        "import numpy as np \n",
        "import pandas as pd \n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WmhwzdGjdXa2"
      },
      "outputs": [],
      "source": [
        "train = pd.read_csv(\"train.csv\", nrows = 1000000)\n",
        "test = pd.read_csv(\"test.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainc = train.copy()"
      ],
      "metadata": {
        "id": "eFjjjvLrkmDB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "testc = test.copy()"
      ],
      "metadata": {
        "id": "bhhAUVDdXjrH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "di6zPMB7sgcV"
      },
      "source": [
        "PART 1 --> DATA CLEANSING \n",
        "\n",
        "Will perform the following activities\n",
        "1.   Shape of train and test sets\n",
        "2.   Check for NaNs and drop them (if any)\n",
        "3.   Check for outliers and drop them (if any)\n",
        "4.   Type conversion of relevant fields\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "uAtwsfCchMpl",
        "outputId": "fd53aa21-dc1b-430c-a70c-ef19c32f8c37"
      },
      "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  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5c8000a4-cdbd-452a-ba38-dc7561e8d313\">\n",
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              "      <th>dropoff_longitude</th>\n",
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              "      <th>passenger_count</th>\n",
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              "  <tbody>\n",
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              "      <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 UTC</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",
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              "      <th>1</th>\n",
              "      <td>2010-01-05 16:52:16.0000002</td>\n",
              "      <td>16.9</td>\n",
              "      <td>2010-01-05 16:52:16 UTC</td>\n",
              "      <td>-74.016048</td>\n",
              "      <td>40.711303</td>\n",
              "      <td>-73.979268</td>\n",
              "      <td>40.782004</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 UTC</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",
              "    </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 UTC</td>\n",
              "      <td>-73.987130</td>\n",
              "      <td>40.733143</td>\n",
              "      <td>-73.991567</td>\n",
              "      <td>40.758092</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2010-03-09 07:51:00.000000135</td>\n",
              "      <td>5.3</td>\n",
              "      <td>2010-03-09 07:51:00 UTC</td>\n",
              "      <td>-73.968095</td>\n",
              "      <td>40.768008</td>\n",
              "      <td>-73.956655</td>\n",
              "      <td>40.783762</td>\n",
              "      <td>1</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A-W5rpF0ePEs",
        "outputId": "abe8ca1c-643e-48ac-fdf3-ff38926fd30e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of the Training data : (1000000, 8)\n",
            "All columns are : ['key', 'fare_amount', 'pickup_datetime', 'pickup_longitude', 'pickup_latitude', 'dropoff_longitude', 'dropoff_latitude', 'passenger_count']\n"
          ]
        }
      ],
      "source": [
        "# Check no of rows & columns in the dataset\n",
        "print(\"Shape of the Training data :\", train.shape)\n",
        "\n",
        "# See all the columns in the dataset\n",
        "print(\"All columns are :\", list(train.columns))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UJVEdRNzePI4",
        "outputId": "ca23722f-52aa-4bf4-f1ba-cee703ba935d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1000000 entries, 0 to 999999\n",
            "Data columns (total 8 columns):\n",
            " #   Column             Non-Null Count    Dtype  \n",
            "---  ------             --------------    -----  \n",
            " 0   key                1000000 non-null  object \n",
            " 1   fare_amount        1000000 non-null  float64\n",
            " 2   pickup_datetime    1000000 non-null  object \n",
            " 3   pickup_longitude   1000000 non-null  float64\n",
            " 4   pickup_latitude    1000000 non-null  float64\n",
            " 5   dropoff_longitude  999990 non-null   float64\n",
            " 6   dropoff_latitude   999990 non-null   float64\n",
            " 7   passenger_count    1000000 non-null  int64  \n",
            "dtypes: float64(5), int64(1), object(2)\n",
            "memory usage: 61.0+ MB\n"
          ]
        }
      ],
      "source": [
        "# Check datatypes & count of not-null values in each field\n",
        "train.info()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check datatypes & count of not-null values in each field\n",
        "test.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CV3O-gLkXZVo",
        "outputId": "4913f04e-c92a-4e7a-bda4-a2f5585a7ec5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 9914 entries, 0 to 9913\n",
            "Data columns (total 7 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   key                9914 non-null   object \n",
            " 1   pickup_datetime    9914 non-null   object \n",
            " 2   pickup_longitude   9914 non-null   float64\n",
            " 3   pickup_latitude    9914 non-null   float64\n",
            " 4   dropoff_longitude  9914 non-null   float64\n",
            " 5   dropoff_latitude   9914 non-null   float64\n",
            " 6   passenger_count    9914 non-null   int64  \n",
            "dtypes: float64(4), int64(1), object(2)\n",
            "memory usage: 542.3+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_5ERpNSDfxZN",
        "outputId": "2461c710-55a9-4b9b-b590-936b90435215"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([120227, 120227, 245696, 245696, 340533, 340533, 428108, 428108,\n",
              "       471472, 471472, 524834, 524834, 574023, 574023, 580338, 580338,\n",
              "       794694, 794694, 895400, 895400])"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "#check for missing values in train data\n",
        "\n",
        "np.where(train.isnull())[0]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wMlhIevBtPXd",
        "outputId": "ab292149-7ab3-426a-b64c-b21553cb29a7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([], dtype=int64)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "#check for missing values in test data\n",
        "np.where(test.isnull())[0]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oNsxwnBDteQK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "outputId": "c15fea9e-fb9f-48bf-d5c4-153b4925bd6c"
      },
      "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",
              "999995    2014-09-13 21:44:38.0000001          7.0  2014-09-13 21:44:38 UTC   \n",
              "999996    2010-09-20 14:50:37.0000002          7.3  2010-09-20 14:50:37 UTC   \n",
              "999997  2013-04-26 14:03:00.000000118         10.5  2013-04-26 14:03:00 UTC   \n",
              "999998   2011-07-08 00:29:00.00000099          6.9  2011-07-08 00:29:00 UTC   \n",
              "999999   2009-12-31 14:30:00.00000021          4.1  2009-12-31 14:30:00 UTC   \n",
              "\n",
              "        pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
              "0             -73.844311        40.721319         -73.841610   \n",
              "1             -74.016048        40.711303         -73.979268   \n",
              "2             -73.982738        40.761270         -73.991242   \n",
              "3             -73.987130        40.733143         -73.991567   \n",
              "4             -73.968095        40.768008         -73.956655   \n",
              "...                  ...              ...                ...   \n",
              "999995        -73.976676        40.785630         -73.959196   \n",
              "999996        -73.992103        40.671385         -73.992103   \n",
              "999997        -73.978423        40.751135         -73.993397   \n",
              "999998        -73.980317        40.759482         -73.976832   \n",
              "999999        -74.006635        40.741598         -73.999450   \n",
              "\n",
              "        dropoff_latitude  passenger_count  \n",
              "0              40.712278                1  \n",
              "1              40.782004                1  \n",
              "2              40.750562                2  \n",
              "3              40.758092                1  \n",
              "4              40.783762                1  \n",
              "...                  ...              ...  \n",
              "999995         40.775114                1  \n",
              "999996         40.671385                2  \n",
              "999997         40.762197                5  \n",
              "999998         40.743122                1  \n",
              "999999         40.745702                1  \n",
              "\n",
              "[999990 rows x 8 columns]"
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              "      <td>-73.979268</td>\n",
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              "      <td>2011-08-18 00:35:00.00000049</td>\n",
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              "      <th>3</th>\n",
              "      <td>2012-04-21 04:30:42.0000001</td>\n",
              "      <td>7.7</td>\n",
              "      <td>2012-04-21 04:30:42 UTC</td>\n",
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              "      <td>40.733143</td>\n",
              "      <td>-73.991567</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",
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              "      <td>2014-09-13 21:44:38.0000001</td>\n",
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              "      <td>-73.976676</td>\n",
              "      <td>40.785630</td>\n",
              "      <td>-73.959196</td>\n",
              "      <td>40.775114</td>\n",
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              "      <td>40.671385</td>\n",
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              "      <th>999998</th>\n",
              "      <td>2011-07-08 00:29:00.00000099</td>\n",
              "      <td>6.9</td>\n",
              "      <td>2011-07-08 00:29:00 UTC</td>\n",
              "      <td>-73.980317</td>\n",
              "      <td>40.759482</td>\n",
              "      <td>-73.976832</td>\n",
              "      <td>40.743122</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999999</th>\n",
              "      <td>2009-12-31 14:30:00.00000021</td>\n",
              "      <td>4.1</td>\n",
              "      <td>2009-12-31 14:30:00 UTC</td>\n",
              "      <td>-74.006635</td>\n",
              "      <td>40.741598</td>\n",
              "      <td>-73.999450</td>\n",
              "      <td>40.745702</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>999990 rows × 8 columns</p>\n",
              "</div>\n",
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              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
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              "\n",
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              "\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-adf8683b-6113-4746-9a85-786b6cf81e13');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "#drop the missing values\n",
        "train = train.dropna(axis = 0, how ='any')\n",
        "train\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#drop the missing values\n",
        "test = test.dropna(axis = 0, how ='any')\n",
        "test\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "g29nFmuUcDcT",
        "outputId": "dc43bebe-7b41-45a8-9bc2-b0759831e54d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                              key          pickup_datetime  pickup_longitude  \\\n",
              "0     2015-01-27 13:08:24.0000002  2015-01-27 13:08:24 UTC        -73.973320   \n",
              "1     2015-01-27 13:08:24.0000003  2015-01-27 13:08:24 UTC        -73.986862   \n",
              "2     2011-10-08 11:53:44.0000002  2011-10-08 11:53:44 UTC        -73.982524   \n",
              "3     2012-12-01 21:12:12.0000002  2012-12-01 21:12:12 UTC        -73.981160   \n",
              "4     2012-12-01 21:12:12.0000003  2012-12-01 21:12:12 UTC        -73.966046   \n",
              "...                           ...                      ...               ...   \n",
              "9909  2015-05-10 12:37:51.0000002  2015-05-10 12:37:51 UTC        -73.968124   \n",
              "9910  2015-01-12 17:05:51.0000001  2015-01-12 17:05:51 UTC        -73.945511   \n",
              "9911  2015-04-19 20:44:15.0000001  2015-04-19 20:44:15 UTC        -73.991600   \n",
              "9912  2015-01-31 01:05:19.0000005  2015-01-31 01:05:19 UTC        -73.985573   \n",
              "9913  2015-01-18 14:06:23.0000006  2015-01-18 14:06:23 UTC        -73.988022   \n",
              "\n",
              "      pickup_latitude  dropoff_longitude  dropoff_latitude  passenger_count  \n",
              "0           40.763805         -73.981430         40.743835                1  \n",
              "1           40.719383         -73.998886         40.739201                1  \n",
              "2           40.751260         -73.979654         40.746139                1  \n",
              "3           40.767807         -73.990448         40.751635                1  \n",
              "4           40.789775         -73.988565         40.744427                1  \n",
              "...               ...                ...               ...              ...  \n",
              "9909        40.796997         -73.955643         40.780388                6  \n",
              "9910        40.803600         -73.960213         40.776371                6  \n",
              "9911        40.726608         -73.789742         40.647011                6  \n",
              "9912        40.735432         -73.939178         40.801731                6  \n",
              "9913        40.754070         -74.000282         40.759220                6  \n",
              "\n",
              "[9914 rows x 7 columns]"
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              "      <td>2011-10-08 11:53:44.0000002</td>\n",
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              "      <td>2012-12-01 21:12:12.0000002</td>\n",
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              "      <td>2012-12-01 21:12:12.0000003</td>\n",
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              "      <th>9909</th>\n",
              "      <td>2015-05-10 12:37:51.0000002</td>\n",
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              "      <td>-73.968124</td>\n",
              "      <td>40.796997</td>\n",
              "      <td>-73.955643</td>\n",
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              "      <td>6</td>\n",
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              "      <th>9910</th>\n",
              "      <td>2015-01-12 17:05:51.0000001</td>\n",
              "      <td>2015-01-12 17:05:51 UTC</td>\n",
              "      <td>-73.945511</td>\n",
              "      <td>40.803600</td>\n",
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              "      <th>9911</th>\n",
              "      <td>2015-04-19 20:44:15.0000001</td>\n",
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              "      <th>9912</th>\n",
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              "      <th>9913</th>\n",
              "      <td>2015-01-18 14:06:23.0000006</td>\n",
              "      <td>2015-01-18 14:06:23 UTC</td>\n",
              "      <td>-73.988022</td>\n",
              "      <td>40.754070</td>\n",
              "      <td>-74.000282</td>\n",
              "      <td>40.759220</td>\n",
              "      <td>6</td>\n",
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              "<p>9914 rows × 7 columns</p>\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "          document.querySelector('#df-55e1870f-10db-47f9-a04f-4bcbc0a4452f 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-55e1870f-10db-47f9-a04f-4bcbc0a4452f');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ftsomnVq1TuS",
        "outputId": "4fb513f2-2cfd-4b6f-83bf-dc22664e7bad"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  2039,   2486,  10002,  13032,  27891,  28839,  36722,  42337,\n",
              "        47302,  56748,  58937,  97838, 102938, 105051, 165146, 175351,\n",
              "       179310, 182340, 211454, 211498, 266483, 288958, 298410, 301354,\n",
              "       323635, 331595, 386731, 399782, 427599, 431815, 436654, 443465,\n",
              "       481414, 489762, 495268, 512489, 519527, 520710, 534745, 549204,\n",
              "       561780, 577718, 605419, 670246, 681334, 689242, 698279, 738396,\n",
              "       740834, 743718, 748544, 760654, 762794, 788458, 857254, 857712,\n",
              "       878014, 888587, 895352, 896057, 897201, 930670, 938010, 942205,\n",
              "       949554, 957580, 963979])"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "#Look for no. of non-positive fare_amount values.\n",
        "np.where(train['fare_amount'] <= 0)[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "id": "Rd-PKokO1eKX",
        "outputId": "941bcba4-70af-42da-f412-56275151a474"
      },
      "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",
              "999995    2014-09-13 21:44:38.0000001          7.0  2014-09-13 21:44:38 UTC   \n",
              "999996    2010-09-20 14:50:37.0000002          7.3  2010-09-20 14:50:37 UTC   \n",
              "999997  2013-04-26 14:03:00.000000118         10.5  2013-04-26 14:03:00 UTC   \n",
              "999998   2011-07-08 00:29:00.00000099          6.9  2011-07-08 00:29:00 UTC   \n",
              "999999   2009-12-31 14:30:00.00000021          4.1  2009-12-31 14:30:00 UTC   \n",
              "\n",
              "        pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
              "0             -73.844311        40.721319         -73.841610   \n",
              "1             -74.016048        40.711303         -73.979268   \n",
              "2             -73.982738        40.761270         -73.991242   \n",
              "3             -73.987130        40.733143         -73.991567   \n",
              "4             -73.968095        40.768008         -73.956655   \n",
              "...                  ...              ...                ...   \n",
              "999995        -73.976676        40.785630         -73.959196   \n",
              "999996        -73.992103        40.671385         -73.992103   \n",
              "999997        -73.978423        40.751135         -73.993397   \n",
              "999998        -73.980317        40.759482         -73.976832   \n",
              "999999        -74.006635        40.741598         -73.999450   \n",
              "\n",
              "        dropoff_latitude  passenger_count  \n",
              "0              40.712278                1  \n",
              "1              40.782004                1  \n",
              "2              40.750562                2  \n",
              "3              40.758092                1  \n",
              "4              40.783762                1  \n",
              "...                  ...              ...  \n",
              "999995         40.775114                1  \n",
              "999996         40.671385                2  \n",
              "999997         40.762197                5  \n",
              "999998         40.743122                1  \n",
              "999999         40.745702                1  \n",
              "\n",
              "[999915 rows x 8 columns]"
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            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "# Remove the non-positive fare_amount values.\n",
        "train = train[train['fare_amount'] > 1]\n",
        "train"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.hist(bins=10, figsize=(15,12))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 716
        },
        "id": "wAL0O8LAdXlw",
        "outputId": "6e7c2c19-c39d-4ad2-900c-939873d7856f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x864 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.plot(kind=\"scatter\", x=\"pickup_longitude\", y=\"pickup_latitude\", figsize=(10, 7), alpha=0.7)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "7_9tfBPAd14p",
        "outputId": "bd4be3ba-fb09-4a06-dba4-5dc1cee029b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.plot(kind=\"scatter\", x=\"dropoff_longitude\", y=\"dropoff_latitude\", figsize=(10, 7), alpha=0.7)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "6nF8xg8YelNR",
        "outputId": "8cf47136-00f5-47fd-8f0b-fef5bb83ec6e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.plot(kind=\"scatter\", x=\"passenger_count\", y=\"fare_amount\", figsize=(10, 7), alpha=0.7)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "EpQsayBniFhh",
        "outputId": "18689225-6fc7-4cce-e12e-3183aa04739c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ioRTlheu13s0",
        "outputId": "420670a3-9568-4f59-d1fb-371c9f68713a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Identified outliers: 27000\n"
          ]
        }
      ],
      "source": [
        "#Remove outliers\n",
        "\n",
        "\n",
        "# calculate summary statistics\n",
        "fare_amount_mean = train[\"fare_amount\"].mean(),\n",
        "fare_amount_std =  train[\"fare_amount\"].std()\n",
        "# identify outliers\n",
        "fare_amount_cut_off = fare_amount_std * 3\n",
        "lower, upper = fare_amount_mean - fare_amount_cut_off, fare_amount_mean + fare_amount_cut_off\n",
        "# identify outliers\n",
        "outliers = [x for x in train[\"fare_amount\"] if x < lower or x > upper]\n",
        "print('Identified outliers: %d' % len(outliers))\n",
        "# remove outliers\n",
        "train = train[(train[\"fare_amount\"] >= lower[0]) & (train[\"fare_amount\"] <= upper[0])]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pickup_longitude_mean = train[\"pickup_longitude\"].mean(),\n",
        "pickup_longitude_std =  train[\"pickup_longitude\"].std()\n",
        "# identify outliers\n",
        "pickup_longitude_cut_off = pickup_longitude_std * 3\n",
        "lower, upper = pickup_longitude_mean - pickup_longitude_cut_off, pickup_longitude_mean + pickup_longitude_cut_off\n",
        "# identify outliers\n",
        "outliers = [x for x in train[\"pickup_longitude\"] if x < lower or x > upper]\n",
        "print('Identified outliers: %d' % len(outliers))\n",
        "# remove outliers\n",
        "train = train[(train[\"pickup_longitude\"] >= lower[0]) & (train[\"pickup_longitude\"] <= upper[0])]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WtKX2T7PvvXr",
        "outputId": "67c50bf9-7c78-4a9b-f825-4ac75e0b4f3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Identified outliers: 18715\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pickup_longitude_mean = test[\"pickup_longitude\"].mean(),\n",
        "pickup_longitude_std =  test[\"pickup_longitude\"].std()\n",
        "# identify outliers\n",
        "pickup_longitude_cut_off = pickup_longitude_std * 3\n",
        "lower, upper = pickup_longitude_mean - pickup_longitude_cut_off, pickup_longitude_mean + pickup_longitude_cut_off\n",
        "# identify outliers\n",
        "outliers = [x for x in test[\"pickup_longitude\"] if x < lower or x > upper]\n",
        "print('Identified outliers: %d' % len(outliers))\n",
        "# remove outliers\n",
        "test = test[(test[\"pickup_longitude\"] >= lower[0]) & (test[\"pickup_longitude\"] <= upper[0])]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tcfTtbU-xpOg",
        "outputId": "9e77a134-78e0-49e9-8e86-0edd7fcbeafe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Identified outliers: 193\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pickup_latitude_mean = train[\"pickup_latitude\"].mean(),\n",
        "pickup_latitude_std =  train[\"pickup_latitude\"].std()\n",
        "# identify outliers\n",
        "pickup_latitude_cut_off = pickup_latitude_std * 3\n",
        "lower, upper = pickup_latitude_mean - pickup_latitude_cut_off, pickup_latitude_mean + pickup_latitude_cut_off\n",
        "# identify outliers\n",
        "outliers = [x for x in train[\"pickup_latitude\"] if x < lower or x > upper]\n",
        "print('Identified outliers: %d' % len(outliers))\n",
        "# remove outliers\n",
        "train = train[(train[\"pickup_latitude\"] >= lower[0]) & (train[\"pickup_latitude\"] <= upper[0])]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "08Qft5iowiEm",
        "outputId": "5f3d0bf6-36b2-41eb-8c1f-b2a3e61f365a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Identified outliers: 51\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pickup_latitude_mean = test[\"pickup_latitude\"].mean(),\n",
        "pickup_latitude_std =  test[\"pickup_latitude\"].std()\n",
        "# identify outliers\n",
        "pickup_latitude_cut_off = pickup_latitude_std * 3\n",
        "lower, upper = pickup_latitude_mean - pickup_latitude_cut_off, pickup_latitude_mean + pickup_latitude_cut_off\n",
        "# identify outliers\n",
        "outliers = [x for x in test[\"pickup_latitude\"] if x < lower or x > upper]\n",
        "print('Identified outliers: %d' % len(outliers))\n",
        "# remove outliers\n",
        "test = test[(test[\"pickup_latitude\"] >= lower[0]) & (test[\"pickup_latitude\"] <= upper[0])]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R3JYZDGvyC8P",
        "outputId": "66fae6f3-43c9-416c-feb4-2d16ccf7699b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Identified outliers: 85\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dropoff_latitude_mean = train[\"dropoff_latitude\"].mean(),\n",
        "dropoff_latitude_std =  train[\"dropoff_latitude\"].std()\n",
        "# identify outliers\n",
        "dropoff_latitude_cut_off = dropoff_latitude_std * 3\n",
        "lower, upper = dropoff_latitude_mean - dropoff_latitude_cut_off, dropoff_latitude_mean + dropoff_latitude_cut_off\n",
        "# identify outliers\n",
        "outliers = [x for x in train[\"dropoff_latitude\"] if x < lower or x > upper]\n",
        "print('Identified outliers: %d' % len(outliers))\n",
        "# remove outliers\n",
        "train = train[(train[\"dropoff_latitude\"] >= lower[0]) & (train[\"dropoff_latitude\"] <= upper[0])]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fbaYScF9wyve",
        "outputId": "90298045-c49d-43ec-bcba-59e940cc1c2c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Identified outliers: 735\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dropoff_latitude_mean = test[\"dropoff_latitude\"].mean(),\n",
        "dropoff_latitude_std =  test[\"dropoff_latitude\"].std()\n",
        "# identify outliers\n",
        "dropoff_latitude_cut_off = dropoff_latitude_std * 3\n",
        "lower, upper = dropoff_latitude_mean - dropoff_latitude_cut_off, dropoff_latitude_mean + dropoff_latitude_cut_off\n",
        "# identify outliers\n",
        "outliers = [x for x in test[\"dropoff_latitude\"] if x < lower or x > upper]\n",
        "print('Identified outliers: %d' % len(outliers))\n",
        "# remove outliers\n",
        "test = test[(test[\"dropoff_latitude\"] >= lower[0]) & (test[\"dropoff_latitude\"] <= upper[0])]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jCjWum6ayXkS",
        "outputId": "1d419f9f-39e9-4ea9-c2c4-01f6cd92ae54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Identified outliers: 190\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dropoff_longitude_mean = train[\"dropoff_longitude\"].mean(),\n",
        "dropoff_longitude_std =  train[\"dropoff_longitude\"].std()\n",
        "# identify outliers\n",
        "dropoff_longitude_cut_off = dropoff_longitude_std * 3\n",
        "lower, upper = dropoff_longitude_mean - dropoff_longitude_cut_off, dropoff_longitude_mean + dropoff_longitude_cut_off\n",
        "# identify outliers\n",
        "outliers = [x for x in train[\"dropoff_longitude\"] if x < lower or x > upper]\n",
        "print('Identified outliers: %d' % len(outliers))\n",
        "# remove outliers\n",
        "train = train[(train[\"dropoff_longitude\"] >= lower[0]) & (train[\"dropoff_longitude\"] <= upper[0])]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8ePcQsNRxJuM",
        "outputId": "969f3f4a-6972-42bf-da38-9e40a935e486"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Identified outliers: 115\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dropoff_longitude_mean = test[\"dropoff_longitude\"].mean(),\n",
        "dropoff_longitude_std =  test[\"dropoff_longitude\"].std()\n",
        "# identify outliers\n",
        "dropoff_longitude_cut_off = dropoff_longitude_std * 3\n",
        "lower, upper = dropoff_longitude_mean - dropoff_longitude_cut_off, dropoff_longitude_mean + dropoff_longitude_cut_off\n",
        "# identify outliers\n",
        "outliers = [x for x in test[\"dropoff_longitude\"] if x < lower or x > upper]\n",
        "print('Identified outliers: %d' % len(outliers))\n",
        "# remove outliers\n",
        "test = test[(train[\"dropoff_longitude\"] >= lower[0]) & (test[\"dropoff_longitude\"] <= upper[0])]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iHEMj3tLynHZ",
        "outputId": "7941cc97-5b20-49b0-bec5-39af0c1cab39"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Identified outliers: 212\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  # Remove the CWD from sys.path while we load stuff.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "passenger_count_mean = train[\"passenger_count\"].mean(),\n",
        "passenger_count_std =  train[\"passenger_count\"].std()\n",
        "# identify outliers\n",
        "passenger_count_cut_off = passenger_count_std * 3\n",
        "lower, upper = passenger_count_mean - passenger_count_cut_off, passenger_count_mean + passenger_count_cut_off\n",
        "# identify outliers\n",
        "outliers = [x for x in train[\"passenger_count\"] if x < lower or x > upper]\n",
        "print('Identified outliers: %d' % len(outliers))\n",
        "# remove outliers\n",
        "train = train[(train[\"passenger_count\"] >= lower[0]) & (train[\"passenger_count\"] <= upper[0])]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LbRkfTLqxeAM",
        "outputId": "29fd9b6d-ac66-4cb0-cb87-604b98fb6942"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Identified outliers: 20092\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "passenger_count_mean = test[\"passenger_count\"].mean(),\n",
        "passenger_count_std =  test[\"passenger_count\"].std()\n",
        "# identify outliers\n",
        "passenger_count_cut_off = passenger_count_std * 3\n",
        "lower, upper = passenger_count_mean - passenger_count_cut_off, passenger_count_mean + passenger_count_cut_off\n",
        "# identify outliers\n",
        "outliers = [x for x in test[\"passenger_count\"] if x < lower or x > upper]\n",
        "print('Identified outliers: %d' % len(outliers))\n",
        "# remove outliers\n",
        "test = test[(test[\"passenger_count\"] >= lower[0]) & (test[\"passenger_count\"] <= upper[0])]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ibky3rnDy4dC",
        "outputId": "ab9cd3e4-0733-458f-d066-df85266e1eff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Identified outliers: 155\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.reset_index(drop = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "id": "AEiazW_RyFKX",
        "outputId": "6e410688-6b58-49b3-db80-9f3b99e11336"
      },
      "execution_count": null,
      "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",
              "933202    2014-09-13 21:44:38.0000001          7.0  2014-09-13 21:44:38 UTC   \n",
              "933203    2010-09-20 14:50:37.0000002          7.3  2010-09-20 14:50:37 UTC   \n",
              "933204  2013-04-26 14:03:00.000000118         10.5  2013-04-26 14:03:00 UTC   \n",
              "933205   2011-07-08 00:29:00.00000099          6.9  2011-07-08 00:29:00 UTC   \n",
              "933206   2009-12-31 14:30:00.00000021          4.1  2009-12-31 14:30:00 UTC   \n",
              "\n",
              "        pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
              "0             -73.844311        40.721319         -73.841610   \n",
              "1             -74.016048        40.711303         -73.979268   \n",
              "2             -73.982738        40.761270         -73.991242   \n",
              "3             -73.987130        40.733143         -73.991567   \n",
              "4             -73.968095        40.768008         -73.956655   \n",
              "...                  ...              ...                ...   \n",
              "933202        -73.976676        40.785630         -73.959196   \n",
              "933203        -73.992103        40.671385         -73.992103   \n",
              "933204        -73.978423        40.751135         -73.993397   \n",
              "933205        -73.980317        40.759482         -73.976832   \n",
              "933206        -74.006635        40.741598         -73.999450   \n",
              "\n",
              "        dropoff_latitude  passenger_count  \n",
              "0              40.712278                1  \n",
              "1              40.782004                1  \n",
              "2              40.750562                2  \n",
              "3              40.758092                1  \n",
              "4              40.783762                1  \n",
              "...                  ...              ...  \n",
              "933202         40.775114                1  \n",
              "933203         40.671385                2  \n",
              "933204         40.762197                5  \n",
              "933205         40.743122                1  \n",
              "933206         40.745702                1  \n",
              "\n",
              "[933207 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cdfcf1db-0844-45b9-a9eb-9fd72a163e26\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>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 UTC</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",
              "    </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 UTC</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",
              "    </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 UTC</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",
              "    </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 UTC</td>\n",
              "      <td>-73.987130</td>\n",
              "      <td>40.733143</td>\n",
              "      <td>-73.991567</td>\n",
              "      <td>40.758092</td>\n",
              "      <td>1</td>\n",
              "    </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 UTC</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",
              "    </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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>933202</th>\n",
              "      <td>2014-09-13 21:44:38.0000001</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2014-09-13 21:44:38 UTC</td>\n",
              "      <td>-73.976676</td>\n",
              "      <td>40.785630</td>\n",
              "      <td>-73.959196</td>\n",
              "      <td>40.775114</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>933203</th>\n",
              "      <td>2010-09-20 14:50:37.0000002</td>\n",
              "      <td>7.3</td>\n",
              "      <td>2010-09-20 14:50:37 UTC</td>\n",
              "      <td>-73.992103</td>\n",
              "      <td>40.671385</td>\n",
              "      <td>-73.992103</td>\n",
              "      <td>40.671385</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>933204</th>\n",
              "      <td>2013-04-26 14:03:00.000000118</td>\n",
              "      <td>10.5</td>\n",
              "      <td>2013-04-26 14:03:00 UTC</td>\n",
              "      <td>-73.978423</td>\n",
              "      <td>40.751135</td>\n",
              "      <td>-73.993397</td>\n",
              "      <td>40.762197</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>933205</th>\n",
              "      <td>2011-07-08 00:29:00.00000099</td>\n",
              "      <td>6.9</td>\n",
              "      <td>2011-07-08 00:29:00 UTC</td>\n",
              "      <td>-73.980317</td>\n",
              "      <td>40.759482</td>\n",
              "      <td>-73.976832</td>\n",
              "      <td>40.743122</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>933206</th>\n",
              "      <td>2009-12-31 14:30:00.00000021</td>\n",
              "      <td>4.1</td>\n",
              "      <td>2009-12-31 14:30:00 UTC</td>\n",
              "      <td>-74.006635</td>\n",
              "      <td>40.741598</td>\n",
              "      <td>-73.999450</td>\n",
              "      <td>40.745702</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>933207 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cdfcf1db-0844-45b9-a9eb-9fd72a163e26')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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-cdfcf1db-0844-45b9-a9eb-9fd72a163e26 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-cdfcf1db-0844-45b9-a9eb-9fd72a163e26');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plotting after removing outliers\n",
        "train.plot(kind=\"scatter\", x=\"pickup_longitude\", y=\"pickup_latitude\", figsize=(10, 7), alpha=0.7)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "qSndJNatjY27",
        "outputId": "939e61f5-3a29-4419-9090-8b6c329ed62d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.plot(kind=\"scatter\", x=\"dropoff_longitude\", y=\"dropoff_latitude\", figsize=(10, 7), alpha=0.7)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "SHzJ1nuekfEX",
        "outputId": "4477255d-4c9a-4860-aba0-97cbab5d482b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.plot(kind=\"scatter\", x=\"passenger_count\", y=\"fare_amount\", figsize=(10, 7), alpha=0.7)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "LNVhY_RIjkrY",
        "outputId": "00102191-48d1-4a2b-f1eb-7089792ffe0c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SsxAk6QT5eKS"
      },
      "source": [
        "**Next let us explore the pickup latitude and longitudes**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NQd_MWpZ91af"
      },
      "source": [
        "Check the data types of each column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p1kSp6Ab93fK",
        "outputId": "7b178ef1-eea6-4f1e-a6b0-2f9143da3bd5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "key                   object\n",
              "fare_amount          float64\n",
              "pickup_datetime       object\n",
              "pickup_longitude     float64\n",
              "pickup_latitude      float64\n",
              "dropoff_longitude    float64\n",
              "dropoff_latitude     float64\n",
              "passenger_count        int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ],
      "source": [
        "train.dtypes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PY-qECSJ-B3O"
      },
      "source": [
        "key and pickup_datetime seem to be datetime columns which are in object format. Let's convert them to datetime"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tiLXtCaY-CzY"
      },
      "outputs": [],
      "source": [
        "train['key'] = pd.to_datetime(train['key'])\n",
        "train['pickup_datetime']  = pd.to_datetime(train['pickup_datetime'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nOEWOcFs-RXV"
      },
      "outputs": [],
      "source": [
        "#Convert for test data\n",
        "test['key'] = pd.to_datetime(test['key'])\n",
        "test['pickup_datetime']  = pd.to_datetime(test['pickup_datetime'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mQVSh9bU-XwF",
        "outputId": "010ac0ef-2c45-481f-83ad-b34394e1c6a6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "key                       datetime64[ns]\n",
              "fare_amount                      float64\n",
              "pickup_datetime      datetime64[ns, UTC]\n",
              "pickup_longitude                 float64\n",
              "pickup_latitude                  float64\n",
              "dropoff_longitude                float64\n",
              "dropoff_latitude                 float64\n",
              "passenger_count                    int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ],
      "source": [
        "#check the dtypes after conversion\n",
        "train.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DqaAlCqI-cBD",
        "outputId": "4465fcea-c4cb-452e-8dc4-56f77d7fc38b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "key                       datetime64[ns]\n",
              "pickup_datetime      datetime64[ns, UTC]\n",
              "pickup_longitude                 float64\n",
              "pickup_latitude                  float64\n",
              "dropoff_longitude                float64\n",
              "dropoff_latitude                 float64\n",
              "passenger_count                    int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ],
      "source": [
        "test.dtypes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vXrSWtM9RRTA"
      },
      "source": [
        "\n",
        "First, let's split the datetime field 'pickup_datetime' to the following - \n",
        "* year\n",
        "* month\n",
        "* date\n",
        "* hour\n",
        "* day of week\n",
        "\n",
        "Using these we shall calculate the day of the week and come to our conclusions about how pickup_location affects the fare.\n",
        "Also, create a new field 'distance' to fetch the distance between the pickup and the drop."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "69oPjV8hRhCc"
      },
      "source": [
        "We can calulate the distance in a sphere when latitudes and longitudes are given by [Haversine formula](https://en.wikipedia.org/wiki/Haversine_formula)\n",
        "\n",
        "**haversine(θ) = sin²(θ/2)**\n",
        "\n",
        "Eventually, the formula boils down to the following where φ is latitude, λ is longitude, R is earth’s radius (mean radius = 6,371km) to include latitude and longitude coordinates (A and B in this case).\n",
        "\n",
        "**a = sin²((φB - φA)/2) + cos φA . cos φB . sin²((λB - λA)/2)**\n",
        "\n",
        "**c = 2 * atan2( √a, √(1−a) )**\n",
        "\n",
        "**d = R ⋅ c**\n",
        "\n",
        "**d = Haversine distance**\n",
        "\n",
        "*Refer [this](https://community.esri.com/groups/coordinate-reference-systems/blog/2017/10/05/haversine-formula) page for more info and examples on Haversine formula*\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kdD-M1pkRSyn"
      },
      "outputs": [],
      "source": [
        "def haversine_distance(lat1, long1, lat2, long2):\n",
        "    data = [train, test]\n",
        "    for i in data:\n",
        "        R = 6371  #radius of earth in kilometers\n",
        "        phi1 = np.radians(i[lat1])\n",
        "        phi2 = np.radians(i[lat2])\n",
        "    \n",
        "        delta_phi = np.radians(i[lat2]-i[lat1])\n",
        "        delta_lambda = np.radians(i[long2]-i[long1])\n",
        "    \n",
        "        #a = sin²((φB - φA)/2) + cos φA . cos φB . sin²((λB - λA)/2)\n",
        "        a = np.sin(delta_phi / 2.0) ** 2 + np.cos(phi1) * np.cos(phi2) * np.sin(delta_lambda / 2.0) ** 2\n",
        "    \n",
        "        #c = 2 * atan2( √a, √(1−a) )\n",
        "        c = 2 * np.arctan2(np.sqrt(a), np.sqrt(1-a))\n",
        "    \n",
        "        #d = R*c\n",
        "        d = (R * c) #in kilometers\n",
        "        i['H_Distance'] = d\n",
        "    return d"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7jU7kBbbR2Df",
        "outputId": "e0c7faaf-af98-41be-8be9-2999ffc94d36"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       2.323260\n",
              "1       2.425353\n",
              "2       0.618628\n",
              "3       1.961033\n",
              "4       5.387301\n",
              "          ...   \n",
              "9732    2.461474\n",
              "9733    1.488777\n",
              "9734    1.609619\n",
              "9735    1.666308\n",
              "9736    2.396137\n",
              "Length: 8665, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ],
      "source": [
        "haversine_distance('pickup_latitude', 'pickup_longitude', 'dropoff_latitude', 'dropoff_longitude')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jR75ePDO1Mo1"
      },
      "source": [
        "Now that we have calculated the distance, we shall create columns for the following -\n",
        "\n",
        "\n",
        "*   year\n",
        "*   month\n",
        "*   date\n",
        "*   hour\n",
        "*   day of week\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ypJEZACs09pe"
      },
      "outputs": [],
      "source": [
        "data = [train,test]\n",
        "for i in data:\n",
        "    i['Year'] = i['pickup_datetime'].dt.year\n",
        "    i['Month'] = i['pickup_datetime'].dt.month\n",
        "    i['Date'] = i['pickup_datetime'].dt.day\n",
        "    i['Day of Week'] = i['pickup_datetime'].dt.dayofweek\n",
        "    i['Hour'] = i['pickup_datetime'].dt.hour"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "id": "aYFLu1Jh1zJV",
        "outputId": "d9ec216a-fcea-4cf6-b443-a0bb2302ef0b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            key  fare_amount           pickup_datetime  \\\n",
              "0 2009-06-15 17:26:21.000000100          4.5 2009-06-15 17:26:21+00:00   \n",
              "1 2010-01-05 16:52:16.000000200         16.9 2010-01-05 16:52:16+00:00   \n",
              "2 2011-08-18 00:35:00.000000490          5.7 2011-08-18 00:35:00+00:00   \n",
              "3 2012-04-21 04:30:42.000000100          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",
              "   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  H_Distance  Year  Month  Date  Day of Week  Hour  \n",
              "0                1    1.030764  2009      6    15            0    17  \n",
              "1                1    8.450134  2010      1     5            1    16  \n",
              "2                2    1.389525  2011      8    18            3     0  \n",
              "3                1    2.799270  2012      4    21            5     4  \n",
              "4                1    1.999157  2010      3     9            1     7  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-52c01d64-b63e-4bed-a478-a98b3770b21e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "    <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>H_Distance</th>\n",
              "      <th>Year</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-06-15 17:26:21.000000100</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",
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              "      <td>1</td>\n",
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              "      <td>2009</td>\n",
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              "      <td>17</td>\n",
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              "      <th>1</th>\n",
              "      <td>2010-01-05 16:52:16.000000200</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>8.450134</td>\n",
              "      <td>2010</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-08-18 00:35:00.000000490</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>1.389525</td>\n",
              "      <td>2011</td>\n",
              "      <td>8</td>\n",
              "      <td>18</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2012-04-21 04:30:42.000000100</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>2.799270</td>\n",
              "      <td>2012</td>\n",
              "      <td>4</td>\n",
              "      <td>21</td>\n",
              "      <td>5</td>\n",
              "      <td>4</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>1.999157</td>\n",
              "      <td>2010</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ],
      "source": [
        "train.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "H_Distance_mean = train[\"H_Distance\"].mean(),\n",
        "H_Distance_std =  train[\"H_Distance\"].std()\n",
        "# identify outliers\n",
        "H_Distance_cut_off = H_Distance_std * 3\n",
        "lower, upper = H_Distance_mean - H_Distance_cut_off, H_Distance_mean + H_Distance_cut_off\n",
        "# identify outliers\n",
        "outliers = [x for x in train[\"H_Distance\"] if x < lower or x > upper]\n",
        "print('Identified outliers: %d' % len(outliers))\n",
        "# remove outliers\n",
        "train = train[(train[\"H_Distance\"] >= lower[0]) & (train[\"H_Distance\"] <= upper[0])]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9dVm_cYlcwLw",
        "outputId": "19d415fa-9283-40b2-d821-a227922a7df3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Identified outliers: 3998\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "10HCMd7o2R6J"
      },
      "source": [
        "**1. Does the number of passengers affect the fare?**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,7))\n",
        "sns.histplot(train, x=\"passenger_count\", y=\"fare_amount\",bins=150, discrete=(True, False),kde = True)"
      ],
      "metadata": {
        "colab": {
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          "height": 460
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        "id": "w0OTqGpUvSXH",
        "outputId": "6f917fd5-8987-4e5e-bc1d-e8d77e18beea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f02be066c90>"
            ]
          },
          "metadata": {},
          "execution_count": 75
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the graph above, we can see that low passenger count has low fare amount proportionately."
      ],
      "metadata": {
        "id": "MqXae-E2K6PA"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "beeVcyOw2ooS"
      },
      "source": [
        "**2. Does the date and time of pickup affect the fare?**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,7))\n",
        "sns.histplot(train, x=\"Date\", y=\"fare_amount\",bins=150, discrete=(True, False))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "id": "3lIx5pS2ZR0-",
        "outputId": "05cf87aa-4f76-46b8-ef9c-48820a66609b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f02b39320d0>"
            ]
          },
          "metadata": {},
          "execution_count": 77
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the above graph, we can infer that the fare amount does not depend on the date."
      ],
      "metadata": {
        "id": "nVDbZAibPzL7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,7))\n",
        "sns.histplot(train, x=\"Hour\", y=\"fare_amount\",bins=150, discrete=(True, False))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "id": "ely94lc-Z96I",
        "outputId": "e5acfc3d-ec83-44ec-c313-36f1723416d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f02b38daad0>"
            ]
          },
          "metadata": {},
          "execution_count": 78
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the above graph we can infer that people do not travel during early morning and travel more during evening and night time."
      ],
      "metadata": {
        "id": "SQs33sjoP9R5"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Isxq9cF3ujw"
      },
      "source": [
        "**3. Does the day of the week affect the fare?**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,7))\n",
        "sns.histplot(train, x=\"Day of Week\", y=\"fare_amount\",bins=150, discrete=(True, False))"
      ],
      "metadata": {
        "id": "lJnTm3ervT0j",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "outputId": "362794cf-b351-478d-a649-3f91a4a1ab1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f02b38366d0>"
            ]
          },
          "metadata": {},
          "execution_count": 79
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the above graph we can infer that there is very less or negligible change in fare during weekends."
      ],
      "metadata": {
        "id": "X_pTIAAhQZq-"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yClerbVz4nxJ"
      },
      "source": [
        "**4. Does the distance affect the fare?**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,7))\n",
        "sns.histplot(train, x=\"H_Distance\", y=\"fare_amount\",bins=200,discrete=(True, False))"
      ],
      "metadata": {
        "id": "IImLjsCVvUaE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "outputId": "435e6a87-d295-47f2-f09a-2955272438f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f02b3836710>"
            ]
          },
          "metadata": {},
          "execution_count": 80
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.plot(kind=\"scatter\", x=\"H_Distance\", y=\"fare_amount\", figsize=(10, 7), alpha=0.1)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "mCOB20RrfCWq",
        "outputId": "7e1cd69a-fd44-4303-9276-7b552fdb8c82"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the above graphs we can infer that fare amount is directly proprtional to H_distance. If H_distance increases,fare also increases."
      ],
      "metadata": {
        "id": "hupSc0WmQxZ3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Data Scaling"
      ],
      "metadata": {
        "id": "4elGf_cMJSNJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#check the target column\n",
        "features = train.drop([\"key\",'fare_amount',\"pickup_datetime\",\"pickup_longitude\",\"pickup_latitude\",\"dropoff_longitude\",\"dropoff_latitude\"],axis=1)   #Feature Matrix\n",
        "target = train['fare_amount']          #Target Variable"
      ],
      "metadata": {
        "id": "h3BiI3peJVkS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "features.reset_index(drop = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "jmeGGHeraDMm",
        "outputId": "6533aee6-ac21-4555-d8e9-4e56155e5107"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        passenger_count  H_Distance  Year  Month  Date  Day of Week  Hour\n",
              "0                     1    1.030764  2009      6    15            0    17\n",
              "1                     1    8.450134  2010      1     5            1    16\n",
              "2                     2    1.389525  2011      8    18            3     0\n",
              "3                     1    2.799270  2012      4    21            5     4\n",
              "4                     1    1.999157  2010      3     9            1     7\n",
              "...                 ...         ...   ...    ...   ...          ...   ...\n",
              "929204                1    1.879763  2014      9    13            5    21\n",
              "929205                2    0.000000  2010      9    20            0    14\n",
              "929206                5    1.761741  2013      4    26            4    14\n",
              "929207                1    1.842683  2011      7     8            4     0\n",
              "929208                1    0.758051  2009     12    31            3    14\n",
              "\n",
              "[929209 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-148be21d-ae6a-4ffd-8485-0367c7d635f1\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>passenger_count</th>\n",
              "      <th>H_Distance</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Date</th>\n",
              "      <th>Day of Week</th>\n",
              "      <th>Hour</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1.030764</td>\n",
              "      <td>2009</td>\n",
              "      <td>6</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>8.450134</td>\n",
              "      <td>2010</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>1.389525</td>\n",
              "      <td>2011</td>\n",
              "      <td>8</td>\n",
              "      <td>18</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>2.799270</td>\n",
              "      <td>2012</td>\n",
              "      <td>4</td>\n",
              "      <td>21</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>1.999157</td>\n",
              "      <td>2010</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>929204</th>\n",
              "      <td>1</td>\n",
              "      <td>1.879763</td>\n",
              "      <td>2014</td>\n",
              "      <td>9</td>\n",
              "      <td>13</td>\n",
              "      <td>5</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>929205</th>\n",
              "      <td>2</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2010</td>\n",
              "      <td>9</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>929206</th>\n",
              "      <td>5</td>\n",
              "      <td>1.761741</td>\n",
              "      <td>2013</td>\n",
              "      <td>4</td>\n",
              "      <td>26</td>\n",
              "      <td>4</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>929207</th>\n",
              "      <td>1</td>\n",
              "      <td>1.842683</td>\n",
              "      <td>2011</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>929208</th>\n",
              "      <td>1</td>\n",
              "      <td>0.758051</td>\n",
              "      <td>2009</td>\n",
              "      <td>12</td>\n",
              "      <td>31</td>\n",
              "      <td>3</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>929209 rows × 7 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-148be21d-ae6a-4ffd-8485-0367c7d635f1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\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-148be21d-ae6a-4ffd-8485-0367c7d635f1 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-148be21d-ae6a-4ffd-8485-0367c7d635f1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "features_train, features_test, target_train, target_test = train_test_split(features, target, test_size=0.2, random_state = 0, shuffle=True)"
      ],
      "metadata": {
        "id": "_CK2dMjs3irJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scale_features_std = StandardScaler()\n",
        "features = scale_features_std.fit_transform(features)\n"
      ],
      "metadata": {
        "id": "xG53JAOj3Z6B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Size of training data :\", features_train.shape)\n",
        "print(\"Size of testing data :\", features_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MKNVYvfvkn0j",
        "outputId": "aa6500c0-60e4-4e91-e70c-0ba4d872c7cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Size of training data : (743367, 7)\n",
            "Size of testing data : (185842, 7)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_AtyBaXF5Bz5",
        "outputId": "b431722b-9ef1-4e0e-9636-bfa6949882bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.51085214, -0.75381321, -1.45053263, ..., -0.07992294,\n",
              "        -1.56083557,  0.5333403 ],\n",
              "       [-0.51085214,  2.25165847, -0.91343442, ..., -1.23149675,\n",
              "        -1.04747544,  0.38003649],\n",
              "       [ 0.35455744, -0.60848457, -0.3763362 , ...,  0.26554921,\n",
              "        -0.02075519, -2.07282442],\n",
              "       ...,\n",
              "       [ 2.95078617, -0.45770578,  0.69786024, ...,  1.18680827,\n",
              "         0.49260493,  0.07342888],\n",
              "       [-0.51085214, -0.42491729, -0.3763362 , ..., -0.88602461,\n",
              "         0.49260493, -2.07282442],\n",
              "       [-0.51085214, -0.8642848 , -1.45053263, ...,  1.76259518,\n",
              "        -0.02075519,  0.07342888]])"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "column_values = ['passenger_count', 'H_Distance', 'Year','Month', 'Date','Day of Week', 'Hour']\n",
        "df = pd.DataFrame(data = features,\n",
        "                  columns = column_values)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "9MVuc9OHdDbl",
        "outputId": "d397e383-03cc-453b-e74e-8d8c6a344ec0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        passenger_count  H_Distance      Year     Month      Date  \\\n",
              "0             -0.510852   -0.753813 -1.450533 -0.075697 -0.079923   \n",
              "1             -0.510852    2.251658 -0.913434 -1.529769 -1.231497   \n",
              "2              0.354557   -0.608485 -0.376336  0.505931  0.265549   \n",
              "3             -0.510852   -0.037419  0.160762 -0.657326  0.611021   \n",
              "4             -0.510852   -0.361532 -0.913434 -0.948140 -0.770867   \n",
              "...                 ...         ...       ...       ...       ...   \n",
              "929204        -0.510852   -0.409897  1.234958  0.796746 -0.310238   \n",
              "929205         0.354557   -1.171360 -0.913434  0.796746  0.495864   \n",
              "929206         2.950786   -0.457706  0.697860 -0.657326  1.186808   \n",
              "929207        -0.510852   -0.424917 -0.376336  0.215117 -0.886025   \n",
              "929208        -0.510852   -0.864285 -1.450533  1.669189  1.762595   \n",
              "\n",
              "        Day of Week      Hour  \n",
              "0         -1.560836  0.533340  \n",
              "1         -1.047475  0.380036  \n",
              "2         -0.020755 -2.072824  \n",
              "3          1.005965 -1.459609  \n",
              "4         -1.047475 -0.999698  \n",
              "...             ...       ...  \n",
              "929204     1.005965  1.146556  \n",
              "929205    -1.560836  0.073429  \n",
              "929206     0.492605  0.073429  \n",
              "929207     0.492605 -2.072824  \n",
              "929208    -0.020755  0.073429  \n",
              "\n",
              "[929209 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7aa89abb-df33-4f5d-bc86-cf84a2b50590\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>passenger_count</th>\n",
              "      <th>H_Distance</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Date</th>\n",
              "      <th>Day of Week</th>\n",
              "      <th>Hour</th>\n",
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              "      <td>-0.753813</td>\n",
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              "      <td>-0.075697</td>\n",
              "      <td>-0.079923</td>\n",
              "      <td>-1.560836</td>\n",
              "      <td>0.533340</td>\n",
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              "      <th>1</th>\n",
              "      <td>-0.510852</td>\n",
              "      <td>2.251658</td>\n",
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              "      <td>-1.047475</td>\n",
              "      <td>0.380036</td>\n",
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              "      <th>3</th>\n",
              "      <td>-0.510852</td>\n",
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              "      <td>-0.657326</td>\n",
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              "      <td>-1.459609</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.510852</td>\n",
              "      <td>-0.361532</td>\n",
              "      <td>-0.913434</td>\n",
              "      <td>-0.948140</td>\n",
              "      <td>-0.770867</td>\n",
              "      <td>-1.047475</td>\n",
              "      <td>-0.999698</td>\n",
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              "    <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",
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              "      <th>929204</th>\n",
              "      <td>-0.510852</td>\n",
              "      <td>-0.409897</td>\n",
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              "      <td>0.796746</td>\n",
              "      <td>-0.310238</td>\n",
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              "      <td>1.146556</td>\n",
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              "    <tr>\n",
              "      <th>929205</th>\n",
              "      <td>0.354557</td>\n",
              "      <td>-1.171360</td>\n",
              "      <td>-0.913434</td>\n",
              "      <td>0.796746</td>\n",
              "      <td>0.495864</td>\n",
              "      <td>-1.560836</td>\n",
              "      <td>0.073429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>929206</th>\n",
              "      <td>2.950786</td>\n",
              "      <td>-0.457706</td>\n",
              "      <td>0.697860</td>\n",
              "      <td>-0.657326</td>\n",
              "      <td>1.186808</td>\n",
              "      <td>0.492605</td>\n",
              "      <td>0.073429</td>\n",
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              "    <tr>\n",
              "      <th>929207</th>\n",
              "      <td>-0.510852</td>\n",
              "      <td>-0.424917</td>\n",
              "      <td>-0.376336</td>\n",
              "      <td>0.215117</td>\n",
              "      <td>-0.886025</td>\n",
              "      <td>0.492605</td>\n",
              "      <td>-2.072824</td>\n",
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              "    <tr>\n",
              "      <th>929208</th>\n",
              "      <td>-0.510852</td>\n",
              "      <td>-0.864285</td>\n",
              "      <td>-1.450533</td>\n",
              "      <td>1.669189</td>\n",
              "      <td>1.762595</td>\n",
              "      <td>-0.020755</td>\n",
              "      <td>0.073429</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>929209 rows × 7 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7aa89abb-df33-4f5d-bc86-cf84a2b50590')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "    }\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",
              "\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-7aa89abb-df33-4f5d-bc86-cf84a2b50590 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-7aa89abb-df33-4f5d-bc86-cf84a2b50590');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
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    {
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      "source": [
        "features = df \n",
        "features"
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        "id": "rxyl0FgtZLGE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "175c4a80-1b0a-4cae-b9b3-b45af9741560"
      },
      "execution_count": null,
      "outputs": [
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              "        passenger_count  H_Distance      Year     Month      Date  \\\n",
              "0             -0.510852   -0.753813 -1.450533 -0.075697 -0.079923   \n",
              "1             -0.510852    2.251658 -0.913434 -1.529769 -1.231497   \n",
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              "...                 ...         ...       ...       ...       ...   \n",
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          },
          "metadata": {},
          "execution_count": 107
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The requried data is scaled and standardized without a pipeline."
      ],
      "metadata": {
        "id": "_eQifN0pUiqZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Pipeline"
      ],
      "metadata": {
        "id": "lErM2MaTlc81"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will use a copy of the main data to write the pipeline."
      ],
      "metadata": {
        "id": "a5e8KFc1U0Gw"
      }
    },
    {
      "cell_type": "code",
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          "height": 641
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        "id": "sAqz3FvQgKN4",
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      "execution_count": null,
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              "                                  key  fare_amount          pickup_datetime  \\\n",
              "0         2009-06-15 17:26:21.0000001          4.5  2009-06-15 17:26:21 UTC   \n",
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              "...                               ...          ...                      ...   \n",
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              "999997  2013-04-26 14:03:00.000000118         10.5  2013-04-26 14:03:00 UTC   \n",
              "999998   2011-07-08 00:29:00.00000099          6.9  2011-07-08 00:29:00 UTC   \n",
              "999999   2009-12-31 14:30:00.00000021          4.1  2009-12-31 14:30:00 UTC   \n",
              "\n",
              "        pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
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              "999999        -74.006635        40.741598         -73.999450   \n",
              "\n",
              "        dropoff_latitude  passenger_count  \n",
              "0              40.712278                1  \n",
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              "3              40.758092                1  \n",
              "4              40.783762                1  \n",
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              "999997         40.762197                5  \n",
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              "999999         40.745702                1  \n",
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              "      <td>2011-07-08 00:29:00.00000099</td>\n",
              "      <td>6.9</td>\n",
              "      <td>2011-07-08 00:29:00 UTC</td>\n",
              "      <td>-73.980317</td>\n",
              "      <td>40.759482</td>\n",
              "      <td>-73.976832</td>\n",
              "      <td>40.743122</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999999</th>\n",
              "      <td>2009-12-31 14:30:00.00000021</td>\n",
              "      <td>4.1</td>\n",
              "      <td>2009-12-31 14:30:00 UTC</td>\n",
              "      <td>-74.006635</td>\n",
              "      <td>40.741598</td>\n",
              "      <td>-73.999450</td>\n",
              "      <td>40.745702</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000000 rows × 8 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.base import BaseEstimator, TransformerMixin\n",
        "from sklearn.pipeline import (Pipeline, FeatureUnion)\n",
        "from sklearn.preprocessing import (FunctionTransformer, StandardScaler)"
      ],
      "metadata": {
        "id": "qEUlTnXMkyCb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove 0 values\n",
        "\n",
        "class drop_na(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self, col):\n",
        "        self.col = col\n",
        "    \n",
        "    def fit(self, X, y=None):\n",
        "        return self\n",
        "    \n",
        "    # Transform updates all NaN values with median\n",
        "    def transform(self, X):\n",
        "        return X.dropna(axis = 0, how ='any')"
      ],
      "metadata": {
        "id": "PePzhtnOg7VP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove negative values in fare amount\n",
        "\n",
        "class remove_neg(BaseEstimator, TransformerMixin):\n",
        "\n",
        "    def fit(self, X, y=None):\n",
        "        return self\n",
        "\n",
        "    def transform(self, X):\n",
        "        return X[X[\"fare_amount\"]>0]\n"
      ],
      "metadata": {
        "id": "VgSUfBHKg_zk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "class Outliers(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self, col):\n",
        "        self.col = col\n",
        "\n",
        "    def fit(self, X, y=None):\n",
        "        return self\n",
        "\n",
        "    # Remove outliers\n",
        "    def transform(self, X):\n",
        "        self.mean = X[self.col].mean()\n",
        "        self.std = X[self.col].std()\n",
        "        self.cut_off = self.std * 3\n",
        "        self.lower = self.mean - self.cut_off\n",
        "        self.upper = self.mean + self.cut_off\n",
        "        return X[(X[self.col] >= self.lower) & (X[self.col] <= self.upper)]"
      ],
      "metadata": {
        "id": "n-j6InGDjS-n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def haversine_distance(lat1, long1, lat2, long2):\n",
        "    data = [trainc]\n",
        "    for i in data:\n",
        "        R = 6371  #radius of earth in kilometers\n",
        "        phi1 = np.radians(i[lat1])\n",
        "        phi2 = np.radians(i[lat2])\n",
        "    \n",
        "        delta_phi = np.radians(i[lat2]-i[lat1])\n",
        "        delta_lambda = np.radians(i[long2]-i[long1])\n",
        "    \n",
        "        #a = sin²((φB - φA)/2) + cos φA . cos φB . sin²((λB - λA)/2)\n",
        "        a = np.sin(delta_phi / 2.0) ** 2 + np.cos(phi1) * np.cos(phi2) * np.sin(delta_lambda / 2.0) ** 2\n",
        "    \n",
        "        #c = 2 * atan2( √a, √(1−a) )\n",
        "        c = 2 * np.arctan2(np.sqrt(a), np.sqrt(1-a))\n",
        "    \n",
        "        #d = R*c\n",
        "        d = (R * c) #in kilometers\n",
        "        i['H_Distance'] = d\n",
        "    return d\n",
        "\n",
        "class attributes(BaseEstimator, TransformerMixin):\n",
        "\n",
        "    def fit(self, X, y=None):\n",
        "        return self\n",
        "\n",
        "    def transform(self, X):\n",
        "        X['key'] = pd.to_datetime(X['key'])\n",
        "        X['pickup_datetime']  = pd.to_datetime(X['pickup_datetime'])\n",
        "        data = [X]\n",
        "        for i in data:\n",
        "          i['Year'] = i['pickup_datetime'].dt.year\n",
        "          i['Month'] = i['pickup_datetime'].dt.month\n",
        "          i['Date'] = i['pickup_datetime'].dt.day\n",
        "          i['Day of Week'] = i['pickup_datetime'].dt.dayofweek\n",
        "          i['Hour'] = i['pickup_datetime'].dt.hour\n",
        "        i['H_Distance'] = haversine_distance('pickup_latitude', 'pickup_longitude', 'dropoff_latitude', 'dropoff_longitude')\n",
        "        return X"
      ],
      "metadata": {
        "id": "Yi96l--RGeke"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class dropcolumns(BaseEstimator, TransformerMixin):\n",
        "\n",
        "    def fit(self, X, y=None):\n",
        "        return self\n",
        "\n",
        "    def transform(self, X):\n",
        "        X = X.drop([\"key\",\"pickup_datetime\",\"pickup_longitude\",\"pickup_latitude\",\"dropoff_longitude\",\"dropoff_latitude\"],axis=1) \n",
        "        return X"
      ],
      "metadata": {
        "id": "srgO0ghnGeaa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "\n",
        "# This is the main pipeline object, in which we are performing four tasks\n",
        "# pipeline takes list of tuples of two values as (name of transform, transform object)\n",
        "pre_pipe = Pipeline([\n",
        "    ('dropna', drop_na(col=\"fare_amount\")),\n",
        "    (\"remove_neg\",remove_neg()),\n",
        "    (\"outliers1\",Outliers(col=\"fare_amount\")),\n",
        "    (\"outliers2\",Outliers(col=\"pickup_latitude\")),\n",
        "    (\"outliers3\",Outliers(col=\"pickup_longitude\")),\n",
        "    (\"outliers4\",Outliers(col=\"dropoff_latitude\")),\n",
        "    (\"outliers5\",Outliers(col=\"dropoff_longitude\")),\n",
        "    (\"outliers6\",Outliers(col=\"passenger_count\")),\n",
        "    (\"attributes\",attributes()),\n",
        "    (\"outliers7\",Outliers(col='H_Distance')),\n",
        "    (\"dropcolumns\",dropcolumns())])\n",
        "\n",
        "train_prepared = pre_pipe.fit_transform(trainc)\n",
        "y = train_prepared[\"fare_amount\"]\n",
        "X = train_prepared.drop([\"fare_amount\"],axis=1) \n",
        "scalar = Pipeline([('Standard_scaling', StandardScaler())])\n",
        "X = scalar.fit_transform(X)\n",
        "column_values = ['passenger_count', 'H_Distance', 'Year','Month', 'Date','Day of Week', 'Hour']\n",
        "X = pd.DataFrame(data = X,columns = column_values)\n",
        "X.reset_index(drop = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "eAZTQzOdfzEo",
        "outputId": "4121ab75-f88d-4fb3-f3a2-763b0bf60035"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        passenger_count  H_Distance      Year     Month      Date  \\\n",
              "0             -0.510846   -1.450583 -0.075667 -0.079934 -1.560835   \n",
              "1             -0.510846   -0.913492 -1.529669 -1.231518 -1.047438   \n",
              "2              0.354601   -0.376402  0.505934  0.265541 -0.020642   \n",
              "3             -0.510846    0.160689 -0.657268  0.611016  1.006153   \n",
              "4             -0.510846   -0.913492 -0.948068 -0.770884 -1.047438   \n",
              "...                 ...         ...       ...       ...       ...   \n",
              "928063        -0.510846    1.234870  0.796734 -0.310251  1.006153   \n",
              "928064         0.354601   -0.913492  0.796734  0.495858 -1.560835   \n",
              "928065         2.950942    0.697780 -0.657268  1.186808  0.492755   \n",
              "928066        -0.510846   -0.376402  0.215134 -0.886043  0.492755   \n",
              "928067        -0.510846   -1.450583  1.669136  1.762600 -0.020642   \n",
              "\n",
              "        Day of Week      Hour  \n",
              "0          0.533344 -0.758492  \n",
              "1          0.379990  2.290021  \n",
              "2         -2.073673 -0.611082  \n",
              "3         -1.460257 -0.031838  \n",
              "4         -1.000196 -0.360593  \n",
              "...             ...       ...  \n",
              "928063     1.146760 -0.409650  \n",
              "928064     0.073282 -1.182018  \n",
              "928065     0.073282 -0.458144  \n",
              "928066    -2.073673 -0.424886  \n",
              "928067     0.073282 -0.870545  \n",
              "\n",
              "[928068 rows x 7 columns]"
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.510846</td>\n",
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              "      <td>-1.231518</td>\n",
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              "      <td>0.379990</td>\n",
              "      <td>2.290021</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.354601</td>\n",
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              "      <td>-0.611082</td>\n",
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              "      <th>3</th>\n",
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              "    <tr>\n",
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              "      <td>-0.770884</td>\n",
              "      <td>-1.047438</td>\n",
              "      <td>-1.000196</td>\n",
              "      <td>-0.360593</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>1.146760</td>\n",
              "      <td>-0.409650</td>\n",
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              "    <tr>\n",
              "      <th>928064</th>\n",
              "      <td>0.354601</td>\n",
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              "      <td>2.950942</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The training data is cleaned,preprocessed and scaled the we have output which is a pandas dataframe ready to be fed into a regression algorithm."
      ],
      "metadata": {
        "id": "jLZToogmVD7I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y = y.reset_index(drop = True)"
      ],
      "metadata": {
        "id": "F6XPQXKLpAO-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The target values should not be scaled because we need the final value of fare amount not its scaled value as output."
      ],
      "metadata": {
        "id": "hbMiE-PyVm_9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "testc"
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        "id": "EA4tH6IprJ2U",
        "outputId": "55535297-73cd-4ac9-cc4e-060c8df53e6e"
      },
      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
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              "                              key          pickup_datetime  pickup_longitude  \\\n",
              "0     2015-01-27 13:08:24.0000002  2015-01-27 13:08:24 UTC        -73.973320   \n",
              "1     2015-01-27 13:08:24.0000003  2015-01-27 13:08:24 UTC        -73.986862   \n",
              "2     2011-10-08 11:53:44.0000002  2011-10-08 11:53:44 UTC        -73.982524   \n",
              "3     2012-12-01 21:12:12.0000002  2012-12-01 21:12:12 UTC        -73.981160   \n",
              "4     2012-12-01 21:12:12.0000003  2012-12-01 21:12:12 UTC        -73.966046   \n",
              "...                           ...                      ...               ...   \n",
              "9909  2015-05-10 12:37:51.0000002  2015-05-10 12:37:51 UTC        -73.968124   \n",
              "9910  2015-01-12 17:05:51.0000001  2015-01-12 17:05:51 UTC        -73.945511   \n",
              "9911  2015-04-19 20:44:15.0000001  2015-04-19 20:44:15 UTC        -73.991600   \n",
              "9912  2015-01-31 01:05:19.0000005  2015-01-31 01:05:19 UTC        -73.985573   \n",
              "9913  2015-01-18 14:06:23.0000006  2015-01-18 14:06:23 UTC        -73.988022   \n",
              "\n",
              "      pickup_latitude  dropoff_longitude  dropoff_latitude  passenger_count  \n",
              "0           40.763805         -73.981430         40.743835                1  \n",
              "1           40.719383         -73.998886         40.739201                1  \n",
              "2           40.751260         -73.979654         40.746139                1  \n",
              "3           40.767807         -73.990448         40.751635                1  \n",
              "4           40.789775         -73.988565         40.744427                1  \n",
              "...               ...                ...               ...              ...  \n",
              "9909        40.796997         -73.955643         40.780388                6  \n",
              "9910        40.803600         -73.960213         40.776371                6  \n",
              "9911        40.726608         -73.789742         40.647011                6  \n",
              "9912        40.735432         -73.939178         40.801731                6  \n",
              "9913        40.754070         -74.000282         40.759220                6  \n",
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              "      <td>2015-01-12 17:05:51.0000001</td>\n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "test_pre_pipe = Pipeline([\n",
        "    (\"outliers1\",Outliers(col=\"pickup_latitude\")),\n",
        "    (\"outliers2\",Outliers(col=\"pickup_longitude\")),\n",
        "    (\"outliers3\",Outliers(col=\"dropoff_latitude\")),\n",
        "    (\"outliers4\",Outliers(col=\"dropoff_longitude\")),\n",
        "    (\"outliers5\",Outliers(col=\"passenger_count\")),\n",
        "    (\"attributes\",attributes()),\n",
        "    (\"outliers6\",Outliers(col='H_Distance')),\n",
        "    (\"dropcolumns\",dropcolumns()),\n",
        "    ('Standard_scaling', StandardScaler())])\n",
        "\n",
        "# we are calling fit_transform() on pipeline, so the pipeline is learning parameters from input data.\n",
        "# Later it also transforms the input data as per the \"transform()\" definition of each object\n",
        "test_prepared = test_pre_pipe.fit_transform(testc)\n",
        "column_values = ['passenger_count', 'H_Distance', 'Year','Month', 'Date','Day of Week', 'Hour']\n",
        "test_prepared = pd.DataFrame(data = test_prepared,columns = column_values)\n",
        "test_prepared.reset_index(drop = True)"
      ],
      "metadata": {
        "id": "a70dvjarkxyD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "520a0169-d5f1-433b-923b-db2ca033cabf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      passenger_count  H_Distance      Year     Month      Date  Day of Week  \\\n",
              "0           -0.512551    1.783640 -1.735836  1.232249 -0.941641    -0.068437   \n",
              "1           -0.512551    1.783640 -1.735836  1.232249 -0.941641    -0.068437   \n",
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              "3           -0.512551    0.113859  1.538763 -1.699267  1.070239     1.100982   \n",
              "4           -0.512551    0.113859  1.538763 -1.699267  1.070239     1.100982   \n",
              "...               ...         ...       ...       ...       ...          ...   \n",
              "8936         2.986377    1.783640 -0.545073  1.457751  0.567269     0.662450   \n",
              "8937         2.986377    1.783640 -1.438145  1.006748 -0.438671     0.662450   \n",
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              "8940         2.986377    1.783640 -1.140455 -0.008008 -1.444611     1.100982   \n",
              "\n",
              "          Hour  \n",
              "0    -0.536416  \n",
              "1     1.197723  \n",
              "2    -0.452562  \n",
              "3    -0.123060  \n",
              "4    -0.310072  \n",
              "...        ...  \n",
              "8936  0.773091  \n",
              "8937 -0.591302  \n",
              "8938 -0.476106  \n",
              "8939 -0.637022  \n",
              "8940 -0.071538  \n",
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              "      <td>0.943381</td>\n",
              "      <td>-0.910013</td>\n",
              "      <td>1.070239</td>\n",
              "      <td>-0.360792</td>\n",
              "      <td>-0.452562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.512551</td>\n",
              "      <td>0.113859</td>\n",
              "      <td>1.538763</td>\n",
              "      <td>-1.699267</td>\n",
              "      <td>1.070239</td>\n",
              "      <td>1.100982</td>\n",
              "      <td>-0.123060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.512551</td>\n",
              "      <td>0.113859</td>\n",
              "      <td>1.538763</td>\n",
              "      <td>-1.699267</td>\n",
              "      <td>1.070239</td>\n",
              "      <td>1.100982</td>\n",
              "      <td>-0.310072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8936</th>\n",
              "      <td>2.986377</td>\n",
              "      <td>1.783640</td>\n",
              "      <td>-0.545073</td>\n",
              "      <td>1.457751</td>\n",
              "      <td>0.567269</td>\n",
              "      <td>0.662450</td>\n",
              "      <td>0.773091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8937</th>\n",
              "      <td>2.986377</td>\n",
              "      <td>1.783640</td>\n",
              "      <td>-1.438145</td>\n",
              "      <td>1.006748</td>\n",
              "      <td>-0.438671</td>\n",
              "      <td>0.662450</td>\n",
              "      <td>-0.591302</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8938</th>\n",
              "      <td>2.986377</td>\n",
              "      <td>1.783640</td>\n",
              "      <td>-1.438145</td>\n",
              "      <td>-0.910013</td>\n",
              "      <td>1.573209</td>\n",
              "      <td>-0.799325</td>\n",
              "      <td>-0.476106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8939</th>\n",
              "      <td>2.986377</td>\n",
              "      <td>1.783640</td>\n",
              "      <td>-0.545073</td>\n",
              "      <td>-0.459010</td>\n",
              "      <td>-0.941641</td>\n",
              "      <td>-0.799325</td>\n",
              "      <td>-0.637022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8940</th>\n",
              "      <td>2.986377</td>\n",
              "      <td>1.783640</td>\n",
              "      <td>-1.140455</td>\n",
              "      <td>-0.008008</td>\n",
              "      <td>-1.444611</td>\n",
              "      <td>1.100982</td>\n",
              "      <td>-0.071538</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8941 rows × 7 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-502bac12-9d2d-463b-87cb-a97b39c1921f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "          document.querySelector('#df-502bac12-9d2d-463b-87cb-a97b39c1921f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-502bac12-9d2d-463b-87cb-a97b39c1921f');\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The test pipeline is different because it does not have a target value, we need to find the target value. The functions used are same for both pipelines."
      ],
      "metadata": {
        "id": "v9cLBjTpV8ki"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Use of Validation Set and Cross Validation Approach"
      ],
      "metadata": {
        "id": "wCfNJDFJyPPz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "K-fold cross validation set algorithm is written from scratch using numpy."
      ],
      "metadata": {
        "id": "YBSedVYiWb3A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Xcopy = X.copy()\n",
        "ycopy = y.copy() #copies of training data "
      ],
      "metadata": {
        "id": "20iCyAQYf9IH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "k = 5\n",
        "Xfolds = np.array_split(Xcopy, k)\n",
        "yfolds = np.array_split(ycopy, k) # using numpy to split data"
      ],
      "metadata": {
        "id": "g2xZFxFssXWs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The cell below contains K-fold cross validation by using loops and the values from the regressions are added into a list."
      ],
      "metadata": {
        "id": "CqUz-K5JXC7H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "LinearRegressionScores = []\n",
        "DecisionTreeRegressorScores = []\n",
        "SGDRegressorScores = []\n",
        "from sklearn.linear_model import SGDRegressor\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "for i in range(k):\n",
        "  Xtrain = []\n",
        "  ytrain = []\n",
        "  Xtest  = []\n",
        "  ytest  = []\n",
        "  Xtrain = Xfolds.copy()\n",
        "  Xtest = Xfolds[i]\n",
        "  ytrain = yfolds.copy()\n",
        "  ytest = yfolds[i]\n",
        "  del Xtrain[i]\n",
        "  del ytrain[i]\n",
        "  Xtrain = pd.concat(Xtrain)\n",
        "  ytrain = pd.concat(ytrain)\n",
        "\n",
        "  regr = LinearRegression()\n",
        "  regr.fit(Xtrain, ytrain)\n",
        "  ypred = regr.predict(Xtest)\n",
        "  a = regr.score(Xtest, ytest)\n",
        "  LinearRegressionScores.append(a)\n",
        "\n",
        "  regressor = DecisionTreeRegressor(random_state = 0) \n",
        "  regressor.fit(Xtrain, ytrain)\n",
        "  y_pred = regressor.predict(Xtest)\n",
        "  b = regressor.score(Xtest, ytest)\n",
        "  DecisionTreeRegressorScores.append(b)\n",
        "\n",
        "  rege = SGDRegressor()\n",
        "  rege.fit(Xtrain, ytrain)\n",
        "  yprede = rege.predict(Xtest)\n",
        "  c = rege.score(Xtest, ytest)\n",
        "  SGDRegressorScores.append(c)\n"
      ],
      "metadata": {
        "id": "P84e5JNy4zJv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Matrix based linear regression"
      ],
      "metadata": {
        "id": "O07_G64LXmV2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "LinearRegressionscore = np.array(LinearRegressionScores)\n",
        "LinearRegressionscore.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mzurQERnIBR6",
        "outputId": "fc0bd896-8512-485b-cc41-fca879942822"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7646597822160217"
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "mean_squared_error(ytest, ypred, squared=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JqXrdVZ0DW90",
        "outputId": "e0600c51-c5ea-40b5-dd7f-878925095627"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.0107974987318533"
            ]
          },
          "metadata": {},
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The accuracy is 76.465% on average."
      ],
      "metadata": {
        "id": "U2UH66kOYj93"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Optimization based linear regression"
      ],
      "metadata": {
        "id": "SCGqbSdCYAbG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "SGDRegressorscore = np.array(SGDRegressorScores)\n",
        "SGDRegressorscore.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Tixw3ACazJH",
        "outputId": "901253ac-0cf2-40f4-8371-a1382c3a7143"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7644642334476878"
            ]
          },
          "metadata": {},
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "mean_squared_error(ytest, yprede, squared=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GferdZNbDXIq",
        "outputId": "f7334616-7144-4e53-a220-be94ae52cbe8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.011635511939391"
            ]
          },
          "metadata": {},
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The accuracy is 76.464% on average."
      ],
      "metadata": {
        "id": "2aU4o-orYzak"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Non parametric based linear regression"
      ],
      "metadata": {
        "id": "l1T1L09DYK0N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "DecisionTreeRegressorscore = np.array(DecisionTreeRegressorScores)\n",
        "DecisionTreeRegressorscore.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CR7sSpe5ao6D",
        "outputId": "4577a823-5c54-4f83-f77c-fd272ee2125b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5850008633408146"
            ]
          },
          "metadata": {},
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "mean_squared_error(ytest, y_pred, squared=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vj8xTm-pSUvA",
        "outputId": "75692173-9ed8-4de5-98ac-4f93dee8a11e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.011657729269587"
            ]
          },
          "metadata": {},
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The accuracy is 58.50% on average. The error is high when compared to the other two models."
      ],
      "metadata": {
        "id": "74UrdSO8Y8E_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "By observation,both Stohastic gradient descent regression and linear regression work well."
      ],
      "metadata": {
        "id": "X8TCUrNjco_k"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Linear Regression"
      ],
      "metadata": {
        "id": "dOqm9MKCYYCa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will calculate the fare amount for the test values using SGD regressor."
      ],
      "metadata": {
        "id": "FhqLsXTsZVhl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rege = SGDRegressor()\n",
        "rege.fit(Xtrain, ytrain)\n",
        "test_prediction = rege.predict(test_prepared)\n",
        "test_prediction"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z1k17rosSUPm",
        "outputId": "0b769abc-ca4c-486a-8076-5c434d368ba3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 8.1431921 , 17.32951715,  7.35498415, ...,  8.8155865 ,\n",
              "        8.31707945, 11.37440673])"
            ]
          },
          "metadata": {},
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_prepared['predicted_fare_amount'] = test_prediction.tolist()"
      ],
      "metadata": {
        "id": "njk6M5y5eW4r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_prepared\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "6spmBFVoeW0l",
        "outputId": "b9db98a7-70f8-42ad-96a7-be36f3ed9507"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      passenger_count  H_Distance      Year     Month      Date  Day of Week  \\\n",
              "0           -0.512551    1.783640 -1.735836  1.232249 -0.941641    -0.068437   \n",
              "1           -0.512551    1.783640 -1.735836  1.232249 -0.941641    -0.068437   \n",
              "2           -0.512551   -0.442735  0.943381 -0.910013  1.070239    -0.360792   \n",
              "3           -0.512551    0.113859  1.538763 -1.699267  1.070239     1.100982   \n",
              "4           -0.512551    0.113859  1.538763 -1.699267  1.070239     1.100982   \n",
              "...               ...         ...       ...       ...       ...          ...   \n",
              "8936         2.986377    1.783640 -0.545073  1.457751  0.567269     0.662450   \n",
              "8937         2.986377    1.783640 -1.438145  1.006748 -0.438671     0.662450   \n",
              "8938         2.986377    1.783640 -1.438145 -0.910013  1.573209    -0.799325   \n",
              "8939         2.986377    1.783640 -0.545073 -0.459010 -0.941641    -0.799325   \n",
              "8940         2.986377    1.783640 -1.140455 -0.008008 -1.444611     1.100982   \n",
              "\n",
              "          Hour  predicted_fare_amount  \n",
              "0    -0.536416               8.143192  \n",
              "1     1.197723              17.329517  \n",
              "2    -0.452562               7.354984  \n",
              "3    -0.123060               9.805103  \n",
              "4    -0.310072               8.814439  \n",
              "...        ...                    ...  \n",
              "8936  0.773091              15.776781  \n",
              "8937 -0.591302               8.428560  \n",
              "8938 -0.476106               8.815586  \n",
              "8939 -0.637022               8.317079  \n",
              "8940 -0.071538              11.374407  \n",
              "\n",
              "[8941 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9244c8e2-e85d-44ef-9fea-8334fe179be9\">\n",
              "    <div 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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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>passenger_count</th>\n",
              "      <th>H_Distance</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Date</th>\n",
              "      <th>Day of Week</th>\n",
              "      <th>Hour</th>\n",
              "      <th>predicted_fare_amount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.512551</td>\n",
              "      <td>1.783640</td>\n",
              "      <td>-1.735836</td>\n",
              "      <td>1.232249</td>\n",
              "      <td>-0.941641</td>\n",
              "      <td>-0.068437</td>\n",
              "      <td>-0.536416</td>\n",
              "      <td>8.143192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.512551</td>\n",
              "      <td>1.783640</td>\n",
              "      <td>-1.735836</td>\n",
              "      <td>1.232249</td>\n",
              "      <td>-0.941641</td>\n",
              "      <td>-0.068437</td>\n",
              "      <td>1.197723</td>\n",
              "      <td>17.329517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.512551</td>\n",
              "      <td>-0.442735</td>\n",
              "      <td>0.943381</td>\n",
              "      <td>-0.910013</td>\n",
              "      <td>1.070239</td>\n",
              "      <td>-0.360792</td>\n",
              "      <td>-0.452562</td>\n",
              "      <td>7.354984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.512551</td>\n",
              "      <td>0.113859</td>\n",
              "      <td>1.538763</td>\n",
              "      <td>-1.699267</td>\n",
              "      <td>1.070239</td>\n",
              "      <td>1.100982</td>\n",
              "      <td>-0.123060</td>\n",
              "      <td>9.805103</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.512551</td>\n",
              "      <td>0.113859</td>\n",
              "      <td>1.538763</td>\n",
              "      <td>-1.699267</td>\n",
              "      <td>1.070239</td>\n",
              "      <td>1.100982</td>\n",
              "      <td>-0.310072</td>\n",
              "      <td>8.814439</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8936</th>\n",
              "      <td>2.986377</td>\n",
              "      <td>1.783640</td>\n",
              "      <td>-0.545073</td>\n",
              "      <td>1.457751</td>\n",
              "      <td>0.567269</td>\n",
              "      <td>0.662450</td>\n",
              "      <td>0.773091</td>\n",
              "      <td>15.776781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8937</th>\n",
              "      <td>2.986377</td>\n",
              "      <td>1.783640</td>\n",
              "      <td>-1.438145</td>\n",
              "      <td>1.006748</td>\n",
              "      <td>-0.438671</td>\n",
              "      <td>0.662450</td>\n",
              "      <td>-0.591302</td>\n",
              "      <td>8.428560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8938</th>\n",
              "      <td>2.986377</td>\n",
              "      <td>1.783640</td>\n",
              "      <td>-1.438145</td>\n",
              "      <td>-0.910013</td>\n",
              "      <td>1.573209</td>\n",
              "      <td>-0.799325</td>\n",
              "      <td>-0.476106</td>\n",
              "      <td>8.815586</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8939</th>\n",
              "      <td>2.986377</td>\n",
              "      <td>1.783640</td>\n",
              "      <td>-0.545073</td>\n",
              "      <td>-0.459010</td>\n",
              "      <td>-0.941641</td>\n",
              "      <td>-0.799325</td>\n",
              "      <td>-0.637022</td>\n",
              "      <td>8.317079</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8940</th>\n",
              "      <td>2.986377</td>\n",
              "      <td>1.783640</td>\n",
              "      <td>-1.140455</td>\n",
              "      <td>-0.008008</td>\n",
              "      <td>-1.444611</td>\n",
              "      <td>1.100982</td>\n",
              "      <td>-0.071538</td>\n",
              "      <td>11.374407</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8941 rows × 8 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We get the predicted fare amounts for the test values which are scaled and standardized in the pipeline.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "rlZniKdeZmZ3"
      }
    }
  ]
}