trip_duration_prediction.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "tede-2.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import datetime"
      ],
      "metadata": {
        "id": "4E-GOXg6z5Cr"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E8MQKU9dyPnr",
        "outputId": "3917b476-d364-44e3-8060-0a70fe726246"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train  = pd.read_csv('/content/gdrive/MyDrive/data/train.csv')  \n",
        "df_test   = pd.read_csv('/content/gdrive/MyDrive/data/test.csv')  \n",
        "df_target = pd.read_csv('/content/gdrive/MyDrive/data/target.csv')  "
      ],
      "metadata": {
        "id": "qw8ynj3AzyXO"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Analysis"
      ],
      "metadata": {
        "id": "IhADWyYK1cVt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **1**. Remove the outliers"
      ],
      "metadata": {
        "id": "4-ytqglq1eLs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "hLZWO9ng5_9d",
        "outputId": "7bf1e157-fbfd-4554-e1ef-d53a94ab84c7"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          vendor_id  passenger_count  pickup_longitude  pickup_latitude  \\\n",
              "count  1.166915e+06     1.166915e+06      1.166915e+06     1.166915e+06   \n",
              "mean   1.534915e+00     1.665292e+00     -7.397346e+01     4.075089e+01   \n",
              "std    4.987797e-01     1.314746e+00      6.231408e-02     3.173031e-02   \n",
              "min    1.000000e+00     0.000000e+00     -1.219332e+02     3.435970e+01   \n",
              "25%    1.000000e+00     1.000000e+00     -7.399186e+01     4.073731e+01   \n",
              "50%    2.000000e+00     1.000000e+00     -7.398175e+01     4.075407e+01   \n",
              "75%    2.000000e+00     2.000000e+00     -7.396733e+01     4.076834e+01   \n",
              "max    2.000000e+00     9.000000e+00     -6.133553e+01     4.437194e+01   \n",
              "\n",
              "       dropoff_longitude  dropoff_latitude  trip_duration  \n",
              "count       1.166915e+06      1.166915e+06   1.166915e+06  \n",
              "mean       -7.397340e+01      4.075182e+01   9.584392e+02  \n",
              "std         6.157028e-02      3.645646e-02   5.334345e+03  \n",
              "min        -1.219332e+02      3.218114e+01   1.000000e+00  \n",
              "25%        -7.399133e+01      4.073591e+01   3.970000e+02  \n",
              "50%        -7.397977e+01      4.075453e+01   6.630000e+02  \n",
              "75%        -7.396303e+01      4.076981e+01   1.075000e+03  \n",
              "max        -6.133553e+01      4.392103e+01   3.526282e+06  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0e3789cd-fe5a-4378-b573-1e21dfd659e3\">\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>vendor_id</th>\n",
              "      <th>passenger_count</th>\n",
              "      <th>pickup_longitude</th>\n",
              "      <th>pickup_latitude</th>\n",
              "      <th>dropoff_longitude</th>\n",
              "      <th>dropoff_latitude</th>\n",
              "      <th>trip_duration</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.166915e+06</td>\n",
              "      <td>1.166915e+06</td>\n",
              "      <td>1.166915e+06</td>\n",
              "      <td>1.166915e+06</td>\n",
              "      <td>1.166915e+06</td>\n",
              "      <td>1.166915e+06</td>\n",
              "      <td>1.166915e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.534915e+00</td>\n",
              "      <td>1.665292e+00</td>\n",
              "      <td>-7.397346e+01</td>\n",
              "      <td>4.075089e+01</td>\n",
              "      <td>-7.397340e+01</td>\n",
              "      <td>4.075182e+01</td>\n",
              "      <td>9.584392e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.987797e-01</td>\n",
              "      <td>1.314746e+00</td>\n",
              "      <td>6.231408e-02</td>\n",
              "      <td>3.173031e-02</td>\n",
              "      <td>6.157028e-02</td>\n",
              "      <td>3.645646e-02</td>\n",
              "      <td>5.334345e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>-1.219332e+02</td>\n",
              "      <td>3.435970e+01</td>\n",
              "      <td>-1.219332e+02</td>\n",
              "      <td>3.218114e+01</td>\n",
              "      <td>1.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>-7.399186e+01</td>\n",
              "      <td>4.073731e+01</td>\n",
              "      <td>-7.399133e+01</td>\n",
              "      <td>4.073591e+01</td>\n",
              "      <td>3.970000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>-7.398175e+01</td>\n",
              "      <td>4.075407e+01</td>\n",
              "      <td>-7.397977e+01</td>\n",
              "      <td>4.075453e+01</td>\n",
              "      <td>6.630000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>-7.396733e+01</td>\n",
              "      <td>4.076834e+01</td>\n",
              "      <td>-7.396303e+01</td>\n",
              "      <td>4.076981e+01</td>\n",
              "      <td>1.075000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>9.000000e+00</td>\n",
              "      <td>-6.133553e+01</td>\n",
              "      <td>4.437194e+01</td>\n",
              "      <td>-6.133553e+01</td>\n",
              "      <td>4.392103e+01</td>\n",
              "      <td>3.526282e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0e3789cd-fe5a-4378-b573-1e21dfd659e3')\"\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-0e3789cd-fe5a-4378-b573-1e21dfd659e3 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-0e3789cd-fe5a-4378-b573-1e21dfd659e3');\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": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean = df_train['trip_duration'].mean()\n",
        "sd = df_train['trip_duration'].std()"
      ],
      "metadata": {
        "id": "ksmO8hHeJmvk"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wWM4PwGyAxFn",
        "outputId": "e27f8022-9fa6-4a6d-f5cd-da34f45fe12a"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1166915, 11)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = df_train[(df_train['trip_duration'] <= mean+(2*sd))] "
      ],
      "metadata": {
        "id": "oWP3DWPOAQxX"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yvj9_EnxAzIv",
        "outputId": "5c2d9bee-a7ea-4bc8-ed04-c1ceb497d7df"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1165250, 11)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Άρα αφαιρέθηκαν 1166915 - 1165250 = **1665** outliers χρησιμοποιώντας standard deviation.\n",
        "\n"
      ],
      "metadata": {
        "id": "YzaiYmGOBEQP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **2**. Define NY borders"
      ],
      "metadata": {
        "id": "fV9f7I1X10jM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "city_long_border = (-74.03, -73.75)\n",
        "city_lat_border = (40.63, 40.85)\n",
        "\n",
        "# Drop all the points outside the city of NY for the train data set\n",
        "df_train = df_train[(df_train['pickup_longitude'] >= city_long_border[0]) & (df_train['pickup_longitude'] <= city_long_border[1])]\n",
        "df_train = df_train[(df_train['dropoff_longitude'] >= city_long_border[0]) & (df_train['dropoff_longitude'] <= city_long_border[1])]\n",
        "\n",
        "df_train = df_train[(df_train['pickup_latitude'] >= city_lat_border[0]) & (df_train['pickup_latitude'] <= city_lat_border[1])]\n",
        "df_train = df_train[(df_train['dropoff_latitude'] >= city_lat_border[0]) & (df_train['dropoff_latitude'] <= city_lat_border[1])]"
      ],
      "metadata": {
        "id": "6rPwLYN69hgV"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0EuDJx_V92YO",
        "outputId": "af8c24cc-3723-4872-e94a-7f043fa215a2"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1149734, 11)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Άρα αφαιρέθηκαν 1165250 - 1149734 = **15516** σημεία στο train set."
      ],
      "metadata": {
        "id": "3ALi5al3jZuZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **3.** Add new columns"
      ],
      "metadata": {
        "id": "NMO8gyDo10te"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert all columns with pd.to_datetime()\n",
        "\n",
        "df_train['pickup_datetime'] = pd.to_datetime(df_train['pickup_datetime'])\n",
        "df_train['dropoff_datetime'] = pd.to_datetime(df_train['dropoff_datetime'])\n",
        "\n",
        "df_test['pickup_datetime'] = pd.to_datetime(df_test['pickup_datetime'])"
      ],
      "metadata": {
        "id": "zG5bwRIOERBx"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Add day_period column"
      ],
      "metadata": {
        "id": "iuPMBh0lC6AL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the day periods\n",
        "morning_1 = datetime.time(6,00,00)    # 6:00 am\n",
        "morning_2 = datetime.time(11,59,59)   # 11.59 am\n",
        "\n",
        "afternoon_1 = datetime.time(12,00,00)   # 12:00 pm\n",
        "afternoon_2 = datetime.time(15,59,59)   # 3:59 pm\n",
        "\n",
        "evening_1 = datetime.time(16,00,00)   # 4:00 pm\n",
        "evening_2 = datetime.time(21,59,59)   # 9:59pm\n",
        "\n",
        "late_night_1 = datetime.time(22,00,00)  # 10:00 pm\n",
        "late_night_2 = datetime.time(5,59,59)   # 5:59 am"
      ],
      "metadata": {
        "id": "UiMOYHyYDV2N"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def add_day_period(df, column_name, new_col_name):\n",
        "\n",
        "  # Extract the timestamps\n",
        "  pickup_timestamps = df[column_name].dt.time\n",
        "\n",
        "  conditions = [\n",
        "                \n",
        "    (pickup_timestamps >= morning_1) & (pickup_timestamps <= morning_2),\n",
        "    (pickup_timestamps >= afternoon_1) & (pickup_timestamps <= afternoon_2),\n",
        "    (pickup_timestamps >= evening_1) & (pickup_timestamps <= evening_2),\n",
        "    ((pickup_timestamps >= late_night_1) & (pickup_timestamps <= datetime.time(23,59,59))) | ((pickup_timestamps >= datetime.time(00,00,00)) & (pickup_timestamps <= late_night_2))]\n",
        "\n",
        "  # create a list of the values we want to assign for each condition\n",
        "  values = ['Morning', 'Afternoon', 'Evening', 'Late Night']\n",
        "  \n",
        "  # create a new column and use np.select to assign values to it using our lists as arguments\n",
        "  df[new_col_name] = np.select(conditions, values)\n",
        "\n",
        "  return df"
      ],
      "metadata": {
        "id": "68yWSCnWCBZg"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Add the new columns to df_train\n",
        "df_train = add_day_period(df_train, 'pickup_datetime', 'pickup_day_period')\n",
        "df_train = add_day_period(df_train, 'dropoff_datetime', 'dropoff_day_period')\n",
        "\n",
        "# Add the new column to df_test\n",
        "df_test = add_day_period(df_test, 'pickup_datetime', 'pickup_day_period')"
      ],
      "metadata": {
        "id": "pCoRNaLBC3L1"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Add day_name column"
      ],
      "metadata": {
        "id": "lNdwqA95E1KF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import calendar"
      ],
      "metadata": {
        "id": "Yqtzcj5lFLj6"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def add_day_name(df, column, new_col_name):\n",
        "  \n",
        "  week_days = df[column].dt.date\n",
        "  day_names = [calendar.day_name[name.weekday()] for name in week_days]\n",
        "  df[new_col_name] = day_names\n",
        "\n",
        "  return df"
      ],
      "metadata": {
        "id": "MZMGpaTFFBib"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Add the new columns to df_train\n",
        "df_train = add_day_name(df_train, 'pickup_datetime', 'pickup_day_name')\n",
        "df_train = add_day_name(df_train, 'dropoff_datetime', 'dropoff_day_name')\n",
        "\n",
        "# Add the new column to df_test\n",
        "df_test = add_day_name(df_test, 'pickup_datetime', 'pickup_day_name')"
      ],
      "metadata": {
        "id": "WLsAl8YMFRmY"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Add month column"
      ],
      "metadata": {
        "id": "cIvsu7giF9wd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def add_month(df, column_name, new_col_name):\n",
        "\n",
        "  months = df[column_name].dt.month_name()\n",
        "  df[new_col_name] = months.tolist()\n",
        "  return df"
      ],
      "metadata": {
        "id": "qkl89EHdGJOz"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Add the new columns to df_train\n",
        "df_train = add_month(df_train, 'pickup_datetime', 'pickup_month')\n",
        "df_train = add_month(df_train, 'dropoff_datetime', 'dropoff_month')\n",
        "\n",
        "# Add the new column to df_test\n",
        "df_test = add_month(df_test, 'pickup_datetime', 'pickup_month')"
      ],
      "metadata": {
        "id": "Sp2CghAmGjm5"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Add hour column"
      ],
      "metadata": {
        "id": "WLKtn0vTGC2Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def add_hour(df, column_name, new_col_name):\n",
        "\n",
        "  hours = df[column_name].dt.hour\n",
        "  df[new_col_name] = hours.tolist()\n",
        "  return df"
      ],
      "metadata": {
        "id": "hpQjgM8JHOWE"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Add the new columns to df_train\n",
        "df_train = add_hour(df_train, 'pickup_datetime', 'pickup_hour')\n",
        "df_train = add_hour(df_train, 'dropoff_datetime', 'dropoff_hour')\n",
        "\n",
        "# Add the new column to df_test\n",
        "df_test = add_hour(df_test, 'pickup_datetime', 'pickup_hour')"
      ],
      "metadata": {
        "id": "yBLUybbCHYut"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Add year column"
      ],
      "metadata": {
        "id": "hGmZ2CDLGGL2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def add_year(df, column_name, new_col_name):\n",
        "  \n",
        "  years = df[column_name].dt.year\n",
        "  df[new_col_name] = years.tolist()\n",
        "  return df"
      ],
      "metadata": {
        "id": "__uX3ZkGHwcU"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Add the new columns to df_train\n",
        "df_train = add_year(df_train, 'pickup_datetime', 'pickup_year')\n",
        "df_train = add_year(df_train, 'dropoff_datetime', 'dropoff_year')\n",
        "\n",
        "# Add the new column to df_test\n",
        "df_test = add_year(df_test, 'pickup_datetime', 'pickup_year')"
      ],
      "metadata": {
        "id": "isIVPUR9H4ay"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Add season column"
      ],
      "metadata": {
        "id": "mxL3llG1HtP3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def add_season(df, column, new_col_name):\n",
        "  \n",
        "  months = df[column].dt.month\n",
        "\n",
        "  #create a list of our conditions\n",
        "  conditions = [\n",
        "      ((months == 12) | (months < 3)),  # Winter\n",
        "      ((months >= 3) & (months < 6)),   # Spring\n",
        "      ((months >= 6) & (months < 9)),   # Summer\n",
        "      ((months >= 9) & (months < 12)),  # Fall\n",
        "      ]\n",
        "\n",
        "  values = ['Winter', 'Spring', 'Summer', 'Fall']\n",
        "\n",
        "  df[new_col_name] = np.select(conditions, values)\n",
        "\n",
        "  return df"
      ],
      "metadata": {
        "id": "NYda9KRezNlN"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Add the new columns to df_train\n",
        "df_train = add_season(df_train, 'pickup_datetime', 'pickup_season')\n",
        "df_train = add_season(df_train, 'dropoff_datetime', 'dropoff_season')\n",
        "\n",
        "# Add the new column to df_test\n",
        "df_test = add_season(df_test, 'pickup_datetime', 'pickup_season')"
      ],
      "metadata": {
        "id": "82UUzOpuIlTW"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check that all the columns have been added in both datasets\n",
        "df_train.head()"
      ],
      "metadata": {
        "id": "ZS2I5YukyhDg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "outputId": "a3fc99af-6127-43dc-b0c8-e6b8d509caa0"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          id  vendor_id     pickup_datetime    dropoff_datetime  \\\n",
              "0  id2575240          2 2016-04-09 08:41:59 2016-04-09 08:48:11   \n",
              "1  id3828278          2 2016-04-07 11:12:45 2016-04-07 11:24:24   \n",
              "2  id1483137          2 2016-04-15 19:53:35 2016-04-15 20:04:49   \n",
              "3  id0416660          2 2016-04-21 00:47:17 2016-04-21 00:53:41   \n",
              "4  id2363476          1 2016-01-26 18:34:23 2016-01-26 19:09:14   \n",
              "\n",
              "   passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
              "0                1        -73.976501        40.788368         -73.957657   \n",
              "1                1        -73.992310        40.769211         -73.990662   \n",
              "2                1        -73.994713        40.746391         -73.969742   \n",
              "3                1        -73.988358        40.718460         -73.951538   \n",
              "4                1        -73.992332        40.743576         -73.979309   \n",
              "\n",
              "   dropoff_latitude store_and_fwd_flag  ...  pickup_day_name dropoff_day_name  \\\n",
              "0         40.782452                  N  ...         Saturday         Saturday   \n",
              "1         40.758137                  N  ...         Thursday         Thursday   \n",
              "2         40.758484                  N  ...           Friday           Friday   \n",
              "3         40.713840                  N  ...         Thursday         Thursday   \n",
              "4         40.678200                  N  ...          Tuesday          Tuesday   \n",
              "\n",
              "  pickup_month dropoff_month pickup_hour dropoff_hour pickup_year  \\\n",
              "0        April         April           8            8        2016   \n",
              "1        April         April          11           11        2016   \n",
              "2        April         April          19           20        2016   \n",
              "3        April         April           0            0        2016   \n",
              "4      January       January          18           19        2016   \n",
              "\n",
              "   dropoff_year  pickup_season  dropoff_season  \n",
              "0          2016         Spring          Spring  \n",
              "1          2016         Spring          Spring  \n",
              "2          2016         Spring          Spring  \n",
              "3          2016         Spring          Spring  \n",
              "4          2016         Winter          Winter  \n",
              "\n",
              "[5 rows x 23 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-aa38ef0c-9a7d-48c6-9110-ae99a011d178\">\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>id</th>\n",
              "      <th>vendor_id</th>\n",
              "      <th>pickup_datetime</th>\n",
              "      <th>dropoff_datetime</th>\n",
              "      <th>passenger_count</th>\n",
              "      <th>pickup_longitude</th>\n",
              "      <th>pickup_latitude</th>\n",
              "      <th>dropoff_longitude</th>\n",
              "      <th>dropoff_latitude</th>\n",
              "      <th>store_and_fwd_flag</th>\n",
              "      <th>...</th>\n",
              "      <th>pickup_day_name</th>\n",
              "      <th>dropoff_day_name</th>\n",
              "      <th>pickup_month</th>\n",
              "      <th>dropoff_month</th>\n",
              "      <th>pickup_hour</th>\n",
              "      <th>dropoff_hour</th>\n",
              "      <th>pickup_year</th>\n",
              "      <th>dropoff_year</th>\n",
              "      <th>pickup_season</th>\n",
              "      <th>dropoff_season</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>id2575240</td>\n",
              "      <td>2</td>\n",
              "      <td>2016-04-09 08:41:59</td>\n",
              "      <td>2016-04-09 08:48:11</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.976501</td>\n",
              "      <td>40.788368</td>\n",
              "      <td>-73.957657</td>\n",
              "      <td>40.782452</td>\n",
              "      <td>N</td>\n",
              "      <td>...</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>April</td>\n",
              "      <td>April</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>2016</td>\n",
              "      <td>2016</td>\n",
              "      <td>Spring</td>\n",
              "      <td>Spring</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>id3828278</td>\n",
              "      <td>2</td>\n",
              "      <td>2016-04-07 11:12:45</td>\n",
              "      <td>2016-04-07 11:24:24</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.992310</td>\n",
              "      <td>40.769211</td>\n",
              "      <td>-73.990662</td>\n",
              "      <td>40.758137</td>\n",
              "      <td>N</td>\n",
              "      <td>...</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>April</td>\n",
              "      <td>April</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>2016</td>\n",
              "      <td>2016</td>\n",
              "      <td>Spring</td>\n",
              "      <td>Spring</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>id1483137</td>\n",
              "      <td>2</td>\n",
              "      <td>2016-04-15 19:53:35</td>\n",
              "      <td>2016-04-15 20:04:49</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.994713</td>\n",
              "      <td>40.746391</td>\n",
              "      <td>-73.969742</td>\n",
              "      <td>40.758484</td>\n",
              "      <td>N</td>\n",
              "      <td>...</td>\n",
              "      <td>Friday</td>\n",
              "      <td>Friday</td>\n",
              "      <td>April</td>\n",
              "      <td>April</td>\n",
              "      <td>19</td>\n",
              "      <td>20</td>\n",
              "      <td>2016</td>\n",
              "      <td>2016</td>\n",
              "      <td>Spring</td>\n",
              "      <td>Spring</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>id0416660</td>\n",
              "      <td>2</td>\n",
              "      <td>2016-04-21 00:47:17</td>\n",
              "      <td>2016-04-21 00:53:41</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.988358</td>\n",
              "      <td>40.718460</td>\n",
              "      <td>-73.951538</td>\n",
              "      <td>40.713840</td>\n",
              "      <td>N</td>\n",
              "      <td>...</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>April</td>\n",
              "      <td>April</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2016</td>\n",
              "      <td>2016</td>\n",
              "      <td>Spring</td>\n",
              "      <td>Spring</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>id2363476</td>\n",
              "      <td>1</td>\n",
              "      <td>2016-01-26 18:34:23</td>\n",
              "      <td>2016-01-26 19:09:14</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.992332</td>\n",
              "      <td>40.743576</td>\n",
              "      <td>-73.979309</td>\n",
              "      <td>40.678200</td>\n",
              "      <td>N</td>\n",
              "      <td>...</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>January</td>\n",
              "      <td>January</td>\n",
              "      <td>18</td>\n",
              "      <td>19</td>\n",
              "      <td>2016</td>\n",
              "      <td>2016</td>\n",
              "      <td>Winter</td>\n",
              "      <td>Winter</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 23 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-aa38ef0c-9a7d-48c6-9110-ae99a011d178')\"\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-aa38ef0c-9a7d-48c6-9110-ae99a011d178 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-aa38ef0c-9a7d-48c6-9110-ae99a011d178');\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": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test.head()"
      ],
      "metadata": {
        "id": "9owgXRsXJRtW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "c566a413-b0a4-4f8d-e818-fb229bbbefa5"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          id  vendor_id     pickup_datetime  passenger_count  \\\n",
              "0  id3206885          2 2016-03-16 05:59:42                5   \n",
              "1  id2931797          1 2016-04-14 13:37:36                1   \n",
              "2  id0759548          1 2016-06-06 14:03:22                1   \n",
              "3  id1998257          1 2016-04-21 12:21:54                1   \n",
              "4  id2645175          2 2016-01-12 20:10:05                1   \n",
              "\n",
              "   pickup_longitude  pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
              "0        -73.977753        40.764610         -73.991142         40.770260   \n",
              "1        -73.996925        40.732578         -73.997932         40.755642   \n",
              "2        -74.006149        40.749966         -74.013985         40.713726   \n",
              "3        -73.967941        40.762707         -73.983833         40.742435   \n",
              "4        -73.973351        40.763561         -73.987679         40.738201   \n",
              "\n",
              "  store_and_fwd_flag pickup_day_period pickup_day_name pickup_month  \\\n",
              "0                  N        Late Night       Wednesday        March   \n",
              "1                  N         Afternoon        Thursday        April   \n",
              "2                  N         Afternoon          Monday         June   \n",
              "3                  N         Afternoon        Thursday        April   \n",
              "4                  N           Evening         Tuesday      January   \n",
              "\n",
              "   pickup_hour  pickup_year pickup_season  \n",
              "0            5         2016        Spring  \n",
              "1           13         2016        Spring  \n",
              "2           14         2016        Summer  \n",
              "3           12         2016        Spring  \n",
              "4           20         2016        Winter  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5d8525a0-ea74-4a74-9439-dd544cb3f836\">\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>id</th>\n",
              "      <th>vendor_id</th>\n",
              "      <th>pickup_datetime</th>\n",
              "      <th>passenger_count</th>\n",
              "      <th>pickup_longitude</th>\n",
              "      <th>pickup_latitude</th>\n",
              "      <th>dropoff_longitude</th>\n",
              "      <th>dropoff_latitude</th>\n",
              "      <th>store_and_fwd_flag</th>\n",
              "      <th>pickup_day_period</th>\n",
              "      <th>pickup_day_name</th>\n",
              "      <th>pickup_month</th>\n",
              "      <th>pickup_hour</th>\n",
              "      <th>pickup_year</th>\n",
              "      <th>pickup_season</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>id3206885</td>\n",
              "      <td>2</td>\n",
              "      <td>2016-03-16 05:59:42</td>\n",
              "      <td>5</td>\n",
              "      <td>-73.977753</td>\n",
              "      <td>40.764610</td>\n",
              "      <td>-73.991142</td>\n",
              "      <td>40.770260</td>\n",
              "      <td>N</td>\n",
              "      <td>Late Night</td>\n",
              "      <td>Wednesday</td>\n",
              "      <td>March</td>\n",
              "      <td>5</td>\n",
              "      <td>2016</td>\n",
              "      <td>Spring</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>id2931797</td>\n",
              "      <td>1</td>\n",
              "      <td>2016-04-14 13:37:36</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.996925</td>\n",
              "      <td>40.732578</td>\n",
              "      <td>-73.997932</td>\n",
              "      <td>40.755642</td>\n",
              "      <td>N</td>\n",
              "      <td>Afternoon</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>April</td>\n",
              "      <td>13</td>\n",
              "      <td>2016</td>\n",
              "      <td>Spring</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>id0759548</td>\n",
              "      <td>1</td>\n",
              "      <td>2016-06-06 14:03:22</td>\n",
              "      <td>1</td>\n",
              "      <td>-74.006149</td>\n",
              "      <td>40.749966</td>\n",
              "      <td>-74.013985</td>\n",
              "      <td>40.713726</td>\n",
              "      <td>N</td>\n",
              "      <td>Afternoon</td>\n",
              "      <td>Monday</td>\n",
              "      <td>June</td>\n",
              "      <td>14</td>\n",
              "      <td>2016</td>\n",
              "      <td>Summer</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>id1998257</td>\n",
              "      <td>1</td>\n",
              "      <td>2016-04-21 12:21:54</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.967941</td>\n",
              "      <td>40.762707</td>\n",
              "      <td>-73.983833</td>\n",
              "      <td>40.742435</td>\n",
              "      <td>N</td>\n",
              "      <td>Afternoon</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>April</td>\n",
              "      <td>12</td>\n",
              "      <td>2016</td>\n",
              "      <td>Spring</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>id2645175</td>\n",
              "      <td>2</td>\n",
              "      <td>2016-01-12 20:10:05</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.973351</td>\n",
              "      <td>40.763561</td>\n",
              "      <td>-73.987679</td>\n",
              "      <td>40.738201</td>\n",
              "      <td>N</td>\n",
              "      <td>Evening</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>January</td>\n",
              "      <td>20</td>\n",
              "      <td>2016</td>\n",
              "      <td>Winter</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5d8525a0-ea74-4a74-9439-dd544cb3f836')\"\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-5d8525a0-ea74-4a74-9439-dd544cb3f836 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-5d8525a0-ea74-4a74-9439-dd544cb3f836');\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": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Create Histograms"
      ],
      "metadata": {
        "id": "vNIbTyJ6HibE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "IbvmMChi8Qii"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_histogram(column_name, title, ylabel, bins):\n",
        "  fig, ax = plt.subplots(1,1, figsize =(10, 7), tight_layout = True)\n",
        "  ax.hist(df_train[column_name], bins = bins, edgecolor='#ffff', linewidth=0.7)\n",
        "  plt.xlabel(\"Bins\")\n",
        "  plt.ylabel(ylabel)\n",
        "  plt.title(title)\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "iPc7bnVyTQGk"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Ιστογραμμα για τις πιο πολυσύχναστες pickup days***"
      ],
      "metadata": {
        "id": "-0NG0Wxsz80w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Busiest pickup days\n",
        "plot_histogram('pickup_day_name', 'Trip duration histogram',\n",
        "               'Number of trips per bin', 7)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "nFMqXY9fTQtK",
        "outputId": "ca50b8a3-d7f5-4416-f570-c560410ebcd0"
      },
      "execution_count": 33,
      "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": [
        "Όπως φαίνεται και στο παραπάνω ιστόγραμμα η πιο πολυσύχναστη pickup day είναι η Παρασκευή ακολουθούμενη απο το Σάββατο και την Πέμπτη."
      ],
      "metadata": {
        "id": "uTcudlk4KK-R"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Ιστογραμμα για τις πιο πολυσύχναστες dropoff days***"
      ],
      "metadata": {
        "id": "a713Qxiiz5cg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Busiest dropoff days\n",
        "plot_histogram('dropoff_day_name', 'Trip duration histogram',\n",
        "               'Number of trips per bin', 7)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "miHip0Y5calP",
        "outputId": "092c31e3-163d-4b72-d178-e1dd28005219"
      },
      "execution_count": 34,
      "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": [
        "Αντίστοιχα όπως φαίνεται στο παραπάνω το ίδιο ακριβώς ισχύει και για τις πιο πολυσύχναστες dropoff days, δηλαδή πρώτα είναι η Παρασκευή και ακολουθεί το Σάββατο και η Πέμπτη."
      ],
      "metadata": {
        "id": "OpsDTtJpK1nv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Ιστόγραμμα για Trips per day_period (pickup)***"
      ],
      "metadata": {
        "id": "uHSm_-JCz2sB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Trips per pickup day period\n",
        "plot_histogram('pickup_day_period', 'Trips per pickup day period',\n",
        "               'Number of trips per bin', 4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "vAbiXtooeAt_",
        "outputId": "a90282e7-77d1-4e28-b3d5-16f27b254cf9"
      },
      "execution_count": 35,
      "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": [
        "Όπως παρατηρούμε στο ιστόγραμμα τα περισσότερα trips per day_period γίνονται με διαφορά το απόγευμα (Evening)."
      ],
      "metadata": {
        "id": "JhKR0687Liaf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Ιστόγραμμα για Trips per day_period (dropoff)***"
      ],
      "metadata": {
        "id": "q4cOXkpzzsF7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Trips per dropoff day period\n",
        "plot_histogram('dropoff_day_period', 'Trips per dropoff day period',\n",
        "               'Number of trips per bin', 4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "6IaJobfefOsg",
        "outputId": "51176ce8-e79a-4720-c697-da86a50fb86f"
      },
      "execution_count": 36,
      "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": [
        "Το ίδιο ισχύει επίσης και για τα dropoff trips per day_period."
      ],
      "metadata": {
        "id": "q2tpMJz8Lut3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Ιστόγραμμα για Trips per month (pickup)***"
      ],
      "metadata": {
        "id": "bvSq0NUVzm2y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Trips per month (pickup)\n",
        "plot_histogram('pickup_month', 'Trips per month (pickup)',\n",
        "               'Number of trips per bin', 6)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "Tqt5gcdtfpmQ",
        "outputId": "5dde9c92-01e3-4ffc-8cc7-fe7bcfe6a418"
      },
      "execution_count": 37,
      "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": [
        "***Ιστόγραμμα για Trips per month (dropoff)***"
      ],
      "metadata": {
        "id": "uxgxltvwzh8T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Trips per month (dropoff)\n",
        "plot_histogram('dropoff_month', 'Trips per month (dropoff)',\n",
        "               'Number of trips per bin', 6)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "AK2dVDAUftRH",
        "outputId": "45d77565-a8df-4298-e936-8f3bd7921b70"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAAHwCAYAAAC7apkrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3de5xdZX33/c/XxAMeEJSUmwIStFFvpBolRdrqrUKVYFXUKkJ95KAVvQWLre0jHrFVWm1LrVbFokSCtSAVLXksFime24KEM4iUiFCgCCkgBxUQ+D1/7GvkIs5MdkL27Mnk83699mvWutbpt/aeTL6z5lrXSlUhSZIkaeBB4y5AkiRJmk0MyJIkSVLHgCxJkiR1DMiSJElSx4AsSZIkdQzIkiRJUseALGmTluTZSS4bdx0bkyRfT/J767D+Q5N8N8k2Uyw/MMm3N1yFw0vysiRXJ7k9ydOTPCnJ+UluS/L7SY5K8n/HUZuk8Zk/7gIkaUNLcns3+3DgTuCeNv+GqvrsxMKq+hbwpBksb6OS5L3Ar1TV//MAdnMw8M2qum7DVLVB/RVwaFWdApDkWOBrVbW4zW8DfCfJsVV11xjrlDSDvIIsac6pqkdOvID/Al7ctf08HCeZ9RcJNoYah/BG4DPrs+EMnP8OwCVTzbdQ/z3gJSOuQ9IsYkCWtMlI8twk1yR5W5IfAp+eaOvWuTLJ21uXgJuTfDrJw9qyrZJ8KcmPktyU5FtJJv05mqTan+ivSPI/Sf6yXzfJa5Nc2o5xWpId1tj2kCSXA5dPsu+FbZ2DWveAm5O8McmvJbmw1ffRbv0HJXlXkquS3JDk+CSPXmNfByT5r1brO9uypcA7gFe1LggXdGXskOTfWleEryTZaor34XHA44GzurbHJlmR5NYk3wGeMMl7d7/zT/L6JKva+74iyS8P815Pde6t28ftwDzggiTfT/JV4HnAR9v5PrEd4uvAb092fpLmJgOypE3N/wIew+BK4cFTrPNqYE8Gwe2JwLta+1uBa4AFwNYMwmNNc6yXAUuAZwB7A68FSLJ32/blbV/fAk5YY9uXAs8Edppm/88EFgGvAv4GeCfwW8BTgH2SPKetd2B7PY9BWH0k8NE19vUsBl1N9gDek+R/V9W/AH8GfK5dfX9at/7vAgcBvwQ8BPijKWr8VeCKqrq7a/sYcAewDYP35LWTbPfz80+yO/DnwD5tm6uAE9dYf9L3eqpzr6o7218YAJ5WVU+oqt0ZfBaHtvP9z7b8UqA/d0lznAFZ0qbmXuCIFpB+OsU6H62qq6vqJuBIYL/W/jMGAW2HqvpZVX2rqqYLyB+sqpuq6r8YBNiJ/bwR+POqurQFxz8DFvdXkdvym6apEeB9VXVHVX0F+DFwQlXdUFXXMgh6T2/rvRr466q6oqpuB94O7LtG94U/qaqfVtUFwAWsPRB+uqr+s9V3ErB4ivW2AG6bmEkyD/gd4D1V9eOquhhYPsl2/fm/GlhWVedW1Z2t/l9PsrBbf6r3ephzX5vb2nlI2kQYkCVtalZX1R1rWefqbvoqYOLP+X8JrAK+0v6cf/h67mcH4MOtK8SPgJuAANtOse1Uru+mfzrJ/MQV0l9ux+9rmc/gKviEH3bTP+m2ncqw698MPKqbX9COveZ7s6Z++f3qb0H3RqZ+v/r3ephzX5tHAT9ah/UlbeQMyJI2NdNd8Z2wfTf9OOC/Aarqtqp6a1U9nsFNW3+YZI913Q+DMPeGqtqie21WVf++jnUO678ZhPK+lru5f6CeygOt40Jgx+6K7ep27DXfm+mOe7/6kzwCeCxwbbfOVO/1Azn3Cf+bwVV1SZsIA7Ik/aJDkmyX5DEM+vV+DiDJi5L8SpIAtzAYOu7eafbzx0m2TLI9cNjEfoBPAG9P8pS230cneeWoToZB/+Y/SLJjkkdyX7/iu9eyHQyC5MKpbkZcm6q6hsFV913b/D3AF4D3Jnl4kp2AA4ao/6Aki5M8tNV/VlVd2a0z1Xv9QM59wnOAL6/D+pI2cgZkSfpF/wB8BbgC+D7w/ta+CPhX4HbgP4CPV9XXptnPKcA5wPnAPwPHAlTVF4EPAicmuRW4GNhrw5/Gzy1jMMzaN4EfMLhB7s1DbvuP7euNSc5dz+P/HfCabv5QBl0yfggcB3x6uo2r6l+BdwMnA9cxuHly3zVWm/S95oGd+8Q4yDsB/zTsNpI2fpn+/hJJ2rQkuRL4vRbKHsh+ClhUVas2SGEbsXbV9zxgj1E8LGSU73WSo4DvV9XHN/S+Jc1ec2EAeknSLNZGnphuuLpZq6reOu4aJM08u1hIkiRJHbtYSJIkSR2vIEuSJEkd+yA3W221VS1cuHDcZUiSJGmGnHPOOf9TVQvWbDcgNwsXLmTlypXjLkOSJEkzJMlkT/K0i4UkSZLUMyBLkiRJHQOyJEmS1DEgS5IkSR0DsiRJktQxIEuSJEkdA7IkSZLUMSBLkiRJHQOyJEmS1DEgS5IkSR0DsiRJktQxIEuSJEkdA7IkSZLUMSBLkiRJHQOyJEmS1DEgS5IkSR0DsiRJktQxIEuSJEkdA7IkSZLUmT/uAiRJksZt9W13ctsdPxt3GZu0Rz3swSx41EPHXQYwwoCcZHvgeGBroIBjqurDSR4DfA5YCFwJ7FNVNycJ8GHghcBPgAOr6ty2rwOAd7Vdv7+qlrf2XYDjgM2AU4HDqqqmOsaozlWSJG3cbrvjZ+x+1DfGXcYm7atvfc6sCcij7GJxN/DWqtoJ2A04JMlOwOHAGVW1CDijzQPsBSxqr4OBowFa2D0CeCawK3BEki3bNkcDr++2W9rapzqGJEmSNK2RBeSqum7iCnBV3QZcCmwL7A0sb6stB17apvcGjq+BM4EtkmwD7AmcXlU3tavApwNL27LNq+rMqioGV6v7fU12DEmSJGlaM3KTXpKFwNOBs4Ctq+q6tuiHDLpgwCA8X91tdk1rm679mknameYYa9Z1cJKVSVauXr163U9MkiRJc87IA3KSRwInA2+pqlv7Ze3Kb43y+NMdo6qOqaolVbVkwYIFoyxDkiRJG4mRBuQkD2YQjj9bVV9ozde37hG0rze09muB7bvNt2tt07VvN0n7dMeQJEmSpjWygNxGpTgWuLSq/rpbtAI4oE0fAJzSte+fgd2AW1o3idOAFyTZst2c9wLgtLbs1iS7tWPtv8a+JjuGJEmSNK1RjoP8m8BrgIuSnN/a3gF8ADgpyeuAq4B92rJTGQzxtorBMG8HAVTVTUneB5zd1vvTqrqpTb+J+4Z5+3J7Mc0xZhXHXBy/2TTmoiRJmh1GFpCr6ttApli8xyTrF3DIFPtaBiybpH0lsPMk7TdOdozZxjEXx282jbkoSZJmBx81LUmSJHUMyJIkSVLHgCxJkiR1RnmTniRpI+ANw+PnDcPS7GJAlqRNnDcMj583DEuzi10sJEmSpI4BWZIkSeoYkCVJkqSOAVmSJEnqGJAlSZKkjgFZkiRJ6hiQJUmSpI4BWZIkSeoYkCVJkqSOAVmSJEnqGJAlSZKkjgFZkiRJ6hiQJUmSpM78cRcgadO2+rY7ue2On427jE3aPffWuEuQpFnFgCxprG6742fsftQ3xl3GJu20tzx73CVI0qxiFwtJkiSpY0CWJEmSOgZkSZIkqWNAliRJkjoGZEmSJKljQJYkSZI6DvMmSdKYzZ/3IK5Yffu4y9ikOR64egZkSZLG7Kd33c2ef/OtcZexSXM8cPXsYiFJkiR1DMiSJElSx4AsSZIkdQzIkiRJUseALEmSJHUcxUKbNIdWGj+HVpIkzTYGZG3SHFpp/BxaSZI029jFQpIkSeoYkCVJkqSOAVmSJEnqGJAlSZKkjgFZkiRJ6hiQJUmSpI4BWZIkSeqMLCAnWZbkhiQXd22fS3J+e12Z5PzWvjDJT7tln+i22SXJRUlWJflIkrT2xyQ5Pcnl7euWrT1tvVVJLkzyjFGdoyRJkuaeUV5BPg5Y2jdU1auqanFVLQZOBr7QLf7+xLKqemPXfjTwemBRe03s83DgjKpaBJzR5gH26tY9uG0vSZIkDWVkAbmqvgncNNmydhV4H+CE6faRZBtg86o6s6oKOB54aVu8N7C8TS9fo/34GjgT2KLtR5IkSVqrcfVBfjZwfVVd3rXtmOS8JN9IMvHs2W2Ba7p1rmltAFtX1XVt+ofA1t02V0+xzf0kOTjJyiQrV69e/QBOR5IkSXPFuALyftz/6vF1wOOq6unAHwL/kGTzYXfWri7XuhZRVcdU1ZKqWrJgwYJ13VySJElz0PyZPmCS+cDLgV0m2qrqTuDONn1Oku8DTwSuBbbrNt+utQFcn2SbqrqudaG4obVfC2w/xTaSJEnStMZxBfm3gO9V1c+7TiRZkGRem348gxvsrmhdKG5Nslvrt7w/cErbbAVwQJs+YI32/dtoFrsBt3RdMSRJkqRpjXKYtxOA/wCelOSaJK9ri/blF2/O+z/AhW3Yt88Db6yqiRv83gR8ClgFfB/4cmv/APD8JJczCN0faO2nAle09T/ZtpckSZKGMrIuFlW13xTtB07SdjKDYd8mW38lsPMk7TcCe0zSXsAh61iuJEmSBPgkPUmSJOl+DMiSJElSx4AsSZIkdQzIkiRJUseALEmSJHUMyJIkSVLHgCxJkiR1DMiSJElSx4AsSZIkdQzIkiRJUseALEmSJHUMyJIkSVLHgCxJkiR1DMiSJElSx4AsSZIkdQzIkiRJUseALEmSJHUMyJIkSVLHgCxJkiR1DMiSJElSx4AsSZIkdQzIkiRJUseALEmSJHUMyJIkSVLHgCxJkiR1DMiSJElSx4AsSZIkdQzIkiRJUseALEmSJHUMyJIkSVLHgCxJkiR1DMiSJElSx4AsSZIkdQzIkiRJUseALEmSJHUMyJIkSVLHgCxJkiR1DMiSJElSx4AsSZIkdQzIkiRJUseALEmSJHUMyJIkSVLHgCxJkiR1RhaQkyxLckOSi7u29ya5Nsn57fXCbtnbk6xKclmSPbv2pa1tVZLDu/Ydk5zV2j+X5CGt/aFtflVbvnBU5yhJkqS5Z5RXkI8Dlk7S/qGqWtxepwIk2QnYF3hK2+bjSeYlmQd8DNgL2AnYr60L8MG2r18BbgZe19pfB9zc2j/U1pMkSZKGMrKAXFXfBG4acvW9gROr6s6q+gGwCti1vVZV1RVVdRdwIrB3kgC7A59v2y8HXtrta3mb/jywR1tfkiRJWqtx9EE+NMmFrQvGlq1tW+Dqbp1rWttU7Y8FflRVd6/Rfr99teW3tPV/QZKDk6xMsnL16tUP/MwkSZK00ZvpgHw08ARgMXAdcNQMH/9+quqYqlpSVUsWLFgwzlIkSZI0S8xoQK6q66vqnqq6F/gkgy4UANcC23erbtfapmq/Edgiyfw12u+3r7b80W19SZIkaa1mNCAn2aabfRkwMcLFCmDfNgLFjsAi4DvA2cCiNmLFQxjcyLeiqgr4GvCKtv0BwCndvg5o068AvtrWlyRJktZq/tpXWT9JTgCeC2yV5BrgCOC5SRYDBVwJvAGgqi5JchLwXeBu4JCquqft51DgNGAesKyqLmmHeBtwYpL3A+cBx7b2Y4HPJFnF4CbBfUd1jpIkSZp7RhaQq2q/SZqPnaRtYv0jgSMnaT8VOHWS9iu4r4tG334H8Mp1KlaSJElqfJKeJEmS1DEgS5IkSR0DsiRJktQxIEuSJEkdA7IkSZLUMSBLkiRJHQOyJEmS1DEgS5IkSR0DsiRJktQxIEuSJEkdA7IkSZLUMSBLkiRJHQOyJEmS1DEgS5IkSR0DsiRJktQxIEuSJEkdA7IkSZLUMSBLkiRJHQOyJEmS1DEgS5IkSR0DsiRJktQxIEuSJEkdA7IkSZLUMSBLkiRJHQOyJEmS1DEgS5IkSR0DsiRJktQxIEuSJEmd+WtbIckC4PXAwn79qnrt6MqSJEmSxmOtARk4BfgW8K/APaMtR5IkSRqvYQLyw6vqbSOvRJIkSZoFhumD/KUkLxx5JZIkSdIsMExAPoxBSP5pkluT3Jbk1lEXJkmSJI3DWrtYVNWjZqIQSZIkaTaYMiAneXJVfS/JMyZbXlXnjq4sSZIkaTymu4L8h8DBwFGTLCtg95FUJEmSJI3RlAG5qg5uX583c+VIkiRJ4zXMg0IeBrwJeBaDK8ffAj5RVXeMuDZJkiRpxg0zDvLxwG3A37b53wU+A7xyVEVJkiRJ4zJMQN65qnbq5r+W5LujKkiSJEkap2HGQT43yW4TM0meCawcXUmSJEnS+Ew3zNtFDPocPxj49yT/1eZ3AL43M+VJkiRJM2u6LhYveiA7TrKs7eOGqtq5tf0l8GLgLuD7wEFV9aMkC4FLgcva5mdW1RvbNrsAxwGbAacCh1VVJXkM8DlgIXAlsE9V3ZwkwIeBFwI/AQ50zGZJkiQNa8ouFlV11XSvIfZ9HLB0jbbTGfRpfirwn8Dbu2Xfr6rF7fXGrv1o4PXAovaa2OfhwBlVtQg4o80D7NWte3DbXpIkSRrKMH2Q10tVfRO4aY22r1TV3W32TGC76faRZBtg86o6s6qKwYgaL22L9waWt+nla7QfXwNnAlu0/UiSJElrNbKAPITXAl/u5ndMcl6SbyR5dmvbFrimW+ea1gawdVVd16Z/CGzdbXP1FNvcT5KDk6xMsnL16tUP4FQkSZI0V0wbkJPMS/K1DX3QJO8E7gY+25quAx5XVU9n8Ijrf0iy+bD7a1eXa13rqKpjqmpJVS1ZsGDBum4uSZKkOWjagFxV9wD3Jnn0hjpgkgMZ3Lz36hZsqao7q+rGNn0Ogxv4nghcy/27YWzX2gCun+g60b7e0NqvBbafYhtJkiRpWsN0sbgduCjJsUk+MvFan4MlWQr8v8BLquonXfuCJPPa9OMZ3GB3RetCcWuS3droFPsDp7TNVgAHtOkD1mjfPwO7Abd0XTEkSZKkaQ3zJL0vtNc6SXIC8FxgqyTXAEcwGLXiocDpg7z78+Hc/g/wp0l+BtwLvLGqJm7wexP3DfP2Ze7rt/wB4KQkrwOuAvZp7acyGOJtFYNh3g5a19olSZK06VprQK6q5Uk2Y9BH+LK1rd9tt98kzcdOse7JwMlTLFsJ7DxJ+43AHpO0F3DIsHVKkiRJvbV2sUjyYuB84F/a/OIkK0ZdmCRJkjQOw/RBfi+wK/AjgKo6H3j8CGuSJEmSxmaYgPyzqrpljbZ7R1GMJEmSNG7D3KR3SZLfBeYlWQT8PvDvoy1LkiRJGo9hriC/GXgKcCdwAnAr8JZRFiVJkiSNyzCjWPwEeGeSDw5m67bRlyVJkiSNxzCjWPxakouACxk8MOSCJLuMvjRJkiRp5g3TB/lY4E1V9S2AJM8CPg08dZSFSZIkSeMwTB/keybCMUBVfRu4e3QlSZIkSeMzzBXkbyT5OwY36BXwKuDrSZ4BUFXnjrA+SZIkaUYNE5Cf1r4esUb70xkE5t03aEWSJEnSGA0zisXzZqIQSZIkaTYYpg+yJEmStMkwIEuSJEkdA7IkSZLUGeZBIa9M8qg2/a4kX5gYwUKSJEmaa4a5gvzuqrqtPSDktxg8OOTo0ZYlSZIkjcdQDwppX38bOKaq/hl4yOhKkiRJksZnmIB8bXtQyKuAU5M8dMjtJEmSpI3OMEF3H+A0YM+q+hHwGOCPR1qVJEmSNCZrDchV9RPgSmCvJG8Gtqmqr4y6MEmSJGkchhnF4j3AcuCxwFbAp5O8a9SFSZIkSeOw1kdNA68GnlZVdwAk+QBwPvD+URYmSZIkjcMwfZD/G3hYN/9Q4NrRlCNJkiSN1zBXkG8BLklyOlDA84HvJPkIQFX9/gjrkyRJkmbUMAH5i+014eujKUWSJEkav7UG5KpaPhOFSJIkSbPBlAE5yUlVtU+Sixh0rbifqnrqSCuTJEmSxmC6K8iHta8vmolCJEmSpNlgyoBcVdclmQccV1XPm8GaJEmSpLGZdpi3qroHuDfJo2eoHkmSJGmshhnF4nbgojbM248nGh3eTZIkSXPRMAH5C+3V+4Wb9iRJkqS5YJiAvEVVfbhvSHLYVCtLkiRJG7NhHjV9wCRtB27gOiRJkqRZYbpxkPcDfhfYMcmKbtGjgJtGXZgkSZI0DtN1sfh34DpgK+Corv024MJRFiVJkiSNy3TjIF8FXAX8+syVI0mSJI3XMH2QJUmSpE2GAVmSJEnqTBmQk5zRvn5w5sqRJEmSxmu6m/S2SfIbwEuSnAikX1hV5460MkmSJGkMputi8R7g3cB2wF8zGMli4vVXw+w8ybIkNyS5uGt7TJLTk1zevm7Z2pPkI0lWJbkwyTO6bQ5o61+e5ICufZckF7VtPpIk0x1DkiRJWpspA3JVfb6q9gL+oqqet8Zr9yH3fxywdI22w4EzqmoRcEabB9gLWNReBwNHwyDsAkcAzwR2BY7oAu/RwOu77Zau5RiSJEnStNZ6k15VvS/JS5L8VXu9aNidV9U3+cWHiuwNLG/Ty4GXdu3H18CZwBZJtgH2BE6vqpuq6mbgdGBpW7Z5VZ1ZVQUcv8a+JjuGJEmSNK21BuQkfw4cBny3vQ5L8mcP4JhbV9V1bfqHwNZtelvg6m69a1rbdO3XTNI+3THuJ8nBSVYmWbl69er1PB1JkiTNJdPdpDfht4HFVXUvQJLlwHnAOx7owauqktQD3c/6HqOqjgGOAViyZMlI65AkSdLGYdhxkLfoph/9AI95feseQft6Q2u/Fti+W2+71jZd+3aTtE93DEmSJGlawwTkPwfOS3Jcu3p8DnDkAzjmCmBiJIoDgFO69v3baBa7Abe0bhKnAS9IsmW7Oe8FwGlt2a1JdmujV+y/xr4mO4YkSZI0rbV2saiqE5J8Hfi11vS2qvrhMDtPcgLwXGCrJNcwGI3iA8BJSV4HXAXs01Y/FXghsAr4CXBQO/5NSd4HnN3W+9Oqmrjx700MRsrYDPhyezHNMSRJkqRpDdMHmXa1dsW67ryq9pti0R6TrFvAIVPsZxmwbJL2lcDOk7TfONkxJEmSpLUZtg+yJEmStEkwIEuSJEmdaQNyknlJvjdTxUiSJEnjNm1Arqp7gMuSPG6G6pEkSZLGapib9LYELknyHeDHE41V9ZKRVSVJkiSNyTAB+d0jr0KSJEmaJYYZB/kbSXYAFlXVvyZ5ODBv9KVJkiRJM2+to1gkeT3weeDvWtO2wD+NsihJkiRpXIYZ5u0Q4DeBWwGq6nLgl0ZZlCRJkjQuwwTkO6vqromZJPOBGl1JkiRJ0vgME5C/keQdwGZJng/8I/D/jbYsSZIkaTyGCciHA6uBi4A3AKcC7xplUZIkSdK4DDOKxb1JlgNnMehacVlV2cVCkiRJc9JaA3KS3wY+AXwfCLBjkjdU1ZdHXZwkSZI004Z5UMhRwPOqahVAkicA/wwYkCVJkjTnDNMH+baJcNxcAdw2onokSZKksZryCnKSl7fJlUlOBU5i0Af5lcDZM1CbJEmSNOOm62Lx4m76euA5bXo1sNnIKpIkSZLGaMqAXFUHzWQhkiRJ0mwwzCgWOwJvBhb261fVS0ZXliRJkjQew4xi8U/AsQyennfvaMuRJEmSxmuYgHxHVX1k5JVIkiRJs8AwAfnDSY4AvgLcOdFYVeeOrCpJkiRpTIYJyL8KvAbYnfu6WFSblyRJkuaUYQLyK4HHV9Vdoy5GkiRJGrdhnqR3MbDFqAuRJEmSZoNhriBvAXwvydncvw+yw7xJkiRpzhkmIB8x8iokSZKkWWKtAbmqvjEThUiSJEmzwTBP0ruNwagVAA8BHgz8uKo2H2VhkiRJ0jgMcwX5URPTSQLsDew2yqIkSZKkcRlmFIufq4F/AvYcUT2SJEnSWA3TxeLl3eyDgCXAHSOrSJIkSRqjYUaxeHE3fTdwJYNuFpIkSdKcM0wf5INmohBJkiRpNpgyICd5zzTbVVW9bwT1SJIkSWM13RXkH0/S9gjgdcBjAQOyJEmS5pwpA3JVHTUxneRRwGHAQcCJwFFTbSdJkiRtzKbtg5zkMcAfAq8GlgPPqKqbZ6IwSZIkaRym64P8l8DLgWOAX62q22esKkmSJGlMpntQyFuBXwbeBfx3klvb67Ykt85MeZIkSdLMmq4P8jo9ZU+SJEmaCwzBkiRJUmfGA3KSJyU5v3vdmuQtSd6b5Nqu/YXdNm9PsirJZUn27NqXtrZVSQ7v2ndMclZr/1ySh8z0eUqSJGnjNOMBuaouq6rFVbUY2AX4CfDFtvhDE8uq6lSAJDsB+wJPAZYCH08yL8k84GPAXsBOwH5tXYAPtn39CnAzg7GbJUmSpLUadxeLPYDvV9VV06yzN3BiVd1ZVT8AVgG7tteqqrqiqu5iMD7z3kkC7A58vm2/HHjpyM5AkiRJc8q4A/K+wAnd/KFJLkyyLMmWrW1b4OpunWta21TtjwV+VFV3r9H+C5IcnGRlkpWrV69+4GcjSZKkjd7YAnLrF/wS4B9b09HAE4DFwHXMwNP6quqYqlpSVUsWLFgw6sNJkiRpIzDtk/RGbC/g3Kq6HmDiK0CSTwJfarPXAtt3223X2pii/UZgiyTz21Xkfn1JkiRpWuPsYrEfXfeKJNt0y14GXNymVwD7Jnlokh2BRcB3gLOBRW3Eiocw6K6xoqoK+Brwirb9AcApIz0TSZIkzRljuYKc5BHA84E3dM1/kWQxUMCVE8uq6pIkJwHfBe4GDqmqe9p+DgVOA+YBy6rqkravtwEnJnk/cB5w7MhPSpIkSXPCWAJyVf2Ywc10fdtrpln/SODISdpPBU6dpP0KBqNcSJIkSetk3KNYSJIkSbOKAVmSJEnqGJAlSZKkjgFZkiRJ6hiQJUmSpI4BWZIkSeoYkCVJkqSOAVmSJEnqGJAlSZKkjgFZkiRJ6hiQJUmSpI4BWZIkSeoYkCVJkqSOAVmSJEnqGJAlSZKkjgFZkiRJ6hiQJUmSpI4BWZIkSeoYkCVJkqSOAVmSJEnqGJAlSZKkjgFZkiRJ6hiQJUmSpI4BWZIkSeoYkCVJkqSOAVmSJEnqGJAlSZKkjgFZkiRJ6hiQJUmSpI4BWZIkSeoYkCVJkqSOAVmSJEnqGJAlSZKkjgFZkiRJ6hiQJUmSpI4BWZIkSeoYkCVJkqSOAVmSJEnqGGGalBsAABAMSURBVJAlSZKkjgFZkiRJ6hiQJUmSpI4BWZIkSeqMLSAnuTLJRUnOT7KytT0myelJLm9ft2ztSfKRJKuSXJjkGd1+DmjrX57kgK59l7b/VW3bzPxZSpIkaWMz7ivIz6uqxVW1pM0fDpxRVYuAM9o8wF7AovY6GDgaBoEaOAJ4JrArcMREqG7rvL7bbunoT0eSJEkbu3EH5DXtDSxv08uBl3btx9fAmcAWSbYB9gROr6qbqupm4HRgaVu2eVWdWVUFHN/tS5IkSZrSOANyAV9Jck6Sg1vb1lV1XZv+IbB1m94WuLrb9prWNl37NZO030+Sg5OsTLJy9erVD/R8JEmSNAfMH+Oxn1VV1yb5JeD0JN/rF1ZVJalRFlBVxwDHACxZsmSkx5IkSdLGYWxXkKvq2vb1BuCLDPoQX9+6R9C+3tBWvxbYvtt8u9Y2Xft2k7RLkiRJ0xpLQE7yiCSPmpgGXgBcDKwAJkaiOAA4pU2vAPZvo1nsBtzSumKcBrwgyZbt5rwXAKe1Zbcm2a2NXrF/ty9JkiRpSuPqYrE18MU28tp84B+q6l+SnA2clOR1wFXAPm39U4EXAquAnwAHAVTVTUneB5zd1vvTqrqpTb8JOA7YDPhye0mSJEnTGktArqorgKdN0n4jsMck7QUcMsW+lgHLJmlfCez8gIuVJEnSJmW2DfMmSZIkjZUBWZIkSeoYkCVJkqSOAVmSJEnqGJAlSZKkjgFZkiRJ6hiQJUmSpI4BWZIkSeoYkCVJkqSOAVmSJEnqGJAlSZKkjgFZkiRJ6hiQJUmSpI4BWZIkSeoYkCVJkqSOAVmSJEnqGJAlSZKkjgFZkiRJ6hiQJUmSpI4BWZIkSeoYkCVJkqSOAVmSJEnqGJAlSZKkjgFZkiRJ6hiQJUmSpI4BWZIkSeoYkCVJkqSOAVmSJEnqGJAlSZKkjgFZkiRJ6hiQJUmSpI4BWZIkSeoYkCVJkqSOAVmSJEnqGJAlSZKkjgFZkiRJ6hiQJUmSpI4BWZIkSeoYkCVJkqSOAVmSJEnqGJAlSZKkjgFZkiRJ6sx4QE6yfZKvJflukkuSHNba35vk2iTnt9cLu23enmRVksuS7Nm1L21tq5Ic3rXvmOSs1v65JA+Z2bOUJEnSxmocV5DvBt5aVTsBuwGHJNmpLftQVS1ur1MB2rJ9gacAS4GPJ5mXZB7wMWAvYCdgv24/H2z7+hXgZuB1M3VykiRJ2rjNeECuquuq6tw2fRtwKbDtNJvsDZxYVXdW1Q+AVcCu7bWqqq6oqruAE4G9kwTYHfh823458NLRnI0kSZLmmrH2QU6yEHg6cFZrOjTJhUmWJdmytW0LXN1tdk1rm6r9scCPquruNdonO/7BSVYmWbl69eoNcEaSJEna2I0tICd5JHAy8JaquhU4GngCsBi4Djhq1DVU1TFVtaSqlixYsGDUh5MkSdJGYP44DprkwQzC8Wer6gsAVXV9t/yTwJfa7LXA9t3m27U2pmi/Edgiyfx2FblfX5IkSZrWOEaxCHAscGlV/XXXvk232suAi9v0CmDfJA9NsiOwCPgOcDawqI1Y8RAGN/KtqKoCvga8om1/AHDKKM9JkiRJc8c4riD/JvAa4KIk57e2dzAYhWIxUMCVwBsAquqSJCcB32UwAsYhVXUPQJJDgdOAecCyqrqk7e9twIlJ3g+cxyCQS5IkSWs14wG5qr4NZJJFp06zzZHAkZO0nzrZdlV1BYNRLiRJkqR14pP0JEmSpI4BWZIkSeoYkCVJkqSOAVmSJEnqGJAlSZKkjgFZkiRJ6hiQJUmSpI4BWZIkSeoYkCVJkqSOAVmSJEnqGJAlSZKkjgFZkiRJ6hiQJUmSpI4BWZIkSeoYkCVJkqSOAVmSJEnqGJAlSZKkjgFZkiRJ6hiQJUmSpI4BWZIkSeoYkCVJkqSOAVmSJEnqGJAlSZKkjgFZkiRJ6hiQJUmSpI4BWZIkSeoYkCVJkqSOAVmSJEnqGJAlSZKkjgFZkiRJ6hiQJUmSpI4BWZIkSeoYkCVJkqSOAVmSJEnqGJAlSZKkjgFZkiRJ6hiQJUmSpI4BWZIkSeoYkCVJkqSOAVmSJEnqGJAlSZKkjgFZkiRJ6hiQJUmSpM6cDchJlia5LMmqJIePux5JkiRtHOZkQE4yD/gYsBewE7Bfkp3GW5UkSZI2BnMyIAO7Aquq6oqqugs4Edh7zDVJkiRpI5CqGncNG1ySVwBLq+r32vxrgGdW1aFrrHcwcHCbfRJw2YwWClsB/zPDx9T9+RmMn5/B+PkZjJ+fwfj5GYzfOD6DHapqwZqN82e4iFmlqo4BjhnX8ZOsrKol4zq+/AxmAz+D8fMzGD8/g/HzMxi/2fQZzNUuFtcC23fz27U2SZIkaVpzNSCfDSxKsmOShwD7AivGXJMkSZI2AnOyi0VV3Z3kUOA0YB6wrKouGXNZkxlb9w79nJ/B+PkZjJ+fwfj5GYyfn8H4zZrPYE7epCdJkiStr7naxUKSJElaLwZkSZIkqWNAHpEkL01SSZ68Htt+auLJf0muTLLVhq9w45fk9nHXsKlJck+S87vXwmnW/XqSWTFcz6as/Rz6+25+fpLVSb40zro2Ff6cmlmj/n7383xg1vb+zab/N+bkTXqzxH7At9vXI4bdKMm8iQecaPZJMr+q7h53HWP006pavKF2tiHez/Zv5p4NVdMc9GNg5ySbVdVPgefjsJeaux7w97s/5wVeQR6JJI8EngW8jsEQcyR5bpJvJvnnJJcl+USSB7Vltyc5KskFwK/Ppt+gZrskj0xyRpJzk1yUZO/WvjDJpUk+meSSJF9Jsllb9vP3N8lWSa7stvlW29e5SX6jtT+3ta8AvpvkT5O8pavhyCSHzfS5zxZJdknyjSTnJDktyTbd4te0K80XJ9m1rf/eJJ9J8m/AZ5IcmOSj3f6+lOS5bfroJCvbZ/gn3TpXJvlgknOBw9vXiWWL+nkBcCrw2216P+CEiQVJdk3yH0nOS/LvSZ7U2r+ZZHG33reTPG1Gq54j2s+QL3XzH01yYJu+MsmfdD/DntzaH5FkWZLvtM9m7zGVvzFan+/3A5OsSPJV4Iz2f8un22dyYZLf6fZxZJILkpyZZOuZPLG5YLp/D13ba5P8TTf/+iQfmsEyDcgjsjfwL1X1n8CNSXZp7bsCbwZ2Ap4AvLy1PwI4q6qeVlXfnvFqN253AC+rqmcAzwOOSpK2bBHwsap6CvAj4Hem2MeEG4Dnt329CvhIt+wZwGFV9URgGbA/QPslZ1/g79k0bJb7uld8McmDgb8FXlFVuzB4b47s1n94u+L8prZswk7Ab1XVfms53jvbU5WeCjwnyVO7ZTdW1TOq6kjgli7MHQR8ev1PcU46Edg3ycMYvJdndcu+Bzy7qp4OvAf4s9Z+LHAgQJInAg+rqgtmrOJNy/+0nztHA3/U2t4JfLWqdmXws+0vkzxiXAVuZNbn+x0GP+dfUVXPAd4N3FJVv1pVTwW+2tZ5BHBmVT0N+Cbw+tGeyibrJODF7f8YGPxcXzbN+hucXSxGYz/gw236xDb/JeA7VXUFQJITGFxl/jxwD3DyGOqcCwL8WZL/A9wLbAtM/Eb/g6o6v02fAyxcy74eDHy0Ba17gCd2y75TVT8AqKork9yY5OntWOdV1Y0b5Gxmv/t1sUiyM7AzcHr7vWQecF23/gkAVfXNJJsn2aK1r2h//lybfZIczOBn1TYMgvWFbdnnuvU+BRyU5A8Z/HKz6zqf2RxWVRdm0F98PwZX13qPBpYnWQQUg38HAP8IvDvJHwOvBY6bkWI3TV9oX8/hvgsnLwBekmQiMD8MeBxw6QzXttFZz+93gNOr6qY2/Vu0vwC3fd7cJu9i8P85DD6v52/Q4gVAVd3erua/KMmlwIOr6qKZrMGAvIEleQywO/CrSYpBYCjgn9vX3sT8HfahXG+vBhYAu1TVz1p3iYe1ZXd2690DbNam7+a+v548rFvnD4Drgae15Xd0y368xnE/xeDq2v9ihn+rnWUCXFJVvz7F8qm+5/v3s/88oH0mSXZkcDXt16rq5iTHcf/Pq9/HyQz6+n8VOGcT+oVlXawA/gp4LvDYrv19wNeq6mUtVHwdoKp+kuR0Bn8R2wfYBa2vSb/HOxM/q+7hvv+XA/xOVV024trmqnX6fm/W/Dk/mZ/VfQ+Q6D8vDW9t/x4mfAp4B4Or/jP+V0G7WGx4rwA+U1U7VNXCqtoe+AHwbGDXDB5//SAGV7nsTvHAPRq4oYXj5wE7DLHNldz3n/0r1tjXdVV1L/AaBr/cTOWLwFLg1xg8sXFTdRmwIMmvAyR5cJKndMtf1dqfxeDPlbdMso8rgcVJHpRke+67+rs5g/+wbmn9/PaaqoiquoPB53A0dq+YyjLgTya5CvNo7ruJ6cA1ln2KQVejs7sraFp3VwE7JXlo+yvKHkNscxrw5okuY+0vVhre+ny/904HDpmYSbLlBq1u0zbUv4eqOgvYHvhdun7kM8WAvOHtxyA89U5u7WcDH2XwJ7IfTLKehpRkPoOrLp8FliS5iEG/4O8NsflfAf83yXlAP4Tex4EDMrhZ8slMczWhqu4CvgactClf/W/vwyuAD7b37XzgN7pV7mjv8ycY3LQ6mX9j8O/huwzC2Llt3xcA5zH4TP+hrTedzzLoZvOV9TqZOa6qrqmqj0yy6C+AP2+f0/w1tjkHuBV/6VgvEz+nqupqBn0qL25fzxti8/cx+PP/hUkuafMa0vp8v6/h/cCWGdxgfAGDfuB6ANbz38NJwL+N4xd0HzU9QzK4K/+PqupF465lLmh303+y3cAyjuM/iEGQe2VVXT6OGnR/ra/mo6vq3eOuZa5I8ssM/gT95PaXFa2Dcf+ckmaT9fn30Ea7+FBVnTG6yibnFWRtdJK8kcGfW941puPvBKwCzjAczw5JvsjgLwgfXtu6Gk6S/Rnc/f9Ow/G6G/fPKWk2Wdd/D0m2SPKfDG4Mn/FwDF5BliRJku7HK8iSJElSx4AsSZIkdQzIkiRJUseALElzVJJ72mPBL0hybpLfaO2/nOTz465PkmYrb9KTpDkqye1V9cg2vSfwjqp6zpjLkqRZzyvIkrRp2By4GSDJwiQXt+kDk3whyb8kuTzJX7T2eUmOaw9KuCjJH4yxdkmaUT5DXJLmrs2SnA88DNgG2H2K9RYDT2fwdMrLkvwt8EvAtlW1MwzGJZ2BeiVpVvAKsiTNXT+tqsVV9WRgKXB8kkyy3hlVdUtV3cHgkd87AFcAj0/yt0mWMnjktCRtEgzIkrQJqKr/ALYCFkyy+M5u+h5gflXdDDyNwaOm3wh8atQ1StJsYRcLSdoEJHkyMA+4EXj4EOtvBdxVVScnuQz4+xGXKEmzhgFZkuauiT7IAAEOqKp7Ju9l8Qu2BT6dZOIvjW8fRYGSNBs5zJskSZLUsQ+yJEmS1DEgS5IkSR0DsiRJktQxIEuSJEkdA7IkSZLUMSBLkiRJHQOyJEmS1Pn/AY+j868lRIYEAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Create diagrams"
      ],
      "metadata": {
        "id": "CyPEA4oYQgku"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def create_diagram(y_values, x_values, title, xlabel, ylabel):\n",
        "  fig = plt.figure(figsize=(10, 5))\n",
        "\n",
        "  plt.bar(y_values, x_values, color='maroon')\n",
        "\n",
        "  plt.title(title)\n",
        "  plt.xlabel(xlabel)\n",
        "  plt.ylabel(ylabel)\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "ay0ycIT8MfTe"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Μέση διάρκεια ταξιδιού ανά pickup hour***"
      ],
      "metadata": {
        "id": "i1bDW5HAlr7Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Average trip duration per pickup hour\n",
        "avg_trip_durations = df_train.groupby(['pickup_hour'])['trip_duration'].mean()\n",
        "\n",
        "create_diagram(avg_trip_durations.keys().tolist(), avg_trip_durations.tolist(),\\\n",
        "              \"Average trip duration per pickup hour\",\\\n",
        "              \"Hours\", \"Trip Duration\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "11BHMOMpM5_W",
        "outputId": "ad8b5d5a-46b9-48e3-aad8-989fe747c245"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmQAAAFNCAYAAACuWnPfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAdP0lEQVR4nO3dfbildV3v8fdHEBAFRgQJBnBQyEQIxDlKiUrRAyAFpSHKkYE0tNQ066RHO+HjOVkpop5LQklQAUXD4ASZHZRKU3JABIRIUhCGAQZ5EAeUBr79cd871mz3w5ph1v6tvff7dV372vfTutd33fdesz7z+/3WfaeqkCRJUjuPal2AJEnSYmcgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJI2mSR/m2TFJtrXGUneuSn2NeTzHZvk83P1fHNl2HOS5IYkvzDiWpYlqSSbj/J5pPnIN4U0IkkuAfYDfqKqftS4nEckyVuBPavqv8+0XVUdNjcVPTJJlgHfAR5dVesAquos4KyGZY3EfDkn0mJnC5k0Av0H/nOBAn51BPsfq/9MpTM2/54k2ax1DaMwbud9nC3UvwEtXGPzD6i0wBwHfBU4A1gBkGTLJHcn2WdioyQ7Jrk/yRP7+SOSXNFv989Jfnpg2xuSvDHJlcDaJJsneVOSf09yb5JrkvzawPabJXlPkjuSfCfJawa7i5Jsl+T0JKuTrEryzqk+xJIcCrwZeHGSHyT5Rr/8kiTvSvJl4D7gyf2yV/Trj0/y5SQfTHJPkn9Ncsh0ByzJM5Jc3r+WTwFbDaw7PsmXJm1fSfbsp89I8qEkFyVZC/xckhck+XqS7ye5qW/lm/CP/e+7+9f0M5OfI8nPJvlaX/vXkvzswLpLkryjf333Jvl8kh2meV0HJ7k5yZv7c3FDkmMH1m+Z5M+TfDfJbUlOTfKYSY99Y5JbgY9Osf8Zj/PgOennfyvJtQN/MwdMsc+n9X8zLxny2J+a5O/7ff5DkidNdSwGHNu/3juSvGXSsXhfklv6n/cl2XLgdW7Q38AsNUhjxUAmjcZxdN1fZwG/nGSnvtvyPOAlA9sdDfxDVd2e5BnAXwKvBJ4A/AVwwcQHUu8lwAuAJX1X27/TtcRtB7wN+ESSnfttfws4DNgfOAA4alKNZwDrgD2BZwC/BLxi0jZU1eeA/w18qqoeV1X7Dax+GXAisA1w4xTH4dl9jTsAJwHnJdl+8kZJtgD+Gvg4sD3waeCFU+xvJi8F3tXX8iVgLd15WEJ3zH47ycQxeF7/e0n/mr4yqZ7tgQuB99Odi/cCFyZ5wqTnOwF4IrAF8Acz1PYTdMdgKV1APy3JU/t1fwL8JN152rPf5o8nPXZ74El0x3oqwx7n3wDeSndctqVrvf3epG0OAP4OeG1VnTPDaxp0LPCO/vmvYPau34OApwKHAH+c5Gn98rcAB9Idi/2AZwF/NGQN8ON/A9K8YSCTNrEkB9F9eJ5bVZfRfVC+tF99NnDMwOYv7ZdB92H7F1V1aVU9WFVnAj+i+4Ca8P6quqmq7geoqk9X1S1V9VBVfQr4Ft2HGHRh75Squrmq7qL74J+ocSfgcOD1VbW2qm4HTp5U2zDOqKpvVtW6qvqPKdbfDryvqv6jr+86unA02YHAowe2/QzwtQ2s5fyq+nJ/LH5YVZdU1VX9/JXAOcDzh9zXC4BvVdXH+9d2DvCvwK8MbPPRqvq3/lycSxciZvK/qupHVfUPdGHv6CShO++/V1V3VtW9dOF38Dw8BJzUP/b+afY97HF+BfCnVfW16lxfVYNB+rnABcBxVfU3s7yeQRdW1T/2/+l4C/AzSXabYfu3VdX9VfUN4Bt04Qu6YPf2qrq9qtbQ/SfjZRtQx3p/AxvwOKk5xyNIm94K4PNVdUc/f3a/7GTgi8DWSZ4N3Eb3If7ZfrsnASuSvHZgX1sAuwzM3zT4REmOA94ALOsXPY6ulYL+cYPbD04/iS4Are4yAdD9B229/Q9htu1XVVUNzN/I+q9nwi7TbLvRtfTH+E+AfeiO45Z0LW/D2GWK57+RrvVqwq0D0/fRHfvp3FVVayftaxdgR2Br4LKB8xBgsOt4zRDhYtjjvBvdfxCm8yq6FttLZnm+yf7r2FfVD5LcyY///Q2a7thNPu7TvY5Z65DmG1vIpE2oH/tzNPD8JLf2435+D9gvyX5V9SBda8pL+p+/6VtFoPsweVdVLRn42XpSt1ENPNeTgA8DrwGeUFVLgKvpPtABVgO7Djx2sMXiJrrWtx0Gnmvbqnr6NC+tNnD5hKUZSBrA7sAtU2y3epptJ6ylCy4AJPmJIWo5m661Z7eq2g44lYePzWx130IXWgftDqya5XHTeXySx07a1y3AHcD9wNMHzsN2VTUY7marFYY/zjcBT5lhP68Cdk9y8sCyYY79bgPrH0fXxTrV889m8nEffB0b8zcgzRsGMmnTOgp4ENibrvVrf+BpwD/RjduBLii8mK575uyBx34YeFWSZ6fz2HQD07eZ5rkeS/cBtAYgyQl0rUETzgVel2RpkiXAGydWVNVq4PPAe5Jsm+RRSZ6SZLouvduAZdnwb1I+EfjdJI/uxy89Dbhoiu2+QjeebWLbX+fhrlfourWenmT/JFvRjYOazTbAnVX1wyTP4uFuY+iO2UPAk6d57EXATyZ5abovT7yY7pxuSDfeZG9LskWS5wJHAJ+uqofozvvJefiLHUuT/PIG7nvY4/wR4A+SPLP/G9tz0gD8e4FDgeclmejiHubYH57koH4s4DuAr1bVxrRWnQP8Ubovu+xAN5buExtQhzRvGcikTWsF3dii71bVrRM/wAfpvlm2eVVdSve//V2Av514YFWtpBuI/0HgLuB64PjpnqiqrgHeQxdmbgP2Bb48sMmH6ULXlcDX6T6g19EFRugC4hbANf3zfQbYmalNdPV9L8nlsx+G/3IpsBddS9C7gBdV1fcmb1RVDwC/Tvd676QLrOcNrP834O3A/6cbJzfMgO3fAd6e5F66D/ZzB/Z3X1/Pl9N9o3VwnB59jUcAv0836P0PgSMGuqE31K10x/gWugHvr6qqf+3XvZHuXH81yff71/jUKfcyvWGP86f79WfTha+/pmvNGtzmbuAXgcOSvGPIY3823ZcJ7gSeCcx4vboZvBNYSfc3exVweb9sY/8GpHkj6w87kLRQJTkMOLWqZrskwaZ6vuOBV1TVQXPxfOMqycHAJ6pq19m23cj9H0/D45zkDODmqtqQb0NKmsQWMmmBSvKYJIf3XW5L6VowPjvb4yRJc89AJi1cobtswF10XZbXsv71rSRJY8IuS0mSpMZsIZMkSWrMQCZJktTYvL5S/w477FDLli1rXYYkSdKsLrvssjuqasep1s3rQLZs2TJWrlzZugxJkqRZJZn2lnB2WUqSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLU2Ly+l6UkaXTeljzifZxUtcn3OdV+pfnOFjJJkqTGDGSSJEmNGcgkSZIacwyZJC0AoxjvJWnu2EImSZLUmIFMkiSpMQOZJElSYwYySZKkxhzUL0ma9/xSg+Y7W8gkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxrwOmSTNMa+ZJWkyW8gkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIP6JWkaDr6XNFcMZJIWBMOTpPnMLktJkqTGDGSSJEmNGcgkSZIaM5BJkiQ15qB+SZKm4BdFNJdsIZMkSWrMFjJJc86WB0lan4FM0rQMTpI0N+yylCRJasxAJkmS1JhdltICYfeiJM1ftpBJkiQ1ZiCTJElqbKSBLMnvJflmkquTnJNkqyR7JLk0yfVJPpVki37bLfv56/v1y0ZZmyRJ0rgYWSBLshT4XWB5Ve0DbAYcA7wbOLmq9gTuAl7eP+TlwF398pP77SRJkha8UXdZbg48JsnmwNbAauDngc/0688Ejuqnj+zn6dcfkmyCUcqSJEljbmSBrKpWAX8OfJcuiN0DXAbcXVXr+s1uBpb200uBm/rHruu3f8Ko6pMkSRoXo+yyfDxdq9cewC7AY4FDN8F+T0yyMsnKNWvWPNLdSZIkNTfK65D9AvCdqloDkOQ84DnAkiSb961guwKr+u1XAbsBN/ddnNsB35u806o6DTgNYPny5V40SZKkRW4hXIdxlGPIvgscmGTrfizYIcA1wBeBF/XbrADO76cv6Ofp13+hyqtUSpKkhW9kLWRVdWmSzwCXA+uAr9O1bF0IfDLJO/tlp/cPOR34eJLrgTvpvpEpSZIWiIXQkjUqI711UlWdBJw0afG3gWdNse0Pgd8YZT2SJEnjyCv1S5IkNebNxSVJmiN22Wk6tpBJkiQ1ZiCTJElqzEAmSZLUmGPIZmF/vyRpMfLzb27ZQiZJktSYLWSSJM1ztmbNfwYyqQH/8ZQkDbLLUpIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrM65A14nWoJEnSBFvIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiD+tWEX2qQJOlhtpBJkiQ1ZiCTJElqzC5LLRh2g0qS5itbyCRJkhozkEmSJDVml+UCYpedJEnzk4FMMzLkSZI0enZZSpIkNWYgkyRJasxAJkmS1JiBTJIkqTEH9Usz8EsNkqS5YAuZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhqb9ebiSbYEXggsG9y+qt4+xGOXAB8B9gEK+E3gOuBT/f5uAI6uqruSBDgFOBy4Dzi+qi7foFcjSZI0Dw3TQnY+cCSwDlg78DOMU4DPVdVPAfsB1wJvAi6uqr2Ai/t5gMOAvfqfE4EPDfkckiRJ89qsLWTArlV16IbuOMl2wPOA4wGq6gHggSRHAgf3m50JXAK8kS70fayqCvhqkiVJdq6q1Rv63JIkSfPJMC1k/5xk343Y9x7AGuCjSb6e5CNJHgvsNBCybgV26qeXAjcNPP7mftl6kpyYZGWSlWvWrNmIsiRJksbLMIHsIOCyJNcluTLJVUmuHOJxmwMHAB+qqmfQdXO+aXCDvjWsNqTgqjqtqpZX1fIdd9xxQx4qSZI0lobpsjxsI/d9M3BzVV3az3+GLpDdNtEVmWRn4PZ+/Spgt4HH79ovkyRJWtBmbSGrqhuBJcCv9D9L+mWzPe5W4KYkT+0XHQJcA1wArOiXraD70gD98uPSORC4x/FjkiRpMRjmshevA34LOK9f9Ikkp1XVB4bY/2uBs5JsAXwbOIEuBJ6b5OXAjcDR/bYX0V3y4nq6y16csCEvRJIkab4apsvy5cCzq2otQJJ3A18BZg1kVXUFsHyKVYdMsW0Brx6iHkmSpAVlmEH9AR4cmH+wXyZJkqRNYJgWso8Clyb5bD9/FHD66EqSJElaXGYNZFX13iSX0F3+AuCEqvr6SKuSJElaRKYNZEm2rarvJ9me7p6TNwys276q7hx9eZIkSQvfTC1kZwNHAJex/sVb088/eYR1SZIkLRrTBrKqOqL/vcfclSNJkrT4zPotyyQXD7NMkiRJG2emMWRbAVsDOyR5PA9f6mJbprjptyRJkjbOTGPIXgm8HtiFbhzZRCD7PvDBEdclSZK0aMw0huwU4JQkrx3yNkmSJEnaCMNch+wDSfYB9ga2Glj+sVEWJkmStFgMc3Pxk4CD6QLZRcBhwJcAA5kkSdImMMy9LF9EdzPwW6vqBGA/YLuRViVJkrSIDBPI7q+qh4B1SbYFbgd2G21ZkiRJi8cwNxdfmWQJ8GG6b1v+APjKSKuSJElaRGYMZEkC/J+quhs4NcnngG2r6so5qU6SJGkRmDGQVVUluQjYt5+/YS6KkiRJWkyGGUN2eZL/NvJKJEmSFqlhxpA9Gzg2yY3AWror9ldV/fRIK5MkSVokhglkvzzyKiRJkhaxYQJZjbwKSZKkRWyYQHYhXSgL3a2T9gCuA54+wrokSZIWjWHuZbnv4HySA4DfGVlFkiRJi8ww37JcT1VdTjfQX5IkSZvAMDcXf8PA7KOAA4BbRlaRJEnSIjPMGLJtBqbX0Y0p+6vRlCNJkrT4DDOG7G1zUYgkSdJiNeMYsiQrklyeZG3/szLJcXNVnCRJ0mIwbQtZkhXA64E3AJfTXfbiAODPklRVfXxuSpQkSVrYZmoh+23g16rqi1V1T1XdXVVfAF4IvHpuypMkSVr4Zgpk21bVDZMX9su2HVVBkiRJi81Mgez+jVwnSZKkDTDTtyyfluTKKZYHePKI6pEkSVp0Zgxkc1aFJEnSIjZtIKuqG+eyEEmSpMVqg+9lKUmSpE3LQCZJktTYMPeyJMkWwE8BBVxXVQ+MtCpJkqRFZNZAluQFwKnAv9N9w3KPJK+sqr8ddXGSJEmLwTAtZO8Bfq6qrgdI8hTgQsBAJkmStAkMM4bs3okw1vs2cO+I6pEkSVp0hmkhW5nkIuBcujFkvwF8LcmvA1TVeSOsT5IkacEbJpBtBdwGPL+fXwM8BvgVuoBmIJMkSXoEZg1kVXXCXBQiSZK0WE0byJL8YVX9aZIP0LWEraeqfneklUmSJC0SM7WQXdv/XvlIniDJZv0+VlXVEUn2AD4JPAG4DHhZVT2QZEvgY8Azge8BL66qGx7Jc0uSJM0HM93L8v/1YWrfqvqDR/Acr6MLd9v28+8GTq6qTyY5FXg58KH+911VtWeSY/rtXvwInleSJGlemPayF0k2r6oHgeds7M6T7Aq8APhIPx/g54HP9JucCRzVTx/Zz9OvP6TfXpIkaUGbqcvyX4ADgCuSXAB8Glg7sXLIy128D/hDYJt+/gnA3VW1rp+/GVjaTy8Fbur3vS7JPf32dwz3UiRJkuanYS978T26lq2iu33SrJe7SHIEcHtVXZbk4EdY5+B+TwROBNh999031W4lSZKamSmQPTHJG4CreTiITfixb11O4TnAryY5nC7UbQucAizpu0PXAbsCq/rtVwG7ATcn2RzYji4IrqeqTgNOA1i+fPkwdUiSJI21mW6dtBnwuP5nm4HpiZ8ZVdX/rKpdq2oZcAzwhao6Fvgi8KJ+sxXA+f30Bf08/fovVJWBS5IkLXgztZCtrqq3j+A53wh8Msk7ga8Dp/fLTwc+nuR64E66ECdJkrTgzRTINtk3HKvqEuCSfvrbwLOm2OaHdPfJlCRJWlRm6rI8ZM6qkCRJWsSmDWRVdedcFiJJkrRYzdRCJkmSpDlgIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqbGRBbIkuyX5YpJrknwzyev65dsn+fsk3+p/P75fniTvT3J9kiuTHDCq2iRJksbJKFvI1gG/X1V7AwcCr06yN/Am4OKq2gu4uJ8HOAzYq/85EfjQCGuTJEkaGyMLZFW1uqou76fvBa4FlgJHAmf2m50JHNVPHwl8rDpfBZYk2XlU9UmSJI2LORlDlmQZ8AzgUmCnqlrdr7oV2KmfXgrcNPCwm/tlk/d1YpKVSVauWbNmZDVLkiTNlZEHsiSPA/4KeH1VfX9wXVUVUBuyv6o6raqWV9XyHXfccRNWKkmS1MZIA1mSR9OFsbOq6rx+8W0TXZH979v75auA3QYevmu/TJIkaUEb5bcsA5wOXFtV7x1YdQGwop9eAZw/sPy4/tuWBwL3DHRtSpIkLVibj3DfzwFeBlyV5Ip+2ZuBPwHOTfJy4Ebg6H7dRcDhwPXAfcAJI6xNkiRpbIwskFXVl4BMs/qQKbYv4NWjqkeSJGlceaV+SZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqbKwCWZJDk1yX5Pokb2pdjyRJ0lwYm0CWZDPg/wKHAXsDL0myd9uqJEmSRm9sAhnwLOD6qvp2VT0AfBI4snFNkiRJIzdOgWwpcNPA/M39MkmSpAUtVdW6BgCSvAg4tKpe0c+/DHh2Vb1m0nYnAif2s08FrpvTQqe2A3BH6yI0FM/V/OB5mj88V/OD52k8PKmqdpxqxeZzXckMVgG7Dczv2i9bT1WdBpw2V0UNI8nKqlreug7NznM1P3ie5g/P1fzgeRp/49Rl+TVgryR7JNkCOAa4oHFNkiRJIzc2LWRVtS7Ja4C/AzYD/rKqvtm4LEmSpJEbm0AGUFUXARe1rmMjjFUXqmbkuZofPE/zh+dqfvA8jbmxGdQvSZK0WI3TGDJJkqRFyUD2CHirp/kjyQ1JrkpyRZKVrevRw5L8ZZLbk1w9sGz7JH+f5Fv978e3rFHTnqe3JlnVv6+uSHJ4yxrVSbJbki8muSbJN5O8rl/u+2qMGcg2krd6mpd+rqr296vfY+cM4NBJy94EXFxVewEX9/Nq6wx+/DwBnNy/r/bvxwGrvXXA71fV3sCBwKv7zyffV2PMQLbxvNWTtAlU1T8Cd05afCRwZj99JnDUnBalHzPNedIYqqrVVXV5P30vcC3dnW98X40xA9nG81ZP80sBn09yWX+3B423napqdT99K7BTy2I0o9ckubLv0rQLbMwkWQY8A7gU31djzUCmxeKgqjqArov51Ume17ogDae6r4L7dfDx9CHgKcD+wGrgPW3L0aAkjwP+Cnh9VX1/cJ3vq/FjINt4Q93qSeOhqlb1v28HPkvX5azxdVuSnQH637c3rkdTqKrbqurBqnoI+DC+r8ZGkkfThbGzquq8frHvqzFmINt43uppnkjy2CTbTEwDvwRcPfOj1NgFwIp+egVwfsNaNI2JD/fer+H7aiwkCXA6cG1VvXdgle+rMeaFYR+B/ive7+PhWz29q3FJmkKSJ9O1ikF3d4qzPVfjI8k5wMHADsBtwEnAXwPnArsDNwJHV5UDyhua5jwdTNddWcANwCsHxiipkSQHAf8EXAU81C9+M904Mt9XY8pAJkmS1JhdlpIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUzSgpXkB5Pmj0/ywVb1SNJ0DGSStIGSbN66BkkLi4FM0qKUZFmSL/Q3xr44ye798jOSvGhgux/0vw9O8k9JLgCu6e8AcWGSbyS5OsmLG70USQuA/8uTtJA9JskVA/Pb8/Atzj4AnFlVZyb5TeD9wFGz7O8AYJ+q+k6SFwK3VNULAJJst4lrl7SI2EImaSG7v6r2n/gB/nhg3c8AZ/fTHwcOGmJ//1JV3+mnrwJ+Mcm7kzy3qu7ZdGVLWmwMZJK0vnX0/zYmeRSwxcC6tRMTVfVvdC1mVwHvTDIY9iRpgxjIJC1W/wwc008fS3czZuhukv3MfvpXgUdP9eAkuwD3VdUngD+jC2eStFEcQyZpsXot8NEk/wNYA5zQL/8wcH6SbwCfY6BVbJJ9gT9L8hDwH8Bvj7heSQtYqqp1DZIkSYuaXZaSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxv4T8cgeBOZrTbwAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Μέση διάρκεια ταξιδιού ανά day_period***"
      ],
      "metadata": {
        "id": "gHIR_2WxlozQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Average trip duration per day_period\n",
        "avg_trip_durations = df_train.groupby(['pickup_day_period'])['trip_duration'].mean()\n",
        "\n",
        "create_diagram(avg_trip_durations.keys().tolist(), avg_trip_durations.tolist(),\\\n",
        "              \"Average trip duration per day period\",\\\n",
        "              \"Day Periods\", \"Trip Duration\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "CunH7X6KO52K",
        "outputId": "eb9f1c4e-dc07-4c32-dd6e-fc5b419ba2bc"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Μέση διάρκεια ταξιδιού ανά Day of Week***"
      ],
      "metadata": {
        "id": "GScw98sallMZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Average trip duration per day of the week\n",
        "avg_trip_durations = df_train.groupby(['pickup_day_name'])['trip_duration'].mean()\n",
        "\n",
        "create_diagram(avg_trip_durations.keys().tolist(), avg_trip_durations.tolist(),\\\n",
        "              \"Average trip duration per day of the week\",\\\n",
        "              \"Trip Duration\", \"Day of the week\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "9PR87G0OPJNp",
        "outputId": "6413d6a2-ae20-4ca6-93db-d271c7541ae9"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4."
      ],
      "metadata": {
        "id": "vQdA_Cvg11Ap"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Trip Duration Histogram (100 bins)\n",
        "plot_histogram('trip_duration', 'Trip Duration Histogram',\n",
        "               'Number of trips per bin', 100)"
      ],
      "metadata": {
        "id": "FH4upZZoOdV3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "outputId": "d5b8804c-b1a5-48cb-ed03-ba164dcfbc4f"
      },
      "execution_count": 43,
      "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": [
        "### a. Apply log transformation"
      ],
      "metadata": {
        "id": "qE-3xc1wp05Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Apply log transformation in 'trip_duration' column and create a new column\n",
        "df_train['log_trip_duration'] = np.log(df_train['trip_duration']) "
      ],
      "metadata": {
        "id": "X0qyq9QdNYYy"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### b. Plot the log_trip_duration histogram"
      ],
      "metadata": {
        "id": "6TxHzXfdp8GX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Trip Duration Histogram (100 bins)\n",
        "plot_histogram('log_trip_duration', 'Log Trip Duration Histogram',\n",
        "               'Number of trips per bin', 100)"
      ],
      "metadata": {
        "id": "PfwqY6xKN9-o",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "outputId": "4974c88c-0c62-4afb-8338-c774fb0cfc98"
      },
      "execution_count": 45,
      "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": [
        "## **5.** Average trip duration per vendor"
      ],
      "metadata": {
        "id": "-4ZfJ5qP141k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the avg trip duration per vendor\n",
        "avg_trip_durations = df_train.groupby(['vendor_id'])['trip_duration'].mean()\n",
        "\n",
        "create_diagram(['vendor_id: 1', 'vendor_id: 2'], avg_trip_durations.tolist(),\n",
        "               'Average trip duration per vendor','Trip Duration','Vendors')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "aN71M-xerJEP",
        "outputId": "bf930399-7c3d-4951-c2e8-01be4591feb6"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Άρα παρατηρούμε πως ο πρώτος πάροχος (vendor 1) έχει μικρότερη μέση διάρκεια ταξιδιού απο τον δεύτερο πάροχο (vendor 2), αλλά η διαφορά είναι ελάχιστη και δεν θα έχει μεγάλο αντίκτυπο στην πρόβλεψη του χρόνου ταξιδιού, όπως θα δούμε παρακάτω."
      ],
      "metadata": {
        "id": "57VseKNw1PH4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **6.** Trip Duration per number of passengers"
      ],
      "metadata": {
        "id": "iLSE_NQx16-A"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Αρχικά θα αφαιρέσουμε τα δεδομένα όπου το passenger_count = 0 καθώς είναι προφανώς invalid. Δεν μπορεί να υπάρξει ταξίδι χωρίς κανέναν επιβάτη. Υπάρχουν 44 τέτοια rows στο df_train."
      ],
      "metadata": {
        "id": "gg-zIWjS7ddz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop the rows where passenger_count is 0\n",
        "df_train.drop(df_train[df_train['passenger_count'] == 0].index, inplace = True)"
      ],
      "metadata": {
        "id": "AInZRhmRewIQ"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pass_counts = df_train.groupby(['passenger_count'])['trip_duration'].mean()\n",
        "\n",
        "create_diagram(pass_counts.keys().tolist(), pass_counts.tolist(),\n",
        "               'Trip Duration per number of passengers',\n",
        "               'Passengers','Trip Duration')"
      ],
      "metadata": {
        "id": "BnpoS1jfdgcp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "56fa5710-23d3-4964-c28f-b06bf7835bc0"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Όπως φαίνεται και απο το παραπάνω διάγραμμα ο αριθμός των επιβατών δεν επηρεάζει σημαντικά τον χρόνο του ταξιδιού. Αυτό θα επιβεβαιωθεί και στην συνέχεια όπου θα χρησιμοποιήσουμε τον αριθμό των επιβατών για την πρόβλεψη του χρόνου ταξιδιού, χωρίς να έχει σημαντικό αποτέλεσμα."
      ],
      "metadata": {
        "id": "BZzNq8x-giz3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **7.** Manhattan Distance"
      ],
      "metadata": {
        "id": "4AzshwYE17HC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics.pairwise import manhattan_distances"
      ],
      "metadata": {
        "id": "xWQHAaF-hseO"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def find_manhattan_dist(dataframe):\n",
        "\n",
        "  distances = []\n",
        "\n",
        "  for index, row in dataframe.iterrows():\n",
        "      long_x = row['pickup_longitude']\n",
        "      lat_x = row['pickup_latitude']\n",
        "\n",
        "      long_y = row['dropoff_longitude']\n",
        "      lat_y = row['dropoff_latitude']\n",
        "\n",
        "      point1 = [long_x, lat_x]\n",
        "      point2 = [long_y, lat_y]\n",
        "\n",
        "      dist = manhattan_distances([point1], [point2])\n",
        "      distances.append(dist[0][0] * 1609.344)        # convert from miles to km\n",
        "\n",
        "  return distances"
      ],
      "metadata": {
        "id": "fCRhtyKUinZ2"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Add the new column to the train as well as the test dataframe\n",
        "df_train['manhattan_dist'] = find_manhattan_dist(df_train)\n",
        "df_test['manhattan_dist'] = find_manhattan_dist(df_test)"
      ],
      "metadata": {
        "id": "id5jM69vmKLe"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Μέση απόσταση διαδρομής ανά ημέρα της εβδομάδας***"
      ],
      "metadata": {
        "id": "h0BEexwuQxTt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mean_dist = df_train.groupby(['pickup_day_name'])['manhattan_dist'].mean()\n",
        "\n",
        "create_diagram(mean_dist.keys().tolist(), mean_dist.tolist(),\\\n",
        "              \"Average trip distance per day of the week\",\\\n",
        "              \"Distance (meters)\", \"Day of the week\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "E9VCQgIxRDRy",
        "outputId": "56ac1625-0eab-4cdd-c38c-108043e79436"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Άρα παρατηρούμε πως τις Κυριακές η μέση απόσταση διαδρομής είναι η μεγαλύτερη και ακολουθεί η Δευτέρα."
      ],
      "metadata": {
        "id": "VI4UffjZDQ3H"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Απόσταση διαδρομής και χρόνος διαδρομής***\n"
      ],
      "metadata": {
        "id": "mUOOmY5sTZ6h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(df_train.manhattan_dist, df_train.trip_duration, s = 0.3)\n",
        "plt.title(\"Distance and trip duration\")\n",
        "plt.xlabel(\"Manhattan Distance\")\n",
        "plt.ylabel(\"Trip Duration\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "FNDZgkYjUJiq",
        "outputId": "d1b0b04b-ca07-49bc-84cd-d33c7acb9b08"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Trip Duration')"
            ]
          },
          "metadata": {},
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Άρα όπως φαίνεται από το παραπάνω scatter plot υπάρχει άμεση σύνδεση της απόστασης (manhattan distance) και του χρόνου διαδρομής (trip duration). Με άλλα λόγια, η αύξηση της απόστασης έχει ως αποτέλεσμα την αύξηση του χρόνου της διαδρομής."
      ],
      "metadata": {
        "id": "m1MGwRhuUsPB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **8.** Average Speed"
      ],
      "metadata": {
        "id": "3MCZ3za317Vz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def find_avg_speed(column_name):\n",
        "\n",
        "  mean_dist = df_train.groupby([column_name])['manhattan_dist'].mean()\n",
        "  mean_time = df_train.groupby([column_name])['trip_duration'].mean()\n",
        "\n",
        "  avg_speed = mean_dist / mean_time\n",
        "\n",
        "  return avg_speed"
      ],
      "metadata": {
        "id": "P-y-VqNhwriC"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Μέση ταχύτητα και ώρα της ημέρας***"
      ],
      "metadata": {
        "id": "fzwN7IrLgw5B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "avg_speed = find_avg_speed('pickup_hour')\n",
        "\n",
        "create_diagram(avg_speed.keys().tolist(), avg_speed.tolist(),\\\n",
        "              \"Average speed per hour\",\\\n",
        "              \"Hours\", \"Average Speed\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "rmpbHF-siDOH",
        "outputId": "b3f16b25-274d-4d87-bbb7-330ee5aef42f"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Άρα όπως φαίνεται στο παραπάνω διάγραμμα στις ώρες 4:00 - 6:00 AM εντοπίζεται η μεγαλύτερη μέση ταχύτητα. Αυτό είναι αναμενόμενο καθώς εκείνες τις ώρες δεν συνηθίζεται να υπάρχει μποτιλιάρισμα και πολυκοσμία, με αποτέλεσμα το κάθε όχημα που κυκλοφορεί να μπορεί να μετακινηθεί με μεγαλύτερη ταχύτητα."
      ],
      "metadata": {
        "id": "Uv8zx9wHENH5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Μέση ταχύτητα και ημέρα της εβδομάδας***"
      ],
      "metadata": {
        "id": "WNNn89KPghMj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "avg_speed = find_avg_speed('pickup_day_name')\n",
        "\n",
        "create_diagram(avg_speed.keys().tolist(), avg_speed.tolist(),\\\n",
        "              \"Average speed per day of the week\",\\\n",
        "              \"Day of the week\", \"Average Speed\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "W83BdA_Hd7-g",
        "outputId": "8fc82424-d126-4004-a349-aeb7359bdb9b"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ""
      ],
      "metadata": {
        "id": "yCnxrDLyGMHX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Μέση ταχύτητα και μήνας του έτους***"
      ],
      "metadata": {
        "id": "IkBm9ZfmiwYI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "avg_speed = find_avg_speed('pickup_month')\n",
        "\n",
        "create_diagram(avg_speed.keys().tolist(), avg_speed.tolist(),\\\n",
        "              \"Average speed per month\",\\\n",
        "              \"Months\", \"Average Speed\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "sOloWftuizvH",
        "outputId": "52adbf6b-584e-46d3-9bf9-8b1a599a6da3"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Αν και δεν παρατηρείται μεγάλη απόκλιση μεταξύ των μηνών όσον αφορά τη μέση ταχύτητα, ο μήνας που παρατηρείται η μεγαλύτερη μέση ταχύτητα είναι ο Ιανουάριος και ο μήνας που παρατηρείται η μικρότερη μέση ταχύτητα είναι ο Ιούνιος. "
      ],
      "metadata": {
        "id": "mjCvZYeeFldm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **9.** Create scatter plots"
      ],
      "metadata": {
        "id": "ykuxPX371-ma"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Scater plot για τα pickup σημεία***"
      ],
      "metadata": {
        "id": "dwmz5dTzKgK6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the first scatter plot for the pickup points\n",
        "plt.scatter(df_train.pickup_longitude, df_train.pickup_latitude, s = 0.1)\n",
        "plt.title(\"Pickup points scatter plot\")\n",
        "plt.xlabel(\"Longitude\")\n",
        "plt.ylabel(\"Latitude\")"
      ],
      "metadata": {
        "id": "CJdPd44cu17b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "bb18cb24-77fb-4e5b-cdfd-6799ebe7146f"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Latitude')"
            ]
          },
          "metadata": {},
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Scater plot για τα dropoff σημεία***"
      ],
      "metadata": {
        "id": "6aiaHAvzKnxP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the second scatter plot for the dropoff points\n",
        "plt.scatter(df_train.dropoff_longitude, df_train.dropoff_latitude, s = 0.1, c = 'maroon')\n",
        "plt.title(\"Dropoff points scatter plot\")\n",
        "plt.xlabel(\"Longitude\")\n",
        "plt.ylabel(\"Latitude\")"
      ],
      "metadata": {
        "id": "ZgS1lqvNv3-I",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "b9f533b4-a85b-42b7-e6f5-afdf611a920e"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Latitude')"
            ]
          },
          "metadata": {},
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Clustering"
      ],
      "metadata": {
        "id": "0JPtVA202AT9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Μέχρι τώρα χρησιμοποιούσαμε όλο το dataset, αλλά απο εδώ και στο εξής, δηλαδή στο κομμάτι του Clustering, θα χρησιμοποιήσουμε **περίπου το 20%**."
      ],
      "metadata": {
        "id": "0L51MZiT_06v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Keep 50% of the data set\n",
        "LIMIT = 200000\n",
        "df_train = df_train[:LIMIT]"
      ],
      "metadata": {
        "id": "XSnrrGgk_u7w"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1."
      ],
      "metadata": {
        "id": "kiCfMVgC2IxH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans"
      ],
      "metadata": {
        "id": "fTY66qFTCkrE"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_array = df_train[[\"pickup_longitude\",\"pickup_latitude\"]].to_numpy()"
      ],
      "metadata": {
        "id": "ZRXQxVGdH5Xc"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ---------- Elbow Method ----------\n",
        "\n",
        "# Calculate the score for different K-mean values\n",
        "sse = []\n",
        "for k in range(1, 11):\n",
        "  kmeans = KMeans(n_clusters=k)\n",
        "  kmeans.fit(df_array)\n",
        "  sse.append(kmeans.inertia_)"
      ],
      "metadata": {
        "id": "E9AxQVJNT92R"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "markers_on = [12, 17, 18, 19]\n",
        "plt.plot(range(1, 11), sse, '-D', label='line with select markers')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "gYmT9kltVi1R",
        "outputId": "a23e96e0-ad0c-4ec3-8a28-944b802d915b"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Άρα χρησιμοποιώντας την μέθοδο elbow και σύμφωνα με το παραπάνω διάγραμμα μπορούμε να συμπαιράνουμε πως ο βέλτιστος αριθμός κέντρων, είναι ο αριθμός 4. Επομένως θα εκπαιδεύσουμε τον K-means με αριθμό κέντρων (n_clusters) = 4.\n",
        "\n",
        "Επιλέγοντας n_clusters = 4 έχουμε ένα λογικό trade-off μεταξύ πολυπλοκότητας και απόδοσης."
      ],
      "metadata": {
        "id": "m0JM9e2q9HT0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Kmean = KMeans(n_clusters=4)\n",
        "Kmean.fit(df_array)"
      ],
      "metadata": {
        "id": "UQxq-cYyV_Dg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a2cbdd42-28ab-459d-ccb4-ffa9607a2a58"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KMeans(n_clusters=4)"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.rcParams[\"figure.figsize\"] = [7.00, 3.50]\n",
        "plt.rcParams[\"figure.autolayout\"] = True\n",
        "\n",
        "fig = plt.figure()\n",
        "ax = fig.add_subplot(111)\n",
        "\n",
        "scatter = ax.scatter(df_train.pickup_longitude, df_train.pickup_latitude,\n",
        "                     c=Kmean.labels_, s=1, alpha = 0.5)\n",
        "\n",
        "for i, j in Kmean.cluster_centers_:\n",
        "   ax.scatter(i, j, s=70, c='red', marker='+')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "id": "F6uN89g-L-Tk",
        "outputId": "58dc8302-f4ea-4075-8f65-92969ba47fc8"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x252 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Στο παραπάνω διάγραμμα φαίνονται τα σημεία του df_train με χρώμα που αντιστοιχεί στο cluster που ανήκουν (χρησιμοποιώντας το 20% των data καθώς και 4 clusters).\n",
        "\n",
        "Επίσης φαίνονται με κόκκινο χρώμα και τα κεντροειδή των τεσσάρων clusters."
      ],
      "metadata": {
        "id": "oOryg1UrMZU5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2."
      ],
      "metadata": {
        "id": "iYOhBiOl2I_m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn import preprocessing\n",
        "from sklearn.metrics import r2_score"
      ],
      "metadata": {
        "id": "1eiYfVD1O2sX"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "number = preprocessing.LabelEncoder()\n",
        "\n",
        "df_train['pickup_day_period'] = number.fit_transform(df_train.pickup_day_period)\n",
        "df_test['pickup_day_period'] = number.fit_transform(df_test.pickup_day_period)\n",
        "\n",
        "df_train['pickup_day_name'] = number.fit_transform(df_train.pickup_day_name)\n",
        "df_test['pickup_day_name'] = number.fit_transform(df_test.pickup_day_name)"
      ],
      "metadata": {
        "id": "D9zpb6HEWpFG"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "important_features = ['vendor_id', 'passenger_count' ,'pickup_longitude', \n",
        "                      'pickup_latitude', 'dropoff_latitude' ,'dropoff_longitude',\n",
        "                      'pickup_day_period', 'pickup_day_name',\n",
        "                      'pickup_hour', 'manhattan_dist']\n",
        "\n",
        "# create regressor object\n",
        "regressor = RandomForestRegressor()\n",
        "\n",
        "# fit the regressor\n",
        "regressor.fit(df_train[important_features], df_train['log_trip_duration']) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KWw015-bOrnQ",
        "outputId": "e5662f87-fcd6-4d63-ea7b-8f95dd4f791e"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor()"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# test the output by changing values\n",
        "Y_pred = regressor.predict(df_test[important_features])"
      ],
      "metadata": {
        "id": "oTXyZQ_DOsp3"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Apply log transformation in 'trip_duration' column on target_csv\n",
        "df_target['log_trip_duration'] = np.log(df_target['trip_duration'])\n",
        "\n",
        "r2_score(df_target['log_trip_duration'], Y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6jyWhAY-aOPq",
        "outputId": "8d37f94a-764a-4ea1-fff0-755c4ecf400e"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7080316119888292"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the important features graph\n",
        "importances = regressor.feature_importances_\n",
        "\n",
        "create_diagram(important_features,  importances.tolist(),\n",
        "              \"Important Features\",\n",
        "              \"Features\", \"Importance\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "HN-A8cwjMe29",
        "outputId": "461b664a-271b-4afa-f5d7-0b4f0db7e538"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Όπως φαίνεται και στο παραπάνω διάγραμμα το πιο σημαντικό χαρακτηριστικό είναι με διαφορά η manhattan_distance εφόσον η απόσταση της διαδρομής σχετίζεται άμεσα με τον χρόνο που θα χρειαστεί.\n",
        "\n",
        "Στη συνέχεια ακολουθούν τα: pickup_longitude, pickup_latitude, dropoff_longitude, dropoff_latitude καθώς και τα pickup_day_name και pickup_hour."
      ],
      "metadata": {
        "id": "0OOu-zyf9fhq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "important_features = ['pickup_longitude', \n",
        "                      'pickup_latitude', 'dropoff_latitude' ,'dropoff_longitude',\n",
        "                      'pickup_day_name',\n",
        "                      'pickup_hour', 'manhattan_dist']"
      ],
      "metadata": {
        "id": "w8z3b5KdXZxY"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3."
      ],
      "metadata": {
        "id": "QFZ4pruM2JHd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV"
      ],
      "metadata": {
        "id": "RT1NnDk568IW"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "param_grid = {'max_depth': [15, None], \n",
        "              'max_features': ['auto', 'log2'], \n",
        "              'n_estimators': [100, 150]}\n",
        "\n",
        "rfr = RandomForestRegressor(random_state = 1)\n",
        "\n",
        "g_search = GridSearchCV(estimator = rfr,\n",
        "param_grid = param_grid, cv = 3, n_jobs = 1, verbose = 0)"
      ],
      "metadata": {
        "id": "7--eLugA71Z8"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "g_search.fit(df_train[important_features], df_train['log_trip_duration'])"
      ],
      "metadata": {
        "id": "Yd7nadYG8CyE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9f3cd1be-ccb1-4920-ab24-e827129e364c"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=3, estimator=RandomForestRegressor(random_state=1), n_jobs=1,\n",
              "             param_grid={'max_depth': [15, None],\n",
              "                         'max_features': ['auto', 'log2'],\n",
              "                         'n_estimators': [100, 150]})"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "g_search.best_params_"
      ],
      "metadata": {
        "id": "JcgkZ0Xn7TQI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "81def6ba-19d6-4cef-d7c6-90094a31ab9a"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'max_depth': None, 'max_features': 'log2', 'n_estimators': 150}"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Έτσι συμπαιρένουμε πως οι βέλτιστες παραμέτροι είναι: max_depth = None, max_features = log2 και n_estimators = 150. "
      ],
      "metadata": {
        "id": "IqCKhras0wrA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4."
      ],
      "metadata": {
        "id": "WuhEdDB02JM4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Στη συνέχεια εκπαιδεύουμε τον RandomForestRegressor με τις νέες παραμέτρους και υπολογίζουμε ξανά το νέο score."
      ],
      "metadata": {
        "id": "pRQsGBysb9Hw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# create regressor object\n",
        "regressor = RandomForestRegressor(max_depth= None, max_features = 'log2', n_estimators= 150, random_state=1)\n",
        "\n",
        "# fit the regressor\n",
        "regressor.fit(df_train[important_features], df_train['log_trip_duration']) "
      ],
      "metadata": {
        "id": "QlF8bf9ieueZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cf76360d-aff1-465c-f2d9-7617817f619b"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(max_features='log2', n_estimators=150, random_state=1)"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# test the output by changing values\n",
        "Y_pred = regressor.predict(df_test[important_features])"
      ],
      "metadata": {
        "id": "fzSNdNGhhFB-"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "score = r2_score(df_target['log_trip_duration'],Y_pred)"
      ],
      "metadata": {
        "id": "a_t70x-whFhq"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4H3C9Uga1NMt",
        "outputId": "449605ad-7e0e-493e-8543-b5e382a27649"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7113417696356481\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Άρα όπως φαίνεται παραπάνω το score αυξήθηκε από 0.70770 σε 0.71134 με την χρήση της GridSearchCV για την εύρεση των βέλτιστων παραμέτρων."
      ],
      "metadata": {
        "id": "lw24Laj1cL1_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5."
      ],
      "metadata": {
        "id": "eSAG_tqV2MIj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Στο βήμα αυτό θα υπολογίσουμε την απόδοση χρησιμοποιώντας τις μετρικές ΜΑE (Mean Absolute Error) και MSE (Mean Squared Error)."
      ],
      "metadata": {
        "id": "VODWUZTIlIYH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.metrics import mean_absolute_error"
      ],
      "metadata": {
        "id": "4XOqP6c6dPn3"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mae = mean_absolute_error(df_target['log_trip_duration'], Y_pred)\n",
        "mse = mean_squared_error(df_target['log_trip_duration'], Y_pred)"
      ],
      "metadata": {
        "id": "YRjMPziDcu-4"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('MAE is: ' , mae)\n",
        "print('MSE is: ', mse)\n",
        "print('Score is:', score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G0qvmoujkX0_",
        "outputId": "e24846a9-41b6-4143-9a43-766997c3911b"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE is:  0.2740423504489232\n",
            "MSE is:  0.18523666588117108\n",
            "Score is: 0.7113417696356481\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Bonus"
      ],
      "metadata": {
        "id": "zNo6oCPyxBIz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1."
      ],
      "metadata": {
        "id": "ylhFualvxGkc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import folium"
      ],
      "metadata": {
        "id": "_flO8KV0xGH7"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_list = df_train[['pickup_longitude','pickup_latitude']].values.tolist()\n",
        "\n",
        "map = folium.Map(location=df_list[0])\n",
        "for cord in df_list:\n",
        "  folium.Marker(location=[cord[0], cord[1]]).add_to(map)"
      ],
      "metadata": {
        "id": "YPl4LcSKxCK4"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "map.save('map.html')"
      ],
      "metadata": {
        "id": "z43lkuYkyMoK"
      },
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2"
      ],
      "metadata": {
        "id": "aDvz9E9sy-8k"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### a."
      ],
      "metadata": {
        "id": "-B3skW5czD0o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "vendor_counts = df_train['vendor_id'].value_counts()\n",
        "\n",
        "create_diagram(['vendor 2', 'vendor 1'], vendor_counts.tolist(),\\\n",
        "              \"Most common vendor\",\\\n",
        "              \"Vendors\", \"Number of occurrences\")"
      ],
      "metadata": {
        "id": "6VnnXIZ30ICV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "outputId": "5852f659-dd60-46a9-b007-fcfc89c68b7b"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vendor_counts"
      ],
      "metadata": {
        "id": "P3HV5IoA1o2z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e44ebe34-a11a-4cd2-8ce1-dd0064e934a2"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    106898\n",
              "1     93102\n",
              "Name: vendor_id, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Άρα ο πιο συχνός vendor ανάμεσα στους δύο είναι ο vendor 2 με αριθμό εμφανίσεων 106898 στα 93102 data samples (εφόσον έχουμε ορίσει LIMIT = 200000)"
      ],
      "metadata": {
        "id": "NRa4CgyL1p2Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "traffic_congestion = df_train['pickup_hour'].value_counts()\n",
        "\n",
        "create_diagram(traffic_congestion.keys().tolist(), traffic_congestion.tolist(),\\\n",
        "              \"Traffic congestion hours\",\\\n",
        "              \"Hours\", \"Number of occurrences\")"
      ],
      "metadata": {
        "id": "kbO45ojq2a5V",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "outputId": "8cdc272c-9021-415b-ad98-bd07e3a4a704"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Όπως φαίνεται και απο το διάγραμμα οι ώρες που παρατηρείται traffic congestion είναι κυρίως 18:00 - 20:00. "
      ],
      "metadata": {
        "id": "pf7LRWn57VsF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### b."
      ],
      "metadata": {
        "id": "HhpV_tN07zsu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Αρχικά θα προσθέσουμε ένα feature που θα δείχνει αν η διαδρομή έγινε ΣΚ ή όχι και θα δούμε αν υπάρχει κάποια βελτίωση στο μοντέλο."
      ],
      "metadata": {
        "id": "-XZKBjIZ8Pss"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def add_is_weekend(df):\n",
        "\n",
        "  day_names = df['pickup_day_name']\n",
        "\n",
        "  conditions = [\n",
        "      ((day_names == 'Saturday') | (day_names == 'Sunday')),    # is weekend\n",
        "      ((day_names != 'Saturday') & (day_names != 'Sunday'))     # is not weekend\n",
        "      ]\n",
        "\n",
        "  values = ['Yes', 'No']\n",
        "\n",
        "  df['is_weekend'] = np.select(conditions, values)\n",
        "\n",
        "  return df"
      ],
      "metadata": {
        "id": "wSJl_ZtP9GEU"
      },
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = add_is_weekend(df_train)\n",
        "df_test = add_is_weekend(df_test)"
      ],
      "metadata": {
        "id": "TrXaworwm3Lh"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train['is_weekend'] = number.fit_transform(df_train.is_weekend)\n",
        "df_test['is_weekend'] = number.fit_transform(df_test.is_weekend)"
      ],
      "metadata": {
        "id": "06OBL2aa2jbs"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "important_features = ['pickup_longitude', \n",
        "                      'pickup_latitude', 'dropoff_latitude' ,'dropoff_longitude',\n",
        "                      'is_weekend',\n",
        "                      'pickup_hour', 'manhattan_dist']"
      ],
      "metadata": {
        "id": "MCjmR0gl2eL1"
      },
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# create regressor object\n",
        "regressor = RandomForestRegressor(max_depth= None, max_features = 'log2', n_estimators= 150, random_state=1)\n",
        "\n",
        "# fit the regressor\n",
        "regressor.fit(df_train[important_features], df_train['log_trip_duration'])\n",
        "\n",
        "# test the output by changing values\n",
        "Y_pred = regressor.predict(df_test[important_features])\n",
        "\n",
        "score = r2_score(df_target['log_trip_duration'],Y_pred)"
      ],
      "metadata": {
        "id": "lNvmf_Fs2QqH"
      },
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "--sksTwC2uWD"
      },
      "execution_count": 95,
      "outputs": []
    }
  ]
}