Bikes_rental_analysis(1).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Bikes_rental_analysis.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1X3i4EXgzKgV"
      },
      "source": [
        "# Used libraries\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wIWdRE0IZH2Q"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import graphviz\n",
        "from sklearn.tree import DecisionTreeRegressor, export_graphviz\n",
        "from sklearn.model_selection import train_test_split, validation_curve, GridSearchCV\n",
        "from sklearn.linear_model import ElasticNet, Lasso, Ridge\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import r2_score, mean_squared_log_error\n",
        "import numpy as np\n",
        "from scipy.stats import randint\n",
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m3N_0JN2zUMR"
      },
      "source": [
        "# Data analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qrLRLOxxZR1L"
      },
      "source": [
        "df = pd.read_csv('/home/marcin/Documents/Project Wojtek/Bike-Sharing-Dataset/hour.csv',\n",
        "                      encoding = \"ISO-8859-1\", parse_dates=[\"dteday\"])"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y9SdI7XO9fVV"
      },
      "source": [
        "## Data description"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "vYAf5JMiT_Rm",
        "outputId": "2288ef86-f8b9-4ae4-ec54-987df8bd1c11"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          instant        season            yr          mnth            hr  \\\n",
              "count  17379.0000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
              "mean    8690.0000      2.501640      0.502561      6.537775     11.546752   \n",
              "std     5017.0295      1.106918      0.500008      3.438776      6.914405   \n",
              "min        1.0000      1.000000      0.000000      1.000000      0.000000   \n",
              "25%     4345.5000      2.000000      0.000000      4.000000      6.000000   \n",
              "50%     8690.0000      3.000000      1.000000      7.000000     12.000000   \n",
              "75%    13034.5000      3.000000      1.000000     10.000000     18.000000   \n",
              "max    17379.0000      4.000000      1.000000     12.000000     23.000000   \n",
              "\n",
              "            holiday       weekday    workingday    weathersit          temp  \\\n",
              "count  17379.000000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
              "mean       0.028770      3.003683      0.682721      1.425283      0.496987   \n",
              "std        0.167165      2.005771      0.465431      0.639357      0.192556   \n",
              "min        0.000000      0.000000      0.000000      1.000000      0.020000   \n",
              "25%        0.000000      1.000000      0.000000      1.000000      0.340000   \n",
              "50%        0.000000      3.000000      1.000000      1.000000      0.500000   \n",
              "75%        0.000000      5.000000      1.000000      2.000000      0.660000   \n",
              "max        1.000000      6.000000      1.000000      4.000000      1.000000   \n",
              "\n",
              "              atemp           hum     windspeed        casual    registered  \\\n",
              "count  17379.000000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
              "mean       0.475775      0.627229      0.190098     35.676218    153.786869   \n",
              "std        0.171850      0.192930      0.122340     49.305030    151.357286   \n",
              "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "25%        0.333300      0.480000      0.104500      4.000000     34.000000   \n",
              "50%        0.484800      0.630000      0.194000     17.000000    115.000000   \n",
              "75%        0.621200      0.780000      0.253700     48.000000    220.000000   \n",
              "max        1.000000      1.000000      0.850700    367.000000    886.000000   \n",
              "\n",
              "                cnt  \n",
              "count  17379.000000  \n",
              "mean     189.463088  \n",
              "std      181.387599  \n",
              "min        1.000000  \n",
              "25%       40.000000  \n",
              "50%      142.000000  \n",
              "75%      281.000000  \n",
              "max      977.000000  "
            ],
            "text/html": [
              "<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>instant</th>\n",
              "      <th>season</th>\n",
              "      <th>yr</th>\n",
              "      <th>mnth</th>\n",
              "      <th>hr</th>\n",
              "      <th>holiday</th>\n",
              "      <th>weekday</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weathersit</th>\n",
              "      <th>temp</th>\n",
              "      <th>atemp</th>\n",
              "      <th>hum</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>casual</th>\n",
              "      <th>registered</th>\n",
              "      <th>cnt</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>17379.0000</td>\n",
              "      <td>17379.000000</td>\n",
              "      <td>17379.000000</td>\n",
              "      <td>17379.000000</td>\n",
              "      <td>17379.000000</td>\n",
              "      <td>17379.000000</td>\n",
              "      <td>17379.000000</td>\n",
              "      <td>17379.000000</td>\n",
              "      <td>17379.000000</td>\n",
              "      <td>17379.000000</td>\n",
              "      <td>17379.000000</td>\n",
              "      <td>17379.000000</td>\n",
              "      <td>17379.000000</td>\n",
              "      <td>17379.000000</td>\n",
              "      <td>17379.000000</td>\n",
              "      <td>17379.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>8690.0000</td>\n",
              "      <td>2.501640</td>\n",
              "      <td>0.502561</td>\n",
              "      <td>6.537775</td>\n",
              "      <td>11.546752</td>\n",
              "      <td>0.028770</td>\n",
              "      <td>3.003683</td>\n",
              "      <td>0.682721</td>\n",
              "      <td>1.425283</td>\n",
              "      <td>0.496987</td>\n",
              "      <td>0.475775</td>\n",
              "      <td>0.627229</td>\n",
              "      <td>0.190098</td>\n",
              "      <td>35.676218</td>\n",
              "      <td>153.786869</td>\n",
              "      <td>189.463088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5017.0295</td>\n",
              "      <td>1.106918</td>\n",
              "      <td>0.500008</td>\n",
              "      <td>3.438776</td>\n",
              "      <td>6.914405</td>\n",
              "      <td>0.167165</td>\n",
              "      <td>2.005771</td>\n",
              "      <td>0.465431</td>\n",
              "      <td>0.639357</td>\n",
              "      <td>0.192556</td>\n",
              "      <td>0.171850</td>\n",
              "      <td>0.192930</td>\n",
              "      <td>0.122340</td>\n",
              "      <td>49.305030</td>\n",
              "      <td>151.357286</td>\n",
              "      <td>181.387599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.020000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>4345.5000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.340000</td>\n",
              "      <td>0.333300</td>\n",
              "      <td>0.480000</td>\n",
              "      <td>0.104500</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>40.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>8690.0000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.484800</td>\n",
              "      <td>0.630000</td>\n",
              "      <td>0.194000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>115.000000</td>\n",
              "      <td>142.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>13034.5000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.660000</td>\n",
              "      <td>0.621200</td>\n",
              "      <td>0.780000</td>\n",
              "      <td>0.253700</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>220.000000</td>\n",
              "      <td>281.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>17379.0000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.850700</td>\n",
              "      <td>367.000000</td>\n",
              "      <td>886.000000</td>\n",
              "      <td>977.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UTtw7Fn39tGh"
      },
      "source": [
        "## Rented bikes per annum"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XLlLxk-qR0aU"
      },
      "source": [
        "year_2011 = df.loc[df['yr']==0].sum(axis=0)\n",
        "year_2012 = df.loc[df['yr']==1].sum(axis=0)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "xOVPpK9uVTCj",
        "outputId": "d2f18986-92a1-4314-e0ea-86e35b6fcd64"
      },
      "source": [
        "df_year = df.groupby(['yr'])['casual', 'registered', 'cnt'].sum()\n",
        "\n",
        "df_year = df_year.rename(index={0: '2011', 1: '2012'})\n",
        "\n",
        "df_year[['casual', 'registered', 'cnt']].plot(kind='bar')\n",
        "\n",
        "df_year.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/home/marcin/anaconda3/envs/tensorflow/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      casual  registered      cnt\n",
              "yr                               \n",
              "2011  247252      995851  1243103\n",
              "2012  372765     1676811  2049576"
            ],
            "text/html": [
              "<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>casual</th>\n",
              "      <th>registered</th>\n",
              "      <th>cnt</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>yr</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2011</th>\n",
              "      <td>247252</td>\n",
              "      <td>995851</td>\n",
              "      <td>1243103</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012</th>\n",
              "      <td>372765</td>\n",
              "      <td>1676811</td>\n",
              "      <td>2049576</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pEryRz59oHUW"
      },
      "source": [
        "It is easy to notice increase in demand, what should be accounted for in the predicitve model for the future."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o9-xM_6V-Jfz"
      },
      "source": [
        "## Rented bikes per season"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 719
        },
        "id": "tZ7K4kvRbwdR",
        "outputId": "cd89be88-05c8-4a41-f5e4-60a4819e429e"
      },
      "source": [
        "df_seasons = df.groupby(['yr', 'season'])['casual', 'registered', 'cnt'].sum()\n",
        "\n",
        "df_seasons = df_seasons.rename(index={0: 2011, 1: 2012}, level=0)\n",
        "df_seasons = df_seasons.rename(index={1: 'spring', 2: 'summer', 3: 'fall', 4: 'winter'}, level=1)\n",
        "\n",
        "\n",
        "df_seasons[['casual', 'registered', 'cnt']].plot(kind='bar')\n",
        "\n",
        "df_seasons"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/home/marcin/anaconda3/envs/tensorflow/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             casual  registered     cnt\n",
              "yr   season                            \n",
              "2011 spring   21425      128575  150000\n",
              "     summer   77564      269752  347316\n",
              "     fall     95450      324200  419650\n",
              "     winter   52813      273324  326137\n",
              "2012 spring   39197      282151  321348\n",
              "     summer  125958      445315  571273\n",
              "     fall    130641      510838  641479\n",
              "     winter   76969      438507  515476"
            ],
            "text/html": [
              "<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></th>\n",
              "      <th>casual</th>\n",
              "      <th>registered</th>\n",
              "      <th>cnt</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>yr</th>\n",
              "      <th>season</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">2011</th>\n",
              "      <th>spring</th>\n",
              "      <td>21425</td>\n",
              "      <td>128575</td>\n",
              "      <td>150000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>summer</th>\n",
              "      <td>77564</td>\n",
              "      <td>269752</td>\n",
              "      <td>347316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fall</th>\n",
              "      <td>95450</td>\n",
              "      <td>324200</td>\n",
              "      <td>419650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>winter</th>\n",
              "      <td>52813</td>\n",
              "      <td>273324</td>\n",
              "      <td>326137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">2012</th>\n",
              "      <th>spring</th>\n",
              "      <td>39197</td>\n",
              "      <td>282151</td>\n",
              "      <td>321348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>summer</th>\n",
              "      <td>125958</td>\n",
              "      <td>445315</td>\n",
              "      <td>571273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fall</th>\n",
              "      <td>130641</td>\n",
              "      <td>510838</td>\n",
              "      <td>641479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>winter</th>\n",
              "      <td>76969</td>\n",
              "      <td>438507</td>\n",
              "      <td>515476</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sVCL6-js-Nav"
      },
      "source": [
        "## Rented bikes count depending on weather condition"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Az63qgb82x0o"
      },
      "source": [
        "df_unnormalized = df.copy()\n",
        "df_unnormalized['temp'] = df_unnormalized['temp'].apply(lambda x: x * 41)\n",
        "df_unnormalized['atemp'] = df_unnormalized['atemp'].apply(lambda x: x * 50)\n",
        "df_unnormalized['hum'] = df_unnormalized['hum'].apply(lambda x: x * 100)\n",
        "df_unnormalized['windspeed'] = df_unnormalized['windspeed'].apply(lambda x: x * 67)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "id": "f42hqows10-v",
        "outputId": "e0b20724-ba73-46f7-8e3e-78d81f7a7f23"
      },
      "source": [
        "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(12, 10))\n",
        "\n",
        "ax1 = df_unnormalized.plot.hexbin(x='temp', y='cnt', gridsize=15, sharex=False,\n",
        "                                  ax=axes[0, 0])\n",
        "ax2 = df_unnormalized.plot.hexbin(x='atemp', y='cnt', gridsize=15, sharex=False,\n",
        "                                  ax=axes[0, 1])\n",
        "ax3 = df_unnormalized.plot.hexbin(x='hum', y='cnt', gridsize=15, sharex=False,\n",
        "                                  ax=axes[1, 0])\n",
        "ax4 = df_unnormalized.plot.hexbin(x='windspeed', y='cnt', gridsize=15, sharex=False,\n",
        "                                  ax=axes[1, 1])"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 8 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "id": "nfO72-oUqa9A",
        "outputId": "9d6ef485-c320-45d1-c0c6-9c790ede07e2"
      },
      "source": [
        "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(12, 10))\n",
        "\n",
        "ax1 = df_unnormalized.plot.hexbin(x='temp', y='casual', gridsize=15, sharex=False,\n",
        "                                  ax=axes[0, 0])\n",
        "ax2 = df_unnormalized.plot.hexbin(x='atemp', y='casual', gridsize=15, sharex=False,\n",
        "                                  ax=axes[0, 1])\n",
        "ax3 = df_unnormalized.plot.hexbin(x='hum', y='casual', gridsize=15, sharex=False,\n",
        "                                  ax=axes[1, 0])\n",
        "ax4 = df_unnormalized.plot.hexbin(x='windspeed', y='casual', gridsize=15, sharex=False,\n",
        "                                  ax=axes[1, 1])"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 8 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "id": "qVhcysvxTfXv",
        "outputId": "86847f2c-e308-40e7-de40-34c0a38df6e7"
      },
      "source": [
        "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 5))\n",
        "\n",
        "ax1 = df.groupby(['workingday'])[['casual', 'registered', 'cnt']].apply(sum).plot.bar(\n",
        "    ax=axes[0])\n",
        "ax1 = df.groupby(['weekday'])[['casual', 'registered', 'cnt']].apply(sum).plot(\n",
        "    ax=axes[1])"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WzW32WdeUVB6"
      },
      "source": [
        "# Mitigating the influence of differences in demand over years"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1ZlYRWzwaulR"
      },
      "source": [
        "For mitigating the increase in demand, I will calculate \n",
        "\n",
        "We possibly can assume linear increase in demand. To be certain, it is advised to get more data from years predate the 2011. At least three points are required to determine linearinty or nonlinearity. \n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "UV1rzc46AXla",
        "outputId": "2a0a75c5-7b8d-470b-968b-42af71b93218"
      },
      "source": [
        "df_seasons"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             casual  registered     cnt\n",
              "yr   season                            \n",
              "2011 spring   21425      128575  150000\n",
              "     summer   77564      269752  347316\n",
              "     fall     95450      324200  419650\n",
              "     winter   52813      273324  326137\n",
              "2012 spring   39197      282151  321348\n",
              "     summer  125958      445315  571273\n",
              "     fall    130641      510838  641479\n",
              "     winter   76969      438507  515476"
            ],
            "text/html": [
              "<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></th>\n",
              "      <th>casual</th>\n",
              "      <th>registered</th>\n",
              "      <th>cnt</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>yr</th>\n",
              "      <th>season</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">2011</th>\n",
              "      <th>spring</th>\n",
              "      <td>21425</td>\n",
              "      <td>128575</td>\n",
              "      <td>150000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>summer</th>\n",
              "      <td>77564</td>\n",
              "      <td>269752</td>\n",
              "      <td>347316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fall</th>\n",
              "      <td>95450</td>\n",
              "      <td>324200</td>\n",
              "      <td>419650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>winter</th>\n",
              "      <td>52813</td>\n",
              "      <td>273324</td>\n",
              "      <td>326137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">2012</th>\n",
              "      <th>spring</th>\n",
              "      <td>39197</td>\n",
              "      <td>282151</td>\n",
              "      <td>321348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>summer</th>\n",
              "      <td>125958</td>\n",
              "      <td>445315</td>\n",
              "      <td>571273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fall</th>\n",
              "      <td>130641</td>\n",
              "      <td>510838</td>\n",
              "      <td>641479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>winter</th>\n",
              "      <td>76969</td>\n",
              "      <td>438507</td>\n",
              "      <td>515476</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Noo0hDeWZ84U"
      },
      "source": [
        "df_2013 = df.copy()"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5r2DDymGDibI"
      },
      "source": [
        "# df_2013.loc[(df_2013['season']==2) & (df_2013['yr']==1)]['cnt']"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mXUAz43v_Kkh",
        "outputId": "ae9c2d59-50bc-4a39-e999-cc1cce6e5839"
      },
      "source": [
        "seasons = ['spring', 'summer', 'fall', 'winter']\n",
        "i = 1\n",
        "\n",
        "for season in seasons:\n",
        "\n",
        "  rented_bikes_in_2012 = df_seasons.loc[2012, season]['cnt'] \n",
        "  rented_bikes_in_2011 = df_seasons.loc[2011, season]['cnt']\n",
        "\n",
        "  ratio_season = rented_bikes_in_2012 / rented_bikes_in_2011\n",
        "  print('Ratio', season, '=', ratio_season)\n",
        "\n",
        "  df_2013.loc[(df_2013['season']==i) & (df_2013['yr']==1), 'cnt'] = round(df_2013['cnt'] * ratio_season)\n",
        "  \n",
        "  df_2013.loc[(df_2013['season']==i) & (df_2013['yr']==0), 'cnt'] = round(df_2013['cnt'] * (ratio_season ** 2))\n",
        "  i += 1"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Ratio spring = 2.14232\n",
            "Ratio summer = 1.6448220064724919\n",
            "Ratio fall = 1.5286047897057071\n",
            "Ratio winter = 1.5805505048491892\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x5zgahwZigIp"
      },
      "source": [
        "# df_2013['dteday'] = df_2013['dteday'].dt.dayofyear"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nHlI9us4mDcA"
      },
      "source": [
        "# df_2013.groupby(['dteday', 'hr']).head()"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dMEm2bOHmQ98"
      },
      "source": [
        "# df_2013"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UEbxcgbGeNML",
        "outputId": "4f33dd41-533c-4901-e2f9-ad93d27273b2"
      },
      "source": [
        "df_2013['yr'].replace([0, 1], 2)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        2\n",
              "1        2\n",
              "2        2\n",
              "3        2\n",
              "4        2\n",
              "        ..\n",
              "17374    2\n",
              "17375    2\n",
              "17376    2\n",
              "17377    2\n",
              "17378    2\n",
              "Name: yr, Length: 17379, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A_q0wBuGZYdt"
      },
      "source": [
        "# df_seasons.cnt.to_numpy()"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GlsmRjeFaJ9-"
      },
      "source": [
        "# df_2013.groupby(['season'])['cnt'].sum().divide(2).to_numpy()"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WTGQkFbNiwl3",
        "outputId": "7a356049-f86f-47be-97bf-f2e040b2f590"
      },
      "source": [
        "seasons_years = 3*seasons\n",
        "starting_year = 2011\n",
        "\n",
        "for i in range(1, len(seasons_years) + 1):\n",
        "  seasons_years[i - 1] = seasons_years[i - 1] + ' ' + str(starting_year)\n",
        "  if i % 4 == 0:\n",
        "    starting_year += 1\n",
        "\n",
        "seasons_years"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['spring 2011',\n",
              " 'summer 2011',\n",
              " 'fall 2011',\n",
              " 'winter 2011',\n",
              " 'spring 2012',\n",
              " 'summer 2012',\n",
              " 'fall 2012',\n",
              " 'winter 2012',\n",
              " 'spring 2013',\n",
              " 'summer 2013',\n",
              " 'fall 2013',\n",
              " 'winter 2013']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "FC27PDRPanPL",
        "outputId": "fab3a741-4045-400d-c1ee-71a21aa66770"
      },
      "source": [
        "numpy_11_12 = df_seasons.cnt.to_numpy()\n",
        "numpy_13 = df_2013.groupby(['season'])['cnt'].sum().divide(2).to_numpy()\n",
        "\n",
        "concenated = np.concatenate((numpy_11_12, numpy_13))\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(seasons_years, concenated)\n",
        "plt.xticks(rotation = 45)\n",
        "plt.title('Quantity of rented bicycles')"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Quantity of rented bicycles')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uNs4r4Lq-O9k"
      },
      "source": [
        "  With these changes, dataset for 2013 contains interpolated data twice as large as for previous years.\n",
        "  \n",
        "  Conluding, each hour of each day in 2013 now has two samples under different weather conditions and algorithm can benefit from it.\n",
        "\n",
        "  Since algorithm won't use information what particular day of the year the sample is from, but only month/weekday/hour will matter, there is no problem with doubling samples. \n",
        "\n",
        "  Another approach would be calculating mean for each month/day/hour from those two years. \n",
        " "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UQ7O1zhlrt7s"
      },
      "source": [
        "# Funtion's declarations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_NQbr2wdZR8I"
      },
      "source": [
        "def get_data_for_validation_curve(model, X_train, y_train, depths_range, cv=5):\n",
        "    \n",
        "    train_score, valid_score = validation_curve(model,\n",
        "                                                 X_train, y_train,\n",
        "                                                 param_name='max_depth',\n",
        "                                                 param_range=depths_range,\n",
        "                                                 cv=cv)\n",
        "    return train_score, valid_score"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m5mBxwhfZR51"
      },
      "source": [
        "def plot_validation_curve(range_depths, y_train, y_cvs, best_depth, title='Score', ):\n",
        "    plt.figure(figsize=(12, 7))\n",
        "    plt.plot(range_depths, y_train)\n",
        "    plt.plot(range_depths, y_cvs)\n",
        "    plt.plot(best_depth, y_cvs[best_depth - range_depths[0]], marker='o', color='black')\n",
        "    plt.title(title)\n",
        "    plt.xlabel('Depth')\n",
        "    plt.ylabel('Score (max=1)')\n",
        "    plt.legend(['Train', 'Cross-validation', \n",
        "                'Best score = {:.03f}'.format(y_cvs[best_depth - range_depths[0]])])\n",
        "    plt.xticks(range_depths)\n",
        "    plt.grid()\n",
        "    plt.show()\n",
        "    "
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ct86iZsSZR3e"
      },
      "source": [
        "def get_final_tree_and_score(tree_model, X_train, X_test, y_train, y_test):\n",
        "    regressor_tree = tree_model\n",
        "    regressor_tree.fit(X_train, y_train)\n",
        "    final_score = regressor_tree.score(X_test, y_test)\n",
        "    return regressor_tree, final_score\n",
        "        "
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8nUY-bffZjVH"
      },
      "source": [
        "def get_bestDepth_scores_plot(trainScores, validScores, depth_range):\n",
        "    train_scores_mean = []\n",
        "    valid_scores_mean = []\n",
        "    \n",
        "    for i in range(len(depth_range)):\n",
        "      train_scores_mean.append(np.mean(trainScores[i]))\n",
        "      valid_scores_mean.append(np.mean(validScores[i]))\n",
        "\n",
        "    best_depth = depth_range[0]\n",
        "\n",
        "    for i in range(len(valid_scores_mean) - 1):\n",
        "      if valid_scores_mean[i + 1] - valid_scores_mean[i] > 0.001:\n",
        "        best_depth = depth_range[i + 1]\n",
        "      else:\n",
        "        break\n",
        "\n",
        "    # best_depth = depth_range[np.argmax(valid_scores_mean)]\n",
        "    \n",
        "    plot_validation_curve(depth_range, train_scores_mean, valid_scores_mean, best_depth)\n",
        "    \n",
        "    return best_depth, train_scores_mean, valid_scores_mean"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ICy8NiPX8_2i"
      },
      "source": [
        "# Predicitve models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SnBLIVtpzjbb"
      },
      "source": [
        "## Decision tree regressor with validation curve\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ys4oh7jobprp"
      },
      "source": [
        "X = df_2013[['mnth', 'hr', 'holiday', \n",
        "        'weekday', 'workingday', 'weathersit',\n",
        "        'temp', 'atemp', 'hum', 'windspeed']]\n",
        "\n",
        "y = df_2013[['cnt']]\n",
        "\n",
        "depths = range(1, 25)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,\n",
        "                                                    random_state=42)"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 580
        },
        "id": "Ek6gNEP4ZRy4",
        "outputId": "ce3cd32f-10ab-4b70-8631-ff4702e8a39c"
      },
      "source": [
        "train_scores, valid_scores = get_data_for_validation_curve(DecisionTreeRegressor(),\n",
        "                                                           X_train,\n",
        "                                                           y_train,\n",
        "                                                           depths)\n",
        "\n",
        "best_depth, train_scores_mean, valid_scores_mean = get_bestDepth_scores_plot(train_scores,\n",
        "                                                                          valid_scores,\n",
        "                                                                          depths)\n",
        "\n",
        "\n",
        "print(\"\"\"\n",
        "Best depth = {}\n",
        "Train score = {:.3f}\n",
        "Cross-validation score = {:.3f}\n",
        "\"\"\".format(best_depth, train_scores_mean[best_depth - 1],\n",
        "valid_scores_mean[best_depth - 1]))\n",
        "\n",
        "\n",
        "regressor_tree, test_score = get_final_tree_and_score(\n",
        "                                DecisionTreeRegressor(max_depth=best_depth), \n",
        "                                X_train, X_test, y_train, y_test)\n",
        "\n",
        "print(\"Test score = {:.3f}\".format(test_score))\n",
        "\n",
        "test_predictions = regressor_tree.predict(X_test)\n",
        "\n",
        "test_msle = mean_squared_log_error(y_test, test_predictions)\n",
        "print('msle =', test_msle)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Best depth = 10\n",
            "Train score = 0.920\n",
            "Cross-validation score = 0.884\n",
            "\n",
            "Test score = 0.860\n",
            "msle = 0.17413270482556095\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KqNc2GAVzsAl"
      },
      "source": [
        "## Random forest regresson with hyperparameter tunning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CHIRZsPIZRwP"
      },
      "source": [
        "clf_randomForest = RandomForestRegressor()\n",
        "\n",
        "y_train_numpy = y_train.to_numpy()\n",
        "y_train_numpy = np.ravel(y_train_numpy)\n",
        "\n",
        "y_test_numpy = y_test.to_numpy()\n",
        "y_test_numpy = np.ravel(y_test_numpy)\n",
        "\n",
        "param_dist = {\n",
        "    \"n_estimators\": range(50, 201, 15),\n",
        "    \"max_depth\": range(10, 21, 2),\n",
        "    \"max_features\": ['auto', 'sqrt', 'log2'],\n",
        "    \"min_samples_split\": [2, 3, 5, 8, 10, 13],\n",
        "    }\n"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "STjZ1T9B9dn_"
      },
      "source": [
        "# gs = GridSearchCV(estimator=clf_randomForest, param_grid=param_dist, verbose=2, cv=5)\n",
        "\n",
        "# gs.fit(X_train, y_train_numpy)\n",
        "\n",
        "# best_params_forest = gs.best_params_\n",
        "\n",
        "# best_params_forest"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OiVbnsLFaWna",
        "outputId": "a672d7da-4d4e-4772-87bb-ffe8e1aecd77"
      },
      "source": [
        "regressor_tree, test_score = get_final_tree_and_score(RandomForestRegressor(max_depth=16,\n",
        "                                                                            max_features='auto',\n",
        "                                                                            min_samples_split=4,\n",
        "                                                                            n_estimators=90),  \n",
        "                                                      X_train, X_test,\n",
        "                                                      y_train_numpy, y_test_numpy)\n",
        "\n",
        "print(\"r2_score [test set] = {:.3f}\".format(test_score))"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "r2_score [test set] = 0.920\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hp0kPZr3TpoT",
        "outputId": "e77e6c25-8f70-4aaf-b4a0-262c19c3da13"
      },
      "source": [
        "test_predictions_forest = regressor_tree.predict(X_test)\n",
        "\n",
        "msle_best_forest = mean_squared_log_error(y_test, test_predictions_forest)\n",
        "\n",
        "print('msle =', msle_best_forest)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "msle = 0.13545616611523903\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MHn6ZaWTz-HK"
      },
      "source": [
        "## Random forest regressor with validation curve"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "id": "eySfKrUaajao",
        "outputId": "92fed626-63ed-48f1-88f3-6dbca88b2551"
      },
      "source": [
        "depths = range(10, 25)\n",
        "\n",
        "train_scores_forest, valid_scores_forest = get_data_for_validation_curve(clf_randomForest,\n",
        "                                                           X_train,\n",
        "                                                           y_train_numpy,\n",
        "                                                           depths)\n",
        "\n",
        "best_depth, train_scores_mean_forest, valid_scores_mean_forest = get_bestDepth_scores_plot(\n",
        "                                                                      train_scores_forest, \n",
        "                                                                      valid_scores_forest,\n",
        "                                                                      depths\n",
        "                                                                      )\n",
        "\n",
        "\n",
        "print(\"\"\"\n",
        "Recommended depth = {}\n",
        "Train score = {:.3f}\n",
        "Cross-validation score = {:.3f}\n",
        "\"\"\".format(best_depth, train_scores_mean_forest[best_depth - depths[0]],\n",
        "valid_scores_mean_forest[best_depth - depths[0]]))\n",
        "\n",
        "\n",
        "regressor_tree, test_score = get_final_tree_and_score(\n",
        "    RandomForestRegressor(), X_train, X_test, y_train_numpy, y_test_numpy)\n",
        "\n",
        "print(\"Test score = {:.3f}\".format(test_score))\n",
        "\n",
        "test_predictions = regressor_tree.predict(X_test)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Recommended depth = 13\n",
            "Train score = 0.967\n",
            "Cross-validation score = 0.918\n",
            "\n",
            "Test score = 0.919\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v1_cwPderuP2"
      },
      "source": [
        "## Neural Network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IHtf3Nfr8z8k"
      },
      "source": [
        "X_primary = df[['yr', 'season', 'mnth', 'hr', 'holiday', \n",
        "        'weekday', 'workingday', 'weathersit',\n",
        "        'temp', 'atemp', 'hum', 'windspeed']]\n",
        "\n",
        "y_primary = df[['cnt']]\n",
        "\n",
        "depths = range(1, 25)\n",
        "\n",
        "X_train_primary, X_test_primary, y_train_primary, y_test_primary = train_test_split(\n",
        "    X, y, test_size=0.2,\n",
        "    random_state=42\n",
        "  )"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hZBKyAr5s42e"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.layers import Dense, BatchNormalization, Dropout\n",
        "from tensorflow.keras.optimizers import RMSprop, Adam"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rEvn44pGvuY7"
      },
      "source": [
        "def build_model(input_shape):\n",
        "    model = tf.keras.models.Sequential([\n",
        "        Dense(256, activation='relu', input_shape=input_shape),\n",
        "        BatchNormalization(),\n",
        "        Dense(256, activation='relu'),\n",
        "        BatchNormalization(),\n",
        "        Dense(256, activation='relu'),\n",
        "        # BatchNormalization(),\n",
        "        # Dense(128, activation='relu'),\n",
        "        Dense(1, activation='relu')\n",
        "        ])\n",
        "    model.compile(optimizer=RMSprop(learning_rate=5*10e-4), loss='msle', metrics=['msle'])\n",
        "    return model"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XMaXJ6kowM59"
      },
      "source": [
        "input_shape = (X_train.shape[1],)\n",
        "\n",
        "model = build_model(input_shape)"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vd4JSJ-dx4qK",
        "outputId": "973994b6-d264-4b3f-c208-a17706bddd6f"
      },
      "source": [
        "history = model.fit(X_train_primary, y_train_primary, batch_size=16, epochs=25)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/25\n",
            "869/869 [==============================] - 2s 2ms/step - loss: 0.8628 - msle: 0.8628\n",
            "Epoch 2/25\n",
            "869/869 [==============================] - 2s 2ms/step - loss: 0.4640 - msle: 0.4640\n",
            "Epoch 3/25\n",
            "869/869 [==============================] - 2s 2ms/step - loss: 0.3879 - msle: 0.3879\n",
            "Epoch 4/25\n",
            "869/869 [==============================] - 2s 2ms/step - loss: 0.3515 - msle: 0.3515\n",
            "Epoch 5/25\n",
            "869/869 [==============================] - 2s 2ms/step - loss: 0.3297 - msle: 0.3297\n",
            "Epoch 6/25\n",
            "869/869 [==============================] - 2s 2ms/step - loss: 0.3072 - msle: 0.3072\n",
            "Epoch 7/25\n",
            "869/869 [==============================] - 2s 2ms/step - loss: 0.2880 - msle: 0.2880\n",
            "Epoch 8/25\n",
            "869/869 [==============================] - 2s 2ms/step - loss: 0.2700 - msle: 0.2700\n",
            "Epoch 9/25\n",
            "869/869 [==============================] - 2s 2ms/step - loss: 0.2676 - msle: 0.2676\n",
            "Epoch 10/25\n",
            "869/869 [==============================] - 2s 2ms/step - loss: 0.2554 - msle: 0.2554\n",
            "Epoch 11/25\n",
            "869/869 [==============================] - 2s 2ms/step - loss: 0.2586 - msle: 0.2586\n",
            "Epoch 12/25\n",
            "869/869 [==============================] - 2s 2ms/step - loss: 0.2606 - msle: 0.2606\n",
            "Epoch 13/25\n",
            "869/869 [==============================] - 2s 2ms/step - loss: 0.2472 - msle: 0.2472\n",
            "Epoch 14/25\n",
            "869/869 [==============================] - 2s 2ms/step - loss: 0.2357 - msle: 0.2357\n",
            "Epoch 15/25\n",
            "869/869 [==============================] - 2s 2ms/step - loss: 0.2363 - msle: 0.2363\n",
            "Epoch 16/25\n",
            "869/869 [==============================] - 2s 2ms/step - loss: 0.2286 - msle: 0.2286\n",
            "Epoch 17/25\n",
            "869/869 [==============================] - 2s 2ms/step - loss: 0.2253 - msle: 0.2253\n",
            "Epoch 18/25\n",
            "869/869 [==============================] - 2s 2ms/step - loss: 0.2318 - msle: 0.2318\n",
            "Epoch 19/25\n",
            "869/869 [==============================] - 2s 2ms/step - loss: 0.2246 - msle: 0.2246\n",
            "Epoch 20/25\n",
            "869/869 [==============================] - 2s 2ms/step - loss: 0.2214 - msle: 0.2214\n",
            "Epoch 21/25\n",
            "869/869 [==============================] - 2s 2ms/step - loss: 0.2149 - msle: 0.2149\n",
            "Epoch 22/25\n",
            "869/869 [==============================] - 2s 2ms/step - loss: 0.2175 - msle: 0.2175\n",
            "Epoch 23/25\n",
            "869/869 [==============================] - 2s 2ms/step - loss: 0.2101 - msle: 0.2101\n",
            "Epoch 24/25\n",
            "869/869 [==============================] - 2s 2ms/step - loss: 0.2084 - msle: 0.2084\n",
            "Epoch 25/25\n",
            "869/869 [==============================] - 2s 2ms/step - loss: 0.2072 - msle: 0.2072\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "86jU6TVFyZKu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "36a5ad0d-406b-4385-ffcd-8a1d5c9b6285"
      },
      "source": [
        "val_msle = model.evaluate(X_test_primary, y_test_primary, verbose=0)\n",
        "print(\"MSLE = {:.03}\".format(val_msle[1]))"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MSLE = 0.178\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b4BmmrEvy3k2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "98368231-e241-4492-9fdb-7c3958d88b1d"
      },
      "source": [
        "y_predicted = model.predict(X_test_primary)\n",
        "y_predicted.min()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l5Oeu-jhzMAF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "093ba291-15ed-497d-f16b-24c9e02d2d27"
      },
      "source": [
        "r2 = r2_score(y_test_primary, y_predicted)\n",
        "print('r2 score =', r2)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "r2 score = 0.796826685413966\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}