Week 4 - Data Analysis (Python).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cf29cbe4",
   "metadata": {},
   "source": [
    "# Hourly Bike Rentals\n",
    "## Evaluation Question: How are hourly bike rentals affected by weather and seasonal factors?\n",
    "### Variable Key\n",
    "* `instant`: record index\n",
    "* `dteday` : date\n",
    "* `season` : season (`1`:winter, `2`:spring, `3`:summer, `4`:fall)\n",
    "* `yr` : year (`0`: 2011, `1`:2012)\n",
    "* `mnth` : month ( 1 to 12)\n",
    "* `hr` : hour (0 to 23)\n",
    "* `holiday` : whether day is holiday (`1`) or not (`0`)\n",
    "* `weekday` : day of the week (`0`: Sunday through `6`: Saturday)\n",
    "* `workingday` : if day is neither weekend nor holiday is `1`, otherwise is `0`.\n",
    "* `weathersit` :\n",
    "    * `1`: Clear, Few clouds, Partly cloudy, Partly cloudy\n",
    "    * `2`: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist\n",
    "    * `3`: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds\n",
    "    * `4`: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog\n",
    "* `temp` : Normalized temperature in Celsius. The values are derived via (t-t_min)/(t_max-t_min), t_min=-8, t_max=+39 (only in hourly scale)\n",
    "* `atemp`: Normalized feeling temperature in Celsius. The values are derived via (t-t_min)/(t_max-t_min), t_min=-16, t_max=+50 (only in hourly scale)\n",
    "* `hum`: Normalized humidity. The values are divided to 100 (max)\n",
    "* `windspeed`: Normalized wind speed. The values are divided to 67 (max)\n",
    "* `casual`: count of casual users\n",
    "* `registered`: count of registered users\n",
    "* `cnt`: count of total rental bikes including both casual and registered"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ae8601e",
   "metadata": {},
   "source": [
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "83959a79",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn import metrics\n",
    "import numpy as np\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3261741",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7073e757",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "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>dteday</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>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17374</th>\n",
       "      <td>17375</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2576</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>11</td>\n",
       "      <td>108</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17375</th>\n",
       "      <td>17376</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2576</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>8</td>\n",
       "      <td>81</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17376</th>\n",
       "      <td>17377</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2576</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>7</td>\n",
       "      <td>83</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17377</th>\n",
       "      <td>17378</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>13</td>\n",
       "      <td>48</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17378</th>\n",
       "      <td>17379</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>12</td>\n",
       "      <td>37</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17379 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       instant      dteday  season  yr  mnth  hr  holiday  weekday  \\\n",
       "0            1  2011-01-01       1   0     1   0        0        6   \n",
       "1            2  2011-01-01       1   0     1   1        0        6   \n",
       "2            3  2011-01-01       1   0     1   2        0        6   \n",
       "3            4  2011-01-01       1   0     1   3        0        6   \n",
       "4            5  2011-01-01       1   0     1   4        0        6   \n",
       "...        ...         ...     ...  ..   ...  ..      ...      ...   \n",
       "17374    17375  2012-12-31       1   1    12  19        0        1   \n",
       "17375    17376  2012-12-31       1   1    12  20        0        1   \n",
       "17376    17377  2012-12-31       1   1    12  21        0        1   \n",
       "17377    17378  2012-12-31       1   1    12  22        0        1   \n",
       "17378    17379  2012-12-31       1   1    12  23        0        1   \n",
       "\n",
       "       workingday  weathersit  temp   atemp   hum  windspeed  casual  \\\n",
       "0               0           1  0.24  0.2879  0.81     0.0000       3   \n",
       "1               0           1  0.22  0.2727  0.80     0.0000       8   \n",
       "2               0           1  0.22  0.2727  0.80     0.0000       5   \n",
       "3               0           1  0.24  0.2879  0.75     0.0000       3   \n",
       "4               0           1  0.24  0.2879  0.75     0.0000       0   \n",
       "...           ...         ...   ...     ...   ...        ...     ...   \n",
       "17374           1           2  0.26  0.2576  0.60     0.1642      11   \n",
       "17375           1           2  0.26  0.2576  0.60     0.1642       8   \n",
       "17376           1           1  0.26  0.2576  0.60     0.1642       7   \n",
       "17377           1           1  0.26  0.2727  0.56     0.1343      13   \n",
       "17378           1           1  0.26  0.2727  0.65     0.1343      12   \n",
       "\n",
       "       registered  cnt  \n",
       "0              13   16  \n",
       "1              32   40  \n",
       "2              27   32  \n",
       "3              10   13  \n",
       "4               1    1  \n",
       "...           ...  ...  \n",
       "17374         108  119  \n",
       "17375          81   89  \n",
       "17376          83   90  \n",
       "17377          48   61  \n",
       "17378          37   49  \n",
       "\n",
       "[17379 rows x 17 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes = pd.read_csv('hour.csv')\n",
    "bikes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e7d0c1a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a copy of the data, just in case\n",
    "bikes1 = bikes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "929d105a",
   "metadata": {},
   "source": [
    "## Data Wrangling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a381976",
   "metadata": {},
   "source": [
    "### Check Variable Types (Transform object types as needed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3f787a0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "instant         int64\n",
       "dteday         object\n",
       "season          int64\n",
       "yr              int64\n",
       "mnth            int64\n",
       "hr              int64\n",
       "holiday         int64\n",
       "weekday         int64\n",
       "workingday      int64\n",
       "weathersit      int64\n",
       "temp          float64\n",
       "atemp         float64\n",
       "hum           float64\n",
       "windspeed     float64\n",
       "casual          int64\n",
       "registered      int64\n",
       "cnt             int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check data types of the entire dataframe\n",
    "bikes1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "de3bbfb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform all categorical variables from `int64` to appropriate types for our analyses\n",
    "bikes1.dteday = bikes1.dteday.astype('datetime64')\n",
    "bikes1.season = bikes1.season.astype('category')\n",
    "bikes1.yr = bikes1.yr.astype('category')\n",
    "bikes1.mnth = bikes1.mnth.astype('category')\n",
    "bikes1.hr = bikes1.hr.astype('category')\n",
    "bikes1.holiday = bikes1.holiday.astype('bool')\n",
    "bikes1.weekday = bikes1.weekday.astype('category')\n",
    "bikes1.workingday = bikes1.workingday.astype('bool')\n",
    "bikes1.weathersit = bikes1.weathersit.astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6d08f25b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "instant                int64\n",
       "dteday        datetime64[ns]\n",
       "season              category\n",
       "yr                  category\n",
       "mnth                category\n",
       "hr                  category\n",
       "holiday                 bool\n",
       "weekday             category\n",
       "workingday              bool\n",
       "weathersit          category\n",
       "temp                 float64\n",
       "atemp                float64\n",
       "hum                  float64\n",
       "windspeed            float64\n",
       "casual                 int64\n",
       "registered             int64\n",
       "cnt                    int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check dtypes to be sure transformations occurred as desired\n",
    "bikes1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8f885e30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>dteday</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>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17374</th>\n",
       "      <td>17375</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2576</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>11</td>\n",
       "      <td>108</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17375</th>\n",
       "      <td>17376</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2576</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>8</td>\n",
       "      <td>81</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17376</th>\n",
       "      <td>17377</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>21</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2576</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>7</td>\n",
       "      <td>83</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17377</th>\n",
       "      <td>17378</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>13</td>\n",
       "      <td>48</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17378</th>\n",
       "      <td>17379</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>23</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>12</td>\n",
       "      <td>37</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17379 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       instant     dteday season yr mnth  hr  holiday weekday  workingday  \\\n",
       "0            1 2011-01-01      1  0    1   0    False       6       False   \n",
       "1            2 2011-01-01      1  0    1   1    False       6       False   \n",
       "2            3 2011-01-01      1  0    1   2    False       6       False   \n",
       "3            4 2011-01-01      1  0    1   3    False       6       False   \n",
       "4            5 2011-01-01      1  0    1   4    False       6       False   \n",
       "...        ...        ...    ... ..  ...  ..      ...     ...         ...   \n",
       "17374    17375 2012-12-31      1  1   12  19    False       1        True   \n",
       "17375    17376 2012-12-31      1  1   12  20    False       1        True   \n",
       "17376    17377 2012-12-31      1  1   12  21    False       1        True   \n",
       "17377    17378 2012-12-31      1  1   12  22    False       1        True   \n",
       "17378    17379 2012-12-31      1  1   12  23    False       1        True   \n",
       "\n",
       "      weathersit  temp   atemp   hum  windspeed  casual  registered  cnt  \n",
       "0              1  0.24  0.2879  0.81     0.0000       3          13   16  \n",
       "1              1  0.22  0.2727  0.80     0.0000       8          32   40  \n",
       "2              1  0.22  0.2727  0.80     0.0000       5          27   32  \n",
       "3              1  0.24  0.2879  0.75     0.0000       3          10   13  \n",
       "4              1  0.24  0.2879  0.75     0.0000       0           1    1  \n",
       "...          ...   ...     ...   ...        ...     ...         ...  ...  \n",
       "17374          2  0.26  0.2576  0.60     0.1642      11         108  119  \n",
       "17375          2  0.26  0.2576  0.60     0.1642       8          81   89  \n",
       "17376          1  0.26  0.2576  0.60     0.1642       7          83   90  \n",
       "17377          1  0.26  0.2727  0.56     0.1343      13          48   61  \n",
       "17378          1  0.26  0.2727  0.65     0.1343      12          37   49  \n",
       "\n",
       "[17379 rows x 17 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50af4cb4",
   "metadata": {},
   "source": [
    "### Descriptive Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "00d898fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8690.0000</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>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>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>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>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>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>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>"
      ],
      "text/plain": [
       "          instant          temp         atemp           hum     windspeed  \\\n",
       "count  17379.0000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
       "mean    8690.0000      0.496987      0.475775      0.627229      0.190098   \n",
       "std     5017.0295      0.192556      0.171850      0.192930      0.122340   \n",
       "min        1.0000      0.020000      0.000000      0.000000      0.000000   \n",
       "25%     4345.5000      0.340000      0.333300      0.480000      0.104500   \n",
       "50%     8690.0000      0.500000      0.484800      0.630000      0.194000   \n",
       "75%    13034.5000      0.660000      0.621200      0.780000      0.253700   \n",
       "max    17379.0000      1.000000      1.000000      1.000000      0.850700   \n",
       "\n",
       "             casual    registered           cnt  \n",
       "count  17379.000000  17379.000000  17379.000000  \n",
       "mean      35.676218    153.786869    189.463088  \n",
       "std       49.305030    151.357286    181.387599  \n",
       "min        0.000000      0.000000      1.000000  \n",
       "25%        4.000000     34.000000     40.000000  \n",
       "50%       17.000000    115.000000    142.000000  \n",
       "75%       48.000000    220.000000    281.000000  \n",
       "max      367.000000    886.000000    977.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes1.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c96ca74",
   "metadata": {},
   "source": [
    "The descriptive statistics above are difficult to make meaning out of for anything except the last 3 columns (the potential dependent variables). Because the `temp`, `atemp`, `hum`, and `windspeed` are already normalized, there is not much physical meaning we can make out of those numbers at this time.\n",
    "\n",
    "We can see that the average number of registered users is much higher than the average number of casual users. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b1108ef",
   "metadata": {},
   "source": [
    "### Correct for Missing Values\n",
    "Based on the Count for all numerical values above being exactly the same, I suspect that our data is fairly \"full.\" However, we will run `.dropna()` to be sure that we won't run in to any trouble with later analyses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a2945dc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>dteday</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>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17374</th>\n",
       "      <td>17375</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2576</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>11</td>\n",
       "      <td>108</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17375</th>\n",
       "      <td>17376</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2576</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>8</td>\n",
       "      <td>81</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17376</th>\n",
       "      <td>17377</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>21</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2576</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>7</td>\n",
       "      <td>83</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17377</th>\n",
       "      <td>17378</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>13</td>\n",
       "      <td>48</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17378</th>\n",
       "      <td>17379</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>23</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>12</td>\n",
       "      <td>37</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17379 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       instant     dteday season yr mnth  hr  holiday weekday  workingday  \\\n",
       "0            1 2011-01-01      1  0    1   0    False       6       False   \n",
       "1            2 2011-01-01      1  0    1   1    False       6       False   \n",
       "2            3 2011-01-01      1  0    1   2    False       6       False   \n",
       "3            4 2011-01-01      1  0    1   3    False       6       False   \n",
       "4            5 2011-01-01      1  0    1   4    False       6       False   \n",
       "...        ...        ...    ... ..  ...  ..      ...     ...         ...   \n",
       "17374    17375 2012-12-31      1  1   12  19    False       1        True   \n",
       "17375    17376 2012-12-31      1  1   12  20    False       1        True   \n",
       "17376    17377 2012-12-31      1  1   12  21    False       1        True   \n",
       "17377    17378 2012-12-31      1  1   12  22    False       1        True   \n",
       "17378    17379 2012-12-31      1  1   12  23    False       1        True   \n",
       "\n",
       "      weathersit  temp   atemp   hum  windspeed  casual  registered  cnt  \n",
       "0              1  0.24  0.2879  0.81     0.0000       3          13   16  \n",
       "1              1  0.22  0.2727  0.80     0.0000       8          32   40  \n",
       "2              1  0.22  0.2727  0.80     0.0000       5          27   32  \n",
       "3              1  0.24  0.2879  0.75     0.0000       3          10   13  \n",
       "4              1  0.24  0.2879  0.75     0.0000       0           1    1  \n",
       "...          ...   ...     ...   ...        ...     ...         ...  ...  \n",
       "17374          2  0.26  0.2576  0.60     0.1642      11         108  119  \n",
       "17375          2  0.26  0.2576  0.60     0.1642       8          81   89  \n",
       "17376          1  0.26  0.2576  0.60     0.1642       7          83   90  \n",
       "17377          1  0.26  0.2727  0.56     0.1343      13          48   61  \n",
       "17378          1  0.26  0.2727  0.65     0.1343      12          37   49  \n",
       "\n",
       "[17379 rows x 17 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes1.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5e07390",
   "metadata": {},
   "source": [
    "Indeed, after running `.dropna()`, we still have the same number of rows as the initial imported set, so we know that we did not lose any data due to having any missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a460253a",
   "metadata": {},
   "source": [
    "### Check the Distribution of Variables and Detect Any Outliers\n",
    "\n",
    "#### Independent Variables\n",
    "Visualizing counts of the independent variables of season, yr, month, hr, and weekday will not tell us much of anything on their own because we would expect the numbers of observations for those hours to be approximately equal. (We assume that every hour of every day is a \"bin\" for reporting the numbers of users.)\n",
    "\n",
    "There is not much point in visualizing the temp, atemp, hum, or windspeed variables either because they have already been reported to us as normalized from 0 to 1. IF there were any outliers, they should have already been accounted for before normalizing the values, so we have no control over whether that step was completed appropriately.\n",
    "\n",
    "Therefore, the only reasonable visualizations are in relation to the dependent variables."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9551380a",
   "metadata": {},
   "source": [
    "#### Dependent Variables\n",
    "As the 3 potential dependent variables are completed related to each other (by `cnt` = `casual` + `registered`), we would expect that a histogram of each varible would show similar trends."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "305b1e2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='cnt', ylabel='Count'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(bikes1[\"cnt\"], kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "282e5864",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='casual', ylabel='Count'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(bikes1[\"casual\"], kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e115d0cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='registered', ylabel='Count'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(bikes1[\"registered\"], kde=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b086eafa",
   "metadata": {},
   "source": [
    "Indeed, we see that the 3 potential dependent variables show approximately the same distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dca526fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a boxplot for total count of bike rentals\n",
    "bikes1.boxplot(column='cnt')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32051834",
   "metadata": {},
   "source": [
    "This boxplot clearly shows a large number of \"outliers\" (1.5*IQR) - as shown by the dark wide line above the top whisker. However, we already know that our data are not normally distributed (we expect to have many more low-rental-counts than high rental counts), which heavily skews the descriptive statistics. Therefore, we will not remove \"outliers\" at this time."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f25b054",
   "metadata": {},
   "source": [
    "# Regression and MANOVA\n",
    "We will use step-wise regression and MANOVA to determine which factors are most significant in predicting the number of bike rentals. This process is easiest to complete in R. So we will refer to a companion R notebook available [HERE](https://github.com/aglisson6/ENTITY-Data-Science-Virtual-Academy-Final-Project/blob/main/Abi_Glisson%20-%20Final%20Project%20-%20R.ipynb).\n",
    "\n",
    "## Dependent Variables\n",
    "Our analysis in R determined that `cnt` is too closely related to `registered`, so we can use `casual` and `registered` without worrying about `cnt` and that will account for nearly all of the variance in `cnt`. This was to be expected as `cnt` is simply the addition of `casual` and `registered` together.\n",
    "\n",
    "## Independent Variables\n",
    "Our analysis in R showed that all possible dependent variables (both continuous and categorical) were strong predictors of the dependent variables. Therefore, we will need to make educated choices about which variables to include and exclude in order to not overfit the model.\n",
    "\n",
    "# Machine Learning Prediction\n",
    "\n",
    "## Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "e628330a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split your variables into x and y\n",
    "\n",
    "# Select remove `dteday` as dates do not play nicely with machine learning.\n",
    "x = bikes1[['season', 'yr', 'mnth', 'hr', 'workingday', 'weekday', 'holiday', 'weathersit', 'temp', 'atemp', 'hum', 'windspeed']]\n",
    "\n",
    "# We will run the model to predict `casual` and `registered` users\n",
    "y = bikes1[['casual', 'registered']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "38e8defa",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x,y, test_size = .4, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "4d6481e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10427, 12) (10427, 2)\n",
      "(6952, 12) (6952, 2)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape, y_train.shape)\n",
    "print(x_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "30e101a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm = LinearRegression()\n",
    "lm.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d5638c45",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = lm.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "1cfadb7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1f442dfd5a0>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "04680f8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.3990655529804673\n"
     ]
    }
   ],
   "source": [
    "print(\"Score:\", lm.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "23dc1aef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56.57793967687116"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.mean_absolute_error(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "6826e1c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8292.157991602679"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.mean_squared_error(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "0bd91ac4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "91.06128700827085"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(metrics.mean_squared_error(y_test, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fce0627",
   "metadata": {},
   "source": [
    "### k-Folds Cross Validation of the Linear Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "79bb80a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "kfold = KFold(n_splits = 3, shuffle = True, random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "ba6becd3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.25906252 0.28968812 0.33158837]\n"
     ]
    }
   ],
   "source": [
    "print(cross_val_score(lm, x, y, cv = 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "513f68ec",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "\n",
    "We can see that the Linear Regression model does not produce particularly robust predictive capabilities. Let's try a Random Forest.\n",
    "\n",
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "d9b48c65",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_cas = bikes1['casual']\n",
    "y_reg = bikes1['registered']\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y_cas, test_size=0.3, random_state=76)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "e83dca92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=10, random_state=76)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=10, random_state=76)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(n_estimators=10, random_state=76)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest = RandomForestClassifier(n_estimators=10, random_state=76)\n",
    "forest.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "8e6d8df4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[272  82  43 ...   0   0   0]\n",
      " [153  75  41 ...   0   0   0]\n",
      " [ 87  53  37 ...   0   0   0]\n",
      " ...\n",
      " [  0   0   0 ...   0   0   0]\n",
      " [  0   0   0 ...   0   0   0]\n",
      " [  0   0   0 ...   0   0   0]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.41      0.60      0.49       450\n",
      "           1       0.21      0.23      0.22       325\n",
      "           2       0.15      0.15      0.15       255\n",
      "           3       0.15      0.15      0.15       196\n",
      "           4       0.07      0.06      0.06       178\n",
      "           5       0.10      0.11      0.11       156\n",
      "           6       0.06      0.05      0.05       132\n",
      "           7       0.05      0.05      0.05       114\n",
      "           8       0.02      0.03      0.02       107\n",
      "           9       0.08      0.07      0.08       108\n",
      "          10       0.09      0.07      0.08       105\n",
      "          11       0.01      0.01      0.01        94\n",
      "          12       0.02      0.03      0.02        75\n",
      "          13       0.09      0.09      0.09        79\n",
      "          14       0.03      0.03      0.03        75\n",
      "          15       0.06      0.07      0.07        74\n",
      "          16       0.08      0.09      0.09        67\n",
      "          17       0.05      0.05      0.05        66\n",
      "          18       0.01      0.01      0.01        68\n",
      "          19       0.08      0.07      0.08        69\n",
      "          20       0.00      0.00      0.00        49\n",
      "          21       0.03      0.04      0.03        52\n",
      "          22       0.02      0.02      0.02        43\n",
      "          23       0.00      0.00      0.00        51\n",
      "          24       0.05      0.07      0.06        41\n",
      "          25       0.03      0.02      0.02        51\n",
      "          26       0.00      0.00      0.00        46\n",
      "          27       0.06      0.08      0.07        52\n",
      "          28       0.03      0.02      0.02        44\n",
      "          29       0.03      0.02      0.02        51\n",
      "          30       0.00      0.00      0.00        43\n",
      "          31       0.04      0.06      0.04        36\n",
      "          32       0.07      0.05      0.06        41\n",
      "          33       0.04      0.05      0.04        44\n",
      "          34       0.00      0.00      0.00        40\n",
      "          35       0.00      0.00      0.00        37\n",
      "          36       0.00      0.00      0.00        37\n",
      "          37       0.00      0.00      0.00        29\n",
      "          38       0.00      0.00      0.00        25\n",
      "          39       0.03      0.03      0.03        33\n",
      "          40       0.03      0.02      0.03        42\n",
      "          41       0.00      0.00      0.00        33\n",
      "          42       0.00      0.00      0.00        30\n",
      "          43       0.00      0.00      0.00        25\n",
      "          44       0.06      0.08      0.07        24\n",
      "          45       0.04      0.05      0.04        22\n",
      "          46       0.00      0.00      0.00        26\n",
      "          47       0.07      0.08      0.07        25\n",
      "          48       0.00      0.00      0.00        28\n",
      "          49       0.04      0.03      0.04        31\n",
      "          50       0.06      0.03      0.04        32\n",
      "          51       0.00      0.00      0.00        26\n",
      "          52       0.00      0.00      0.00        28\n",
      "          53       0.00      0.00      0.00        24\n",
      "          54       0.00      0.00      0.00        26\n",
      "          55       0.04      0.04      0.04        24\n",
      "          56       0.00      0.00      0.00        29\n",
      "          57       0.07      0.05      0.05        22\n",
      "          58       0.00      0.00      0.00        18\n",
      "          59       0.00      0.00      0.00        18\n",
      "          60       0.00      0.00      0.00        16\n",
      "          61       0.00      0.00      0.00        26\n",
      "          62       0.00      0.00      0.00        17\n",
      "          63       0.00      0.00      0.00        17\n",
      "          64       0.00      0.00      0.00        16\n",
      "          65       0.08      0.06      0.07        18\n",
      "          66       0.00      0.00      0.00        20\n",
      "          67       0.00      0.00      0.00        15\n",
      "          68       0.00      0.00      0.00        24\n",
      "          69       0.00      0.00      0.00        21\n",
      "          70       0.00      0.00      0.00        14\n",
      "          71       0.00      0.00      0.00        20\n",
      "          72       0.07      0.05      0.06        19\n",
      "          73       0.00      0.00      0.00        16\n",
      "          74       0.00      0.00      0.00        22\n",
      "          75       0.00      0.00      0.00        14\n",
      "          76       0.14      0.07      0.10        14\n",
      "          77       0.06      0.06      0.06        18\n",
      "          78       0.04      0.07      0.05        14\n",
      "          79       0.06      0.08      0.06        13\n",
      "          80       0.00      0.00      0.00        10\n",
      "          81       0.00      0.00      0.00        11\n",
      "          82       0.00      0.00      0.00        10\n",
      "          83       0.00      0.00      0.00        12\n",
      "          84       0.00      0.00      0.00        10\n",
      "          85       0.00      0.00      0.00        14\n",
      "          86       0.00      0.00      0.00         8\n",
      "          87       0.00      0.00      0.00        10\n",
      "          88       0.17      0.11      0.13        19\n",
      "          89       0.00      0.00      0.00         8\n",
      "          90       0.00      0.00      0.00        16\n",
      "          91       0.00      0.00      0.00        13\n",
      "          92       0.00      0.00      0.00         8\n",
      "          93       0.00      0.00      0.00         9\n",
      "          94       0.09      0.14      0.11         7\n",
      "          95       0.00      0.00      0.00         4\n",
      "          96       0.00      0.00      0.00         9\n",
      "          97       0.14      0.12      0.13         8\n",
      "          98       0.11      0.11      0.11         9\n",
      "          99       0.00      0.00      0.00         7\n",
      "         100       0.00      0.00      0.00        10\n",
      "         101       0.00      0.00      0.00         7\n",
      "         102       0.00      0.00      0.00        10\n",
      "         103       0.00      0.00      0.00         7\n",
      "         104       0.00      0.00      0.00         6\n",
      "         105       0.00      0.00      0.00         5\n",
      "         106       0.00      0.00      0.00         4\n",
      "         107       0.00      0.00      0.00         4\n",
      "         108       0.00      0.00      0.00         7\n",
      "         109       0.25      0.11      0.15         9\n",
      "         110       0.00      0.00      0.00         4\n",
      "         111       0.00      0.00      0.00         7\n",
      "         112       0.00      0.00      0.00         3\n",
      "         113       0.00      0.00      0.00         5\n",
      "         114       0.00      0.00      0.00        10\n",
      "         115       0.00      0.00      0.00         6\n",
      "         116       0.00      0.00      0.00         3\n",
      "         117       0.00      0.00      0.00         7\n",
      "         118       0.00      0.00      0.00         6\n",
      "         119       0.00      0.00      0.00         4\n",
      "         120       0.25      0.10      0.14        10\n",
      "         121       0.00      0.00      0.00         4\n",
      "         122       0.00      0.00      0.00         6\n",
      "         123       0.00      0.00      0.00         7\n",
      "         124       0.00      0.00      0.00         3\n",
      "         125       0.00      0.00      0.00         6\n",
      "         126       0.00      0.00      0.00         4\n",
      "         127       0.00      0.00      0.00         4\n",
      "         128       0.00      0.00      0.00         5\n",
      "         129       0.00      0.00      0.00         4\n",
      "         130       0.00      0.00      0.00         2\n",
      "         131       0.00      0.00      0.00         2\n",
      "         132       0.00      0.00      0.00         5\n",
      "         133       0.00      0.00      0.00         6\n",
      "         134       0.00      0.00      0.00         4\n",
      "         135       0.00      0.00      0.00         5\n",
      "         136       0.00      0.00      0.00         3\n",
      "         137       0.00      0.00      0.00         4\n",
      "         138       0.00      0.00      0.00         1\n",
      "         139       0.00      0.00      0.00         6\n",
      "         140       0.00      0.00      0.00         2\n",
      "         141       0.00      0.00      0.00         3\n",
      "         142       0.00      0.00      0.00         7\n",
      "         143       0.00      0.00      0.00         7\n",
      "         144       0.00      0.00      0.00         3\n",
      "         145       0.00      0.00      0.00         5\n",
      "         146       0.00      0.00      0.00         4\n",
      "         147       0.00      0.00      0.00         1\n",
      "         148       0.00      0.00      0.00         4\n",
      "         149       0.00      0.00      0.00         2\n",
      "         150       0.00      0.00      0.00         4\n",
      "         151       0.00      0.00      0.00         6\n",
      "         152       0.00      0.00      0.00         3\n",
      "         153       0.00      0.00      0.00         2\n",
      "         155       0.00      0.00      0.00         1\n",
      "         156       0.00      0.00      0.00         3\n",
      "         157       0.00      0.00      0.00         2\n",
      "         158       0.00      0.00      0.00         2\n",
      "         159       0.00      0.00      0.00         2\n",
      "         160       0.00      0.00      0.00         2\n",
      "         161       0.00      0.00      0.00         2\n",
      "         162       0.00      0.00      0.00         1\n",
      "         163       0.00      0.00      0.00         0\n",
      "         164       0.00      0.00      0.00         4\n",
      "         165       0.00      0.00      0.00         2\n",
      "         166       0.00      0.00      0.00         2\n",
      "         167       0.00      0.00      0.00         5\n",
      "         168       0.00      0.00      0.00         6\n",
      "         169       0.00      0.00      0.00         2\n",
      "         170       0.00      0.00      0.00         3\n",
      "         171       0.00      0.00      0.00         1\n",
      "         172       0.00      0.00      0.00         0\n",
      "         173       0.00      0.00      0.00         3\n",
      "         174       0.00      0.00      0.00         3\n",
      "         175       0.08      0.33      0.13         3\n",
      "         176       0.00      0.00      0.00         1\n",
      "         177       0.00      0.00      0.00         3\n",
      "         178       0.00      0.00      0.00         4\n",
      "         179       0.00      0.00      0.00         3\n",
      "         180       0.00      0.00      0.00         0\n",
      "         181       0.00      0.00      0.00         4\n",
      "         182       0.00      0.00      0.00         2\n",
      "         183       0.00      0.00      0.00         6\n",
      "         184       0.00      0.00      0.00         2\n",
      "         185       0.00      0.00      0.00         5\n",
      "         186       0.00      0.00      0.00         3\n",
      "         187       0.00      0.00      0.00         1\n",
      "         188       0.00      0.00      0.00         2\n",
      "         189       0.00      0.00      0.00         3\n",
      "         190       0.00      0.00      0.00         2\n",
      "         191       0.00      0.00      0.00         2\n",
      "         192       0.00      0.00      0.00         1\n",
      "         193       0.00      0.00      0.00         1\n",
      "         194       0.00      0.00      0.00         1\n",
      "         195       0.00      0.00      0.00         3\n",
      "         196       0.00      0.00      0.00         3\n",
      "         197       0.00      0.00      0.00         5\n",
      "         198       0.00      0.00      0.00         0\n",
      "         199       0.00      0.00      0.00         2\n",
      "         200       0.00      0.00      0.00         1\n",
      "         201       0.00      0.00      0.00         2\n",
      "         202       0.00      0.00      0.00         0\n",
      "         203       0.00      0.00      0.00         2\n",
      "         204       0.00      0.00      0.00         1\n",
      "         205       0.00      0.00      0.00         2\n",
      "         206       0.00      0.00      0.00         3\n",
      "         207       0.00      0.00      0.00         2\n",
      "         208       0.00      0.00      0.00         4\n",
      "         209       0.00      0.00      0.00         1\n",
      "         212       0.00      0.00      0.00         1\n",
      "         213       0.00      0.00      0.00         3\n",
      "         214       0.00      0.00      0.00         2\n",
      "         215       0.00      0.00      0.00         1\n",
      "         216       0.00      0.00      0.00         1\n",
      "         217       0.00      0.00      0.00         3\n",
      "         218       0.00      0.00      0.00         3\n",
      "         219       0.00      0.00      0.00         1\n",
      "         221       0.00      0.00      0.00         2\n",
      "         222       0.00      0.00      0.00         2\n",
      "         223       0.00      0.00      0.00         1\n",
      "         224       0.00      0.00      0.00         0\n",
      "         225       0.00      0.00      0.00         2\n",
      "         226       0.00      0.00      0.00         1\n",
      "         227       0.00      0.00      0.00         3\n",
      "         228       0.00      0.00      0.00         2\n",
      "         229       0.00      0.00      0.00         4\n",
      "         231       0.00      0.00      0.00         0\n",
      "         232       0.00      0.00      0.00         2\n",
      "         233       0.00      0.00      0.00         1\n",
      "         235       0.00      0.00      0.00         1\n",
      "         236       0.00      0.00      0.00         2\n",
      "         237       0.00      0.00      0.00         4\n",
      "         238       0.00      0.00      0.00         3\n",
      "         240       0.00      0.00      0.00         2\n",
      "         242       0.00      0.00      0.00         0\n",
      "         243       0.00      0.00      0.00         1\n",
      "         245       0.00      0.00      0.00         2\n",
      "         246       0.00      0.00      0.00         0\n",
      "         248       0.00      0.00      0.00         1\n",
      "         249       0.00      0.00      0.00         1\n",
      "         250       0.00      0.00      0.00         1\n",
      "         251       0.00      0.00      0.00         0\n",
      "         253       0.00      0.00      0.00         2\n",
      "         254       0.00      0.00      0.00         2\n",
      "         256       0.00      0.00      0.00         2\n",
      "         257       0.00      0.00      0.00         0\n",
      "         258       0.00      0.00      0.00         3\n",
      "         260       0.00      0.00      0.00         3\n",
      "         261       0.00      0.00      0.00         1\n",
      "         262       0.00      0.00      0.00         0\n",
      "         263       0.00      0.00      0.00         1\n",
      "         264       0.00      0.00      0.00         1\n",
      "         265       0.00      0.00      0.00         1\n",
      "         266       0.00      0.00      0.00         1\n",
      "         267       0.00      0.00      0.00         2\n",
      "         268       0.00      0.00      0.00         1\n",
      "         269       0.00      0.00      0.00         2\n",
      "         271       0.00      0.00      0.00         1\n",
      "         273       0.00      0.00      0.00         1\n",
      "         274       0.00      0.00      0.00         1\n",
      "         278       0.00      0.00      0.00         1\n",
      "         279       0.00      0.00      0.00         1\n",
      "         280       0.00      0.00      0.00         1\n",
      "         283       0.00      0.00      0.00         1\n",
      "         284       0.00      0.00      0.00         0\n",
      "         286       0.00      0.00      0.00         1\n",
      "         287       0.00      0.00      0.00         1\n",
      "         290       0.00      0.00      0.00         0\n",
      "         291       0.00      0.00      0.00         0\n",
      "         293       0.00      0.00      0.00         1\n",
      "         294       0.00      0.00      0.00         0\n",
      "         295       0.00      0.00      0.00         1\n",
      "         298       0.00      0.00      0.00         0\n",
      "         299       0.00      0.00      0.00         1\n",
      "         301       0.00      0.00      0.00         0\n",
      "         302       0.00      0.00      0.00         0\n",
      "         304       0.00      0.00      0.00         1\n",
      "         307       0.00      0.00      0.00         0\n",
      "         310       0.00      0.00      0.00         0\n",
      "         311       0.00      0.00      0.00         0\n",
      "         321       0.00      0.00      0.00         1\n",
      "         325       0.00      0.00      0.00         1\n",
      "         326       0.00      0.00      0.00         0\n",
      "         331       0.00      0.00      0.00         0\n",
      "         335       0.00      0.00      0.00         0\n",
      "         354       0.00      0.00      0.00         1\n",
      "         362       0.00      0.00      0.00         1\n",
      "         367       0.00      0.00      0.00         1\n",
      "\n",
      "    accuracy                           0.10      5214\n",
      "   macro avg       0.01      0.01      0.01      5214\n",
      "weighted avg       0.09      0.10      0.09      5214\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1327: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Python3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1327: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Python3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1327: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Python3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1327: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Python3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1327: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Python3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1327: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "forestPredictions = forest.predict(x_test)\n",
    "print(confusion_matrix(y_test, forestPredictions))\n",
    "print(classification_report(y_test, forestPredictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "00f0cae3",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x,y_reg, test_size=0.3, random_state=76)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "4da39b05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=10, random_state=76)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=10, random_state=76)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(n_estimators=10, random_state=76)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest = RandomForestClassifier(n_estimators=10, random_state=76)\n",
    "forest.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "4ee32219",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  2  3 ...  0  0  0]\n",
      " [ 1 12 13 ...  0  0  0]\n",
      " [ 2 14 10 ...  0  0  0]\n",
      " ...\n",
      " [ 0  0  0 ...  0  0  0]\n",
      " [ 0  0  0 ...  0  0  0]\n",
      " [ 0  0  0 ...  0  0  0]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00         8\n",
      "           1       0.13      0.20      0.15        61\n",
      "           2       0.08      0.14      0.10        70\n",
      "           3       0.24      0.27      0.25        94\n",
      "           4       0.05      0.06      0.05        86\n",
      "           5       0.07      0.10      0.08        78\n",
      "           6       0.10      0.12      0.11        75\n",
      "           7       0.02      0.03      0.02        39\n",
      "           8       0.09      0.09      0.09        55\n",
      "           9       0.06      0.06      0.06        51\n",
      "          10       0.00      0.00      0.00        45\n",
      "          11       0.11      0.12      0.11        41\n",
      "          12       0.07      0.09      0.08        22\n",
      "          13       0.00      0.00      0.00        25\n",
      "          14       0.05      0.03      0.03        39\n",
      "          15       0.00      0.00      0.00        33\n",
      "          16       0.00      0.00      0.00        28\n",
      "          17       0.04      0.04      0.04        26\n",
      "          18       0.04      0.04      0.04        28\n",
      "          19       0.00      0.00      0.00        36\n",
      "          20       0.02      0.04      0.03        23\n",
      "          21       0.00      0.00      0.00        26\n",
      "          22       0.05      0.07      0.06        29\n",
      "          23       0.04      0.03      0.03        32\n",
      "          24       0.03      0.05      0.04        21\n",
      "          25       0.00      0.00      0.00        24\n",
      "          26       0.00      0.00      0.00        25\n",
      "          27       0.05      0.06      0.05        18\n",
      "          28       0.04      0.04      0.04        24\n",
      "          29       0.00      0.00      0.00        17\n",
      "          30       0.00      0.00      0.00        19\n",
      "          31       0.00      0.00      0.00        21\n",
      "          32       0.08      0.05      0.06        20\n",
      "          33       0.00      0.00      0.00        15\n",
      "          34       0.00      0.00      0.00        24\n",
      "          35       0.07      0.05      0.06        20\n",
      "          36       0.00      0.00      0.00        20\n",
      "          37       0.05      0.06      0.05        18\n",
      "          38       0.00      0.00      0.00        12\n",
      "          39       0.00      0.00      0.00        20\n",
      "          40       0.05      0.05      0.05        20\n",
      "          41       0.00      0.00      0.00        18\n",
      "          42       0.00      0.00      0.00        15\n",
      "          43       0.00      0.00      0.00        20\n",
      "          44       0.05      0.07      0.06        15\n",
      "          45       0.00      0.00      0.00        12\n",
      "          46       0.00      0.00      0.00        16\n",
      "          47       0.00      0.00      0.00        15\n",
      "          48       0.00      0.00      0.00        22\n",
      "          49       0.00      0.00      0.00        23\n",
      "          50       0.04      0.08      0.05        12\n",
      "          51       0.00      0.00      0.00        13\n",
      "          52       0.00      0.00      0.00        11\n",
      "          53       0.00      0.00      0.00        23\n",
      "          54       0.00      0.00      0.00        17\n",
      "          55       0.06      0.06      0.06        18\n",
      "          56       0.00      0.00      0.00        15\n",
      "          57       0.00      0.00      0.00        18\n",
      "          58       0.05      0.07      0.06        14\n",
      "          59       0.00      0.00      0.00        22\n",
      "          60       0.00      0.00      0.00        13\n",
      "          61       0.04      0.05      0.04        19\n",
      "          62       0.00      0.00      0.00         8\n",
      "          63       0.00      0.00      0.00        11\n",
      "          64       0.00      0.00      0.00        26\n",
      "          65       0.07      0.07      0.07        14\n",
      "          66       0.00      0.00      0.00        20\n",
      "          67       0.08      0.06      0.07        18\n",
      "          68       0.00      0.00      0.00        19\n",
      "          69       0.00      0.00      0.00        11\n",
      "          70       0.00      0.00      0.00        10\n",
      "          71       0.00      0.00      0.00        16\n",
      "          72       0.00      0.00      0.00        16\n",
      "          73       0.09      0.14      0.11        14\n",
      "          74       0.00      0.00      0.00         6\n",
      "          75       0.00      0.00      0.00        17\n",
      "          76       0.00      0.00      0.00        13\n",
      "          77       0.09      0.08      0.09        12\n",
      "          78       0.00      0.00      0.00         9\n",
      "          79       0.00      0.00      0.00        23\n",
      "          80       0.00      0.00      0.00        26\n",
      "          81       0.00      0.00      0.00        19\n",
      "          82       0.00      0.00      0.00        21\n",
      "          83       0.00      0.00      0.00        15\n",
      "          84       0.00      0.00      0.00        16\n",
      "          85       0.00      0.00      0.00        17\n",
      "          86       0.00      0.00      0.00        20\n",
      "          87       0.00      0.00      0.00        14\n",
      "          88       0.00      0.00      0.00        18\n",
      "          89       0.00      0.00      0.00        15\n",
      "          90       0.00      0.00      0.00        13\n",
      "          91       0.00      0.00      0.00        13\n",
      "          92       0.00      0.00      0.00        18\n",
      "          93       0.08      0.07      0.08        14\n",
      "          94       0.08      0.05      0.06        20\n",
      "          95       0.04      0.04      0.04        28\n",
      "          96       0.00      0.00      0.00        19\n",
      "          97       0.00      0.00      0.00        16\n",
      "          98       0.00      0.00      0.00        18\n",
      "          99       0.00      0.00      0.00        16\n",
      "         100       0.00      0.00      0.00        19\n",
      "         101       0.00      0.00      0.00         6\n",
      "         102       0.00      0.00      0.00        15\n",
      "         103       0.00      0.00      0.00        15\n",
      "         104       0.00      0.00      0.00        19\n",
      "         105       0.00      0.00      0.00        14\n",
      "         106       0.00      0.00      0.00        17\n",
      "         107       0.00      0.00      0.00        17\n",
      "         108       0.00      0.00      0.00        19\n",
      "         109       0.00      0.00      0.00        21\n",
      "         110       0.00      0.00      0.00        16\n",
      "         111       0.00      0.00      0.00        14\n",
      "         112       0.00      0.00      0.00        21\n",
      "         113       0.00      0.00      0.00        12\n",
      "         114       0.06      0.06      0.06        18\n",
      "         115       0.00      0.00      0.00        18\n",
      "         116       0.00      0.00      0.00        22\n",
      "         117       0.00      0.00      0.00        15\n",
      "         118       0.00      0.00      0.00        15\n",
      "         119       0.00      0.00      0.00        19\n",
      "         120       0.00      0.00      0.00        13\n",
      "         121       0.05      0.07      0.06        14\n",
      "         122       0.00      0.00      0.00        11\n",
      "         123       0.00      0.00      0.00        13\n",
      "         124       0.00      0.00      0.00        11\n",
      "         125       0.00      0.00      0.00        12\n",
      "         126       0.00      0.00      0.00        18\n",
      "         127       0.00      0.00      0.00        18\n",
      "         128       0.00      0.00      0.00        11\n",
      "         129       0.00      0.00      0.00        13\n",
      "         130       0.08      0.12      0.09        17\n",
      "         131       0.00      0.00      0.00        10\n",
      "         132       0.00      0.00      0.00        14\n",
      "         133       0.00      0.00      0.00        10\n",
      "         134       0.00      0.00      0.00        19\n",
      "         135       0.00      0.00      0.00        14\n",
      "         136       0.00      0.00      0.00        13\n",
      "         137       0.00      0.00      0.00        12\n",
      "         138       0.00      0.00      0.00        10\n",
      "         139       0.00      0.00      0.00        15\n",
      "         140       0.12      0.10      0.11        10\n",
      "         141       0.00      0.00      0.00        12\n",
      "         142       0.00      0.00      0.00        10\n",
      "         143       0.00      0.00      0.00        10\n",
      "         144       0.00      0.00      0.00        21\n",
      "         145       0.09      0.14      0.11         7\n",
      "         146       0.00      0.00      0.00        15\n",
      "         147       0.00      0.00      0.00        12\n",
      "         148       0.00      0.00      0.00        13\n",
      "         149       0.00      0.00      0.00         7\n",
      "         150       0.00      0.00      0.00        12\n",
      "         151       0.00      0.00      0.00        16\n",
      "         152       0.00      0.00      0.00        12\n",
      "         153       0.00      0.00      0.00        20\n",
      "         154       0.00      0.00      0.00        11\n",
      "         155       0.00      0.00      0.00        16\n",
      "         156       0.03      0.07      0.04        15\n",
      "         157       0.00      0.00      0.00        11\n",
      "         158       0.00      0.00      0.00        12\n",
      "         159       0.05      0.08      0.06        12\n",
      "         160       0.00      0.00      0.00         4\n",
      "         161       0.07      0.05      0.06        20\n",
      "         162       0.00      0.00      0.00        18\n",
      "         163       0.00      0.00      0.00        10\n",
      "         164       0.00      0.00      0.00        13\n",
      "         165       0.00      0.00      0.00        12\n",
      "         166       0.00      0.00      0.00        12\n",
      "         167       0.00      0.00      0.00         6\n",
      "         168       0.00      0.00      0.00        19\n",
      "         169       0.07      0.09      0.08        11\n",
      "         170       0.00      0.00      0.00        19\n",
      "         171       0.08      0.08      0.08        12\n",
      "         172       0.00      0.00      0.00        10\n",
      "         173       0.00      0.00      0.00         8\n",
      "         174       0.00      0.00      0.00        15\n",
      "         175       0.00      0.00      0.00        19\n",
      "         176       0.06      0.06      0.06        16\n",
      "         177       0.14      0.08      0.11        12\n",
      "         178       0.00      0.00      0.00        11\n",
      "         179       0.00      0.00      0.00        14\n",
      "         180       0.00      0.00      0.00        16\n",
      "         181       0.00      0.00      0.00         6\n",
      "         182       0.00      0.00      0.00         7\n",
      "         183       0.00      0.00      0.00        10\n",
      "         184       0.00      0.00      0.00        14\n",
      "         185       0.00      0.00      0.00        11\n",
      "         186       0.00      0.00      0.00         6\n",
      "         187       0.00      0.00      0.00        10\n",
      "         188       0.00      0.00      0.00        15\n",
      "         189       0.08      0.06      0.07        17\n",
      "         190       0.00      0.00      0.00        10\n",
      "         191       0.08      0.10      0.09        10\n",
      "         192       0.00      0.00      0.00        10\n",
      "         193       0.00      0.00      0.00         5\n",
      "         194       0.00      0.00      0.00         9\n",
      "         195       0.00      0.00      0.00         9\n",
      "         196       0.00      0.00      0.00         7\n",
      "         197       0.00      0.00      0.00        10\n",
      "         198       0.00      0.00      0.00         7\n",
      "         199       0.00      0.00      0.00        10\n",
      "         200       0.00      0.00      0.00         9\n",
      "         201       0.00      0.00      0.00        12\n",
      "         202       0.00      0.00      0.00        12\n",
      "         203       0.00      0.00      0.00        10\n",
      "         204       0.00      0.00      0.00         6\n",
      "         205       0.00      0.00      0.00        11\n",
      "         206       0.00      0.00      0.00        11\n",
      "         207       0.00      0.00      0.00        17\n",
      "         208       0.00      0.00      0.00         9\n",
      "         209       0.00      0.00      0.00        15\n",
      "         210       0.00      0.00      0.00         4\n",
      "         211       0.00      0.00      0.00         9\n",
      "         212       0.00      0.00      0.00        13\n",
      "         213       0.00      0.00      0.00         6\n",
      "         214       0.00      0.00      0.00        14\n",
      "         215       0.00      0.00      0.00         2\n",
      "         216       0.00      0.00      0.00        10\n",
      "         217       0.00      0.00      0.00         6\n",
      "         218       0.00      0.00      0.00        13\n",
      "         219       0.00      0.00      0.00        12\n",
      "         220       0.00      0.00      0.00        13\n",
      "         221       0.00      0.00      0.00         8\n",
      "         222       0.00      0.00      0.00        10\n",
      "         223       0.00      0.00      0.00        12\n",
      "         224       0.00      0.00      0.00         6\n",
      "         225       0.00      0.00      0.00        14\n",
      "         226       0.00      0.00      0.00         9\n",
      "         227       0.00      0.00      0.00         9\n",
      "         228       0.00      0.00      0.00        15\n",
      "         229       0.00      0.00      0.00         9\n",
      "         230       0.00      0.00      0.00        14\n",
      "         231       0.14      0.11      0.12         9\n",
      "         232       0.25      0.08      0.12        12\n",
      "         233       0.00      0.00      0.00        11\n",
      "         234       0.00      0.00      0.00        10\n",
      "         235       0.00      0.00      0.00        10\n",
      "         236       0.00      0.00      0.00         5\n",
      "         237       0.40      0.33      0.36         6\n",
      "         238       0.00      0.00      0.00         8\n",
      "         239       0.00      0.00      0.00        12\n",
      "         240       0.00      0.00      0.00        11\n",
      "         241       0.00      0.00      0.00        10\n",
      "         242       0.00      0.00      0.00         8\n",
      "         243       0.00      0.00      0.00        16\n",
      "         244       0.00      0.00      0.00         8\n",
      "         245       0.00      0.00      0.00         9\n",
      "         246       0.00      0.00      0.00         9\n",
      "         247       0.14      0.09      0.11        11\n",
      "         248       0.00      0.00      0.00         9\n",
      "         249       0.00      0.00      0.00         4\n",
      "         250       0.00      0.00      0.00         8\n",
      "         251       0.00      0.00      0.00         9\n",
      "         252       0.00      0.00      0.00         9\n",
      "         253       0.00      0.00      0.00         8\n",
      "         254       0.09      0.14      0.11         7\n",
      "         255       0.00      0.00      0.00         2\n",
      "         256       0.00      0.00      0.00        12\n",
      "         257       0.00      0.00      0.00         3\n",
      "         258       0.09      0.17      0.12         6\n",
      "         259       0.00      0.00      0.00         2\n",
      "         260       0.00      0.00      0.00         9\n",
      "         261       0.00      0.00      0.00         5\n",
      "         262       0.00      0.00      0.00        14\n",
      "         263       0.00      0.00      0.00         7\n",
      "         264       0.00      0.00      0.00         5\n",
      "         265       0.00      0.00      0.00         9\n",
      "         266       0.00      0.00      0.00         8\n",
      "         267       0.00      0.00      0.00         6\n",
      "         268       0.00      0.00      0.00         5\n",
      "         269       0.00      0.00      0.00         3\n",
      "         270       0.00      0.00      0.00        11\n",
      "         271       0.00      0.00      0.00         4\n",
      "         272       0.00      0.00      0.00         3\n",
      "         273       0.00      0.00      0.00         9\n",
      "         274       0.00      0.00      0.00         9\n",
      "         275       0.00      0.00      0.00         5\n",
      "         276       0.12      0.17      0.14         6\n",
      "         277       0.00      0.00      0.00         6\n",
      "         278       0.00      0.00      0.00         7\n",
      "         279       0.00      0.00      0.00         7\n",
      "         280       0.00      0.00      0.00         6\n",
      "         281       0.00      0.00      0.00         5\n",
      "         282       0.00      0.00      0.00         3\n",
      "         283       0.00      0.00      0.00         8\n",
      "         284       0.00      0.00      0.00        10\n",
      "         285       0.00      0.00      0.00         4\n",
      "         286       0.00      0.00      0.00         2\n",
      "         287       0.00      0.00      0.00        10\n",
      "         288       0.00      0.00      0.00         8\n",
      "         289       0.00      0.00      0.00         7\n",
      "         290       0.00      0.00      0.00         7\n",
      "         291       0.00      0.00      0.00         7\n",
      "         292       0.00      0.00      0.00         7\n",
      "         293       0.00      0.00      0.00         4\n",
      "         294       0.00      0.00      0.00         3\n",
      "         295       0.00      0.00      0.00         0\n",
      "         296       0.00      0.00      0.00         4\n",
      "         297       0.00      0.00      0.00         3\n",
      "         298       0.00      0.00      0.00         4\n",
      "         299       0.00      0.00      0.00         8\n",
      "         300       0.00      0.00      0.00         2\n",
      "         301       0.00      0.00      0.00         4\n",
      "         302       0.00      0.00      0.00         5\n",
      "         303       0.00      0.00      0.00         5\n",
      "         304       0.00      0.00      0.00         4\n",
      "         305       0.00      0.00      0.00         5\n",
      "         306       0.00      0.00      0.00         2\n",
      "         307       0.00      0.00      0.00         2\n",
      "         308       0.00      0.00      0.00         3\n",
      "         309       0.00      0.00      0.00         4\n",
      "         310       0.00      0.00      0.00         5\n",
      "         311       0.00      0.00      0.00         6\n",
      "         312       0.00      0.00      0.00         7\n",
      "         313       0.00      0.00      0.00         4\n",
      "         314       0.00      0.00      0.00         5\n",
      "         315       0.00      0.00      0.00         2\n",
      "         316       0.00      0.00      0.00         6\n",
      "         317       0.00      0.00      0.00         1\n",
      "         318       0.00      0.00      0.00         5\n",
      "         319       0.00      0.00      0.00         4\n",
      "         320       0.00      0.00      0.00         5\n",
      "         321       0.00      0.00      0.00         3\n",
      "         322       0.00      0.00      0.00         1\n",
      "         323       0.00      0.00      0.00         7\n",
      "         324       0.00      0.00      0.00         4\n",
      "         325       0.00      0.00      0.00         1\n",
      "         326       0.00      0.00      0.00         6\n",
      "         327       0.00      0.00      0.00         2\n",
      "         328       0.00      0.00      0.00         5\n",
      "         329       0.00      0.00      0.00        10\n",
      "         330       0.00      0.00      0.00         4\n",
      "         331       0.00      0.00      0.00         4\n",
      "         332       0.00      0.00      0.00         5\n",
      "         333       0.00      0.00      0.00         6\n",
      "         334       0.00      0.00      0.00         5\n",
      "         335       0.00      0.00      0.00         6\n",
      "         336       0.00      0.00      0.00         1\n",
      "         337       0.00      0.00      0.00         4\n",
      "         338       0.00      0.00      0.00         5\n",
      "         339       0.00      0.00      0.00         1\n",
      "         340       0.00      0.00      0.00         4\n",
      "         341       0.00      0.00      0.00         4\n",
      "         342       0.00      0.00      0.00         5\n",
      "         343       0.00      0.00      0.00         2\n",
      "         344       0.00      0.00      0.00         5\n",
      "         345       0.00      0.00      0.00         1\n",
      "         346       0.00      0.00      0.00         6\n",
      "         347       0.00      0.00      0.00         7\n",
      "         348       0.00      0.00      0.00         7\n",
      "         349       0.00      0.00      0.00         1\n",
      "         350       0.00      0.00      0.00         5\n",
      "         351       0.00      0.00      0.00         4\n",
      "         352       0.00      0.00      0.00         6\n",
      "         353       0.00      0.00      0.00         2\n",
      "         354       0.00      0.00      0.00         5\n",
      "         355       0.00      0.00      0.00         4\n",
      "         356       0.00      0.00      0.00         3\n",
      "         357       0.00      0.00      0.00         3\n",
      "         358       0.00      0.00      0.00         4\n",
      "         359       0.00      0.00      0.00         1\n",
      "         360       0.00      0.00      0.00         3\n",
      "         361       0.00      0.00      0.00         6\n",
      "         362       0.00      0.00      0.00         4\n",
      "         363       0.00      0.00      0.00         3\n",
      "         364       0.00      0.00      0.00         2\n",
      "         365       0.00      0.00      0.00         3\n",
      "         366       0.00      0.00      0.00         4\n",
      "         367       0.00      0.00      0.00         3\n",
      "         368       0.00      0.00      0.00         1\n",
      "         369       0.25      0.11      0.15         9\n",
      "         370       0.00      0.00      0.00         3\n",
      "         371       0.00      0.00      0.00         1\n",
      "         372       0.00      0.00      0.00         2\n",
      "         373       0.00      0.00      0.00         2\n",
      "         374       0.00      0.00      0.00         7\n",
      "         375       0.00      0.00      0.00         3\n",
      "         376       0.00      0.00      0.00         4\n",
      "         377       0.00      0.00      0.00         2\n",
      "         378       0.00      0.00      0.00         3\n",
      "         379       0.00      0.00      0.00         3\n",
      "         380       0.00      0.00      0.00         2\n",
      "         381       0.00      0.00      0.00         3\n",
      "         382       0.00      0.00      0.00         2\n",
      "         383       0.00      0.00      0.00         1\n",
      "         384       0.00      0.00      0.00         2\n",
      "         385       0.00      0.00      0.00         2\n",
      "         386       0.00      0.00      0.00         2\n",
      "         387       0.00      0.00      0.00         2\n",
      "         388       0.00      0.00      0.00         2\n",
      "         389       0.00      0.00      0.00         3\n",
      "         390       0.00      0.00      0.00         1\n",
      "         391       0.00      0.00      0.00         4\n",
      "         392       0.00      0.00      0.00         1\n",
      "         393       0.00      0.00      0.00         4\n",
      "         394       0.00      0.00      0.00         0\n",
      "         396       0.00      0.00      0.00         1\n",
      "         397       0.00      0.00      0.00         2\n",
      "         398       0.00      0.00      0.00         1\n",
      "         399       0.00      0.00      0.00         4\n",
      "         400       0.00      0.00      0.00         1\n",
      "         401       0.00      0.00      0.00         2\n",
      "         402       0.00      0.00      0.00         2\n",
      "         403       0.00      0.00      0.00         1\n",
      "         404       0.00      0.00      0.00         2\n",
      "         405       0.00      0.00      0.00         1\n",
      "         406       0.00      0.00      0.00         3\n",
      "         407       0.00      0.00      0.00         2\n",
      "         408       0.00      0.00      0.00         3\n",
      "         409       0.00      0.00      0.00         2\n",
      "         410       0.00      0.00      0.00         1\n",
      "         411       0.00      0.00      0.00         4\n",
      "         412       0.00      0.00      0.00         2\n",
      "         413       0.00      0.00      0.00         1\n",
      "         414       0.00      0.00      0.00         3\n",
      "         415       0.00      0.00      0.00         5\n",
      "         417       0.00      0.00      0.00         4\n",
      "         418       0.00      0.00      0.00         2\n",
      "         419       0.00      0.00      0.00         1\n",
      "         420       0.00      0.00      0.00         3\n",
      "         421       0.00      0.00      0.00         0\n",
      "         423       0.00      0.00      0.00         2\n",
      "         424       0.00      0.00      0.00         1\n",
      "         425       0.00      0.00      0.00         2\n",
      "         426       0.00      0.00      0.00         3\n",
      "         427       0.00      0.00      0.00         1\n",
      "         428       0.00      0.00      0.00         1\n",
      "         429       0.00      0.00      0.00         5\n",
      "         430       0.00      0.00      0.00         1\n",
      "         431       0.00      0.00      0.00         0\n",
      "         432       0.00      0.00      0.00         1\n",
      "         433       0.00      0.00      0.00         2\n",
      "         434       0.00      0.00      0.00         1\n",
      "         435       0.00      0.00      0.00         1\n",
      "         436       0.00      0.00      0.00         4\n",
      "         437       0.00      0.00      0.00         6\n",
      "         438       0.00      0.00      0.00         1\n",
      "         439       0.00      0.00      0.00         0\n",
      "         440       0.00      0.00      0.00         1\n",
      "         441       0.00      0.00      0.00         1\n",
      "         442       0.00      0.00      0.00         1\n",
      "         443       0.00      0.00      0.00         2\n",
      "         444       0.00      0.00      0.00         1\n",
      "         445       0.00      0.00      0.00         4\n",
      "         446       0.00      0.00      0.00         2\n",
      "         447       0.00      0.00      0.00         1\n",
      "         448       0.00      0.00      0.00         1\n",
      "         449       0.00      0.00      0.00         2\n",
      "         450       0.00      0.00      0.00         1\n",
      "         451       0.00      0.00      0.00         1\n",
      "         452       0.00      0.00      0.00         1\n",
      "         453       0.00      0.00      0.00         2\n",
      "         454       0.00      0.00      0.00         1\n",
      "         455       0.00      0.00      0.00         2\n",
      "         456       0.00      0.00      0.00         3\n",
      "         457       0.00      0.00      0.00         1\n",
      "         458       0.00      0.00      0.00         0\n",
      "         459       0.00      0.00      0.00         1\n",
      "         460       0.00      0.00      0.00         1\n",
      "         461       0.00      0.00      0.00         2\n",
      "         462       0.00      0.00      0.00         1\n",
      "         463       0.00      0.00      0.00         3\n",
      "         464       0.00      0.00      0.00         2\n",
      "         465       0.00      0.00      0.00         4\n",
      "         466       0.00      0.00      0.00         0\n",
      "         467       0.00      0.00      0.00         3\n",
      "         468       0.00      0.00      0.00         3\n",
      "         469       0.00      0.00      0.00         1\n",
      "         471       0.00      0.00      0.00         3\n",
      "         472       0.00      0.00      0.00         2\n",
      "         473       0.00      0.00      0.00         2\n",
      "         474       0.00      0.00      0.00         3\n",
      "         475       0.00      0.00      0.00         3\n",
      "         477       0.00      0.00      0.00         0\n",
      "         478       0.00      0.00      0.00         2\n",
      "         479       0.00      0.00      0.00         2\n",
      "         480       0.00      0.00      0.00         2\n",
      "         481       0.00      0.00      0.00         1\n",
      "         483       0.00      0.00      0.00         1\n",
      "         484       0.00      0.00      0.00         3\n",
      "         485       0.00      0.00      0.00         1\n",
      "         486       0.00      0.00      0.00         3\n",
      "         487       0.00      0.00      0.00         2\n",
      "         488       0.00      0.00      0.00         2\n",
      "         489       0.00      0.00      0.00         2\n",
      "         491       0.00      0.00      0.00         2\n",
      "         492       0.00      0.00      0.00         2\n",
      "         493       0.00      0.00      0.00         1\n",
      "         494       0.00      0.00      0.00         1\n",
      "         495       0.00      0.00      0.00         4\n",
      "         496       0.00      0.00      0.00         1\n",
      "         497       0.00      0.00      0.00         2\n",
      "         498       0.00      0.00      0.00         2\n",
      "         500       0.00      0.00      0.00         0\n",
      "         501       0.00      0.00      0.00         0\n",
      "         502       0.00      0.00      0.00         2\n",
      "         503       0.00      0.00      0.00         1\n",
      "         504       0.00      0.00      0.00         3\n",
      "         505       0.00      0.00      0.00         0\n",
      "         506       0.00      0.00      0.00         2\n",
      "         507       0.00      0.00      0.00         2\n",
      "         508       0.00      0.00      0.00         1\n",
      "         509       0.00      0.00      0.00         0\n",
      "         510       0.00      0.00      0.00         0\n",
      "         511       0.00      0.00      0.00         3\n",
      "         512       0.00      0.00      0.00         2\n",
      "         513       0.00      0.00      0.00         1\n",
      "         514       0.00      0.00      0.00         1\n",
      "         515       0.00      0.00      0.00         2\n",
      "         516       0.00      0.00      0.00         2\n",
      "         517       0.00      0.00      0.00         2\n",
      "         518       0.00      0.00      0.00         0\n",
      "         519       0.00      0.00      0.00         0\n",
      "         522       0.00      0.00      0.00         1\n",
      "         523       0.00      0.00      0.00         1\n",
      "         525       0.00      0.00      0.00         2\n",
      "         527       0.00      0.00      0.00         2\n",
      "         529       0.00      0.00      0.00         2\n",
      "         530       0.00      0.00      0.00         0\n",
      "         531       0.00      0.00      0.00         2\n",
      "         532       0.00      0.00      0.00         2\n",
      "         533       0.00      0.00      0.00         1\n",
      "         534       0.00      0.00      0.00         1\n",
      "         536       0.00      0.00      0.00         0\n",
      "         537       0.00      0.00      0.00         0\n",
      "         539       0.00      0.00      0.00         3\n",
      "         540       0.00      0.00      0.00         5\n",
      "         543       0.00      0.00      0.00         1\n",
      "         544       0.00      0.00      0.00         1\n",
      "         547       0.00      0.00      0.00         2\n",
      "         549       0.00      0.00      0.00         3\n",
      "         551       0.00      0.00      0.00         2\n",
      "         552       0.00      0.00      0.00         1\n",
      "         553       0.00      0.00      0.00         2\n",
      "         554       0.00      0.00      0.00         0\n",
      "         555       0.00      0.00      0.00         1\n",
      "         556       0.00      0.00      0.00         2\n",
      "         557       0.00      0.00      0.00         1\n",
      "         558       0.00      0.00      0.00         0\n",
      "         559       0.00      0.00      0.00         1\n",
      "         560       0.00      0.00      0.00         1\n",
      "         561       0.00      0.00      0.00         0\n",
      "         562       0.00      0.00      0.00         1\n",
      "         563       0.00      0.00      0.00         0\n",
      "         564       0.00      0.00      0.00         3\n",
      "         565       0.00      0.00      0.00         1\n",
      "         567       0.00      0.00      0.00         2\n",
      "         568       0.00      0.00      0.00         1\n",
      "         570       0.00      0.00      0.00         1\n",
      "         571       0.00      0.00      0.00         0\n",
      "         572       0.00      0.00      0.00         1\n",
      "         573       0.00      0.00      0.00         0\n",
      "         575       0.00      0.00      0.00         2\n",
      "         576       0.00      0.00      0.00         1\n",
      "         577       0.00      0.00      0.00         0\n",
      "         578       0.00      0.00      0.00         1\n",
      "         579       0.00      0.00      0.00         2\n",
      "         580       0.00      0.00      0.00         2\n",
      "         581       0.00      0.00      0.00         2\n",
      "         584       0.00      0.00      0.00         0\n",
      "         585       0.00      0.00      0.00         0\n",
      "         586       0.00      0.00      0.00         1\n",
      "         589       0.00      0.00      0.00         1\n",
      "         591       0.00      0.00      0.00         0\n",
      "         593       0.00      0.00      0.00         2\n",
      "         594       0.00      0.00      0.00         1\n",
      "         596       0.00      0.00      0.00         0\n",
      "         597       0.00      0.00      0.00         1\n",
      "         598       0.00      0.00      0.00         0\n",
      "         599       0.00      0.00      0.00         0\n",
      "         600       0.00      0.00      0.00         1\n",
      "         601       0.00      0.00      0.00         1\n",
      "         602       0.00      0.00      0.00         1\n",
      "         604       0.00      0.00      0.00         1\n",
      "         605       0.00      0.00      0.00         1\n",
      "         608       0.00      0.00      0.00         1\n",
      "         610       0.00      0.00      0.00         1\n",
      "         611       0.00      0.00      0.00         0\n",
      "         613       0.00      0.00      0.00         1\n",
      "         615       0.00      0.00      0.00         1\n",
      "         616       0.00      0.00      0.00         1\n",
      "         617       0.00      0.00      0.00         1\n",
      "         618       0.00      0.00      0.00         0\n",
      "         619       0.00      0.00      0.00         1\n",
      "         620       0.00      0.00      0.00         0\n",
      "         623       0.00      0.00      0.00         1\n",
      "         625       0.00      0.00      0.00         3\n",
      "         626       0.00      0.00      0.00         1\n",
      "         627       0.00      0.00      0.00         1\n",
      "         629       0.00      0.00      0.00         1\n",
      "         632       0.00      0.00      0.00         1\n",
      "         633       0.00      0.00      0.00         1\n",
      "         634       0.00      0.00      0.00         3\n",
      "         636       0.00      0.00      0.00         1\n",
      "         639       0.00      0.00      0.00         0\n",
      "         640       0.00      0.00      0.00         0\n",
      "         642       0.00      0.00      0.00         4\n",
      "         643       0.00      0.00      0.00         1\n",
      "         644       0.00      0.00      0.00         0\n",
      "         646       0.00      0.00      0.00         2\n",
      "         647       0.00      0.00      0.00         2\n",
      "         648       0.00      0.00      0.00         1\n",
      "         649       0.00      0.00      0.00         1\n",
      "         651       0.00      0.00      0.00         0\n",
      "         652       0.00      0.00      0.00         3\n",
      "         653       0.00      0.00      0.00         0\n",
      "         654       0.00      0.00      0.00         1\n",
      "         655       0.00      0.00      0.00         1\n",
      "         656       0.00      0.00      0.00         1\n",
      "         658       0.00      0.00      0.00         0\n",
      "         659       0.00      0.00      0.00         0\n",
      "         661       0.00      0.00      0.00         2\n",
      "         662       0.00      0.00      0.00         1\n",
      "         663       0.00      0.00      0.00         1\n",
      "         664       0.00      0.00      0.00         1\n",
      "         665       0.00      0.00      0.00         4\n",
      "         666       0.00      0.00      0.00         0\n",
      "         667       0.00      0.00      0.00         1\n",
      "         669       0.00      0.00      0.00         0\n",
      "         670       0.00      0.00      0.00         1\n",
      "         672       0.00      0.00      0.00         0\n",
      "         674       0.00      0.00      0.00         1\n",
      "         675       0.00      0.00      0.00         2\n",
      "         677       0.00      0.00      0.00         2\n",
      "         679       0.00      0.00      0.00         1\n",
      "         681       0.00      0.00      0.00         1\n",
      "         684       0.00      0.00      0.00         2\n",
      "         688       0.00      0.00      0.00         0\n",
      "         689       0.00      0.00      0.00         1\n",
      "         691       0.00      0.00      0.00         0\n",
      "         692       0.00      0.00      0.00         0\n",
      "         693       0.00      0.00      0.00         0\n",
      "         695       0.00      0.00      0.00         1\n",
      "         697       0.00      0.00      0.00         2\n",
      "         699       0.00      0.00      0.00         0\n",
      "         700       0.00      0.00      0.00         1\n",
      "         703       0.00      0.00      0.00         1\n",
      "         704       0.00      0.00      0.00         1\n",
      "         707       0.00      0.00      0.00         0\n",
      "         708       0.00      0.00      0.00         0\n",
      "         709       0.00      0.00      0.00         1\n",
      "         711       0.00      0.00      0.00         1\n",
      "         715       0.00      0.00      0.00         1\n",
      "         716       0.00      0.00      0.00         2\n",
      "         718       0.00      0.00      0.00         0\n",
      "         719       0.00      0.00      0.00         3\n",
      "         720       0.00      0.00      0.00         1\n",
      "         725       0.00      0.00      0.00         0\n",
      "         726       0.00      0.00      0.00         1\n",
      "         730       0.00      0.00      0.00         1\n",
      "         733       0.00      0.00      0.00         2\n",
      "         734       0.00      0.00      0.00         1\n",
      "         735       0.00      0.00      0.00         1\n",
      "         739       0.00      0.00      0.00         0\n",
      "         740       0.00      0.00      0.00         1\n",
      "         742       0.00      0.00      0.00         1\n",
      "         743       0.00      0.00      0.00         1\n",
      "         744       0.00      0.00      0.00         3\n",
      "         745       0.00      0.00      0.00         2\n",
      "         746       0.00      0.00      0.00         1\n",
      "         749       0.00      0.00      0.00         1\n",
      "         751       0.00      0.00      0.00         2\n",
      "         756       0.00      0.00      0.00         0\n",
      "         757       0.00      0.00      0.00         0\n",
      "         760       0.00      0.00      0.00         0\n",
      "         762       0.00      0.00      0.00         1\n",
      "         764       0.00      0.00      0.00         1\n",
      "         765       0.00      0.00      0.00         0\n",
      "         767       0.00      0.00      0.00         0\n",
      "         768       0.00      0.00      0.00         0\n",
      "         769       0.00      0.00      0.00         1\n",
      "         774       0.00      0.00      0.00         2\n",
      "         775       0.00      0.00      0.00         1\n",
      "         777       0.00      0.00      0.00         1\n",
      "         778       0.00      0.00      0.00         0\n",
      "         779       0.00      0.00      0.00         1\n",
      "         780       0.00      0.00      0.00         1\n",
      "         782       0.00      0.00      0.00         0\n",
      "         786       0.00      0.00      0.00         1\n",
      "         788       0.00      0.00      0.00         1\n",
      "         794       0.00      0.00      0.00         0\n",
      "         802       0.00      0.00      0.00         1\n",
      "         811       0.00      0.00      0.00         1\n",
      "         812       0.00      0.00      0.00         1\n",
      "         818       0.00      0.00      0.00         1\n",
      "         833       0.00      0.00      0.00         0\n",
      "         857       0.00      0.00      0.00         1\n",
      "         860       0.00      0.00      0.00         0\n",
      "         876       0.00      0.00      0.00         1\n",
      "\n",
      "    accuracy                           0.03      5214\n",
      "   macro avg       0.01      0.01      0.01      5214\n",
      "weighted avg       0.02      0.03      0.02      5214\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1327: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Python3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1327: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Python3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1327: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Python3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1327: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Python3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1327: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Python3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1327: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "forestPredictions = forest.predict(x_test)\n",
    "print(confusion_matrix(y_test, forestPredictions))\n",
    "print(classification_report(y_test, forestPredictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b833b6d4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}