tree_types_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sqlalchemy import create_engine\n",
    "from IPython.core.pylabtools import figsize\n",
    "from scipy.stats import zscore\n",
    "from scipy import stats\n",
    "from numpy import percentile\n",
    "import sqlite3\n",
    "from sklearn.metrics import accuracy_score, f1_score, classification_report, confusion_matrix\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, TimeSeriesSplit, GridSearchCV\n",
    "from statsmodels.formula.api import ols\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.datasets import make_classification, load_digits\n",
    "from xgboost import XGBClassifier\n",
    "from lightgbm import LGBMClassifier\n",
    "from yellowbrick.classifier import ClassificationReport\n",
    "from yellowbrick.datasets import load_occupancy\n",
    "from yellowbrick.classifier import ClassPredictionError, ConfusionMatrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree = pd.read_csv(\"covtype.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Elevation</th>\n",
       "      <th>Aspect</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Horizontal_Distance_To_Hydrology</th>\n",
       "      <th>Vertical_Distance_To_Hydrology</th>\n",
       "      <th>Horizontal_Distance_To_Roadways</th>\n",
       "      <th>Hillshade_9am</th>\n",
       "      <th>Hillshade_Noon</th>\n",
       "      <th>Hillshade_3pm</th>\n",
       "      <th>Horizontal_Distance_To_Fire_Points</th>\n",
       "      <th>...</th>\n",
       "      <th>Soil_Type32</th>\n",
       "      <th>Soil_Type33</th>\n",
       "      <th>Soil_Type34</th>\n",
       "      <th>Soil_Type35</th>\n",
       "      <th>Soil_Type36</th>\n",
       "      <th>Soil_Type37</th>\n",
       "      <th>Soil_Type38</th>\n",
       "      <th>Soil_Type39</th>\n",
       "      <th>Soil_Type40</th>\n",
       "      <th>Cover_Type</th>\n",
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       "      <td>212</td>\n",
       "      <td>-6</td>\n",
       "      <td>390</td>\n",
       "      <td>220</td>\n",
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       "      <td>2</td>\n",
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       "      <th>3</th>\n",
       "      <td>2785</td>\n",
       "      <td>155</td>\n",
       "      <td>18</td>\n",
       "      <td>242</td>\n",
       "      <td>118</td>\n",
       "      <td>3090</td>\n",
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       "      <td>-1</td>\n",
       "      <td>391</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Elevation  Aspect  Slope  Horizontal_Distance_To_Hydrology  \\\n",
       "0       2596      51      3                               258   \n",
       "1       2590      56      2                               212   \n",
       "2       2804     139      9                               268   \n",
       "3       2785     155     18                               242   \n",
       "4       2595      45      2                               153   \n",
       "\n",
       "   Vertical_Distance_To_Hydrology  Horizontal_Distance_To_Roadways  \\\n",
       "0                               0                              510   \n",
       "1                              -6                              390   \n",
       "2                              65                             3180   \n",
       "3                             118                             3090   \n",
       "4                              -1                              391   \n",
       "\n",
       "   Hillshade_9am  Hillshade_Noon  Hillshade_3pm  \\\n",
       "0            221             232            148   \n",
       "1            220             235            151   \n",
       "2            234             238            135   \n",
       "3            238             238            122   \n",
       "4            220             234            150   \n",
       "\n",
       "   Horizontal_Distance_To_Fire_Points  ...  Soil_Type32  Soil_Type33  \\\n",
       "0                                6279  ...            0            0   \n",
       "1                                6225  ...            0            0   \n",
       "2                                6121  ...            0            0   \n",
       "3                                6211  ...            0            0   \n",
       "4                                6172  ...            0            0   \n",
       "\n",
       "   Soil_Type34  Soil_Type35  Soil_Type36  Soil_Type37  Soil_Type38  \\\n",
       "0            0            0            0            0            0   \n",
       "1            0            0            0            0            0   \n",
       "2            0            0            0            0            0   \n",
       "3            0            0            0            0            0   \n",
       "4            0            0            0            0            0   \n",
       "\n",
       "   Soil_Type39  Soil_Type40  Cover_Type  \n",
       "0            0            0           5  \n",
       "1            0            0           5  \n",
       "2            0            0           2  \n",
       "3            0            0           2  \n",
       "4            0            0           5  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(581012, 55)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 581012 entries, 0 to 581011\n",
      "Data columns (total 55 columns):\n",
      " #   Column                              Non-Null Count   Dtype\n",
      "---  ------                              --------------   -----\n",
      " 0   Elevation                           581012 non-null  int64\n",
      " 1   Aspect                              581012 non-null  int64\n",
      " 2   Slope                               581012 non-null  int64\n",
      " 3   Horizontal_Distance_To_Hydrology    581012 non-null  int64\n",
      " 4   Vertical_Distance_To_Hydrology      581012 non-null  int64\n",
      " 5   Horizontal_Distance_To_Roadways     581012 non-null  int64\n",
      " 6   Hillshade_9am                       581012 non-null  int64\n",
      " 7   Hillshade_Noon                      581012 non-null  int64\n",
      " 8   Hillshade_3pm                       581012 non-null  int64\n",
      " 9   Horizontal_Distance_To_Fire_Points  581012 non-null  int64\n",
      " 10  Wilderness_Area1                    581012 non-null  int64\n",
      " 11  Wilderness_Area2                    581012 non-null  int64\n",
      " 12  Wilderness_Area3                    581012 non-null  int64\n",
      " 13  Wilderness_Area4                    581012 non-null  int64\n",
      " 14  Soil_Type1                          581012 non-null  int64\n",
      " 15  Soil_Type2                          581012 non-null  int64\n",
      " 16  Soil_Type3                          581012 non-null  int64\n",
      " 17  Soil_Type4                          581012 non-null  int64\n",
      " 18  Soil_Type5                          581012 non-null  int64\n",
      " 19  Soil_Type6                          581012 non-null  int64\n",
      " 20  Soil_Type7                          581012 non-null  int64\n",
      " 21  Soil_Type8                          581012 non-null  int64\n",
      " 22  Soil_Type9                          581012 non-null  int64\n",
      " 23  Soil_Type10                         581012 non-null  int64\n",
      " 24  Soil_Type11                         581012 non-null  int64\n",
      " 25  Soil_Type12                         581012 non-null  int64\n",
      " 26  Soil_Type13                         581012 non-null  int64\n",
      " 27  Soil_Type14                         581012 non-null  int64\n",
      " 28  Soil_Type15                         581012 non-null  int64\n",
      " 29  Soil_Type16                         581012 non-null  int64\n",
      " 30  Soil_Type17                         581012 non-null  int64\n",
      " 31  Soil_Type18                         581012 non-null  int64\n",
      " 32  Soil_Type19                         581012 non-null  int64\n",
      " 33  Soil_Type20                         581012 non-null  int64\n",
      " 34  Soil_Type21                         581012 non-null  int64\n",
      " 35  Soil_Type22                         581012 non-null  int64\n",
      " 36  Soil_Type23                         581012 non-null  int64\n",
      " 37  Soil_Type24                         581012 non-null  int64\n",
      " 38  Soil_Type25                         581012 non-null  int64\n",
      " 39  Soil_Type26                         581012 non-null  int64\n",
      " 40  Soil_Type27                         581012 non-null  int64\n",
      " 41  Soil_Type28                         581012 non-null  int64\n",
      " 42  Soil_Type29                         581012 non-null  int64\n",
      " 43  Soil_Type30                         581012 non-null  int64\n",
      " 44  Soil_Type31                         581012 non-null  int64\n",
      " 45  Soil_Type32                         581012 non-null  int64\n",
      " 46  Soil_Type33                         581012 non-null  int64\n",
      " 47  Soil_Type34                         581012 non-null  int64\n",
      " 48  Soil_Type35                         581012 non-null  int64\n",
      " 49  Soil_Type36                         581012 non-null  int64\n",
      " 50  Soil_Type37                         581012 non-null  int64\n",
      " 51  Soil_Type38                         581012 non-null  int64\n",
      " 52  Soil_Type39                         581012 non-null  int64\n",
      " 53  Soil_Type40                         581012 non-null  int64\n",
      " 54  Cover_Type                          581012 non-null  int64\n",
      "dtypes: int64(55)\n",
      "memory usage: 243.8 MB\n"
     ]
    }
   ],
   "source": [
    "tree.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Elevation</th>\n",
       "      <th>Aspect</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Horizontal_Distance_To_Hydrology</th>\n",
       "      <th>Vertical_Distance_To_Hydrology</th>\n",
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       "      <th>Soil_Type36</th>\n",
       "      <th>Soil_Type37</th>\n",
       "      <th>Soil_Type38</th>\n",
       "      <th>Soil_Type39</th>\n",
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       "      <td>581012.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2959.365301</td>\n",
       "      <td>155.656807</td>\n",
       "      <td>14.103704</td>\n",
       "      <td>269.428217</td>\n",
       "      <td>46.418855</td>\n",
       "      <td>2350.146611</td>\n",
       "      <td>212.146049</td>\n",
       "      <td>223.318716</td>\n",
       "      <td>142.528263</td>\n",
       "      <td>1980.291226</td>\n",
       "      <td>...</td>\n",
       "      <td>0.090392</td>\n",
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       "      <td>0.026803</td>\n",
       "      <td>0.023762</td>\n",
       "      <td>0.015060</td>\n",
       "      <td>2.051471</td>\n",
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       "      <th>std</th>\n",
       "      <td>279.984734</td>\n",
       "      <td>111.913721</td>\n",
       "      <td>7.488242</td>\n",
       "      <td>212.549356</td>\n",
       "      <td>58.295232</td>\n",
       "      <td>1559.254870</td>\n",
       "      <td>26.769889</td>\n",
       "      <td>19.768697</td>\n",
       "      <td>38.274529</td>\n",
       "      <td>1324.195210</td>\n",
       "      <td>...</td>\n",
       "      <td>0.286743</td>\n",
       "      <td>0.267725</td>\n",
       "      <td>0.052584</td>\n",
       "      <td>0.056957</td>\n",
       "      <td>0.014310</td>\n",
       "      <td>0.022641</td>\n",
       "      <td>0.161508</td>\n",
       "      <td>0.152307</td>\n",
       "      <td>0.121791</td>\n",
       "      <td>1.396504</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
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       "      <td>58.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>108.000000</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2996.000000</td>\n",
       "      <td>127.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>218.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>1997.000000</td>\n",
       "      <td>218.000000</td>\n",
       "      <td>226.000000</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>1710.000000</td>\n",
       "      <td>...</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3163.000000</td>\n",
       "      <td>260.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>384.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>3328.000000</td>\n",
       "      <td>231.000000</td>\n",
       "      <td>237.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>2550.000000</td>\n",
       "      <td>...</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3858.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>1397.000000</td>\n",
       "      <td>601.000000</td>\n",
       "      <td>7117.000000</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>7173.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Elevation         Aspect          Slope  \\\n",
       "count  581012.000000  581012.000000  581012.000000   \n",
       "mean     2959.365301     155.656807      14.103704   \n",
       "std       279.984734     111.913721       7.488242   \n",
       "min      1859.000000       0.000000       0.000000   \n",
       "25%      2809.000000      58.000000       9.000000   \n",
       "50%      2996.000000     127.000000      13.000000   \n",
       "75%      3163.000000     260.000000      18.000000   \n",
       "max      3858.000000     360.000000      66.000000   \n",
       "\n",
       "       Horizontal_Distance_To_Hydrology  Vertical_Distance_To_Hydrology  \\\n",
       "count                     581012.000000                   581012.000000   \n",
       "mean                         269.428217                       46.418855   \n",
       "std                          212.549356                       58.295232   \n",
       "min                            0.000000                     -173.000000   \n",
       "25%                          108.000000                        7.000000   \n",
       "50%                          218.000000                       30.000000   \n",
       "75%                          384.000000                       69.000000   \n",
       "max                         1397.000000                      601.000000   \n",
       "\n",
       "       Horizontal_Distance_To_Roadways  Hillshade_9am  Hillshade_Noon  \\\n",
       "count                    581012.000000  581012.000000   581012.000000   \n",
       "mean                       2350.146611     212.146049      223.318716   \n",
       "std                        1559.254870      26.769889       19.768697   \n",
       "min                           0.000000       0.000000        0.000000   \n",
       "25%                        1106.000000     198.000000      213.000000   \n",
       "50%                        1997.000000     218.000000      226.000000   \n",
       "75%                        3328.000000     231.000000      237.000000   \n",
       "max                        7117.000000     254.000000      254.000000   \n",
       "\n",
       "       Hillshade_3pm  Horizontal_Distance_To_Fire_Points  ...    Soil_Type32  \\\n",
       "count  581012.000000                       581012.000000  ...  581012.000000   \n",
       "mean      142.528263                         1980.291226  ...       0.090392   \n",
       "std        38.274529                         1324.195210  ...       0.286743   \n",
       "min         0.000000                            0.000000  ...       0.000000   \n",
       "25%       119.000000                         1024.000000  ...       0.000000   \n",
       "50%       143.000000                         1710.000000  ...       0.000000   \n",
       "75%       168.000000                         2550.000000  ...       0.000000   \n",
       "max       254.000000                         7173.000000  ...       1.000000   \n",
       "\n",
       "         Soil_Type33    Soil_Type34    Soil_Type35    Soil_Type36  \\\n",
       "count  581012.000000  581012.000000  581012.000000  581012.000000   \n",
       "mean        0.077716       0.002773       0.003255       0.000205   \n",
       "std         0.267725       0.052584       0.056957       0.014310   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       0.000000       0.000000   \n",
       "75%         0.000000       0.000000       0.000000       0.000000   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "         Soil_Type37    Soil_Type38    Soil_Type39    Soil_Type40  \\\n",
       "count  581012.000000  581012.000000  581012.000000  581012.000000   \n",
       "mean        0.000513       0.026803       0.023762       0.015060   \n",
       "std         0.022641       0.161508       0.152307       0.121791   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       0.000000       0.000000   \n",
       "75%         0.000000       0.000000       0.000000       0.000000   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "          Cover_Type  \n",
       "count  581012.000000  \n",
       "mean        2.051471  \n",
       "std         1.396504  \n",
       "min         1.000000  \n",
       "25%         1.000000  \n",
       "50%         2.000000  \n",
       "75%         2.000000  \n",
       "max         7.000000  \n",
       "\n",
       "[8 rows x 55 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column Elevation has 1978 unique values\n",
      "column Aspect has 361 unique values\n",
      "column Slope has 67 unique values\n",
      "column Horizontal_Distance_To_Hydrology has 551 unique values\n",
      "column Vertical_Distance_To_Hydrology has 700 unique values\n",
      "column Horizontal_Distance_To_Roadways has 5785 unique values\n",
      "column Hillshade_9am has 207 unique values\n",
      "column Hillshade_Noon has 185 unique values\n",
      "column Hillshade_3pm has 255 unique values\n",
      "column Horizontal_Distance_To_Fire_Points has 5827 unique values\n",
      "column Wilderness_Area1 has 2 unique values\n",
      "column Wilderness_Area2 has 2 unique values\n",
      "column Wilderness_Area3 has 2 unique values\n",
      "column Wilderness_Area4 has 2 unique values\n",
      "column Soil_Type1 has 2 unique values\n",
      "column Soil_Type2 has 2 unique values\n",
      "column Soil_Type3 has 2 unique values\n",
      "column Soil_Type4 has 2 unique values\n",
      "column Soil_Type5 has 2 unique values\n",
      "column Soil_Type6 has 2 unique values\n",
      "column Soil_Type7 has 2 unique values\n",
      "column Soil_Type8 has 2 unique values\n",
      "column Soil_Type9 has 2 unique values\n",
      "column Soil_Type10 has 2 unique values\n",
      "column Soil_Type11 has 2 unique values\n",
      "column Soil_Type12 has 2 unique values\n",
      "column Soil_Type13 has 2 unique values\n",
      "column Soil_Type14 has 2 unique values\n",
      "column Soil_Type15 has 2 unique values\n",
      "column Soil_Type16 has 2 unique values\n",
      "column Soil_Type17 has 2 unique values\n",
      "column Soil_Type18 has 2 unique values\n",
      "column Soil_Type19 has 2 unique values\n",
      "column Soil_Type20 has 2 unique values\n",
      "column Soil_Type21 has 2 unique values\n",
      "column Soil_Type22 has 2 unique values\n",
      "column Soil_Type23 has 2 unique values\n",
      "column Soil_Type24 has 2 unique values\n",
      "column Soil_Type25 has 2 unique values\n",
      "column Soil_Type26 has 2 unique values\n",
      "column Soil_Type27 has 2 unique values\n",
      "column Soil_Type28 has 2 unique values\n",
      "column Soil_Type29 has 2 unique values\n",
      "column Soil_Type30 has 2 unique values\n",
      "column Soil_Type31 has 2 unique values\n",
      "column Soil_Type32 has 2 unique values\n",
      "column Soil_Type33 has 2 unique values\n",
      "column Soil_Type34 has 2 unique values\n",
      "column Soil_Type35 has 2 unique values\n",
      "column Soil_Type36 has 2 unique values\n",
      "column Soil_Type37 has 2 unique values\n",
      "column Soil_Type38 has 2 unique values\n",
      "column Soil_Type39 has 2 unique values\n",
      "column Soil_Type40 has 2 unique values\n",
      "column Cover_Type has 7 unique values\n"
     ]
    }
   ],
   "source": [
    "for col in tree.columns:\n",
    "    print(\"column\", col, \"has\", tree[col].nunique(), \"unique values\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Cover_Type', ylabel='count'>"
      ]
     },
     "execution_count": 8,
     "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.countplot(tree[\"Cover_Type\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Elevation                             0.0\n",
       "Aspect                                0.0\n",
       "Slope                                 0.0\n",
       "Horizontal_Distance_To_Hydrology      0.0\n",
       "Vertical_Distance_To_Hydrology        0.0\n",
       "Horizontal_Distance_To_Roadways       0.0\n",
       "Hillshade_9am                         0.0\n",
       "Hillshade_Noon                        0.0\n",
       "Hillshade_3pm                         0.0\n",
       "Horizontal_Distance_To_Fire_Points    0.0\n",
       "Wilderness_Area1                      0.0\n",
       "Wilderness_Area2                      0.0\n",
       "Wilderness_Area3                      0.0\n",
       "Wilderness_Area4                      0.0\n",
       "Soil_Type1                            0.0\n",
       "Soil_Type2                            0.0\n",
       "Soil_Type3                            0.0\n",
       "Soil_Type4                            0.0\n",
       "Soil_Type5                            0.0\n",
       "Soil_Type6                            0.0\n",
       "Soil_Type7                            0.0\n",
       "Soil_Type8                            0.0\n",
       "Soil_Type9                            0.0\n",
       "Soil_Type10                           0.0\n",
       "Soil_Type11                           0.0\n",
       "Soil_Type12                           0.0\n",
       "Soil_Type13                           0.0\n",
       "Soil_Type14                           0.0\n",
       "Soil_Type15                           0.0\n",
       "Soil_Type16                           0.0\n",
       "Soil_Type17                           0.0\n",
       "Soil_Type18                           0.0\n",
       "Soil_Type19                           0.0\n",
       "Soil_Type20                           0.0\n",
       "Soil_Type21                           0.0\n",
       "Soil_Type22                           0.0\n",
       "Soil_Type23                           0.0\n",
       "Soil_Type24                           0.0\n",
       "Soil_Type25                           0.0\n",
       "Soil_Type26                           0.0\n",
       "Soil_Type27                           0.0\n",
       "Soil_Type28                           0.0\n",
       "Soil_Type29                           0.0\n",
       "Soil_Type30                           0.0\n",
       "Soil_Type31                           0.0\n",
       "Soil_Type32                           0.0\n",
       "Soil_Type33                           0.0\n",
       "Soil_Type34                           0.0\n",
       "Soil_Type35                           0.0\n",
       "Soil_Type36                           0.0\n",
       "Soil_Type37                           0.0\n",
       "Soil_Type38                           0.0\n",
       "Soil_Type39                           0.0\n",
       "Soil_Type40                           0.0\n",
       "Cover_Type                            0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.isnull().sum()*100/tree.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric = []\n",
    "\n",
    "for col in tree.columns:\n",
    "    if tree[col].nunique() > 7:\n",
    "        numeric.append(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Elevation',\n",
       " 'Aspect',\n",
       " 'Slope',\n",
       " 'Horizontal_Distance_To_Hydrology',\n",
       " 'Vertical_Distance_To_Hydrology',\n",
       " 'Horizontal_Distance_To_Roadways',\n",
       " 'Hillshade_9am',\n",
       " 'Hillshade_Noon',\n",
       " 'Hillshade_3pm',\n",
       " 'Horizontal_Distance_To_Fire_Points']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def outlier_zscore(df, col, min_z=1, max_z=5, step=0.1, print_list=False):\n",
    "    z_scores = zscore(df[col].dropna())\n",
    "    threshold_list = []\n",
    "    for threshold in np.arange(min_z, max_z, step):\n",
    "        threshold_list.append((threshold, len(np.where(z_scores > threshold)[0])))\n",
    "        df_outlier = pd.DataFrame(threshold_list, columns = [\"threshold\", \"outlier_count\"])\n",
    "        df_outlier[\"pct\"] = (df_outlier.outlier_count - df_outlier.outlier_count.shift(-1))/df_outlier.outlier_count*100\n",
    "    plt.plot(df_outlier.threshold, df_outlier.outlier_count)\n",
    "    best_threshold = round(df_outlier.iloc[df_outlier.pct.argmax(), 0], 2)\n",
    "    outlier_limit = int(df[col].dropna().mean() + (df[col].dropna().std()) * df_outlier.iloc[df_outlier.pct.argmax(), 0])\n",
    "    percentile_threshold = stats.percentileofscore(df[col].dropna(), outlier_limit)\n",
    "    plt.vlines(best_threshold, 0, df_outlier.outlier_count.max(),\n",
    "              colors=\"r\", ls=\":\")\n",
    "    plt.annotate(\"Zscore: {}\\nValue: {}\\nPercentile: {}\".format(best_threshold, outlier_limit,\n",
    "                                                               (np.round(percentile_threshold, 3),\n",
    "                                                               np.round(100-percentile_threshold, 3))),\n",
    "                (best_threshold, df_outlier.outlier_count.max()/2))\n",
    "    #plt.show()\n",
    "    if print_list:\n",
    "        print(df_outlier)\n",
    "    return (plt, df_outlier, best_threshold, outlier_limit, percentile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def outlier_inspect(df, col, min_z=1, max_z=5, step=0.5, max_hist=None, bins=50):\n",
    "    fig = plt.figure(figsize=(20, 6))\n",
    "    fig.suptitle(col, fontsize=16)\n",
    "    plt.subplot(1, 3, 1)\n",
    "    if max_hist == None:\n",
    "        sns.distplot(df[col], kde=False, bins=50)\n",
    "    else:\n",
    "        sns.distplot(df[df[col]<=max_hist][col], kde=False, bins=50)\n",
    "    plt.subplot(1, 3, 2)\n",
    "    sns.boxplot(df[col])\n",
    "    plt.subplot(1, 3, 3)\n",
    "    z_score_inspect = outlier_zscore(df, col, min_z=min_z, max_z=max_z, step=step)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in numeric:\n",
    "    outlier_inspect(tree, col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def detect_outliers(df, col_name):\n",
    "    \"\"\"\n",
    "    This function detects outliers based on 3 time IQR and returns the number of\n",
    "    lower and upper limit and number of outliers respectively\n",
    "    \"\"\"\n",
    "    first_quartile = np.percentile(np.array(df[col_name].tolist()), 25)\n",
    "    third_quartile = np.percentile(np.array(df[col_name].tolist()), 75)\n",
    "    IQR = third_quartile - first_quartile\n",
    "    \n",
    "    upper_limit = third_quartile + (3 * IQR)\n",
    "    lower_limit = first_quartile - (3 * IQR)\n",
    "    outlier_count = 0\n",
    "    \n",
    "    for value in df[col_name].tolist():\n",
    "        if (value < lower_limit) | (value > upper_limit):\n",
    "            outlier_count += 1\n",
    "    return lower_limit, upper_limit, outlier_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 275 outliers in Slope\n",
      "There are 414 outliers in Horizontal_Distance_To_Hydrology\n",
      "There are 5339 outliers in Vertical_Distance_To_Hydrology\n",
      "There are 1027 outliers in Hillshade_9am\n",
      "There are 1191 outliers in Hillshade_Noon\n",
      "There are 10 outliers in Horizontal_Distance_To_Fire_Points\n"
     ]
    }
   ],
   "source": [
    "for col in numeric:\n",
    "    if detect_outliers(tree, col)[2] > 0:\n",
    "        print(\"There are {} outliers in {}\".format(detect_outliers(tree, col)[2], col))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree1 = tree[(tree[\"Slope\"] > detect_outliers(tree, \"Slope\")[0]) &\n",
    "            (tree[\"Slope\"] < detect_outliers(tree, \"Slope\")[1])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(580640, 55)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree1 = tree1[(tree1[\"Horizontal_Distance_To_Fire_Points\"] > detect_outliers(tree1, \"Horizontal_Distance_To_Fire_Points\")[0]) &\n",
    "            (tree1[\"Horizontal_Distance_To_Fire_Points\"] < detect_outliers(tree1, \"Horizontal_Distance_To_Fire_Points\")[1])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(580630, 55)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree1 = tree1[(tree1[\"Horizontal_Distance_To_Hydrology\"] > detect_outliers(tree1, \"Horizontal_Distance_To_Hydrology\")[0]) &\n",
    "            (tree1[\"Horizontal_Distance_To_Hydrology\"] < detect_outliers(tree1, \"Horizontal_Distance_To_Hydrology\")[1])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree1 = tree1[(tree1[\"Vertical_Distance_To_Hydrology\"] > detect_outliers(tree1, \"Vertical_Distance_To_Hydrology\")[0]) &\n",
    "            (tree1[\"Vertical_Distance_To_Hydrology\"] < detect_outliers(tree1, \"Vertical_Distance_To_Hydrology\")[1])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(574967, 55)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6045"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tree) - len(tree1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree1 = tree1.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(574967, 55)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the shape of the value of 1 (210004, 55)\n",
      "the shape of the value of 2 (280193, 55)\n",
      "the shape of the value of 3 (35546, 55)\n",
      "the shape of the value of 4 (2741, 55)\n",
      "the shape of the value of 5 (9453, 55)\n",
      "the shape of the value of 6 (17345, 55)\n",
      "the shape of the value of 7 (19685, 55)\n"
     ]
    }
   ],
   "source": [
    "for i in range(1, 8):\n",
    "    print(\"the shape of the value of\", i, tree1[tree1[\"Cover_Type\"] == i].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Cover_Type', ylabel='Size'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAggAAAF0CAYAAABG9MFEAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAaDElEQVR4nO3dfbCedX3n8fenxFKKYnmISBO6YYW6FXaKEiNbuopmC9S6BbewxJkK09KNwyCrU3cd0JnS1c1sXas4dlc6FCJglYcFXdkOFFNQ8REIFA0PIqlQiVCIDYvoFhT87h/37wx3zu/k5AA550rOeb9m7rnv+3tfv+v6XjhjPud3PaWqkCRJGvczQzcgSZJ2PgYESZLUMSBIkqSOAUGSJHUMCJIkqWNAkCRJnUVDN7Cz2G+//WrZsmVDtyFJ0py59dZbv19Vi6f6zYDQLFu2jPXr1w/dhiRJcybJ32/rNw8xSJKkjgFBkiR1DAiSJKljQJAkSR0DgiRJ6hgQJElSx4AgSZI6BgRJktQxIEiSpI4BQZIkdQwIkiSpY0CQJEkdA4IkSer4NEdt13ff9y+HbmGH+aU/2jB0C5K0S3AGQZIkdQwIkiSpY0CQJEkdA4IkSeoYECRJUseAIEmSOgYESZLUMSBIkqSOAUGSJHUMCJIkqWNAkCRJHQOCJEnqGBAkSVLHgCBJkjoGBEmS1DEgSJKkjgFBkiR1DAiSJKljQJAkSR0DgiRJ6hgQJElSx4AgSZI6BgRJktQxIEiSpI4BQZIkdQwIkiSpY0CQJEkdA4IkSeoYECRJUmfWAkKSA5N8PsndSe5M8o5W/+Mk30tye3u9cWzM2Uk2JrknybFj9SOSbGi/fTRJWn33JJe3+k1Jlo2NOTXJve116mztpyRJ89GiWVz3U8C7quq2JC8Cbk2yrv12blX96fjCSV4BrAIOBX4R+Jskv1xVTwPnAauBrwPXAMcB1wKnAY9W1cFJVgEfAE5Osg9wDrAcqLbtq6vq0VncX0mS5o1Zm0Goqoeq6rb2+XHgbmDJNEOOBy6rqier6j5gI7AiyQHAXlX1taoq4BLghLExF7fPVwIr2+zCscC6qtrSQsE6RqFCkiTNwJycg9Cm/l8J3NRKb0/yzSRrk+zdakuAB8aGbWq1Je3z5PpWY6rqKeAxYN9p1iVJkmZg1gNCkhcCVwHvrKofMDpc8DLgcOAh4EMTi04xvKapP9cx472tTrI+yfrNmzdPtxuSJC0osxoQkryAUTj4ZFV9GqCqHq6qp6vqp8BfACva4puAA8eGLwUebPWlU9S3GpNkEfBiYMs069pKVZ1fVcuravnixYufz65KkjSvzOZVDAEuBO6uqg+P1Q8YW+zNwB3t89XAqnZlwkHAIcDNVfUQ8HiSI9s6TwE+OzZm4gqFE4Eb2nkK1wHHJNm7HcI4ptUkSdIMzOZVDEcBbwU2JLm91d4DvCXJ4Yym/O8H3gZQVXcmuQK4i9EVEGe0KxgATgcuAvZgdPXCta1+IfCJJBsZzRysauvakuT9wC1tufdV1ZZZ2UtJkuahWQsIVfVlpj4X4JppxqwB1kxRXw8cNkX9CeCkbaxrLbB2pv1KkqRneCdFSZLUMSBIkqSOAUGSJHUMCJIkqWNAkCRJHQOCJEnqGBAkSVLHgCBJkjoGBEmS1DEgSJKkjgFBkiR1DAiSJKljQJAkSR0DgiRJ6hgQJElSx4AgSZI6BgRJktQxIEiSpI4BQZIkdQwIkiSpY0CQJEkdA4IkSeoYECRJUseAIEmSOgYESZLUMSBIkqSOAUGSJHUMCJIkqWNAkCRJHQOCJEnqGBAkSVLHgCBJkjoGBEmS1DEgSJKkjgFBkiR1DAiSJKljQJAkSR0DgiRJ6hgQJElSZ9HQDewKjvjPlwzdwg5z6wdPGboFSdIuwBkESZLUMSBIkqSOAUGSJHUMCJIkqWNAkCRJHQOCJEnqGBAkSVJn1gJCkgOTfD7J3UnuTPKOVt8nybok97b3vcfGnJ1kY5J7khw7Vj8iyYb220eTpNV3T3J5q9+UZNnYmFPbNu5Ncups7ackSfPRbM4gPAW8q6p+BTgSOCPJK4CzgOur6hDg+vad9tsq4FDgOOBjSXZr6zoPWA0c0l7HtfppwKNVdTBwLvCBtq59gHOA1wArgHPGg4gkSZrerAWEqnqoqm5rnx8H7gaWAMcDF7fFLgZOaJ+PBy6rqier6j5gI7AiyQHAXlX1taoq4JJJYybWdSWwss0uHAusq6otVfUosI5nQoUkSdqOOTkHoU39vxK4Cdi/qh6CUYgAXtIWWwI8MDZsU6staZ8n17caU1VPAY8B+06zrsl9rU6yPsn6zZs3P489lCRpfpn1gJDkhcBVwDur6gfTLTpFraapP9cxzxSqzq+q5VW1fPHixdO0JknSwjKrASHJCxiFg09W1adb+eF22ID2/kirbwIOHBu+FHiw1ZdOUd9qTJJFwIuBLdOsS5IkzcBsXsUQ4ELg7qr68NhPVwMTVxWcCnx2rL6qXZlwEKOTEW9uhyEeT3JkW+cpk8ZMrOtE4IZ2nsJ1wDFJ9m4nJx7TapIkaQZm83HPRwFvBTYkub3V3gP8CXBFktOA7wInAVTVnUmuAO5idAXEGVX1dBt3OnARsAdwbXvBKIB8IslGRjMHq9q6tiR5P3BLW+59VbVllvZTkqR5Z9YCQlV9manPBQBYuY0xa4A1U9TXA4dNUX+CFjCm+G0tsHam/UqSpGd4J0VJktQxIEiSpI4BQZIkdQwIkiSpY0CQJEkdA4IkSeoYECRJUseAIEmSOgYESZLUMSBIkqSOAUGSJHUMCJIkqWNAkCRJHQOCJEnqGBAkSVLHgCBJkjoGBEmS1DEgSJKkjgFBkiR1DAiSJKljQJAkSR0DgiRJ6hgQJElSx4AgSZI6BgRJktQxIEiSpI4BQZIkdQwIkiSpY0CQJEkdA4IkSeoYECRJUseAIEmSOgYESZLUMSBIkqSOAUGSJHUMCJIkqWNAkCRJHQOCJEnqGBAkSVLHgCBJkjoGBEmS1DEgSJKkjgFBkiR1DAiSJKljQJAkSZ0ZBYQk+ye5MMm17fsrkpw2u61JkqShzHQG4SLgOuAX2/dvA++cbkCStUkeSXLHWO2Pk3wvye3t9cax385OsjHJPUmOHasfkWRD++2jSdLquye5vNVvSrJsbMypSe5tr1NnuI+SJKmZaUDYr6quAH4KUFVPAU9vZ8xFwHFT1M+tqsPb6xoYzUgAq4BD25iPJdmtLX8esBo4pL0m1nka8GhVHQycC3ygrWsf4BzgNcAK4Jwke89wPyVJEjMPCD9Ksi9QAEmOBB6bbkBV3QhsmeH6jwcuq6onq+o+YCOwIskBwF5V9bWqKuAS4ISxMRe3z1cCK9vswrHAuqraUlWPAuuYOqhIkqRtmGlAeBdwNfCyJF9h9A/1f3yO23x7km+2QxATf9kvAR4YW2ZTqy1pnyfXtxrTZjQeA/adZl2SJGmGZhQQqupW4HXArwFvAw6tqm88h+2dB7wMOBx4CPhQq2eqzU5Tf65jtpJkdZL1SdZv3rx5mrYlSVpYZnoVw98Bf1BVd1bVHVX1kyR/9Ww3VlUPV9XTVfVT4C8YnSMAo7/yDxxbdCnwYKsvnaK+1Zgki4AXMzqksa11TdXP+VW1vKqWL168+NnujiRJ89ZMDzH8BHh9ko8n+dlWe9bT9u2cgglvBiaucLgaWNWuTDiI0cmIN1fVQ8DjSY5s5xecAnx2bMzEFQonAje08xSuA45Jsnc7hHFMq0mSpBlaNMPl/l9VnZzk3cCXkvx7tjFtPyHJpcDRwH5JNjG6suDoJIe3sfczOlxBVd2Z5ArgLuAp4IyqmrhK4nRGV0TsAVzbXgAXAp9IspHRzMGqtq4tSd4P3NKWe19VzfRkSUmSxMwDQgCq6r8nuZXRX+T7TDegqt4yRfnCaZZfA6yZor4eOGyK+hPASdtY11pg7XT9SZKkbZtpQPijiQ9VdX27kZE3IJIkaZ6aNiAk+RdV9S3ge0leNennZ32SoiRJ2jVsbwbhDxndxXDicsTJ5x28YYd3JEmSBre9qxguSPLSqnp9Vb2e0Z0Lf8jo6oMTZ707SZI0iO0FhD8HfgyQ5LXAf2MUEh4Dzp/d1iRJ0lC2d4hht7FLBE8Gzq+qq4Crktw+q51JkqTBbG8GYbd2l0KAlcANY7/N9AoISZK0i9neP/KXAl9M8n3gn4AvASQ5mO08zVGSJO26pg0IVbUmyfXAAcDn2q2MYTTzcOZsNydJkoax3cMEVfX1KWrfnp12JEnSzmCmD2uSJEkLiAFBkiR1DAiSJKljQJAkSR0DgiRJ6hgQJElSx4AgSZI6BgRJktQxIEiSpI4BQZIkdQwIkiSpY0CQJEkdA4IkSeoYECRJUseAIEmSOgYESZLUMSBIkqSOAUGSJHUMCJIkqWNAkCRJHQOCJEnqGBAkSVLHgCBJkjoGBEmS1DEgSJKkjgFBkiR1DAiSJKljQJAkSR0DgiRJ6hgQJElSx4AgSZI6BgRJktQxIEiSpI4BQZIkdQwIkiSpY0CQJEmdWQsISdYmeSTJHWO1fZKsS3Jve9977Lezk2xMck+SY8fqRyTZ0H77aJK0+u5JLm/1m5IsGxtzatvGvUlOna19lCRpvprNGYSLgOMm1c4Crq+qQ4Dr23eSvAJYBRzaxnwsyW5tzHnAauCQ9ppY52nAo1V1MHAu8IG2rn2Ac4DXACuAc8aDiCRJ2r5ZCwhVdSOwZVL5eODi9vli4ISx+mVV9WRV3QdsBFYkOQDYq6q+VlUFXDJpzMS6rgRWttmFY4F1VbWlqh4F1tEHFUmSNI25Pgdh/6p6CKC9v6TVlwAPjC23qdWWtM+T61uNqaqngMeAfadZlyRJmqGd5STFTFGraerPdczWG01WJ1mfZP3mzZtn1KgkSQvBXAeEh9thA9r7I62+CThwbLmlwIOtvnSK+lZjkiwCXszokMa21tWpqvOranlVLV+8ePHz2C1JkuaXuQ4IVwMTVxWcCnx2rL6qXZlwEKOTEW9uhyEeT3JkO7/glEljJtZ1InBDO0/hOuCYJHu3kxOPaTVJkjRDi2ZrxUkuBY4G9kuyidGVBX8CXJHkNOC7wEkAVXVnkiuAu4CngDOq6um2qtMZXRGxB3BtewFcCHwiyUZGMwer2rq2JHk/cEtb7n1VNflkSUmSNI1ZCwhV9ZZt/LRyG8uvAdZMUV8PHDZF/QlawJjit7XA2hk3K0mStrKznKQoSZJ2IgYESZLUMSBIkqSOAUGSJHUMCJIkqWNAkCRJHQOCJEnqGBAkSVLHgCBJkjoGBEmS1DEgSJKkjgFBkiR1DAiSJKljQJAkSR0DgiRJ6hgQJElSx4AgSZI6BgRJktQxIEiSpI4BQZIkdQwIkiSpY0CQJEkdA4IkSeoYECRJUseAIEmSOgYESZLUMSBIkqSOAUGSJHUMCJIkqWNAkCRJHQOCJEnqGBAkSVLHgCBJkjoGBEmS1DEgSJKkjgFBkiR1DAiSJKljQJAkSR0DgiRJ6hgQJElSx4AgSZI6BgRJktQxIEiSpI4BQZIkdQwIkiSpY0CQJEkdA4IkSeoMEhCS3J9kQ5Lbk6xvtX2SrEtyb3vfe2z5s5NsTHJPkmPH6ke09WxM8tEkafXdk1ze6jclWTbnOylJ0i5syBmE11fV4VW1vH0/C7i+qg4Brm/fSfIKYBVwKHAc8LEku7Ux5wGrgUPa67hWPw14tKoOBs4FPjAH+yNJ0ryxMx1iOB64uH2+GDhhrH5ZVT1ZVfcBG4EVSQ4A9qqqr1VVAZdMGjOxriuBlROzC5IkafuGCggFfC7JrUlWt9r+VfUQQHt/SasvAR4YG7up1Za0z5PrW42pqqeAx4B9JzeRZHWS9UnWb968eYfsmCRJ88GigbZ7VFU9mOQlwLok35pm2an+8q9p6tON2bpQdT5wPsDy5cu73yVJWqgGmUGoqgfb+yPAZ4AVwMPtsAHt/ZG2+CbgwLHhS4EHW33pFPWtxiRZBLwY2DIb+yJJ0nw05wEhyZ5JXjTxGTgGuAO4Gji1LXYq8Nn2+WpgVbsy4SBGJyPe3A5DPJ7kyHZ+wSmTxkys60TghnaegiRJmoEhDjHsD3ymnTO4CPhUVf11kluAK5KcBnwXOAmgqu5McgVwF/AUcEZVPd3WdTpwEbAHcG17AVwIfCLJRkYzB6vmYsckSZov5jwgVNV3gF+dov6PwMptjFkDrJmivh44bIr6E7SAIUmSnr2d6TJHSZK0kzAgSJKkjgFBkiR1DAiSJKljQJAkSR0DgiRJ6hgQJElSx4AgSZI6BgRJktQxIEiSpM5Qj3uWdglH/dlRQ7eww3zlzK8M3YKkXYgzCJIkqWNAkCRJHQOCJEnqGBAkSVLHgCBJkjoGBEmS1DEgSJKkjgFBkiR1DAiSJKljQJAkSR0DgiRJ6hgQJElSx4AgSZI6BgRJktQxIEiSpI4BQZIkdQwIkiSps2joBiRJ2tncveaGoVvYYX7lvW94TuOcQZAkSR0DgiRJ6niIQZI0pTW/e+LQLeww7/3LK4duYZfjDIIkSeoYECRJUseAIEmSOgYESZLUMSBIkqSOAUGSJHUMCJIkqeN9ECRpG/7Hu/7P0C3sMG//0L8dugXtYpxBkCRJHQOCJEnqGBAkSVLHgCBJkjoGBEmS1DEgSJKkzrwOCEmOS3JPko1Jzhq6H0mSdhXz9j4ISXYD/ifwG8Am4JYkV1fVXcN2Ju06vvja1w3dwg7zuhu/OHQL0i5lPs8grAA2VtV3qurHwGXA8QP3JEnSLmE+B4QlwANj3ze1miRJ2o5U1dA9zIokJwHHVtUftO9vBVZU1Zljy6wGVrevLwfumfNGn7Ef8P0Btz8099/9X6j7v5D3Hdz/off/n1XV4ql+mLfnIDCaMThw7PtS4MHxBarqfOD8uWxqW5Ksr6rlQ/cxFPff/V+o+7+Q9x3c/515/+fzIYZbgEOSHJTkZ4FVwNUD9yRJ0i5h3s4gVNVTSd4OXAfsBqytqjsHbkuSpF3CvA0IAFV1DXDN0H3M0E5xqGNA7v/CtpD3fyHvO7j/O+3+z9uTFCVJ0nM3n89BkCRJz5EBYWBJ1iZ5JMkdQ/cyhCQHJvl8kruT3JnkHUP3NFeS/FySm5N8o+37fxm6pyEk2S3J3yb5q6F7mWtJ7k+yIcntSdYP3c9cS/ILSa5M8q32/wH/auie5kqSl7f/3SdeP0jyzqH7GuchhoEleS3wQ+CSqjps6H7mWpIDgAOq6rYkLwJuBU5YCLfEThJgz6r6YZIXAF8G3lFVXx+4tTmV5A+B5cBeVfWmofuZS0nuB5ZX1YK8D0CSi4EvVdUF7Wqzn6+q/ztwW3OuPRrge8Brqurvh+5ngjMIA6uqG4EtQ/cxlKp6qKpua58fB+5mgdzxskZ+2L6+oL0WVGJPshT4LeCCoXvR3EqyF/Ba4EKAqvrxQgwHzUrg73amcAAGBO1EkiwDXgncNHArc6ZNr98OPAKsq6oFs+/NR4B3Az8duI+hFPC5JLe2O7suJP8c2Ax8vB1iuiDJnkM3NZBVwKVDNzGZAUE7hSQvBK4C3llVPxi6n7lSVU9X1eGM7vS5IsmCOcyU5E3AI1V169C9DOioqnoV8JvAGe2Q40KxCHgVcF5VvRL4EXDWsC3NvXZo5beB/zV0L5MZEDS4dvz9KuCTVfXpofsZQpta/QJw3LCdzKmjgN9ux+EvA96Q5C+HbWluVdWD7f0R4DOMnkK7UGwCNo3Nml3JKDAsNL8J3FZVDw/dyGQGBA2qnah3IXB3VX146H7mUpLFSX6hfd4D+DfAtwZtag5V1dlVtbSqljGaYr2hqn534LbmTJI924m5tKn1Y4AFczVTVf0D8ECSl7fSSmDen5w8hbewEx5egHl+J8VdQZJLgaOB/ZJsAs6pqguH7WpOHQW8FdjQjsUDvKfdBXO+OwC4uJ3B/DPAFVW14C71W8D2Bz4zysgsAj5VVX89bEtz7kzgk22a/TvA7w3cz5xK8vPAbwBvG7qXqXiZoyRJ6niIQZIkdQwIkiSpY0CQJEkdA4IkSeoYECRJUseAIEmSOt4HQdKUkryU0bMSXg08CdzP6FbY356Dbe8LXN++vhR4mtF9+wFWVNWPZ7sHaaHzPgiSOu0Ol18FLq6qP2+1w4EXVdWXdvC2dquqp6f5/Y+BH1bVn+7I7UqanocYJE3l9cBPJsIBQFXdDnw5yQeT3JFkQ5KTAZJcnuSNE8smuSjJ77SnVX4wyS1Jvpnkbe33o5N8PsmngA0zaSjJi5Lc157dQZK9ktyf5AVJvpDkI0m+2npb0ZbZM8natv2/TXL8jvoPJM13HmKQNJXDgKmesvjvgMOBXwX2A25JciOjhy2dDFzTbpu7EjgdOA14rKpenWR34CtJPtfWtQI4rKrum0lDVfV4ki8AvwX8b0bPb7iqqn7Sble8Z1X9Wnsi4tq2D+9l9IyH32/Pvbg5yd9U1Y+e1X8NaQFyBkHSs/HrwKXtMdUPA19kdI7CtYyexrg7o6fT3VhV/8ToAUSntOds3ATsCxzS1nXzTMPBmAt45n79vwd8fOy3SwGq6kZgrxYIjgHOatv/AvBzwC89y21KC5IzCJKmcidw4hT1TLVwVT3R/ro/ltFMwqVjy59ZVddttZLkaOBZ/xVfVV9JsizJ64Ddqmr86YeTT6iqtv3fqap7nu22pIXOGQRJU7kB2D3Jf5goJHk18Chwcju3YDHwWuDmtshljP6q/9fARCC4Djh97LyBX26PNn4+LmEUQD4+qT5xPsSvMzqs8Vjb/pntpEuSvPJ5bltaMJxBkNSpqkryZuAjSc4CnqBd5gi8EPgGo7/Q311V/9CGfY7RP95Xj12GeAGwDLit/SO9GTjhebb3SeC/8swsxYRHk3wV2Av4/VZ7P6NLNb/Ztn8/8KbnuX1pQfAyR0m7lCQnAsdX1VvHal8A/lNVrR+sMWmecQZB0i4jyZ8xOgnyjdtbVtLz4wyCpEFNumviuJVV9Y9z3Y+kEQOCJEnqeBWDJEnqGBAkSVLHgCBJkjoGBEmS1DEgSJKkzv8HG+rYlTBkqPkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_tree = tree1.groupby(\"Cover_Type\").size()\n",
    "class_label = pd.DataFrame(class_tree, columns=[\"Size\"])\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.barplot(x=class_label.index, y=\"Size\", data=class_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As we can see from the graph above, the dataset is highly imbalanced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cover_Type 1\n",
      "0.37\n",
      "Cover_Type 2\n",
      "0.49\n",
      "Cover_Type 3\n",
      "0.06\n",
      "Cover_Type 4\n",
      "0.00\n",
      "Cover_Type 5\n",
      "0.02\n",
      "Cover_Type 6\n",
      "0.03\n",
      "Cover_Type 7\n",
      "0.03\n"
     ]
    }
   ],
   "source": [
    "for i, number in enumerate(class_tree):\n",
    "    percent = (number / class_tree.sum())\n",
    "    print(\"Cover_Type\", class_tree.index[i])\n",
    "    print(\"%.2f\"% percent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x936 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_matrix = tree1[numeric].corr()\n",
    "\n",
    "plt.figure(figsize=(25, 13))\n",
    "sns.heatmap(corr_matrix, square=True, annot=True, linewidth=.5, vmin=-1, vmax=1, cmap=\"viridis\")\n",
    "plt.title(\"Correlation Matrix\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree1.to_csv(\"covtype_EDA.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creation of the connection and importing dataframe into database.\n",
    "\n",
    "with sqlite3.connect(\"tree_database.db\") as cnnct:\n",
    "    tree1.to_sql(\"covtype2\", cnnct, if_exists=\"replace\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "query1 = \"\"\"SELECT *,\n",
    "(Horizontal_Distance_To_Hydrology*Horizontal_Distance_To_Hydrology)+(Vertical_Distance_To_Hydrology*Vertical_Distance_To_Hydrology) as Square_Hypo_Distance,\n",
    "(Horizontal_Distance_To_Hydrology + Horizontal_Distance_To_Roadways)/2 as Average_Dist_Road_Hydro, (Elevation + Vertical_Distance_To_Hydrology) /2 as Average_Elevation_Hydro\n",
    "FROM covtype2;\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "query2 = \"\"\"SELECT Elevation, Aspect, Slope, Horizontal_Distance_To_Roadways, Hillshade_9am, Hillshade_Noon, Horizontal_Distance_To_Fire_Points, Wilderness_Area1, Wilderness_Area2, Wilderness_Area3,\n",
    "Wilderness_Area4, Soil_Type1, Soil_Type2, Soil_Type3, Soil_Type4, Soil_Type5, Soil_Type6,\n",
    "Soil_Type9, Soil_Type10, Soil_Type11, Soil_Type12, Soil_Type13, Soil_Type16, Soil_Type17, \n",
    "Soil_Type18, Soil_Type19, Soil_Type20, Soil_Type22, Soil_Type23, Soil_Type24, \n",
    "Soil_Type26, Soil_Type27, Soil_Type29, Soil_Type30, Soil_Type31, Soil_Type32, Soil_Type33, \n",
    "Soil_Type34, Soil_Type35, Soil_Type38, Soil_Type39, Soil_Type40, Cover_Type,\n",
    "Square_Hypo_Distance, Average_Dist_Road_Hydro, Average_Elevation_Hydro\n",
    "FROM covtype2;\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "with sqlite3.connect(\"tree_database.db\") as cnnct:\n",
    "    df1 = pd.read_sql_query(query1, cnnct)  # transforming and exporting to df1\n",
    "    df1.to_sql(\"covtype2\", cnnct, if_exists = \"replace\")  # importing again to dropping columns\n",
    "    df2 = pd.read_sql_query(query2, cnnct)  # exporting table to df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.to_csv(\"covtype2.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"covtype2.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>Elevation</th>\n",
       "      <th>Aspect</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Horizontal_Distance_To_Roadways</th>\n",
       "      <th>Hillshade_9am</th>\n",
       "      <th>Hillshade_Noon</th>\n",
       "      <th>Horizontal_Distance_To_Fire_Points</th>\n",
       "      <th>Wilderness_Area1</th>\n",
       "      <th>Wilderness_Area2</th>\n",
       "      <th>Wilderness_Area3</th>\n",
       "      <th>...</th>\n",
       "      <th>Soil_Type33</th>\n",
       "      <th>Soil_Type34</th>\n",
       "      <th>Soil_Type35</th>\n",
       "      <th>Soil_Type38</th>\n",
       "      <th>Soil_Type39</th>\n",
       "      <th>Soil_Type40</th>\n",
       "      <th>Cover_Type</th>\n",
       "      <th>Square_Hypo_Distance</th>\n",
       "      <th>Average_Dist_Road_Hydro</th>\n",
       "      <th>Average_Elevation_Hydro</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2596</td>\n",
       "      <td>51</td>\n",
       "      <td>3</td>\n",
       "      <td>510</td>\n",
       "      <td>221</td>\n",
       "      <td>232</td>\n",
       "      <td>6279</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>66564</td>\n",
       "      <td>384</td>\n",
       "      <td>1298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2590</td>\n",
       "      <td>56</td>\n",
       "      <td>2</td>\n",
       "      <td>390</td>\n",
       "      <td>220</td>\n",
       "      <td>235</td>\n",
       "      <td>6225</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>44980</td>\n",
       "      <td>301</td>\n",
       "      <td>1292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2804</td>\n",
       "      <td>139</td>\n",
       "      <td>9</td>\n",
       "      <td>3180</td>\n",
       "      <td>234</td>\n",
       "      <td>238</td>\n",
       "      <td>6121</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>76049</td>\n",
       "      <td>1724</td>\n",
       "      <td>1434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2785</td>\n",
       "      <td>155</td>\n",
       "      <td>18</td>\n",
       "      <td>3090</td>\n",
       "      <td>238</td>\n",
       "      <td>238</td>\n",
       "      <td>6211</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>72488</td>\n",
       "      <td>1666</td>\n",
       "      <td>1451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2595</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>391</td>\n",
       "      <td>220</td>\n",
       "      <td>234</td>\n",
       "      <td>6172</td>\n",
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       "      <td>23410</td>\n",
       "      <td>272</td>\n",
       "      <td>1297</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Elevation  Aspect  Slope  Horizontal_Distance_To_Roadways  Hillshade_9am  \\\n",
       "0       2596      51      3                              510            221   \n",
       "1       2590      56      2                              390            220   \n",
       "2       2804     139      9                             3180            234   \n",
       "3       2785     155     18                             3090            238   \n",
       "4       2595      45      2                              391            220   \n",
       "\n",
       "   Hillshade_Noon  Horizontal_Distance_To_Fire_Points  Wilderness_Area1  \\\n",
       "0             232                                6279                 1   \n",
       "1             235                                6225                 1   \n",
       "2             238                                6121                 1   \n",
       "3             238                                6211                 1   \n",
       "4             234                                6172                 1   \n",
       "\n",
       "   Wilderness_Area2  Wilderness_Area3  ...  Soil_Type33  Soil_Type34  \\\n",
       "0                 0                 0  ...            0            0   \n",
       "1                 0                 0  ...            0            0   \n",
       "2                 0                 0  ...            0            0   \n",
       "3                 0                 0  ...            0            0   \n",
       "4                 0                 0  ...            0            0   \n",
       "\n",
       "   Soil_Type35  Soil_Type38  Soil_Type39  Soil_Type40  Cover_Type  \\\n",
       "0            0            0            0            0           5   \n",
       "1            0            0            0            0           5   \n",
       "2            0            0            0            0           2   \n",
       "3            0            0            0            0           2   \n",
       "4            0            0            0            0           5   \n",
       "\n",
       "   Square_Hypo_Distance  Average_Dist_Road_Hydro  Average_Elevation_Hydro  \n",
       "0                 66564                      384                     1298  \n",
       "1                 44980                      301                     1292  \n",
       "2                 76049                     1724                     1434  \n",
       "3                 72488                     1666                     1451  \n",
       "4                 23410                      272                     1297  \n",
       "\n",
       "[5 rows x 46 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(574967, 46)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(\"Cover_Type\", axis=1)\n",
    "y = df[\"Cover_Type\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_classifier = XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20:28:02] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost_1607604574104/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "              importance_type='gain', interaction_constraints='',\n",
       "              learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=100, n_jobs=8, num_parallel_tree=1,\n",
       "              objective='multi:softprob', random_state=0, reg_alpha=0,\n",
       "              reg_lambda=1, scale_pos_weight=None, subsample=1,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predicted = xgb_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_accuracy = accuracy_score(y_test, y_predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8714599602298091"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.86      0.84      0.85     62911\n",
      "           2       0.87      0.90      0.88     84187\n",
      "           3       0.90      0.91      0.91     10576\n",
      "           4       0.89      0.87      0.88       838\n",
      "           5       0.88      0.60      0.71      2877\n",
      "           6       0.86      0.82      0.84      5224\n",
      "           7       0.95      0.91      0.93      5878\n",
      "\n",
      "    accuracy                           0.87    172491\n",
      "   macro avg       0.89      0.84      0.86    172491\n",
      "weighted avg       0.87      0.87      0.87    172491\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#XGBoost performed very well!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/base.py:209: FutureWarning: From version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an instance attribute. Previously it would return None.\n",
      "  warnings.warn('From version 0.24, get_params will raise an '\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Class Prediction Error for XGBClassifier'}, xlabel='actual class', ylabel='number of predicted class'>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visualizer = ClassPredictionError(xgb_classifier)\n",
    "\n",
    "visualizer.fit(X_train, y_train)\n",
    "visualizer.score(X_test, y_test)\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/base.py:209: FutureWarning: From version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an instance attribute. Previously it would return None.\n",
      "  warnings.warn('From version 0.24, get_params will raise an '\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'XGBClassifier Confusion Matrix'}, xlabel='Predicted Class', ylabel='True Class'>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = ConfusionMatrix(xgb_classifier)\n",
    "\n",
    "cm.fit(X_train, y_train)\n",
    "cm.score(X_test, y_test)\n",
    "cm.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'XGBClassifier Classification Report'}>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visualizer = ClassificationReport(xgb_classifier, support=True)\n",
    "visualizer.fit(X_train, y_train)\n",
    "visualizer.score(X_test, y_test)\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's try Decision Tree now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelTree = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelTree.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = modelTree.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree_accuracy = accuracy_score(pred, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9352314033775675"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# It performed even better than XGBoost."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/base.py:209: FutureWarning: From version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an instance attribute. Previously it would return None.\n",
      "  warnings.warn('From version 0.24, get_params will raise an '\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Class Prediction Error for DecisionTreeClassifier'}, xlabel='actual class', ylabel='number of predicted class'>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visualizer = ClassPredictionError(modelTree)\n",
    "\n",
    "visualizer.fit(X_train, y_train)\n",
    "visualizer.score(X_test, y_test)\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/base.py:209: FutureWarning: From version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an instance attribute. Previously it would return None.\n",
      "  warnings.warn('From version 0.24, get_params will raise an '\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'DecisionTreeClassifier Confusion Matrix'}, xlabel='Predicted Class', ylabel='True Class'>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = ConfusionMatrix(modelTree)\n",
    "cm.fit(X_train, y_train)\n",
    "cm.score(X_test, y_test)\n",
    "cm.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/base.py:209: FutureWarning: From version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an instance attribute. Previously it would return None.\n",
      "  warnings.warn('From version 0.24, get_params will raise an '\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'DecisionTreeClassifier Classification Report'}>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visualizer = ClassificationReport(modelTree, support=True)\n",
    "\n",
    "visualizer.fit(X_train, y_train)\n",
    "visualizer.score(X_test, y_test)\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's try KNN now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "neighbors = np.arange(1, 7)\n",
    "train_accuracy = np.empty(len(neighbors))\n",
    "test_accuracy = np.empty(len(neighbors))\n",
    "\n",
    "for i, k in enumerate(neighbors):\n",
    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
    "    knn.fit(X_train, y_train)\n",
    "    train_accuracy[i] = knn.score(X_train, y_train)\n",
    "    test_accuracy[i] = knn.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.title(\"k-NN assessment of number of neighbors\")\n",
    "plt.plot(neighbors, test_accuracy, label=\"Accuracy of Test Data\")\n",
    "plt.plot(neighbors, train_accuracy, label=\"Accuracy of Traininig Data\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"Number of Neighbors\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn5 = KNeighborsClassifier(n_neighbors=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn5.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_accuracy = knn5.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7210115310364019"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/base.py:209: FutureWarning: From version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an instance attribute. Previously it would return None.\n",
      "  warnings.warn('From version 0.24, get_params will raise an '\n"
     ]
    },
    {
     "data": {
      "image/png": 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uTLdu3UhMTGTgwIE89NBDwM3v22uvvUZGRgbVq1dn2rRpdxyjV69eTJkyxfZ+UFAQLVu2xGQy8eGHHzJ//ny6deuGyWTC3d2d3r17236/bnnqqafYunUrgYGB1K5dmzFjxvD6668THh6e4/heXl53bL99z0NOwsLCmDRpku3fQdeuXalcubK24MUuk54HLyKFzWq12q753717N/Pnz7/jnhgRyTvtoheRQnXlyhUef/xxzp8/D8A///nPLCfqiUj+0Ba8iBS6L774gqVLlwJQu3ZtZsyYkatLOUXEPgW8iIiIAWkXvYiIiAEZ+ix6i8VCSkoKrq6uDj3EQ0RESh6r1Up6ejqlS5fO00OqjMrQAZ+SkkJcXFxRlyEiIoWgTp06d7wp0/3K0AF/68ERderUyfJAk+Lg+PHj+Pv7F3UZ98wo8wDNpTgyyjxAcylIZrOZuLi4bA8Lut8ZOuBv7ZZ3c3PLduOR4qA41pQXRpkHaC7FkVHmAZpLQdOh2Kx0sEJERMSAFPAiIiIGZOhd9JKd86hP83/QlT/k63CZc1/O1/FERO5H2oIXERExIAW8iIiIAWkX/X3mnRceL+oSRESkEGgLXkRExIAU8CIiIgakgBcRETEgBbyIiIgB6SS7+0zXzPpFXYIDMoq6ABGREk9b8CIiIgakgBcRETEgBbyIiIgBKeBFREQMSAEvIiJiQDqL/j6T1K6oK3BAclEXICJS8mkLXkRExIAU8CIiIgakgBcRETEgBbyIiIgBKeBFREQMSAEvIiJiQAp4ERERA1LAi4iIGJACXkRExIB0J7v7zInto4u6BLuaF3UBIiIGoC14ERERA1LAi4iIGJACXkRExIAU8CIiIgakgBcRETEgBbyIiIgBKeBFREQMSAEvIiJiQAp4ERERA1LAi4iIGJACXkRExIAK7F70KSkpjB07luvXr5OWlsaQIUPw9/dn7NixJCUlUblyZebMmYObmxtRUVEsWbKEtLQ0+vTpQ48ePcjMzCQsLIy4uDgAZs+eTfXq1fn1118JDQ0lNTUVf39/pkyZgslkKqhpiIiIlEgFtgW/YcMGatWqxaeffsr777/PO++8w6xZs+jevTtr1qyhatWqbN68meTkZMLDw1myZAmrVq1iyZIlpKSksHHjRkwmE6tXr2bw4MFEREQAEBoaypgxY1i/fj2JiYnExMQU1BRERERKrAIL+AoVKpCQkADAtWvX8Pb25uDBgwQFBQEQHBxMdHQ0x44do2HDhpQtWxZPT0+aNm3KoUOHOHDgAMHBwQC0bt2agwcPYjabOX36NI0bNwYgKCiI6OjogpqCiIhIiVVgu+g7d+7MunXr6NChA9euXWPRokUMHjwYDw8PALy9vYmPj+ePP/7A29vbtpyPj0+2dhcXFzIzM0lMTKR8+fJZ+u7fv99uLcePH8/fyeWT2NjYoi6hWCrK74uRfiZGmYtR5gGaixSuAgv4TZs2UaVKFSIjI/nxxx95++23cXV1tb1vtVoxmUxZ2u7WDuDs7HzHvvb4+/vj7u6ex5kUjNjYWAICAgp9vcei1xb6OnOrKL4vUHQ/k4JglLkYZR6guRSktLS0YrshV5QKbBf94cOHadOmDQD16tXj8uXLuLu7k5qaCkB8fDy+vr5UqlTJtis/p3az2Yyrqyve3t5cv349W18RERHJqsACvkaNGrZPVJcuXaJ06dK0bduWnTt3AhAVFUVgYCCNGjXip59+IikpiZSUFI4ePUqzZs1o06aNre/u3btp1aoVTk5O1K9fn8OHD2cZQ0RERLIqsF30L7zwAiEhIfTp04f09HSmTJlC7dq1GTVqFJGRkdSqVYvOnTvj4uLCsGHD6N27N05OTgwZMgQPDw/atWvHrl276NatG56ensydOxeA0aNHM378eDIzM2nRokWx2k0kIiJSXBRYwJcuXdp2advtPv3002xtnTp1olOnTlnanJ2dCQ8Pz9b34YcfZu3a4n8cWUREpCjpTnYiIiIGpIAXERExIAW8iIiIASngRUREDEgBLyIiYkAKeBEREQNSwIuIiBiQAl5ERMSAFPAiIiIGpIAXERExIAW8iIiIASngRUREDEgBLyIiYkAKeBEREQNSwIuIiBiQAl5ERMSAFPAiIiIGpIAXERExIAW8iIiIASngRUREDEgBLyIiYkAKeBEREQNSwIuIiBiQAl5ERMSAFPAiIiIGpIAXERExIAW8iIiIASngRUREDEgBLyIiYkAKeBEREQNSwIuIiBiQAl5ERMSAFPAiIiIGpIAXERExIAW8iIiIASngRUREDEgBLyIiYkAKeBEREQNSwIuIiBiQAl5ERMSAFPAiIiIGpIAXERExIAW8iIiIASngRUREDEgBLyIiYkAKeBEREQNSwIuIiBiQAl5ERMSAFPAiIiIGpIAXERExIAW8iIiIAbkUdQElhfOoT/N/0JU/5NtQmXNfzrexRESk5NMWvIiIiAHZDfivv/6adevWYbVaee2112jRogUbN24shNJEREQkr+wG/MKFC+nYsSPffPMNTk5ObNmyhVWrVhVGbSIiIpJHdgO+dOnSlClThr1799KtWzf8/Pxwd3cvjNpEREQkj+wGfHp6OosXL2bXrl20bNmSn376iT///LMwahMREZE8shvw06ZN4/Lly8ycOZPSpUvz7bff8tZbbxVGbSIiIpJHdi+Tq169On379qV69eocPnwYi8VC/fr1C6M2ERERySO7W/Bjx44lLi6O8+fPM2LECOLi4hg3blxh1CYiIiJ5ZDfgL168SHBwMDt27KBPnz5Mnz6dlJSUwqhNRERE8sihG91YrVaioqIIDAwEbp54JyIiIsWX3WPwLVq0oGnTpjRv3pw6derw8ccf89BDDzk0+JYtW4iMjMRqtTJ8+HAaNmzI2LFjSUpKonLlysyZMwc3NzeioqJYsmQJaWlp9OnThx49epCZmUlYWBhxcXEAzJ49m+rVq/Prr78SGhpKamoq/v7+TJkyBZPJdG/fBQec6Nm/wNdxb3SrWhER+T92A3706NEMGjQILy8vADp16sTly5ftDpySkkJkZCSrVq3i6tWrRERE8NVXX9G9e3c6d+5MeHg4mzdvpmPHjoSHh7NhwwZcXFx47rnn6NSpE9u3b8dkMrF69Wq++eYbIiIimDVrFqGhoYwZM4bGjRszbNgwYmJiaNmy5b1/J+xIalfgq7g3yUVdgIiIFCd2Az45OZktW7aQmJgI3Nw9v3HjRvbs2XPX5aKjowkMDMTd3R0/Pz+mT59O27ZtCQsLAyA4OJjPPvuMqlWr0rBhQ8qWLQtA06ZNOXToEAcOHOCpp54CoHXr1oSFhWE2mzl9+jSNGzcGICgoiOjo6EIJ+BPbRxf4Ou5F86IuQETEoDItFk4l5O9WVG2fMjg72T9KHhcXxxtvvEG/fv3o06dPrtZhN+BHjBhBlSpViI6Opl27duzbt4/Q0FC7A1+4cIHU1FSGDh3KlStXePPNN0lJScHDwwMAb29v4uPj+eOPP/D29rYt5+Pjk63dxcWFzMxMEhMTKV++fJa++/fvt1vL8ePH7fYp6WJjY4u6hHxTlHPR97H4Mco8QHMpqU4lJFN/5qZ8HfNkSFfqVPK6a58bN24wbdq0PG/E2g34tLQ0pk6dyssvv8zbb7/NlStXmDJlCu3a3X2ftdls5ty5c8yfP5+zZ8/Sr18/nJ2dbe9brVZMJhOurq5ZlsupHciy/O197fH397/n2+sei157T8sXtICAAIf6Ffd5gONzyW+xsbFFtu78ZpS5GGUeoLkUpLS0NENuyLm5ubF48WIWL16cp+Xt7h9IS0sjISEBi8VCQkIC3t7eXLhwwe7AlSpVokmTJjg7O/Pggw9SpkwZPD09SU1NBSA+Ph5fX18qVapEQkKCbbk7tZvNZlxdXfH29ub69evZ+oqIiBiNi4uLba93XtgN+J49e7Jt2za6du1Kly5daNeunUOh2qpVK2JiYrBarSQkJJCSkkLbtm3ZuXMngO2yu0aNGvHTTz+RlJRESkoKR48epVmzZrRp08bWd/fu3bRq1QonJyfq16/P4cOHs4whIiIiWdndRd+zZ0/b161ateL69es0aNDA7sB+fn60b9+evn37kpKSwsSJE2nYsCGjRo0iMjKSWrVq0blzZ1xcXBg2bBi9e/fGycmJIUOG4OHhQbt27di1axfdunXD09OTuXPnAjfP6h8/fjyZmZm0aNGiWO0mEhERKS5yDPj58+fnuFBUVBTDhw+3O3ivXr3o1atXlrZPP/00W79OnTrRqVOnLG3Ozs6Eh4dn6/vwww+zdm3xP44sIiJSlHIM+P89oU1EREQKz/HjxwkPD+f8+fO4uLiwY8cOIiIislxNdjc5BvzQoUOBm9e9HzlyhObNb15pvXPnTp544ol7LlxERKQkqO1ThpMhXfN9THv8/f3vuNfbUXaPwYeGhuLl5WUL+G+//ZaoqChmzpyZ55WKiIiUFM5OTnavWS+O7J5Ff/bsWd5++23b65CQEM6ePVugRYmIiMi9sRvwmZmZXL161fb60qVLepqciIhIMWd3F/3rr79O165dqVixIhaLhfj4eGbMmFEYtYmIiEge2Q34wMBAduzYwW+//YbVaqVWrVr3dGcdERERKXh2Ax7Aw8ODevXqFXQtIiIikk8cCngREZH7lcVqIenPBPsdc6Gshw9OJvuPi/3HP/7BgQMHSE9PZ+DAgdluCnc3CngREZG7SPozgQ2xc/N1zOcCRlHOs9Jd+3z77becPHmSL774gqtXr/LMM8/kT8AvWLAgx4WsVitvvvmmwysRERGR3Hn00UeZN28eAF5eXqSnp2OxWHBysr/lD3cJ+IyMDABOnz7NmTNnaN68ORaLhQMHDjj0sBkRERHJOxcXF1xcbsb02rVrCQwMdDjc4S4BP2LECACGDx/OmjVrbPemT09PZ+TIkfdQsoiIiDjq66+/Zs2aNURGRuZqObvH4M+cOYPVarW9NplMnD9/PvcVioiISK7s3buXhQsXsnTpUry8cne7XLsB36ZNGzp06MAjjzwCwA8//ED79u3zVqmIiIg4JCkpiZkzZ7J8+XIqVKiQ6+XtBvzIkSN57rnniIuLw2q1Mnz4cGrXrp2nYkVERMQx27Zt49q1a1kOi4eHh1OlShWHlrcb8GlpaezevZuLFy8SEhJCbGwsKSkplC5dOu9Vi4iIlBBlPXx4LmBUvo9pT69evejVq1ee12E34CdOnEilSpX47rvvAPj5559Zvnw577//fp5XKiIiUlI4mZzsXrNeHNk93/7ixYuMHTsWd3d3AF544QXi4+MLvDARERHJO7sBf+sMepPJBMCNGzdIS0sr2KpERETkntjdRd+uXTv69+/PuXPnmD59Onv37uXFF18sjNpEREQkj+wGfL9+/QgICOC7777DZDIxd+5c/P39C6M2ERERySO7AR8SEsLMmTNp2LChrW3gwIEsXry4QAsTERGRvMsx4Ddv3szq1av5+eef6d27t609NTWVq1evFkZtIiIiRc6amUnaf0/l65juD9XG9P9vAZ+T1NRUQkJCSEhI4MaNGwwZMoTg4GCH15FjwD/zzDM89thjjB49OsuT45ycnHj44YcdXoGIiEhJlvbfUxx7NH8fstbw8A94/KXOXfvs2rULf39/Bg4cyPnz53n11VfzJ+AB/Pz8WLRoEf/+97/p3LkzACtXrqRevXoOr0BERERyr0uXLravL168iJ+fX66Wt3uZ3OjRozl37pztdVpaGmPGjMnVSkRERCRvevbsyejRowkNDc3VcnYDPiUlhUGDBtle9+/fn+Tk5NxXKCIiIrm2du1aFixYwFtvvYXFYnF4OYdudHPq1P+dXHDs2DEyMjLyVqWIiIg45NixY/z+++8APPLII1gsFhITEx1e3u5lcmPHjuXNN9/kypUrWCwWKlWqxLvvvpv3ikVERMSuw4cPc/78ecaPH098fDwpKSm5emys3YBv1KgR27ZtIykpCavVmusHzouIiEjuvfDCC4wfP56XXnoJs9nM5MmTcXKyu+PdJseA/+ijjxg8eDBjxoyx3Yf+drNmzcpbxSIiIiWI+0O1aXj4h3wf0x43Nzfmzp2b53XkGPANGty85q9Vq1Z5HlxERKSkMzk7271mvTjKMeBr167N77//zmOPPZbtvdycxSciIiKFL8eAf/HFFzGZTFitVi5fvoyXlxcWi4Xk5GRq1KjBjh07CrNOERERyYUcA37Pnj0AvPvuuzz77LPUr18fgKNHj7Jly5bCqU5ERETyxO7peHFxcbZwB2jcuDG//PJLgRYlIiIi98buZXIZGRn84x//oGnTpsDN6/LS0tIKvDARERHJO7sBP2/ePFasWMHq1auBmyffzZs3r6DrEhERKRYsmRaST13K1zHL1PbDydmxa9r//PNPunTpwpAhQ+jWrZvD67Ab8D4+Prz88sucPXuWRx99lIyMDFxc7C4mIiJiCMmnLrG1/lv5OuZTJ/+BV50HHOq7aNEiypcvn+t12P34sGHDBnr37s2kSZMAmDFjBitXrsz1ikRERCR3Tp06xalTp3jiiSdyvazdgP/iiy/YtGmT7dNDSEgIX375Za5XJCIiIrkza9YsQkJC8rSs3YD39PTEw8PD9trd3R03N7c8rUxEREQcs3HjRpo1a0a1atXytLzdg+lly5Zl06ZNpKWlceLECb766it8fHzytDIRERFxzO7duzl37hxRUVFcvHgRNzc3Kleu7PAt5O0G/NSpU3n//fcxm81MnDiRgIAApk2bds+Fi4iISM5uv2ItIiKCqlWr5ur5MHcNeKvVyuHDh20n2ImIiEjJcNeAN5lMfPHFF7Ru3fq+vzRuwIZHirqEu+r3t6KuQETEmMrU9uOpk//I9zFz480338z1OuymtoeHB507d6ZBgwZZTq7T8+BFROR+4OTs5PA168WJ3YAPDAwkMDCwMGoRERGRfGI34J977jl++eUXfv75Z0wmE3Xq1OGhhx4qjNpEREQkj+wGfHh4OFFRUTRq1IjMzEzmzJlDly5dGDlyZGHUJyIiInlgN+APHDjAV199haurKwBms5nnn39eAS8iIlKM2b2TnZ+fny3cAVxcXPD19S3QokREROTe2N2CL1euHD179qRZs2YAHDx4kOrVqzN//nwAhg8fXrAVioiIFCGrNZP0P0/l65iuHrUxmZzv2uf48eO88cYb1KxZE4A6deoQGhrq8DrsBvwDDzyQ5T64bdu2dXhwERGRki79z1P892iDfB3zocY/4OZZ5659bty4QYcOHZgwYUKe1mE34LWFLiIiUvhSUlLuaXm7x+BFRESk8N24cYPY2Fj69+9Pnz592L9/f66Wv7/vPysiIlJM1atXj8GDB9OhQwdOnz5Nv3792LFjh8OPbM9xC37q1KlZ/i8iIiKFp3bt2nTo0AGAmjVrUrFiRS5duuTw8jluwUdHRzNq1CgOHDhAcnJytvd1L3oREZGCs2HDBq5du0a/fv1ISEggISEBPz/HH1KTY8AvXbqU2NhYfvjhB1q2bJkvxYqIiIhj2rVrx5gxY/jXv/5FRkYGkydPdnj3PNwl4KtXr0716tVp2rQpNWrUIDExEScnJ8qVK5cvhYuIiJQErh61eajxD/k+pj1ly5blww8/zPM67J5kd+nSJfr168eff/6JxWKhfPnyzJ49m4YNG9od/M8//6RLly4MGTKEwMBAxo4dS1JSEpUrV2bOnDm4ubkRFRXFkiVLSEtLo0+fPvTo0YPMzEzCwsKIi4sDYPbs2VSvXp1ff/2V0NBQUlNT8ff3Z8qUKZhMpjxPXkRExB6TydnuNevFkd3L5ObOncuiRYvYt28fMTExzJ49m5kzZzo0+KJFiyhfvjxw85h99+7dWbNmDVWrVmXz5s0kJycTHh7OkiVLWLVqFUuWLCElJYWNGzdiMplYvXo1gwcPJiIiAoDQ0FDGjBnD+vXrSUxMJCYmJu8zFxERMTC7Ae/q6krdunVtrxs2bIiTk/3L50+dOsWpU6d44okngJu3uA0KCgIgODiY6Ohojh07RsOGDSlbtiyenp40bdqUQ4cOceDAAYKDgwFo3bo1Bw8exGw2c/r0aRo3bgxAUFAQ0dHRuZ6wiIjI/cDuLnonJye2b9/O3/72NwD27t3rUMDPmjWL0NBQNmzYANy8I4+HhwcA3t7exMfH88cff+Dt7W1bxsfHJ1u7i4sLmZmZJCYm2vYG3Orr6EX/x48fd6hfSRYbG1vUJeSbopyLvo/Fj1HmAZqLFC67AR8WFsa0adMIDQ3FZDLRpEkTu9fGb9y4kWbNmmW5h/3tT6SzWq2YTKYsbXdrB3B2dr5jX0f4+/vj7u7uUN8crczfEyzyW0BAgEP9jkWvLeBK7p2jc8lvsbGxRbbu/GaUuRhlHqC5FKS0tLT7YkMut+wG/IMPPsjSpUtzNeju3bs5d+4cUVFRXLx4ETc3N9zd3UlNTcXT05P4+Hh8fX2pVKkSCQkJtuXi4+N5/PHHs7SbzWZcXV3x9vbm+vXrWfrqsbUiIiJ3ViC3qp03b57t64iICKpWrcqJEyfYuXMnTz31FFFRUQQGBtKoUSN++uknkpKScHJy4ujRo0yZMoWUlBR27txJYGAgu3fvplWrVjg5OVG/fn0OHz7Mo48+SlRUFH//+98LonwREREbi9XK1bT0fB2zvLsrTg7shd6yZQuRkZFYrVaGDx9uO6/NEYV2L/rBgwczatQoIiMjqVWrFp07d8bFxYVhw4bRu3dvnJycGDJkCB4eHrRr145du3bRrVs3PD09mTt3LgCjR49m/PjxZGZm0qJFi2K1i0hERIzpalo6i4+dztcxBzasibfH3W9ak5KSQmRkJKtWreLq1atERETkb8DfOtM9r958803b159++mm29zt16kSnTp2ytDk7OxMeHp6t78MPP8zatcX/GLKIiMi9io6OJjAwEHd3d/z8/Jg+fXqulrd7Ovztu9tFRESkcFy4cIHU1FSGDh3KSy+9lP+Pi/X19aVPnz40btw4yz1whw8fnvtqRURExCFms5lz584xf/58zp49S79+/di1a5dDl6qDAwFftWpVqlates+FioiIiOMqVapEkyZNcHZ25sEHH6RMmTIkJibi4+Pj0PJ2A37o0KHEx8dz9uxZHn30UTIyMnBxKbRz80RERO5LrVq1YsKECQwYMIArV66QkpJChQoVHF7eblJv3LiRRYsW4ebmxpYtW5gxYwZ16tThxRdfvKfCRUREJGd+fn60b9+evn37kpKSwsSJEx3ePQ8OBPzq1avZtGkTAwcOBCAkJITevXsr4EVE5L5Q3t2VgQ1r5vuYjujVqxe9evXK0zrsBrynp6ftHvIA7u7uuXrgvIiISEnmZDLZvWa9OLIb8GXLlmXTpk2kpaVx4sQJvvrqK4cP8IuIiEjRsLszf+rUqRw9ehSz2czEiRP5888/mTZtWmHUJiIiInlkdwu+fPnyTJo0iStXrgBkebyriIiIFE92A37btm288847AFgsFlxcXAgNDaV9+/YFXpyIiIjkjd2AX7hwIatWraJ69eoAnDp1iuHDhyvgRUREijG7AV+zZk1buAPUrl2batWqFWhRIiIixUWmxcKphOR8HbO2Txmc7VzTvnbtWjZv3mx7ffz4cQ4fPuzwOnIM+Fs3ta9SpQrTp0+nZcuWODk5sX//fmrVquXwCkREREqyUwnJ1J+5KV/HPBnSlTqVvO7ap2fPnvTs2ROAQ4cOsWXLllytI8eAX7hwYZbXP/30k+1rq9Waq5WIiIhI3kVERDBr1qxcLZNjwN/p2e0iIiJSuL7//nv8/Pzw8/PL1XJ2j8Hv37+fL774gqSkpCxb7p988knuqxQREZFcWbNmDZ07d871cnYDftKkSQwePDjXnxxERETk3n377beEhobmejm7Af/ggw/So0ePPBUlIiIieXfx4kXc3Nxwd3fP9bJ2A/75559n4sSJNGnSJMtz4J999tlcr0xEREQcd/nyZXx9ffO0rN2A/+ijjyhVqhRpaWm2NpPJpIAXEZH7Qm2fMpwM6ZrvYzqiUaNGLF26NE/rsBvwpUuXZvny5XkaXEREpKRzdnKye816cWT3aXJt2rTh4MGDmM1mLBaL7T8REREpvuxuwS9YsIDU1NQsbSaTiZMnTxZYUSIiInJv7AZ8bGwsTnbulysiIiLFi92Aj4iIyNZmsVgYOXJkgRQkIiIi987uprmzs7PtP6vVyvfff8+VK1cKozYRERHJI7tb8EOHDs3WNnXq1AIpRkRERPJHrg+uZ2Rk8MsvvxRELSIiIpJP7G7BBwYGYjKZgJuPiU1OTqZbt24FXpiIiIjknd2AX7lype1rk8mEl5cXZco4dgceERERKRp2A97Hx4fo6GiuXbuW5XGxegCNiIhI8WU34AcMGICLiwtVqlTJ0q6AFxERKb7sBnx6ejqff/55YdQiIiIi+cTuWfR169bVde8iIiIljN0t+AsXLvDkk09Su3btLM+D11a9iIhI8eXQMXhnZ+fCqEVERETyid2Ab9myZWHUISIiIvlIj4kTERExIAW8iIiIASngRUREDEgBLyIiYkAKeBEREQNSwIuIiBiQAl5ERMSAFPAiIiIGpIAXERExIAW8iIiIASngRUREDEgBLyIiYkAKeBEREQNSwIuIiBiQAl5ERMSAFPAiIiIGpIAXERExIAW8iIiIASngRUREDEgBLyIiYkAKeBEREQNSwIuIiBiQAl5ERMSAFPAiIiIG5FKQg//jH//gwIEDpKenM3DgQFq0aMHYsWNJSkqicuXKzJkzBzc3N6KioliyZAlpaWn06dOHHj16kJmZSVhYGHFxcQDMnj2b6tWr8+uvvxIaGkpqair+/v5MmTIFk8lUkNMQEREpcQpsC/7bb7/l5MmTfPHFF3zyySe8++67zJo1i+7du7NmzRqqVq3K5s2bSU5OJjw8nCVLlrBq1SqWLFlCSkoKGzduxGQysXr1agYPHkxERAQAoaGhjBkzhvXr15OYmEhMTExBTUFERKTEKrCAf/TRR5k3bx4AXl5epKenExMTQ1BQEADBwcFER0dz7NgxGjZsSNmyZfH09KRp06YcOnSIAwcOEBwcDEDr1q05ePAgZrOZ06dP07hxYwCCgoKIjo4uqCmIiIiUWAW2i97FxQUXl5vDr127lsDAQHbt2oWHhwcA3t7exMfH88cff+Dt7W1bzsfHJ1u7i4sLmZmZJCYmUr58+Sx99+/fb7eW48eP5+PMiqfY2NiiLiHfFOVc9H0sfowyD9BcpHAV6DF4gK+//po1a9YQGRnJ3r17be1WqxWTyYSrq2uW/jm1Azg7O9+xrz3+/v64u7vncQb/38of7m35AhYQEOBQv2PRawu4knvn6FzyW2xsbJGtO78ZZS5GmQdoLgUpLS3tvtiQy60CPYt+7969LFy4kCVLluDl5UXp0qVJTU0FID4+Hl9fXypVqkRCQoJtmTu1m81mXF1d8fb25vr169n6ioiISFYFFvBJSUnMnDmTjz/+mAoVKgA3j6Xv3LkTgKioKAIDA2nUqBE//fQTSUlJpKSkcPToUZo1a0abNm1sfXfv3k2rVq1wcnKifv36HD58OMsYIiIiklWB7aLftm0b165dY+TIkba2mTNnEhISQmRkJLVq1aJz5864uLgwbNgwevfujZOTE0OGDMHDw4N27dqxa9cuunXrhqenJ3PnzgVg9OjRjB8/nszMTFq0aFGsdhOJiIgUFwUW8L169aJXr17Z2j/99NNsbZ06daJTp05Z2pydnQkPD8/W9+GHH2bt2uJ/HFlERKQo6U52IiIiBqSAFxERMSAFvIiIiAEp4EVERAxIAS8iImJABX4nOyleBmx4pKhLsKvf34q6AhGRkk9b8CIiIgakgBcRETEgBbyIiIgBKeBFREQMSAEvIiJiQAp4ERERA1LAi4iIGJACXkRExIAU8CIiIgakgBcRETEgBbyIiIgBKeBFREQMSAEvIiJiQAp4ERERA1LAi4iIGJACXkRExIAU8CIiIgakgBcRETEgBbyIiIgBKeBFREQMSAEvIiJiQAp4ERERA1LAi4iIGJACXkRExIBciroAkbwK//bnfB7Ri6/zccxxzf+Sb2OJiOSWtuBFREQMSAEvIiJiQAp4ERERA1LAi4iIGJACXkRExIB0Fr2UWNUfn1TUJdxd5qqirkBE7mMKeCmx/uK5tqhLsEMBLyJFR7voRUREDEgBLyIiYkAKeBEREQNSwIuIiBiQAl5ERMSAFPAiIiIGpIAXERExIAW8iIiIASngRUREDEgBLyIiYkAKeBEREQNSwIuIiBiQAl5ERMSAFPAiIiIGpIAXERExIAW8iIiIAbkUdQEiIsVR+Lc/5/OIXnydj2OOa/6XfBtLjElb8CIiIgakLXgREQNzHvVpwQy88od8Gypz7sv5Npb8H23Bi4iIGJACXkRExIAU8CIiIgakgBcRETEgnWQnJdaJ7aOLuoS7al7UBRSBH2Py909KaeDHmHwdknqPZzjUL6ht/fxdcX5Ldmwecv8qkVvw8+fP54UXXqBbt24cO3asqMsREREpdkrcFnxMTAzHjh1j9erVxMXFERYWxueff17UZYnck5XOL+b7mD/l41gvZa7Kx9GkMC197kRRlyBFpMQF/IEDBwgODgagTp06XL58mdTUVDw9PbP1tVqtAJjN5nte7wOlXe95jIKUlpbmUL/iPg9wfC6uplIFXMm9cXQeAC4PeBVgJffO0bnEZk4q4EruXS0H5xK3o3jPpZGD85j4ryYFW0g+eLG54/9W7uTW3/hbf/PlJpO1hH1HJk6cSOvWrenQoQMAvXr1Ys6cOVSvXj1b36SkJOLi4gq7RBERKQJ16tShbNmyRV1GsVHituBdXbNugVqtVkwm0x37li5dmjp16uDq6ppjHxERKdmsVivp6emULl26qEspVkpcwFeqVImEhATb6ytXrlCxYsU79nVyctKnORGR+4CHh0dRl1DslLiz6Nu0acPOnTsBOHHiBNWrV9cPVkRE5H+UuC14f39/6tWrx3PPPYezszMzZswo6pJERESKnRJ3kp2IiIjYV+J20YuIiIh9CngREREDUsAXsevXrxd1Cfni4sWLRV3CPbly5UpRl5Bv9u/fX9Ql5Iv09HTOnz9PRoYx7rlupN8xKRkU8EVs6NChRV2Cw7Zv305gYCB//etfGT9+PMnJybb3xo4dW4SV5c6uXbvo0KED/fr149SpUzz99NP07duXoKAg9uzZU9Tl5crGjRuz/LdhwwYmT55se12STJ8+3fZ1TEwMHTp0YOTIkXTo0IG9e/cWYWW5t2fPHiZNunknvJiYGNq2bcsrr7xCUFAQu3fvLtricqFp06aEhYVx+fLloi5F8qDEnUVfEt3tXvmXLl0qxEruzZIlS9i8eTNeXl6sWbOG/v37s3TpUry8vErULSIXLlxIZGQkFy5cYODAgSxYsIAGDRoQHx/PG2+8QWBgYFGX6LAFCxZQoUKFLDWnpaVx7ty5Iqwqb3766f/unv/BBx+wbNkyatSoQUJCAkOGDKF169ZFWF3uzJ8/n48++gi4+TNatmwZNWvWJCEhgUGDBvHEE08UbYEOeuSRR+jSpQtjx46lcuXKdOrUiZYtW+Lm5lbUpYkDFPCFYNmyZbRs2RJfX99s75Wk3Y8uLi6UK1cOuHmLYB8fH/r378/ixYtL1J0CPT09qVKlClWqVKFixYo0aNAAgIoVK2a7U2Jx989//pOFCxcSFxdHSEgIVapUYe/evSVqz9Att/8Oubm5UaNGDQB8fHxwdnYuqrLyrHz58gBUqFAhy1xKlSrez1C4nclkolmzZixbtozvv/+etWvX8s477+Dh4YGvry+LFy8u6hLlLhTwheCDDz5g+vTpTJw4Mdsn3wMHDhRRVbnXokULBg8ezLx58/D09KRdu3a4u7vTt2/fEnUugY+PD0uXLmXAgAGsWbMGgAsXLrB8+XIqV65cxNXljru7OyNHjuS///0vU6dOpXnz5iVqb8rtTp8+zbvvvovVauXq1atERUXRvn17li9fXuLuSNm/f3969epF27ZtqVatGsOHD6dJkybExMTwzDPPFHV5Drv9d6lRo0Y0atQIuLnnUbvtiz9dB19IUlNTcXd3x8kp62kPJ06c4JFHHimiqnLv0KFDNG3aNMs8kpOT2bZtG88//3wRVua4P//8k127dtG5c2db2/Hjxzl8+DC9evUq0bsfN27cyJ49e3jvvfeKupRcO3HiBDdu3CAlJYUbN25Qr149HnroITZu3Ei7du0oU6ZMUZeYKzdu3CA6OpoLFy5gNpupVKkSLVu2xM/Pr6hLc9i6devo0aNHUZcheaSAFxERMSCdRS8iImJACngREREDUsCLFDOXLl3K881qDhw4wIsvvpjvfUWk5FHAixQzBw4cICYmpqjLEJESTpfJiRQwq9VKWFgYP/74I5mZmTRu3JiJEycCsH79etasWYPFYqFZs2a89NJLzJs3D6vVSvny5UlOTiYjI4ORI0cCEBQURGRkJH5+fowbN474+HhSU1Pp2LEjgwYNyrGG3377jbCwMNt9F2bNmpXl/SNHjvDuu+/i5uZGWloakydP5pFHHmHbtm0sXboUNzc3rFYr77zzDn5+fowaNYrr169z48YNnnjiCUaMGFEw3zwRyTMFvEgBu379OjVq1GDy5MkAdOzYkbi4OMqWLcuiRYvYunUrHh4ejBw5ErPZzHPPPUdGRgb9+/cnIiLijmPGx8fTqlUrevXqhdlspmXLlrz00ks51jB9+nT69u1L27Zt+fLLL9mxYwf169e3vZ+QkMD48eNp0qQJW7Zs4aOPPuL999/nww8/ZOrUqTRp0oSTJ09y6dIlTp06RXp6OitXrsRqtfL5559jNptL9OWFIkakgBcpYKVKleLChQu89NJLuLi4EB8fT2JiImfOnKF+/fp4eHgA5Ora9XLlynHkyBE2btyIi4sLaWlpXL16Ncf+R44cYd68eQB069YNyHqTpXLlyjFv3jwyMjK4fv267Y6Ft25T2qlTJ4KCgmjZsiWXL19m9uzZDBs2jDZt2tC1a1eFu0gxpGPwIgVsy5YtnDx5kuXLl/Ppp59SrVo14Oaue4vFctdl//cWwLd2sS9btgyz2czKlStZsWIFnp6eduu42y0vxo4dy9///nc+++wzhg0bZmsfPHgwK1asoFq1aoSGhvLJJ5/g6+vLtm3b6N27N3FxcTzzzDMl6k6GIvcLBbxIAbt27RrVq1fHzc2No0ePcu7cOcxmM/7+/hw/ftz2VL7hw4dz7NgxTCYTaWlpAHh5edkeM3rhwgXi4+NtY9aqVQuTycSOHTswm82YzeYca2jSpIntiWzbtm1j7ty5Wd6/evUqtWrVwmKxsG3bNsxmM5mZmYSHh1O2bFl69uzJgAEDOHToENHR0URFRfHYY4/x9ttv4+rqyvnz5/P9+yYi90Z3shMpYJcuXWLw4MGUKlWKxo0b4+XlxdatW1m5ciVRUVGsXr0aJycnAgICGDduHDExMbz11lu88MIL9OrVizfeeINatWpRuXJl9uzZw/vvv09aWhpvvfUWFSpUIDg4mN9++40ff/yRUaNGMW/ePFatWpWlht9++41JkyZhsVhwcnIiPDycM2fO2Pp+/PHHbNq0iYoVK/Laa68REhLC3//+dzIzM9myZQulSpUiPT2dt99+G19fX8aNG0dmZibp6ek0bNiQCRMmlKgHDoncDxTwIiIiBqRd9CIiIgakgBcRETEgBbyIiIgBKeBFREQMSAEvIiJiQAp4ERERA1LAi4iIGJACXkRExID+H8kBq1W7uKlJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Class Prediction Error for KNeighborsClassifier'}, xlabel='actual class', ylabel='number of predicted class'>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visualizer = ClassPredictionError(knn5)\n",
    "\n",
    "visualizer.fit(X_train, y_train)\n",
    "visualizer.score(X_test, y_test)\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# KNN performed badly without scaling. We should perhaps try to scale the data and try it again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's try random forest now. Scaling is not necessary for tree based models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# I want to tune our hyperparameters with GridSearch First. Might take a long time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_params = {\"n_estimators\": [50, 100, 300, 400, 500],\n",
    "            \"max_depth\": [1, 3, 5, 7, 9],\n",
    "            \"max_features\": [2, 4, 6, 8],\n",
    "            \"min_samples_split\": [1, 2, 3, 4],\n",
    "            }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rf_cv_model = GridSearchCV(rf, rf_params, cv=5, n_jobs=-1, verbose=2).fit(X_train, y_train)\n",
    "# Okay this took like 10 hours so I'll comment it out so that I won't accidentally run it again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 9,\n",
       " 'max_features': 8,\n",
       " 'min_samples_split': 3,\n",
       " 'n_estimators': 500}"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_cv_model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_tuned = RandomForestClassifier(max_depth=9,\n",
    "                                 max_features=8,\n",
    "                                 min_samples_split=3,\n",
    "                                 n_estimators=500).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = rf_tuned.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[46783, 15901,     3,     0,     0,     0,   224],\n",
       "       [13812, 69741,   471,     1,     0,   157,     5],\n",
       "       [    0,  1298,  9155,    17,     0,   106,     0],\n",
       "       [    0,     0,   532,   304,     0,     2,     0],\n",
       "       [   10,  2819,    48,     0,     0,     0,     0],\n",
       "       [    0,  1476,  2577,    13,     0,  1158,     0],\n",
       "       [ 2847,    16,     0,     0,     0,     0,  3015]])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.74      0.74      0.74     62911\n",
      "           2       0.76      0.83      0.80     84187\n",
      "           3       0.72      0.87      0.78     10576\n",
      "           4       0.91      0.36      0.52       838\n",
      "           5       0.00      0.00      0.00      2877\n",
      "           6       0.81      0.22      0.35      5224\n",
      "           7       0.93      0.51      0.66      5878\n",
      "\n",
      "    accuracy                           0.75    172491\n",
      "   macro avg       0.70      0.51      0.55    172491\n",
      "weighted avg       0.75      0.75      0.74    172491\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1221: 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"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/base.py:209: FutureWarning: From version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an instance attribute. Previously it would return None.\n",
      "  warnings.warn('From version 0.24, get_params will raise an '\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1221: 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"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'RandomForestClassifier Classification Report'}>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visualizer = ClassificationReport(rf_tuned, support=True)\n",
    "\n",
    "visualizer.fit(X_train, y_train)\n",
    "visualizer.score(X_test, y_test)\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Tuned model didn't perform well. Let's try it with the default hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc = RandomForestClassifier(n_estimators=100)\n",
    "rfc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc_pred = rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc_accuracy = rfc.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9616907548799648"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/base.py:209: FutureWarning: From version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an instance attribute. Previously it would return None.\n",
      "  warnings.warn('From version 0.24, get_params will raise an '\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'RandomForestClassifier Classification Report'}>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visualizer = ClassificationReport(rfc, support=True)\n",
    "\n",
    "visualizer.fit(X_train, y_train)\n",
    "visualizer.score(X_test, y_test)\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's compare our models now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Accuracy Score of Different Models')"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare = pd.DataFrame({\"Model\": [\"K-Nearest Neighbor\", \"Random Forest\", \"XGBoost\", \"Decision Tree\"],\n",
    "                       \"Accuracy\": [knn_accuracy, rfc_accuracy, xgb_accuracy, tree_accuracy]})\n",
    "\n",
    "compare = compare.sort_values(by=\"Accuracy\", ascending=True)\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "ax = sns.barplot(x=\"Accuracy\", y=\"Model\", data=compare, palette=\"Blues_d\")\n",
    "\n",
    "plt.yticks(size=14)\n",
    "plt.xticks(size=14)\n",
    "plt.title(\"Accuracy Score of Different Models\", size=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As we can see from the graph above, Random Forest with the default hyperparameters performed\n",
    "# the best."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's try SVM now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelSVM = LinearSVC(max_iter = 500000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_X_train = sc.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVC(max_iter=500000)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelSVM.fit(scaled_X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = modelSVM.predict(scaled_X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "SVM_accuracy = accuracy_score(pred, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7113472586975552"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SVM_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's try knn with scaled data now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_scaled = KNeighborsClassifier(n_neighbors=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_scaled.fit(scaled_X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_accuracy = knn_scaled.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/sklearn/base.py:209: FutureWarning: From version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an instance attribute. Previously it would return None.\n",
      "  warnings.warn('From version 0.24, get_params will raise an '\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Class Prediction Error for KNeighborsClassifier'}, xlabel='actual class', ylabel='number of predicted class'>"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visualizer = ClassPredictionError(knn_scaled)\n",
    "\n",
    "visualizer.fit(scaled_X_train, y_train)\n",
    "visualizer.score(scaled_X_test, y_test)\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
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   "outputs": [],
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