PY_ML_GLASS_Gradient_Booster_Predict.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "277f1674",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d121fac5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(r\"C:\\Users\\ibast\\Downloads\\glass.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dad3b91a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RI</th>\n",
       "      <th>Na</th>\n",
       "      <th>Mg</th>\n",
       "      <th>Al</th>\n",
       "      <th>Si</th>\n",
       "      <th>K</th>\n",
       "      <th>Ca</th>\n",
       "      <th>Ba</th>\n",
       "      <th>Fe</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.52101</td>\n",
       "      <td>13.64</td>\n",
       "      <td>4.49</td>\n",
       "      <td>1.10</td>\n",
       "      <td>71.78</td>\n",
       "      <td>0.06</td>\n",
       "      <td>8.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.51761</td>\n",
       "      <td>13.89</td>\n",
       "      <td>3.60</td>\n",
       "      <td>1.36</td>\n",
       "      <td>72.73</td>\n",
       "      <td>0.48</td>\n",
       "      <td>7.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.51618</td>\n",
       "      <td>13.53</td>\n",
       "      <td>3.55</td>\n",
       "      <td>1.54</td>\n",
       "      <td>72.99</td>\n",
       "      <td>0.39</td>\n",
       "      <td>7.78</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.51766</td>\n",
       "      <td>13.21</td>\n",
       "      <td>3.69</td>\n",
       "      <td>1.29</td>\n",
       "      <td>72.61</td>\n",
       "      <td>0.57</td>\n",
       "      <td>8.22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.51742</td>\n",
       "      <td>13.27</td>\n",
       "      <td>3.62</td>\n",
       "      <td>1.24</td>\n",
       "      <td>73.08</td>\n",
       "      <td>0.55</td>\n",
       "      <td>8.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        RI     Na    Mg    Al     Si     K    Ca   Ba   Fe  Type\n",
       "0  1.52101  13.64  4.49  1.10  71.78  0.06  8.75  0.0  0.0     1\n",
       "1  1.51761  13.89  3.60  1.36  72.73  0.48  7.83  0.0  0.0     1\n",
       "2  1.51618  13.53  3.55  1.54  72.99  0.39  7.78  0.0  0.0     1\n",
       "3  1.51766  13.21  3.69  1.29  72.61  0.57  8.22  0.0  0.0     1\n",
       "4  1.51742  13.27  3.62  1.24  73.08  0.55  8.07  0.0  0.0     1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7328de9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(214, 10)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b268cfa4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['RI', 'Na', 'Mg', 'Al', 'Si', 'K', 'Ca', 'Ba', 'Fe', 'Type'], dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "965148e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0d6319aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RI      0\n",
       "Na      0\n",
       "Mg      0\n",
       "Al      0\n",
       "Si      0\n",
       "K       0\n",
       "Ca      0\n",
       "Ba      0\n",
       "Fe      0\n",
       "Type    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "33499f5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 214 entries, 0 to 213\n",
      "Data columns (total 10 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   RI      214 non-null    float64\n",
      " 1   Na      214 non-null    float64\n",
      " 2   Mg      214 non-null    float64\n",
      " 3   Al      214 non-null    float64\n",
      " 4   Si      214 non-null    float64\n",
      " 5   K       214 non-null    float64\n",
      " 6   Ca      214 non-null    float64\n",
      " 7   Ba      214 non-null    float64\n",
      " 8   Fe      214 non-null    float64\n",
      " 9   Type    214 non-null    int64  \n",
      "dtypes: float64(9), int64(1)\n",
      "memory usage: 16.8 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b8ab4bb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.describe of           RI     Na    Mg    Al     Si     K    Ca    Ba   Fe  Type\n",
       "0    1.52101  13.64  4.49  1.10  71.78  0.06  8.75  0.00  0.0     1\n",
       "1    1.51761  13.89  3.60  1.36  72.73  0.48  7.83  0.00  0.0     1\n",
       "2    1.51618  13.53  3.55  1.54  72.99  0.39  7.78  0.00  0.0     1\n",
       "3    1.51766  13.21  3.69  1.29  72.61  0.57  8.22  0.00  0.0     1\n",
       "4    1.51742  13.27  3.62  1.24  73.08  0.55  8.07  0.00  0.0     1\n",
       "..       ...    ...   ...   ...    ...   ...   ...   ...  ...   ...\n",
       "209  1.51623  14.14  0.00  2.88  72.61  0.08  9.18  1.06  0.0     7\n",
       "210  1.51685  14.92  0.00  1.99  73.06  0.00  8.40  1.59  0.0     7\n",
       "211  1.52065  14.36  0.00  2.02  73.42  0.00  8.44  1.64  0.0     7\n",
       "212  1.51651  14.38  0.00  1.94  73.61  0.00  8.48  1.57  0.0     7\n",
       "213  1.51711  14.23  0.00  2.08  73.36  0.00  8.62  1.67  0.0     7\n",
       "\n",
       "[214 rows x 10 columns]>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9f6e3022",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RI      178\n",
       "Na      142\n",
       "Mg       94\n",
       "Al      118\n",
       "Si      133\n",
       "K        65\n",
       "Ca      143\n",
       "Ba       34\n",
       "Fe       32\n",
       "Type      6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f3447829",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "92435ec0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "08124880",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 5, 6, 7], dtype=int64)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Type'].unique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ece8a1bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    76\n",
       "1    70\n",
       "7    29\n",
       "3    17\n",
       "5    13\n",
       "6     9\n",
       "Name: Type, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "de0ffd80",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (14,6))\n",
    "sns.countplot('Type',data=df, palette = 'hls')\n",
    "plt.title('Type')\n",
    "plt.xticks(rotation = 90)\n",
    "plt.yticks(rotation = 90)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "39ccf6e8",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (2075970409.py, line 4)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"C:\\Users\\ibast\\AppData\\Local\\Temp\\ipykernel_62460\\2075970409.py\"\u001b[1;36m, line \u001b[1;32m4\u001b[0m\n\u001b[1;33m    patches, texts, pcts = plt.pie(glass_data,labels = glass_data.index,colors = ['blue', 'red', 'green', 'orange','yellow', 'pink']pctdistance = 0.65,shadow = True,startangle = 90,explode = explode,autopct = '%1.1f%%',textprops={ 'fontsize': 15,'color': 'black''weight': 'bold','family': 'serif' })\u001b[0m\n\u001b[1;37m                                                                                                                                    ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "glass_data = df['Type '].value_counts()\n",
    "explode = (0.1, 0.1, 0.1, 0.1, 0.1, 0.1)\n",
    "plt.figure(figsize=(14, 10))\n",
    "patches, texts, pcts = plt.pie(glass_data,labels = glass_data.index,colors = ['blue', 'red', 'green', 'orange','yellow', 'pink']pctdistance = 0.65,shadow = True,startangle = 90,explode = explode,autopct = '%1.1f%%',textprops={ 'fontsize': 15,'color': 'black''weight': 'bold','family': 'serif' })\n",
    "plt.setp(pcts, color='black')\n",
    "hfont = {'fontname':'serif', 'weight': 'bold'}\n",
    "plt.title('Type', size=45, **hfont)\n",
    "centre_circle = plt.Circle((0,0),0.40,fc='white')\n",
    "fig = plt.gcf()\n",
    "fig.gca().add_artist(centre_circle)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fd5f36bd",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['Id'] not found in axis\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_62460\\4170553689.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Id\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\util\\_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    309\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    310\u001b[0m                 )\n\u001b[1;32m--> 311\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    312\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    313\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   4955\u001b[0m                 \u001b[0mweight\u001b[0m  \u001b[1;36m1.0\u001b[0m     \u001b[1;36m0.8\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4956\u001b[0m         \"\"\"\n\u001b[1;32m-> 4957\u001b[1;33m         return super().drop(\n\u001b[0m\u001b[0;32m   4958\u001b[0m             \u001b[0mlabels\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4959\u001b[0m             \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   4265\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4266\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4267\u001b[1;33m                 \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4268\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4269\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[1;34m(self, labels, axis, level, errors, consolidate, only_slice)\u001b[0m\n\u001b[0;32m   4309\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4310\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4311\u001b[1;33m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4312\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_axis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4313\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, errors)\u001b[0m\n\u001b[0;32m   6659\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6660\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m\"ignore\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 6661\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"{list(labels[mask])} not found in axis\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   6662\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6663\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: \"['Id'] not found in axis\""
     ]
    }
   ],
   "source": [
    "df=df.drop(\"Id\",axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "aa78a35b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RI</th>\n",
       "      <th>Na</th>\n",
       "      <th>Mg</th>\n",
       "      <th>Al</th>\n",
       "      <th>Si</th>\n",
       "      <th>K</th>\n",
       "      <th>Ca</th>\n",
       "      <th>Ba</th>\n",
       "      <th>Fe</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RI</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.191885</td>\n",
       "      <td>-0.122274</td>\n",
       "      <td>-0.407326</td>\n",
       "      <td>-0.542052</td>\n",
       "      <td>-0.289833</td>\n",
       "      <td>0.810403</td>\n",
       "      <td>-0.000386</td>\n",
       "      <td>0.143010</td>\n",
       "      <td>-0.164237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Na</th>\n",
       "      <td>-0.191885</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.273732</td>\n",
       "      <td>0.156794</td>\n",
       "      <td>-0.069809</td>\n",
       "      <td>-0.266087</td>\n",
       "      <td>-0.275442</td>\n",
       "      <td>0.326603</td>\n",
       "      <td>-0.241346</td>\n",
       "      <td>0.502898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mg</th>\n",
       "      <td>-0.122274</td>\n",
       "      <td>-0.273732</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.481799</td>\n",
       "      <td>-0.165927</td>\n",
       "      <td>0.005396</td>\n",
       "      <td>-0.443750</td>\n",
       "      <td>-0.492262</td>\n",
       "      <td>0.083060</td>\n",
       "      <td>-0.744993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Al</th>\n",
       "      <td>-0.407326</td>\n",
       "      <td>0.156794</td>\n",
       "      <td>-0.481799</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005524</td>\n",
       "      <td>0.325958</td>\n",
       "      <td>-0.259592</td>\n",
       "      <td>0.479404</td>\n",
       "      <td>-0.074402</td>\n",
       "      <td>0.598829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Si</th>\n",
       "      <td>-0.542052</td>\n",
       "      <td>-0.069809</td>\n",
       "      <td>-0.165927</td>\n",
       "      <td>-0.005524</td>\n",
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       "      <td>-0.193331</td>\n",
       "      <td>-0.208732</td>\n",
       "      <td>-0.102151</td>\n",
       "      <td>-0.094201</td>\n",
       "      <td>0.151565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K</th>\n",
       "      <td>-0.289833</td>\n",
       "      <td>-0.266087</td>\n",
       "      <td>0.005396</td>\n",
       "      <td>0.325958</td>\n",
       "      <td>-0.193331</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.317836</td>\n",
       "      <td>-0.042618</td>\n",
       "      <td>-0.007719</td>\n",
       "      <td>-0.010054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ca</th>\n",
       "      <td>0.810403</td>\n",
       "      <td>-0.275442</td>\n",
       "      <td>-0.443750</td>\n",
       "      <td>-0.259592</td>\n",
       "      <td>-0.208732</td>\n",
       "      <td>-0.317836</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.112841</td>\n",
       "      <td>0.124968</td>\n",
       "      <td>0.000952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ba</th>\n",
       "      <td>-0.000386</td>\n",
       "      <td>0.326603</td>\n",
       "      <td>-0.492262</td>\n",
       "      <td>0.479404</td>\n",
       "      <td>-0.102151</td>\n",
       "      <td>-0.042618</td>\n",
       "      <td>-0.112841</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.058692</td>\n",
       "      <td>0.575161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fe</th>\n",
       "      <td>0.143010</td>\n",
       "      <td>-0.241346</td>\n",
       "      <td>0.083060</td>\n",
       "      <td>-0.074402</td>\n",
       "      <td>-0.094201</td>\n",
       "      <td>-0.007719</td>\n",
       "      <td>0.124968</td>\n",
       "      <td>-0.058692</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.188278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type</th>\n",
       "      <td>-0.164237</td>\n",
       "      <td>0.502898</td>\n",
       "      <td>-0.744993</td>\n",
       "      <td>0.598829</td>\n",
       "      <td>0.151565</td>\n",
       "      <td>-0.010054</td>\n",
       "      <td>0.000952</td>\n",
       "      <td>0.575161</td>\n",
       "      <td>-0.188278</td>\n",
       "      <td>1.000000</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            RI        Na        Mg        Al        Si         K        Ca  \\\n",
       "RI    1.000000 -0.191885 -0.122274 -0.407326 -0.542052 -0.289833  0.810403   \n",
       "Na   -0.191885  1.000000 -0.273732  0.156794 -0.069809 -0.266087 -0.275442   \n",
       "Mg   -0.122274 -0.273732  1.000000 -0.481799 -0.165927  0.005396 -0.443750   \n",
       "Al   -0.407326  0.156794 -0.481799  1.000000 -0.005524  0.325958 -0.259592   \n",
       "Si   -0.542052 -0.069809 -0.165927 -0.005524  1.000000 -0.193331 -0.208732   \n",
       "K    -0.289833 -0.266087  0.005396  0.325958 -0.193331  1.000000 -0.317836   \n",
       "Ca    0.810403 -0.275442 -0.443750 -0.259592 -0.208732 -0.317836  1.000000   \n",
       "Ba   -0.000386  0.326603 -0.492262  0.479404 -0.102151 -0.042618 -0.112841   \n",
       "Fe    0.143010 -0.241346  0.083060 -0.074402 -0.094201 -0.007719  0.124968   \n",
       "Type -0.164237  0.502898 -0.744993  0.598829  0.151565 -0.010054  0.000952   \n",
       "\n",
       "            Ba        Fe      Type  \n",
       "RI   -0.000386  0.143010 -0.164237  \n",
       "Na    0.326603 -0.241346  0.502898  \n",
       "Mg   -0.492262  0.083060 -0.744993  \n",
       "Al    0.479404 -0.074402  0.598829  \n",
       "Si   -0.102151 -0.094201  0.151565  \n",
       "K    -0.042618 -0.007719 -0.010054  \n",
       "Ca   -0.112841  0.124968  0.000952  \n",
       "Ba    1.000000 -0.058692  0.575161  \n",
       "Fe   -0.058692  1.000000 -0.188278  \n",
       "Type  0.575161 -0.188278  1.000000  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e36554d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "515ef4a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_matrix = df.corr().abs()\n",
    "# Select upper triangle of correlation matrix\n",
    "upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))\n",
    "# Find features with correlation greater than 0.95\n",
    "to_drop = [column for column in upper.columns if any(upper[column] > 0.95)]\n",
    "# Drop features \n",
    "df.drop(to_drop, axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f4fcd8f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (14,6))\n",
    "sns.heatmap(corr_matrix, annot = True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "40973dcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('Type', axis = 1)\n",
    "y = df['Type']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e4e4c3b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6e5e3c10",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "395a8587",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = preprocessing.RobustScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a072eec9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', random_state=0)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier \n",
    "dt = DecisionTreeClassifier(criterion='entropy', random_state=0) \n",
    "dt.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "740cea9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_dt = dt.predict(X_test) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "93dc690f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f79cef85",
   "metadata": {},
   "outputs": [],
   "source": [
    "cm_dt = confusion_matrix(y_test, y_pred_dt) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "cfc8db09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 7, 14,  1,  0,  0,  1],\n",
       "       [ 4,  5,  2,  2,  1,  2],\n",
       "       [ 1,  6,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  1,  0,  0],\n",
       "       [ 0,  0,  0,  0,  1,  0],\n",
       "       [ 0,  0,  0,  0,  0,  6]], dtype=int64)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm_dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "77786899",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4tOuxXdKRSLE2W7eJiIjl73+QcBLSSg0BSfIZRDVo9gb/9ddfjzPOOKO5T1uV9hncP3bovXP87se/SjoKKbTk2YXx0EW3xG9O/VE8+v3/jK222ya++ZsrYott2iQdjZQaMXp4zH36uXh1wWtJRyGl1BCQJJ9BVaopX76jAjX7Jfrvvfde3H777fGLX/ziM1/T2NgYjY2NBWtr8mujZa5Fc8dJrW26doh/+eGwuPm0MbGmcXXScUihPz/xv5eevbtgcbxZvzDOePInscc3Dok5P38wwWSk0SVjRsQuu+8c/2fI+UlHIaXUEJAkn0FUi6Ib/ClTpnzu86+++uoXnqOuri6uvPLKgrUD2vWK/tv0LjZOZu2wZ89ou902MfL+unVrLVq2iJ333y2+etoxcclXTo18hf7ViMq05qPGWLrg9dimZ+eko5AyF199YRw68OA4+1/Oj4a33kk6DimkhoAk+Qyqbnlfk/f5hgwZErlcLvL5z24uc7nc556jtrY2Ro4cWbA2as8zi42SaS//fn78aODFBWsn//t3o+GVN+N/bv6t5p6itdi8ZbTfZft44w8Lko5Cilx8zYVx+D8dEt/9xoXx5utLko5DCqkhIEk+g6g2RTf4Xbt2jZ/97GcxZMiQ9T4/d+7c6Nu37+eeo6amJmpqagqDuDy/QOOqj2PJS4sL1j75qDFWvf/Bp9ZhfQ4ZdXK8+uiz8cGb78ZWHbaOAy44PjZvs2X86b+eTDoaKfG9MRfFMf9yVFx8+qj4cOVH0WG79hERsfKDldH48ScJpyMN1BCl2qr1lrFTzx7rHnffcfvYo/eu8f6y5fHmG5o1Pp/PICKiYvfKl0vRDX7fvn1jzpw5n9ngf9F0H9g02nZtH18bNzy23LZtfPTeinhrzsK4e8gV8cEb7yYdjZT4xreHRETELb/5acH6lSPq4v/++qEEEpE2aohS9dm7d9x7/8R1j0dfc2lERPx60uQYed5lScUiJXwGERFV9zV5uXyR3fiTTz4Zq1atin/6p39a7/OrVq2K2bNnx2GHHVZUkIt2Oqmo18Pf69HU7PeLpMr8as3rSUcAqtibH/vjK6XptkWHpCOQcn94c1rSEcpi1dWnlu3crS+7q2zn3lhFd0WHHHLI5z7funXropt7AAAAaHZVdon+ZkkHAAAAAErnumYAAACyqcq+Js8EHwAAADLABB8AAIBssgcfAAAASBsTfAAAALIpbw8+AAAApF9TvnxHEaZPnx6DBw+Obt26RS6Xi8mTJxc8n8/nY/To0dGtW7fYcsst4/DDD4/nn3++6F9Xgw8AAABltGrVqujTp0+MGzduvc//+Mc/juuuuy7GjRsXzzzzTHTp0iWOPvro+OCDD4p6H5foAwAAkEn5CvmavEGDBsWgQYPW+1w+n4+xY8fGqFGj4utf/3pERNx+++3RuXPnmDRpUpx99tkb/D4m+AAAAFCkxsbGWLFiRcHR2NhY9Hlee+21WLJkSQwcOHDdWk1NTRx22GExc+bMos6lwQcAACCbyrgHv66uLtq1a1dw1NXVFR1xyZIlERHRuXPngvXOnTuve25DuUQfAAAAilRbWxsjR44sWKupqdno8+VyuYLH+Xz+U2tfRIMPAABANhV5t/ti1NTUlNTQ/02XLl0i4q+T/K5du65bb2ho+NRU/4u4RB8AAAAS0rNnz+jSpUtMnTp13donn3wS06ZNi4MOOqioc5ngAwAAkE35yriL/sqVK2PhwoXrHr/22msxd+7caN++ffTo0SNGjBgRY8aMiS9/+cvx5S9/OcaMGRNbbbVVfOtb3yrqfTT4AAAAZFMZL9EvxuzZs+OII45Y9/hve/eHDRsWt912W3zve9+Ljz76KM4999xYtmxZHHDAAfHII49E27Zti3ofDT4AAACU0eGHHx75/Gf/sSGXy8Xo0aNj9OjRJb2PBh8AAIBMylfIBH9TcZM9AAAAyAATfAAAALLJBB8AAABIGxN8AAAAsqmpMr4mb1MxwQcAAIAMMMEHAAAgm6psD74GHwAAgGyqsgbfJfoAAACQASb4AAAAZFI+b4IPAAAApIwJPgAAANlkDz4AAACQNib4AAAAZJMJPgAAAJA2FTPBv3fF/KQjkGI/3XyvpCOQcnPeW5h0BADYaEtWLks6AlSkfJVN8CumwQcAAIBmVWUNvkv0AQAAIANM8AEAAMimpqQDbFom+AAAAJABJvgAAABkUrXdZM8EHwAAADLABB8AAIBsMsEHAAAA0sYEHwAAgGxyF30AAAAgbUzwAQAAyKRqu4u+Bh8AAIBscok+AAAAkDYm+AAAAGRStV2ib4IPAAAAGWCCDwAAQDbZgw8AAACkjQk+AAAAmZQ3wQcAAADSxgQfAACAbKqyCb4GHwAAgExyiT4AAACQOib4AAAAZJMJPgAAAJA2JvgAAABkkj34AAAAQOqY4AMAAJBJJvgAAABA6pjgAwAAkEnVNsHX4AMAAJBN+VzSCTYpl+gDAABABpjgAwAAkEnVdom+CX4FO6B/35g4aVzMfv6xWPze/Djma0cmHYmU2aLLttFv3Llx7J9uieNenRhHPjomttmrZ9KxSJlzzh4WLy94KlaueCWenvVgfPXg/ZOORIqoH0qlhiiVGqKaaPAr2Fatt4w/zV8Ql186JukopFCrdq3jsPtHR37N2ph5yo/j0cMuiXmjfxmrl69KOhopcsIJx8V1Pxkdddf+NPrtf0zMmPGH+N39d0X37t2SjkYKqB9KpYYolRoi35Qr21GJcvl8Pp90iIiIHdr3TjpCRVv83vw489QL4uEHHks6SkX66eZ7JR2h4vQadVJ02O8rMX3IVUlHSYUT35uWdISKNHPG/THn2flx3vm169bmPfdETJnyUIy67NoEk5EG6odSqSFKpYY23JpP3kg6Qlm89dUjynburjMeL9u5N5YJPmRU12P2jWV/fDX2v/XC+Nr88XHk1DGx0ynl+4Aje1q1ahX77rtXTH208I8fU6dOi/4H9ksoFWmhfiiVGqJUaoiIv+7BL9dRiYpu8D/66KOYMWNG/OlPf/rUcx9//HHccccdzRIMKE3rHp1i52EDYtWrS+L3J10br93xaPS5elj0OOGQpKOREh07to+WLVtGw9tLC9YbGpZG5y6dEkpFWqgfSqWGKJUaohoV1eC/9NJLsfvuu8ehhx4ae+65Zxx++OHx1ltvrXt++fLlcfrpp3/heRobG2PFihUFR75S/wQCKZXbbLN4f96f4/m6e2L5/L/Ea3c+Fq/98rHoOWxA0tFImX/cyZXL5T61Bp9F/VAqNUSp1FB1y+dzZTsqUVEN/qWXXhp77rlnNDQ0xIIFC2LrrbeOgw8+OBYtWlTUm9bV1UW7du0Kjg8+XvrFPwhssI8blsUHLxXupfrg5Tdjq+07JJSItFm69L1Ys2ZNdO6yXcH6dtt1iIa330koFWmhfiiVGqJUaogIl+h/rpkzZ8aYMWOiY8eOscsuu8SUKVNi0KBBccghh8Srr766weepra2N5cuXFxxtt+hYdHjgs737h5eizZe6Fqy12blLfLjYH9PYMKtXr445c56LAUcdWrA+YMCh8dSs2QmlIi3UD6VSQ5RKDVFJ1qxZE5dddln07Nkzttxyy9h5553jqquuiqam5v1LQctiXvzRRx9Fy5aFP/Kzn/0sNttsszjssMNi0qRJG3SempqaqKmpKVjL5dzv7x9t1XrL2Klnj3WPu++4fezRe9d4f9nyePONJQkmIw0WTngwDrt/dOx6wfGxeMqs2HafL0XPoUfGsxf/Z9LRSJHrb7g1bp94Q9TX/zFmPV0fZ515avTovn3cMuHOpKORAuqHUqkhSqWGqJSvs/vRj34UN998c9x+++3Rq1evmD17dpx++unRrl27uPDCC5vtfYpq8HfbbbeYPXt27L777gXrN954Y+Tz+TjuuOOaLRgRffbuHffeP3Hd49HXXBoREb+eNDlGnndZUrFIiWVzX41ZZ1wfvX7wzdht5L/EqkXvxHOX3xmv/+b3SUcjRe69d0p0aL9tXDbqoujatVPMf35BDD5uaCxalM2v0qF5qR9KpYYolRqiUjz11FNx/PHHx7HHHhsRETvttFP86le/itmzm/dqkly+iDtM1NXVxZNPPhkPPPDAep8/99xz4+abb96oywx2aN+76J+Bv/np5nslHYGUO/G9aV/8IgCAjFrzSTb/6LGo31FlO3fn3z8QjY2NBWvru1o9IuLaa6+Nm2++OR555JH4yle+En/84x9j4MCBMXbs2Dj55JObLVNR18XX1tZ+ZnMfEXHTTTc1+x4CAAAAqDTru3l8XV3del976aWXxsknnxy77bZbtGrVKvbZZ58YMWJEszb3EUVeog8AAABpUc49+LW1tTFy5MiCtfVN7yMi7rnnnrjrrrti0qRJ0atXr5g7d26MGDEiunXrFsOGDWu2TBp8AAAAKNJnXY6/Ppdcckl8//vfj5NOOikiIvbcc8/4y1/+EnV1dRp8AAAA+CKVchf9Dz/8MDbbrHCHfIsWLZL9mjwAAABIiw2/pXx5DR48OK655pro0aNH9OrVK5599tm47rrr4owzzmjW99HgAwAAQBndeOONcfnll8e5554bDQ0N0a1btzj77LPjhz/8YbO+jwYfAACATKqUS/Tbtm0bY8eOjbFjx5b1fYr6mjwAAACgMpngAwAAkEn5fGVM8DcVE3wAAADIABN8AAAAMinfvN9CV/FM8AEAACADTPABAADIpKYq24OvwQcAACCT3GQPAAAASB0TfAAAADIp32SCDwAAAKSMCT4AAACZlM8nnWDTMsEHAACADDDBBwAAIJPswQcAAABSxwQfAACATGrKV9cEX4MPAABAJuWrrMF3iT4AAABkgAk+AAAAmeRr8gAAAIDUMcEHAAAgk6rtJnsm+AAAAJABJvgAAABkkrvoAwAAAKljgg8AAEAmVdtd9DX4AAAAZJKb7AEAAACpUzET/CUrlyUdgRQ7MaYlHQGocl3abJt0BFLM/w8CKA832QMAAABSp2Im+AAAANCc7MEHAAAAUscEHwAAgEyqsm/JM8EHAACALDDBBwAAIJOqbQ++Bh8AAIBM8jV5AAAAQOqY4AMAAJBJTUkH2MRM8AEAACADTPABAADIpHzYgw8AAACkjAk+AAAAmdSUTzrBpmWCDwAAABlggg8AAEAmNdmDDwAAAKSNCT4AAACZVG130dfgAwAAkElNSQfYxFyiDwAAABlggg8AAEAmVdsl+ib4AAAAkAEm+AAAAGSSPfgAAABA6pjgAwAAkEkm+AAAAEDqmOADAACQSdV2F30NPgAAAJnUVF39vUv0AQAAIAs0+AAAAGRSU+TKdhTrjTfeiFNPPTU6dOgQW221Vey9995RX1/frL+vS/QBAACgjJYtWxYHH3xwHHHEEfHggw9Gp06d4pVXXoltttmmWd9Hgw8AAEAm5ZMO8P/86Ec/iu7du8fEiRPXre20007N/j4u0QcAAIAiNTY2xooVKwqOxsbG9b52ypQp0a9fvzjhhBOiU6dOsc8++8Stt97a7Jk0+BXunLOHxcsLnoqVK16Jp2c9GF89eP+kI5EyaohSqSE21gH9+8bESeNi9vOPxeL35scxXzsy6UikkM8gSqWGqltTGY+6urpo165dwVFXV7feHK+++mqMHz8+vvzlL8fDDz8c55xzTlxwwQVxxx13NOvvq8GvYCeccFxc95PRUXftT6Pf/sfEjBl/iN/df1d0794t6WikhBqiVGqIUmzVesv40/wFcfmlY5KOQkr5DKJUaohyqq2tjeXLlxcctbW1631tU1NT7LvvvjFmzJjYZ5994uyzz46zzjorxo8f36yZcvl8viK2JbTcfPukI1ScmTPujznPzo/zzv/fIpn33BMxZcpDMeqyaxNMRlqoIUqlhjZclzbbJh2hoi1+b36ceeoF8fADjyUdpSItWbks6QgVyWcQpVJDG27NJ28kHaEs/qvrKWU79zfe+uUGv3bHHXeMo48+On7+85+vWxs/fnxcffXV8cYbzfe/vQl+hWrVqlXsu+9eMfXRaQXrU6dOi/4H9ksoFWmihiiVGgKS5DOIUqkhIv56k71yHcU4+OCDY8GCBQVrL730Uuy4444b82t9pqIb/BdeeCEmTpwYL774YkREvPjii/Hd7343zjjjjHjsMX+Vby4dO7aPli1bRsPbSwvWGxqWRucunRJKRZqoIUqlhoAk+QyiVGqISnLRRRfFrFmzYsyYMbFw4cKYNGlSTJgwIYYPH96s71PU1+Q99NBDcfzxx0ebNm3iww8/jPvuuy9OO+206NOnT+Tz+TjmmGPi4YcfjiOP/Pyb6DQ2Nn7q7oL5fD5yuVzxv0HG/eMOilwu96k1+DxqiFKpISBJPoMolRqqbk1JB/h/9ttvv7jvvvuitrY2rrrqqujZs2eMHTs2TjmlebcQFDXBv+qqq+KSSy6Jd999NyZOnBjf+ta34qyzzoqpU6fGo48+Gt/73vfi2mu/eC/L+u42mG/6YKN/iSxauvS9WLNmTXTusl3B+nbbdYiGt99JKBVpooYolRoCkuQziFKpISrNP//zP8e8efPi448/jhdeeCHOOuusZn+Pohr8559/Pr797W9HRMSJJ54YH3zwQfzrv/7ruudPPvnkeO65577wPOu722Bus7bFJc+41atXx5w5z8WAow4tWB8w4NB4atbshFKRJmqIUqkhIEk+gyiVGiIioilXvqMSFXWJ/t/bbLPNYosttohtttlm3Vrbtm1j+fLlX/izNTU1UVNTU7Dm8vxPu/6GW+P2iTdEff0fY9bT9XHWmadGj+7bxy0T7kw6GimhhiiVGqIUW7XeMnbq2WPd4+47bh979N413l+2PN58Y0mCyUgLn0GUSg1RbYpq8HfaaadYuHBh7LLLLhER8dRTT0WPHv/7D/frr78eXbt2bd6EVezee6dEh/bbxmWjLoquXTvF/OcXxODjhsaiRdn8CguanxqiVGqIUvTZu3fce//EdY9HX3NpRET8etLkGHneZUnFIkV8BlEqNURTVNcgOZcv4g4TN998c3Tv3j2OPfbY9T4/atSoePvttwu+229Dtdx8+6J/BgAqRZc22yYdgRRbsnJZ0hGAKrfmk2z+0eOX3U4t27lPefOusp17YxXV4JeTBh+ANNPgUwoNPpC0rDb4d5WxwT+1Ahv8jd6DDwAAAJWsUm+GVy5F3UUfAAAAqEwm+AAAAGRSU9IBNjETfAAAAMgAE3wAAAAyqSLuKL8JmeADAABABpjgAwAAkEnuog8AAACkjgk+AAAAmVRtd9HX4AMAAJBJ1dbgu0QfAAAAMsAEHwAAgEzKu8keAAAAkDYm+AAAAGSSPfgAAABA6pjgAwAAkEkm+AAAAEDqmOADAACQSfmkA2xiGnwAAAAyqcnX5AEAAABpY4IPAABAJrnJHgAAAJA6JvgAAABkkgk+AAAAkDom+AAAAGRStX1Nngk+AAAAZIAJPgAAAJnUlEs6waalwQcAACCT3GQPAAAASB0TfAAAADLJTfYAAACA1DHBBwAAIJOaqmyGr8EHgGawZOWypCOQYl3abJt0BFLOZxAQocEHAAAgo9xFHwAAAEgdE3wAAAAyqbp24GvwAQAAyCiX6AMAAACpY4IPAABAJjXlkk6waZngAwAAQAaY4AMAAJBJTVV2mz0TfAAAAMgAE3wAAAAyqbrm9yb4AAAAkAkm+AAAAGRSU9IBNjETfAAAAMgAE3wAAAAyqdruoq/BBwAAIJOqq713iT4AAABkggk+AAAAmeQmewAAAEBZ1NXVRS6XixEjRjT7uU3wAQAAyKRKu8neM888ExMmTIi99tqrLOc3wQcAAIAyW7lyZZxyyilx6623xrbbbluW99DgAwAAkEn5Mh6NjY2xYsWKgqOxsfEzswwfPjyOPfbYGDBgQDl+1YjQ4AMAAEDR6urqol27dgVHXV3del979913R319/Wc+31zswQcAACCTynkX/dra2hg5cmTBWk1Nzade9/rrr8eFF14YjzzySGyxxRZlTKTBBwAAIKPyZbzJXk1NzXob+n9UX18fDQ0N0bdv33Vra9eujenTp8e4ceOisbExWrRo0SyZNPgAAABQJkcddVTMmzevYO3000+P3XbbLS699NJma+4jNPgAAABkVDkv0d9Qbdu2jd69exestW7dOjp06PCp9VK5yR4AAABkgAk+AAAAmdRUxj34pXjiiSfKcl4TfAAAAMgAE3wAAAAyqTLn9+Vjgg8AAAAZYIIPAABAJlXqHvxyMcGvcOecPSxeXvBUrFzxSjw968H46sH7Jx2JlFFDlEoNUQr1QykO6N83Jk4aF7OffywWvzc/jvnakUlHIoV8DlW3pjIelUiDX8FOOOG4uO4no6Pu2p9Gv/2PiRkz/hC/u/+u6N69W9LRSAk1RKnUEKVQP5Rqq9Zbxp/mL4jLLx2TdBRSyucQ1SaXz+dLvmYhn89HLpcr6RwtN9++1BiZM3PG/THn2flx3vm169bmPfdETJnyUIy67NoEk5EWaohSqSFKoX42XJc22yYdoeItfm9+nHnqBfHwA48lHaUiLVm5LOkIFcnn0IZb88kbSUcoi+/s9I2ynfvnf/6vsp17YzXLBL+mpiZeeOGF5jgV/0+rVq1i3333iqmPTitYnzp1WvQ/sF9CqUgTNUSp1BClUD9A0nwOUY2KusneyJEj17u+du3auPbaa6NDhw4REXHdddeVnqzKdezYPlq2bBkNby8tWG9oWBqdu3RKKBVpooYolRqiFOoHSJrPISIqd698uRTV4I8dOzb69OkT22yzTcF6Pp+PF154IVq3br1Bl+o3NjZGY2Pjp85R6mX+WfSPOyhyudyn1uDzqCFKpYYohfoBkuZziGpSVIN/zTXXxK233ho/+clP4sgj//cupq1atYrbbrst9thjjw06T11dXVx55ZUFa7nN2kSuxdbFxMm0pUvfizVr1kTnLtsVrG+3XYdoePudhFKRJmqIUqkhSqF+gKT5HCIiIu9r8j5bbW1t3HPPPfHd7343Lr744li9evVGvWltbW0sX7684Mht1najzpVVq1evjjlznosBRx1asD5gwKHx1KzZCaUiTdQQpVJDlEL9AEnzOUQ1KmqCHxGx3377RX19fQwfPjz69u0bv/zlL4u+tL6mpiZqamoK1lye/2nX33Br3D7xhqiv/2PMero+zjrz1OjRffu4ZcKdSUcjJdQQpVJDlEL9UKqtWm8ZO/Xsse5x9x23jz167xrvL1seb76xJMFkpIXPIezB3wBt2rSJ22+/Pe6+++44+uijY+3atc2di4i4994p0aH9tnHZqIuia9dOMf/5BTH4uKGxaFE2v8KC5qeGKJUaohTqh1L12bt33Hv/xHWPR19zaURE/HrS5Bh53mVJxSJFfA7RVGX3W8jlS7zDxOLFi6O+vj4GDBgQrVu33ujztNx8+1JiAACkVpc22yYdgZRbsnJZ0hFIuTWfZPOPHkN3/HrZzn3nX35TtnNvrI2a4P+9HXbYIXbYYYfmyAIAAADNprrm90XeZA8AAACoTCVP8AEAAKASNVXZDN8EHwAAADLABB8AAIBMypvgAwAAAGljgg8AAEAmNSUdYBPT4AMAAJBJbrIHAAAApI4JPgAAAJnkJnsAAABA6pjgAwAAkEnVdpM9E3wAAADIABN8AAAAMimftwcfAAAASBkTfAAAADKpqcruoq/BBwAAIJPcZA8AAABIHRN8AAAAMilfZZfom+ADAABABpjgAwAAkEnVdpM9E3wAAADIABN8AAAAMimfN8EHAAAAUsYEHwAAgExqSjrAJqbBBwAAIJN8TR4AAACQOib4AAAAZJKvyQMAAABSxwQfAACATPI1eQAAAEDqmOADAACQSfbgAwAAAKljgg8AkLAlK5clHYGU+3X7w5KOABUpX2UTfA0+AAAAmdTkJnsAAABA2pjgAwAAkEnVNb83wQcAAIBMMMEHAAAgk3xNHgAAAJA6GnwAAAAyqSnyZTuKUVdXF/vtt1+0bds2OnXqFEOGDIkFCxY0+++rwQcAAIAymjZtWgwfPjxmzZoVU6dOjTVr1sTAgQNj1apVzfo+9uADAACQSfl8ZezBf+ihhwoeT5w4MTp16hT19fVx6KGHNtv7aPABAACgSI2NjdHY2FiwVlNTEzU1NV/4s8uXL4+IiPbt2zdrJpfoAwAAkEnl3INfV1cX7dq1Kzjq6uq+MFM+n4+RI0fGV7/61ejdu3ez/r4m+AAAAGRSvoxfk1dbWxsjR44sWNuQ6f15550Xzz33XMyYMaPZM2nwAQAAoEgbejn+3zv//PNjypQpMX369Nhhhx2aPZMGHwAAgEyqlJvs5fP5OP/88+O+++6LJ554Inr27FmW99HgAwAAQBkNHz48Jk2aFL/97W+jbdu2sWTJkoiIaNeuXWy55ZbN9j4afAAAADKpqYx78Isxfvz4iIg4/PDDC9YnTpwY3/72t5vtfTT4AAAAUEabaquABh8AAIBMqpQ9+JvKZkkHAAAAAEpngg8AAEAmVcoe/E1Fgw8AAEAm5auswXeJPgAAAGSACT4AAACZ1OQmewAAAEDamOADAACQSfbgAwAAAKljgg8AAEAm2YMPAAAApI4JPgAAAJlUbXvwNfgAAABkkkv0AQAAgNQxwQcAACCTqu0SfRP8CnfO2cPi5QVPxcoVr8TTsx6Mrx68f9KRSBk1RKnUEKVQP5RKDVGKLbpsG/3GnRvH/umWOO7ViXHko2Nim716Jh0LykaDX8FOOOG4uO4no6Pu2p9Gv/2PiRkz/hC/u/+u6N69W9LRSAk1RKnUEKVQP5RKDVGKVu1ax2H3j478mrUx85Qfx6OHXRLzRv8yVi9flXQ0NqGmfL5sRyXK5fOVkazl5tsnHaHizJxxf8x5dn6cd37turV5zz0RU6Y8FKMuuzbBZKSFGqJUaohSqB9KpYY23K/bH5Z0hIrTa9RJ0WG/r8T0IVclHSUVvr5kUtIRyuJLHfct27lfWTqnbOfeWCb4FapVq1ax7757xdRHpxWsT506Lfof2C+hVKSJGqJUaohSqB9KpYYoVddj9o1lf3w19r/1wvja/PFx5NQxsdMpRyQdi00sX8b/VKKSbrK3bNmyuP322+Pll1+Orl27xrBhw6J79+7Nla2qdezYPlq2bBkNby8tWG9oWBqdu3RKKBVpooYolRqiFOqHUqkhStW6R6fYediAWHjLg7HghsnRfp8vRZ+rh0XTJ2ti0b1PJh0PyqKoBr9bt24xb9686NChQ7z22mtx0EEHRUTEnnvuGVOmTIn/+I//iFmzZsVuu+32uedpbGyMxsbGgrV8Ph+5XK7I+Nn3jzsocrncp9bg86ghSqWGKIX6oVRqiI2V22yzWPbHV+P5unsiImL5/L9E2113iJ7DBmjwq0g+35R0hE2qqEv0lyxZEmvXro2IiB/84Aex2267xSuvvBKPPPJILFy4MA455JC4/PLLv/A8dXV10a5du4Ij3/TBxv0GGbV06XuxZs2a6Nxlu4L17bbrEA1vv5NQKtJEDVEqNUQp1A+lUkOU6uOGZfHBS28UrH3w8pux1fYdEkpEEpoiX7ajEm30Hvynn346Lr/88thqq60iIqKmpiYuu+yymDVr1hf+bG1tbSxfvrzgyG3WdmOjZNLq1atjzpznYsBRhxasDxhwaDw1a3ZCqUgTNUSp1BClUD+USg1Rqnf/8FK0+VLXgrU2O3eJDxcv/YyfgPQreg/+3y6jb2xsjM6dOxc817lz53jnnS/+i2pNTU3U1NSs97z8r+tvuDVun3hD1Nf/MWY9XR9nnXlq9Oi+fdwy4c6ko5ESaohSqSFKoX4olRqiFAsnPBiH3T86dr3g+Fg8ZVZsu8+XoufQI+PZi/8z6WhsQtW2pafoBv+oo46Kli1bxooVK+Kll16KXr16rXtu0aJF0bFjx2YNWM3uvXdKdGi/bVw26qLo2rVTzH9+QQw+bmgsWvTGF/8whBqidGqIUqgfSqWGKMWyua/GrDOuj14/+GbsNvJfYtWid+K5y++M13/z+6SjQdnk8kX8SePKK68seHzggQfGMcccs+7xJZdcEosXL45f/epXRQdpufn2Rf8MAAAQ8ev2hyUdgZT7+pJJSUcoix3a9y7buRe/N79s595YRTX45aTBBwCAjaPBp1Qa/OJVYoNf9CX6AAAAkAYVMs/eZDb6LvoAAABA5TDBBwAAIJOaqmyCr8EHAAAgk/JRXQ2+S/QBAAAgA0zwAQAAyCQ32QMAAABSxwQfAACATGqyBx8AAABIGxN8AAAAMskefAAAACB1TPABAADIpKYqm+Br8AEAAMgkl+gDAAAAqWOCDwAAQCb5mjwAAAAgdUzwAQAAyCR78AEAAIDUMcEHAAAgk6rta/JM8AEAACADTPABAADIpHyV3UVfgw8AAEAmuUQfAAAASB0TfAAAADLJ1+QBAAAAqWOCDwAAQCZV2032TPABAAAgA0zwAQAAyCR78AEAAIBmd9NNN0XPnj1jiy22iL59+8aTTz7ZrOfX4AMAAJBJ+Xy+bEex7rnnnhgxYkSMGjUqnn322TjkkENi0KBBsWjRomb7fTX4AAAAZFK+jEexrrvuujjzzDPjO9/5Tuy+++4xduzY6N69e4wfP76E37CQBh8AAACK1NjYGCtWrCg4Ghsb1/vaTz75JOrr62PgwIEF6wMHDoyZM2c2W6aKucnemk/eSDpCxWpsbIy6urqora2NmpqapOOQQmqIUqgfSqWGKJUaohTqp7qVs88cPXp0XHnllQVrV1xxRYwePfpTr126dGmsXbs2OnfuXLDeuXPnWLJkSbNlyuWr7baCKbRixYpo165dLF++PLbeeuuk45BCaohSqB9KpYYolRqiFOqHcmlsbPzUxL6mpma9f0h68803Y/vtt4+ZM2dG//79161fc801ceedd8aLL77YLJkqZoIPAAAAafFZzfz6dOzYMVq0aPGpaX1DQ8OnpvqlsAcfAAAAymjzzTePvn37xtSpUwvWp06dGgcddFCzvY8JPgAAAJTZyJEjY+jQodGvX7/o379/TJgwIRYtWhTnnHNOs72HBj8Fampq4oorrnBTEDaaGqIU6odSqSFKpYYohfqhUnzzm9+Md999N6666qp46623onfv3vHAAw/Ejjvu2Gzv4SZ7AAAAkAH24AMAAEAGaPABAAAgAzT4AAAAkAEafAAAAMgADX6Fu+mmm6Jnz56xxRZbRN++fePJJ59MOhIpMn369Bg8eHB069YtcrlcTJ48OelIpEhdXV3st99+0bZt2+jUqVMMGTIkFixYkHQsUmT8+PGx1157xdZbbx1bb7119O/fPx588MGkY5FSdXV1kcvlYsSIEUlHISVGjx4duVyu4OjSpUvSsaCsNPgV7J577okRI0bEqFGj4tlnn41DDjkkBg0aFIsWLUo6GimxatWq6NOnT4wbNy7pKKTQtGnTYvjw4TFr1qyYOnVqrFmzJgYOHBirVq1KOhopscMOO8S1114bs2fPjtmzZ8eRRx4Zxx9/fDz//PNJRyNlnnnmmZgwYULstddeSUchZXr16hVvvfXWumPevHlJR4Ky8jV5FeyAAw6IfffdN8aPH79ubffdd48hQ4ZEXV1dgslIo1wuF/fdd18MGTIk6Sik1DvvvBOdOnWKadOmxaGHHpp0HFKqffv28e///u9x5plnJh2FlFi5cmXsu+++cdNNN8XVV18de++9d4wdOzbpWKTA6NGjY/LkyTF37tyko8AmY4JfoT755JOor6+PgQMHFqwPHDgwZs6cmVAqoJotX748Iv7aoEGx1q5dG3fffXesWrUq+vfvn3QcUmT48OFx7LHHxoABA5KOQgq9/PLL0a1bt+jZs2ecdNJJ8eqrryYdCcqqZdIBWL+lS5fG2rVro3PnzgXrnTt3jiVLliSUCqhW+Xw+Ro4cGV/96lejd+/eScchRebNmxf9+/ePjz/+ONq0aRP33Xdf7LHHHknHIiXuvvvuqK+vj9mzZycdhRQ64IAD4o477oivfOUr8fbbb8fVV18dBx10UDz//PPRoUOHpONBWWjwK1wulyt4nM/nP7UGUG7nnXdePPfcczFjxoyko5Ayu+66a8ydOzfef//9+O///u8YNmxYTJs2TZPPF3r99dfjwgsvjEceeSS22GKLpOOQQoMGDVr33/fcc8/o379/fOlLX4rbb789Ro4cmWAyKB8NfoXq2LFjtGjR4lPT+oaGhk9N9QHK6fzzz48pU6bE9OnTY4cddkg6Dimz+eabxy677BIREf369YtnnnkmbrjhhrjlllsSTkalq6+vj4aGhujbt++6tbVr18b06dNj3Lhx0djYGC1atEgwIWnTunXr2HPPPePll19OOgqUjT34FWrzzTePvn37xtSpUwvWp06dGgcddFBCqYBqks/n47zzzovf/OY38dhjj0XPnj2TjkQG5PP5aGxsTDoGKXDUUUfFvHnzYu7cueuOfv36xSmnnBJz587V3FO0xsbGeOGFF6Jr165JR4GyMcGvYCNHjoyhQ4dGv379on///jFhwoRYtGhRnHPOOUlHIyVWrlwZCxcuXPf4tddei7lz50b79u2jR48eCSYjDYYPHx6TJk2K3/72t9G2bdt1VxS1a9cuttxyy4TTkQY/+MEPYtCgQdG9e/f44IMP4u67744nnngiHnrooaSjkQJt27b91D0/WrduHR06dHAvEDbIxRdfHIMHD44ePXpEQ0NDXH311bFixYoYNmxY0tGgbDT4Feyb3/xmvPvuu3HVVVfFW2+9Fb17944HHnggdtxxx6SjkRKzZ8+OI444Yt3jv+03GzZsWNx2220JpSIt/vYVnYcffnjB+sSJE+Pb3/72pg9E6rz99tsxdOjQeOutt6Jdu3ax1157xUMPPRRHH3100tGAKrB48eI4+eSTY+nSpbHddtvFgQceGLNmzfL/pcm0XD6fzycdAgAAACiNPfgAAACQARp8AAAAyAANPgAAAGSABh8AAAAyQIMPAAAAGaDBBwAAgAzQ4AMAAEAGaPABAAAgAzT4AAAAkAEafAAAAMgADT4AAABkgAYfAAAAMuD/Bx1WphhXian4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (14,6))\n",
    "sns.heatmap(cm_dt, annot = True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "8bb8c965",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "376b9951",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.58      0.30      0.40        23\n",
      "           2       0.20      0.31      0.24        16\n",
      "           3       0.00      0.00      0.00         7\n",
      "           5       0.33      1.00      0.50         1\n",
      "           6       0.50      1.00      0.67         1\n",
      "           7       0.67      1.00      0.80         6\n",
      "\n",
      "    accuracy                           0.37        54\n",
      "   macro avg       0.38      0.60      0.44        54\n",
      "weighted avg       0.40      0.37      0.35        54\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_dt))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "400752ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# rejecting decision tree because of low accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "7799581c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(criterion='entropy', n_estimators=10)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier \n",
    "rf= RandomForestClassifier(n_estimators= 10, criterion=\"entropy\") \n",
    "rf.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "a0bcadae",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_rf = rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "8056ee72",
   "metadata": {},
   "outputs": [],
   "source": [
    "cm_rf = confusion_matrix(y_test, y_pred_rf) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "43508304",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[17,  5,  1,  0,  0,  0],\n",
       "       [ 6,  8,  0,  1,  1,  0],\n",
       "       [ 4,  3,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  1,  0,  0],\n",
       "       [ 0,  0,  0,  0,  1,  0],\n",
       "       [ 0,  0,  0,  0,  0,  6]], dtype=int64)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "7412f3dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (14,6))\n",
    "sns.heatmap(cm_rf, annot = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "c449243c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.63      0.74      0.68        23\n",
      "           2       0.50      0.50      0.50        16\n",
      "           3       0.00      0.00      0.00         7\n",
      "           5       0.50      1.00      0.67         1\n",
      "           6       0.50      1.00      0.67         1\n",
      "           7       1.00      1.00      1.00         6\n",
      "\n",
      "    accuracy                           0.61        54\n",
      "   macro avg       0.52      0.71      0.59        54\n",
      "weighted avg       0.55      0.61      0.57        54\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_rf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "1b1bc9d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "48d74803",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier()"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gradient_booster = GradientBoostingClassifier(learning_rate=0.1)\n",
    "gradient_booster.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "77025fe2",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_gb = gradient_booster.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "156256c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "cm_gb = confusion_matrix(y_test, y_pred_gb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "f4fea14e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[18,  3,  1,  1,  0,  0],\n",
       "       [ 5, 11,  0,  0,  0,  0],\n",
       "       [ 3,  3,  1,  0,  0,  0],\n",
       "       [ 0,  0,  0,  1,  0,  0],\n",
       "       [ 0,  1,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  6]], dtype=int64)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm_gb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "cc80da36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (14,6))\n",
    "sns.heatmap(cm_gb, annot = True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "350b42c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.69      0.78      0.73        23\n",
      "           2       0.61      0.69      0.65        16\n",
      "           3       0.50      0.14      0.22         7\n",
      "           5       0.50      1.00      0.67         1\n",
      "           6       0.00      0.00      0.00         1\n",
      "           7       1.00      1.00      1.00         6\n",
      "\n",
      "    accuracy                           0.69        54\n",
      "   macro avg       0.55      0.60      0.55        54\n",
      "weighted avg       0.66      0.69      0.66        54\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_gb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17e576ba",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}