{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "22b79559-7f85-44f0-899c-9278185eefc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/gptuser/courses/ai-feb-2024'"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "c6af55f9-ab10-4cde-98bd-aefa36df56d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/gptuser/courses/ai-feb-2024/datasets\n"
     ]
    }
   ],
   "source": [
    "cd datasets/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "e711dc9f-df40-4ea0-974d-65e32b91f72e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2024-03-03 18:53:33--  https://online.innosoft.ai/assets/courseware/v1/4578813524c4e208db1323828843093d/asset-v1:InnosoftGulf+AI-200+2024_02+type@asset+block/Classified_Data\n",
      "Resolving online.innosoft.ai (online.innosoft.ai)... 212.71.253.244\n",
      "Connecting to online.innosoft.ai (online.innosoft.ai)|212.71.253.244|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 194323 (190K) [application/octet-stream]\n",
      "Saving to: ‘Classified_Data.2’\n",
      "\n",
      "Classified_Data.2   100%[===================>] 189.77K   725KB/s    in 0.3s    \n",
      "\n",
      "2024-03-03 18:53:34 (725 KB/s) - ‘Classified_Data.2’ saved [194323/194323]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://online.innosoft.ai/assets/courseware/v1/4578813524c4e208db1323828843093d/asset-v1:InnosoftGulf+AI-200+2024_02+type@asset+block/Classified_Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "abcba3dd-5607-4deb-9bc0-5cf93947e5bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classified_Data    \u001b[0m\u001b[01;34mml-100k\u001b[0m/       Salaries.csv    titanic_train.csv\n",
      "Classified_Data.1  ml-100k.zip    Salaries.csv.1  USA_Housing.csv\n",
      "Classified_Data.2  my_report.csv  Salaries.csv.2\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "7337c5f2-585b-4889-8334-ec86e61b431f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/gptuser/courses/ai-feb-2024\n"
     ]
    }
   ],
   "source": [
    "cd .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "b793a9e8-89c5-4032-a38d-c5a6231cda19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/gptuser/courses/ai-feb-2024'"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "c4a4d88d-4e69-4231-ae40-0ba8bd937b71",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns \n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "ceb3ae60-b970-4293-88a7-d61baeaa8009",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "datasets/Classified_Data: CSV text\n"
     ]
    }
   ],
   "source": [
    "!file datasets/Classified_Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "dc7b61f2-c8b5-4e80-b178-69f9aa5c7451",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('datasets/Classified_Data', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "45452306-9f80-454f-b5b7-96d362986a9d",
   "metadata": {},
   "outputs": [
    {
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       "        WTT       PTI       EQW       SBI       LQE       QWG       FDJ  \\\n",
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       "\n",
       "        PJF       HQE       NXJ  TARGET CLASS  \n",
       "0  0.643798  0.879422  1.231409             1  \n",
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       "3  1.380003  1.522692  1.153093             1  \n",
       "4  0.646691  1.463812  1.419167             1  "
      ]
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     "execution_count": 118,
     "metadata": {},
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "4f4dfefd-ccfb-4551-9603-4c336eefb834",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "b5fd66b0-8782-4b93-8a62-987d78b50d0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "e40006a1-dc2b-4131-b3e3-473d5d7c373e",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = df.drop('TARGET CLASS', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "c41c8278-a44c-470b-98cb-102c0f513603",
   "metadata": {},
   "outputs": [
    {
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     "metadata": {},
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    }
   ],
   "source": [
    "features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "0ed75bf2-5d49-4085-a1c3-124f7df08073",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.fit(features) # Calculates the mean and std for the features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "7101449a-ec53-4c6a-8a35-145eaf68f1b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_features = scaler.transform(features) # Normalize the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "23b47afa-63f9-44c9-99ac-5ec7e37c791b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.12354188,  0.18590747, -0.91343069,  0.31962911, -1.03363681,\n",
       "        -2.3083747 , -0.79895135, -1.48236813, -0.9497194 , -0.64331425],\n",
       "       [-1.08483602, -0.43034845, -1.02531333,  0.62538826, -0.44484713,\n",
       "        -1.15270604, -1.12979749, -0.20224031, -1.82805088,  0.63675862]])"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_features[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "15ce702e-362a-4382-8115-9d4af26c7372",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(scaled_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "f156622a-36e2-4680-a000-98a870e37a1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 10)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "13bf78e6-ced0-4fb2-8c00-085fcfc06266",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['WTT', 'PTI', 'EQW', 'SBI', 'LQE', 'QWG', 'FDJ', 'PJF', 'HQE', 'NXJ',\n",
       "       'TARGET CLASS'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "d2699b07-beb6-4852-8f5a-3693bf5a64a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_feat = pd.DataFrame(scaled_features, columns=df.columns[:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "991d79e8-05b7-4a98-b6d3-2958d174800f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WTT</th>\n",
       "      <th>PTI</th>\n",
       "      <th>EQW</th>\n",
       "      <th>SBI</th>\n",
       "      <th>LQE</th>\n",
       "      <th>QWG</th>\n",
       "      <th>FDJ</th>\n",
       "      <th>PJF</th>\n",
       "      <th>HQE</th>\n",
       "      <th>NXJ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.123542</td>\n",
       "      <td>0.185907</td>\n",
       "      <td>-0.913431</td>\n",
       "      <td>0.319629</td>\n",
       "      <td>-1.033637</td>\n",
       "      <td>-2.308375</td>\n",
       "      <td>-0.798951</td>\n",
       "      <td>-1.482368</td>\n",
       "      <td>-0.949719</td>\n",
       "      <td>-0.643314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.084836</td>\n",
       "      <td>-0.430348</td>\n",
       "      <td>-1.025313</td>\n",
       "      <td>0.625388</td>\n",
       "      <td>-0.444847</td>\n",
       "      <td>-1.152706</td>\n",
       "      <td>-1.129797</td>\n",
       "      <td>-0.202240</td>\n",
       "      <td>-1.828051</td>\n",
       "      <td>0.636759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.788702</td>\n",
       "      <td>0.339318</td>\n",
       "      <td>0.301511</td>\n",
       "      <td>0.755873</td>\n",
       "      <td>2.031693</td>\n",
       "      <td>-0.870156</td>\n",
       "      <td>2.599818</td>\n",
       "      <td>0.285707</td>\n",
       "      <td>-0.682494</td>\n",
       "      <td>-0.377850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.982841</td>\n",
       "      <td>1.060193</td>\n",
       "      <td>-0.621399</td>\n",
       "      <td>0.625299</td>\n",
       "      <td>0.452820</td>\n",
       "      <td>-0.267220</td>\n",
       "      <td>1.750208</td>\n",
       "      <td>1.066491</td>\n",
       "      <td>1.241325</td>\n",
       "      <td>-1.026987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.139275</td>\n",
       "      <td>-0.640392</td>\n",
       "      <td>-0.709819</td>\n",
       "      <td>-0.057175</td>\n",
       "      <td>0.822886</td>\n",
       "      <td>-0.936773</td>\n",
       "      <td>0.596782</td>\n",
       "      <td>-1.472352</td>\n",
       "      <td>1.040772</td>\n",
       "      <td>0.276510</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        WTT       PTI       EQW       SBI       LQE       QWG       FDJ  \\\n",
       "0 -0.123542  0.185907 -0.913431  0.319629 -1.033637 -2.308375 -0.798951   \n",
       "1 -1.084836 -0.430348 -1.025313  0.625388 -0.444847 -1.152706 -1.129797   \n",
       "2 -0.788702  0.339318  0.301511  0.755873  2.031693 -0.870156  2.599818   \n",
       "3  0.982841  1.060193 -0.621399  0.625299  0.452820 -0.267220  1.750208   \n",
       "4  1.139275 -0.640392 -0.709819 -0.057175  0.822886 -0.936773  0.596782   \n",
       "\n",
       "        PJF       HQE       NXJ  \n",
       "0 -1.482368 -0.949719 -0.643314  \n",
       "1 -0.202240 -1.828051  0.636759  \n",
       "2  0.285707 -0.682494 -0.377850  \n",
       "3  1.066491  1.241325 -1.026987  \n",
       "4 -1.472352  1.040772  0.276510  "
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_feat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "2571acea-d01c-4939-b542-c008a5f276ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 10 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   WTT     1000 non-null   float64\n",
      " 1   PTI     1000 non-null   float64\n",
      " 2   EQW     1000 non-null   float64\n",
      " 3   SBI     1000 non-null   float64\n",
      " 4   LQE     1000 non-null   float64\n",
      " 5   QWG     1000 non-null   float64\n",
      " 6   FDJ     1000 non-null   float64\n",
      " 7   PJF     1000 non-null   float64\n",
      " 8   HQE     1000 non-null   float64\n",
      " 9   NXJ     1000 non-null   float64\n",
      "dtypes: float64(10)\n",
      "memory usage: 78.2 KB\n"
     ]
    }
   ],
   "source": [
    "df_feat.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "9887a821-13a5-4eac-aad6-d97c8d8ff6fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>WTT</th>\n",
       "      <th>PTI</th>\n",
       "      <th>EQW</th>\n",
       "      <th>SBI</th>\n",
       "      <th>LQE</th>\n",
       "      <th>QWG</th>\n",
       "      <th>FDJ</th>\n",
       "      <th>PJF</th>\n",
       "      <th>HQE</th>\n",
       "      <th>NXJ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>1.000000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.119105e-16</td>\n",
       "      <td>-2.939871e-16</td>\n",
       "      <td>-1.203482e-16</td>\n",
       "      <td>-1.882938e-16</td>\n",
       "      <td>-6.057377e-16</td>\n",
       "      <td>3.552714e-17</td>\n",
       "      <td>2.255973e-16</td>\n",
       "      <td>-4.760636e-16</td>\n",
       "      <td>3.197442e-16</td>\n",
       "      <td>4.503065e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000500e+00</td>\n",
       "      <td>1.000500e+00</td>\n",
       "      <td>1.000500e+00</td>\n",
       "      <td>1.000500e+00</td>\n",
       "      <td>1.000500e+00</td>\n",
       "      <td>1.000500e+00</td>\n",
       "      <td>1.000500e+00</td>\n",
       "      <td>1.000500e+00</td>\n",
       "      <td>1.000500e+00</td>\n",
       "      <td>1.000500e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.678050e+00</td>\n",
       "      <td>-2.618747e+00</td>\n",
       "      <td>-2.275858e+00</td>\n",
       "      <td>-2.775551e+00</td>\n",
       "      <td>-2.947206e+00</td>\n",
       "      <td>-2.660802e+00</td>\n",
       "      <td>-2.620466e+00</td>\n",
       "      <td>-2.674465e+00</td>\n",
       "      <td>-2.701361e+00</td>\n",
       "      <td>-3.542140e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-7.161683e-01</td>\n",
       "      <td>-6.702761e-01</td>\n",
       "      <td>-7.504105e-01</td>\n",
       "      <td>-7.279635e-01</td>\n",
       "      <td>-6.637361e-01</td>\n",
       "      <td>-7.127975e-01</td>\n",
       "      <td>-7.020467e-01</td>\n",
       "      <td>-7.120098e-01</td>\n",
       "      <td>-7.626629e-01</td>\n",
       "      <td>-6.863610e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-3.180217e-02</td>\n",
       "      <td>1.628137e-02</td>\n",
       "      <td>-7.159299e-02</td>\n",
       "      <td>-2.293699e-02</td>\n",
       "      <td>1.433731e-02</td>\n",
       "      <td>-7.940354e-03</td>\n",
       "      <td>-7.093937e-02</td>\n",
       "      <td>-2.236584e-02</td>\n",
       "      <td>2.488297e-02</td>\n",
       "      <td>6.194010e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.378939e-01</td>\n",
       "      <td>7.534412e-01</td>\n",
       "      <td>6.664646e-01</td>\n",
       "      <td>6.631695e-01</td>\n",
       "      <td>6.820374e-01</td>\n",
       "      <td>7.012930e-01</td>\n",
       "      <td>6.723000e-01</td>\n",
       "      <td>7.311915e-01</td>\n",
       "      <td>7.661087e-01</td>\n",
       "      <td>6.961851e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.667092e+00</td>\n",
       "      <td>2.799904e+00</td>\n",
       "      <td>3.049325e+00</td>\n",
       "      <td>4.151021e+00</td>\n",
       "      <td>2.538987e+00</td>\n",
       "      <td>2.825739e+00</td>\n",
       "      <td>2.940974e+00</td>\n",
       "      <td>2.470109e+00</td>\n",
       "      <td>2.477734e+00</td>\n",
       "      <td>2.602476e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                WTT           PTI           EQW           SBI           LQE  \\\n",
       "count  1.000000e+03  1.000000e+03  1.000000e+03  1.000000e+03  1.000000e+03   \n",
       "mean   1.119105e-16 -2.939871e-16 -1.203482e-16 -1.882938e-16 -6.057377e-16   \n",
       "std    1.000500e+00  1.000500e+00  1.000500e+00  1.000500e+00  1.000500e+00   \n",
       "min   -2.678050e+00 -2.618747e+00 -2.275858e+00 -2.775551e+00 -2.947206e+00   \n",
       "25%   -7.161683e-01 -6.702761e-01 -7.504105e-01 -7.279635e-01 -6.637361e-01   \n",
       "50%   -3.180217e-02  1.628137e-02 -7.159299e-02 -2.293699e-02  1.433731e-02   \n",
       "75%    7.378939e-01  7.534412e-01  6.664646e-01  6.631695e-01  6.820374e-01   \n",
       "max    2.667092e+00  2.799904e+00  3.049325e+00  4.151021e+00  2.538987e+00   \n",
       "\n",
       "                QWG           FDJ           PJF           HQE           NXJ  \n",
       "count  1.000000e+03  1.000000e+03  1.000000e+03  1.000000e+03  1.000000e+03  \n",
       "mean   3.552714e-17  2.255973e-16 -4.760636e-16  3.197442e-16  4.503065e-16  \n",
       "std    1.000500e+00  1.000500e+00  1.000500e+00  1.000500e+00  1.000500e+00  \n",
       "min   -2.660802e+00 -2.620466e+00 -2.674465e+00 -2.701361e+00 -3.542140e+00  \n",
       "25%   -7.127975e-01 -7.020467e-01 -7.120098e-01 -7.626629e-01 -6.863610e-01  \n",
       "50%   -7.940354e-03 -7.093937e-02 -2.236584e-02  2.488297e-02  6.194010e-02  \n",
       "75%    7.012930e-01  6.723000e-01  7.311915e-01  7.661087e-01  6.961851e-01  \n",
       "max    2.825739e+00  2.940974e+00  2.470109e+00  2.477734e+00  2.602476e+00  "
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_feat.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "b92d2ccc-3ef5-4ed1-a3e3-9768574129ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_feat\n",
    "y = df['TARGET CLASS']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "a35b52ca-69f7-4fb2-bb74-e22e8a752a3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "c282ed5f-b493-414d-9de9-3c51d55d8639",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "     X, y, test_size=0.30, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "4b3faef7-94fa-4f90-8701-40a45d64cc11",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "10d1fbab-83d0-4af3-9b73-55d5233ff146",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=39)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "0562cd79-ffc1-429c-bc75-2d53baff5d79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {color: black;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=39)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=39)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=39)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "09695f4b-6a3d-49c8-a298-bcec2faf8a87",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "80fe35b1-c27a-4408-8ff3-0461042d646e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "8ce67762-89c9-4e04-a01b-7dd36209f62e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.95      0.95       159\n",
      "           1       0.94      0.95      0.95       141\n",
      "\n",
      "    accuracy                           0.95       300\n",
      "   macro avg       0.95      0.95      0.95       300\n",
      "weighted avg       0.95      0.95      0.95       300\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "62f14fde-e48f-4cc9-8aa3-621e3436de63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[151   8]\n",
      " [  7 134]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "4d0bad8d-56f1-4741-9b07-d3230a99a1d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tuning our model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "dc17af45-967f-44a5-8957-95478da0e3d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "error_rate = []\n",
    "\n",
    "for k in range(1, 200):\n",
    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
    "    knn.fit(X_train, y_train)\n",
    "    prediction_k = knn.predict(X_test)\n",
    "    error_rate.append(np.mean(prediction_k != y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "274b6324-a7ca-47eb-8d44-4f4341960610",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Error Rate')"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(range(1, 200), error_rate, color = 'blue',\n",
    "          linestyle = 'dashed', marker = 'o', markerfacecolor = 'red',\n",
    "          markersize = 10 )\n",
    "plt.title('Error Rate vs. K-value')\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('Error Rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "473ed97c-59b5-4467-80ce-661dbfef4935",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(prediction_k != y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "7110390a-b7b4-4fe0-ac1d-5b3d96b9036b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "300"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "7c5b1ce6-93f3-4de1-ba98-372ca7e5ce41",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_errors_array = np.array(error_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "76334d85-24b1-414c-8aef-e6cd1fdca0d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmin(my_errors_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b0fdd0a-75c7-4a10-a8aa-0dcc6913923e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (ai-env4)",
   "language": "python",
   "name": "ai-env4"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}