GSOC 2021 Task3  (A) Machine Learning Classifier _Dimensionality Reduction for Studying Diffuse Circumgalactic Medium.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imported Libraries\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.decomposition import PCA, TruncatedSVD\n",
    "import matplotlib.patches as mpatches\n",
    "import time\n",
    "\n",
    "# Classifier Libraries\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import collections\n",
    "\n",
    "# Other Libraries\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from imblearn.pipeline import make_pipeline as imbalanced_make_pipeline\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.under_sampling import NearMiss\n",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score, accuracy_score, classification_report\n",
    "from collections import Counter\n",
    "from sklearn.model_selection import KFold, StratifiedKFold\n",
    "import warnings\n",
    "# For handling string\n",
    "import string\n",
    "# For performing mathematical operations\n",
    "import math\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <th>1.00E+00</th>\n",
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       "      <td>0.833</td>\n",
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       "      <td>0.978</td>\n",
       "      <td>0.780</td>\n",
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       "      <td>0.454</td>\n",
       "      <td>0.426</td>\n",
       "      <td>1.100</td>\n",
       "      <td>1.2800</td>\n",
       "      <td>1.3800</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>1.1300</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.910</td>\n",
       "      <td>1.110</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.951</td>\n",
       "      <td>0.803</td>\n",
       "      <td>0.866</td>\n",
       "      <td>0.780</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.340</td>\n",
       "      <td>-0.877</td>\n",
       "      <td>0.93600</td>\n",
       "      <td>1.990</td>\n",
       "      <td>0.882</td>\n",
       "      <td>1.790</td>\n",
       "      <td>-1.6500</td>\n",
       "      <td>-0.9420</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.6780</td>\n",
       "      <td>-1.360000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.947</td>\n",
       "      <td>1.030</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.728</td>\n",
       "      <td>0.869</td>\n",
       "      <td>1.030</td>\n",
       "      <td>0.958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.110</td>\n",
       "      <td>0.321</td>\n",
       "      <td>1.52000</td>\n",
       "      <td>0.883</td>\n",
       "      <td>-1.210</td>\n",
       "      <td>0.681</td>\n",
       "      <td>-1.0700</td>\n",
       "      <td>-0.9220</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.3740</td>\n",
       "      <td>0.113000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.756</td>\n",
       "      <td>1.360</td>\n",
       "      <td>0.987</td>\n",
       "      <td>0.838</td>\n",
       "      <td>1.130</td>\n",
       "      <td>0.872</td>\n",
       "      <td>0.808</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.600</td>\n",
       "      <td>-0.608</td>\n",
       "      <td>0.00707</td>\n",
       "      <td>1.820</td>\n",
       "      <td>-0.112</td>\n",
       "      <td>0.848</td>\n",
       "      <td>-0.5660</td>\n",
       "      <td>1.5800</td>\n",
       "      <td>2.17</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.6540</td>\n",
       "      <td>-1.270000</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.824</td>\n",
       "      <td>0.938</td>\n",
       "      <td>0.972</td>\n",
       "      <td>0.789</td>\n",
       "      <td>0.431</td>\n",
       "      <td>0.961</td>\n",
       "      <td>0.958</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.409</td>\n",
       "      <td>-1.880</td>\n",
       "      <td>-1.03000</td>\n",
       "      <td>1.670</td>\n",
       "      <td>-1.600</td>\n",
       "      <td>1.340</td>\n",
       "      <td>0.0554</td>\n",
       "      <td>0.0135</td>\n",
       "      <td>2.17</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0695</td>\n",
       "      <td>1.380000</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.869</td>\n",
       "      <td>1.220</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.545</td>\n",
       "      <td>0.699</td>\n",
       "      <td>0.977</td>\n",
       "      <td>0.829</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.934</td>\n",
       "      <td>0.629</td>\n",
       "      <td>0.52800</td>\n",
       "      <td>0.238</td>\n",
       "      <td>-0.967</td>\n",
       "      <td>0.548</td>\n",
       "      <td>-0.0594</td>\n",
       "      <td>-1.7100</td>\n",
       "      <td>2.17</td>\n",
       "      <td>...</td>\n",
       "      <td>1.2900</td>\n",
       "      <td>-1.470000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.902</td>\n",
       "      <td>1.080</td>\n",
       "      <td>0.980</td>\n",
       "      <td>0.783</td>\n",
       "      <td>0.849</td>\n",
       "      <td>0.894</td>\n",
       "      <td>0.775</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.410</td>\n",
       "      <td>0.537</td>\n",
       "      <td>0.69000</td>\n",
       "      <td>1.180</td>\n",
       "      <td>-0.110</td>\n",
       "      <td>3.200</td>\n",
       "      <td>-1.5300</td>\n",
       "      <td>-1.5800</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1510</td>\n",
       "      <td>1.160000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.670</td>\n",
       "      <td>4.040</td>\n",
       "      <td>1.180</td>\n",
       "      <td>1.050</td>\n",
       "      <td>1.540</td>\n",
       "      <td>3.530</td>\n",
       "      <td>2.740</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.180</td>\n",
       "      <td>0.104</td>\n",
       "      <td>1.40000</td>\n",
       "      <td>0.480</td>\n",
       "      <td>0.266</td>\n",
       "      <td>1.140</td>\n",
       "      <td>1.5300</td>\n",
       "      <td>-0.2530</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2690</td>\n",
       "      <td>0.530000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.774</td>\n",
       "      <td>0.986</td>\n",
       "      <td>1.100</td>\n",
       "      <td>0.849</td>\n",
       "      <td>0.937</td>\n",
       "      <td>0.812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.946</td>\n",
       "      <td>1.110</td>\n",
       "      <td>1.22000</td>\n",
       "      <td>0.908</td>\n",
       "      <td>0.822</td>\n",
       "      <td>1.150</td>\n",
       "      <td>-0.3650</td>\n",
       "      <td>-1.5700</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.4510</td>\n",
       "      <td>0.063700</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.829</td>\n",
       "      <td>0.981</td>\n",
       "      <td>0.994</td>\n",
       "      <td>0.908</td>\n",
       "      <td>0.776</td>\n",
       "      <td>0.783</td>\n",
       "      <td>0.725</td>\n",
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       "   1.00E+00  8.69E-01  -6.35E-01  2.26E-01  3.27E-01  -6.90E-01  7.54E-01  \\\n",
       "0       1.0     0.908      0.329   0.35900     1.500     -0.313     1.100   \n",
       "1       1.0     0.799      1.470  -1.64000     0.454      0.426     1.100   \n",
       "2       0.0     1.340     -0.877   0.93600     1.990      0.882     1.790   \n",
       "3       1.0     1.110      0.321   1.52000     0.883     -1.210     0.681   \n",
       "4       0.0     1.600     -0.608   0.00707     1.820     -0.112     0.848   \n",
       "5       1.0     0.409     -1.880  -1.03000     1.670     -1.600     1.340   \n",
       "6       1.0     0.934      0.629   0.52800     0.238     -0.967     0.548   \n",
       "7       1.0     1.410      0.537   0.69000     1.180     -0.110     3.200   \n",
       "8       1.0     1.180      0.104   1.40000     0.480      0.266     1.140   \n",
       "9       1.0     0.946      1.110   1.22000     0.908      0.822     1.150   \n",
       "\n",
       "   -2.49E-01  -1.09E+00  0.00E+00  ...  -1.05E-02  -4.58E-02  3.10E+00  \\\n",
       "0    -0.5580    -1.5900      2.17  ...    -1.1400  -0.000819       0.0   \n",
       "1     1.2800     1.3800      0.00  ...     1.1300   0.900000       0.0   \n",
       "2    -1.6500    -0.9420      0.00  ...    -0.6780  -1.360000       0.0   \n",
       "3    -1.0700    -0.9220      0.00  ...    -0.3740   0.113000       0.0   \n",
       "4    -0.5660     1.5800      2.17  ...    -0.6540  -1.270000       3.1   \n",
       "5     0.0554     0.0135      2.17  ...     0.0695   1.380000       3.1   \n",
       "6    -0.0594    -1.7100      2.17  ...     1.2900  -1.470000       0.0   \n",
       "7    -1.5300    -1.5800      0.00  ...    -0.1510   1.160000       0.0   \n",
       "8     1.5300    -0.2530      0.00  ...     0.2690   0.530000       0.0   \n",
       "9    -0.3650    -1.5700      0.00  ...    -0.4510   0.063700       3.1   \n",
       "\n",
       "   1.35E+00  9.80E-01  9.78E-01  9.20E-01  7.22E-01  9.89E-01  8.77E-01  \n",
       "0     0.302     0.833     0.986     0.978     0.780     0.992     0.798  \n",
       "1     0.910     1.110     0.986     0.951     0.803     0.866     0.780  \n",
       "2     0.947     1.030     0.999     0.728     0.869     1.030     0.958  \n",
       "3     0.756     1.360     0.987     0.838     1.130     0.872     0.808  \n",
       "4     0.824     0.938     0.972     0.789     0.431     0.961     0.958  \n",
       "5     0.869     1.220     1.000     0.545     0.699     0.977     0.829  \n",
       "6     0.902     1.080     0.980     0.783     0.849     0.894     0.775  \n",
       "7     1.670     4.040     1.180     1.050     1.540     3.530     2.740  \n",
       "8     0.833     0.774     0.986     1.100     0.849     0.937     0.812  \n",
       "9     0.829     0.981     0.994     0.908     0.776     0.783     0.725  \n",
       "\n",
       "[10 rows x 29 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Importing dataset\n",
    "data=pd.read_csv('HIGGS_6M.csv')\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.302</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.986</td>\n",
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       "      <td>0.780</td>\n",
       "      <td>0.992</td>\n",
       "      <td>0.798</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>0.426</td>\n",
       "      <td>1.100</td>\n",
       "      <td>1.2800</td>\n",
       "      <td>1.3800</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.1300</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.910</td>\n",
       "      <td>1.110</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.951</td>\n",
       "      <td>0.803</td>\n",
       "      <td>0.866</td>\n",
       "      <td>0.780</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>-0.877</td>\n",
       "      <td>0.93600</td>\n",
       "      <td>1.990</td>\n",
       "      <td>0.882</td>\n",
       "      <td>1.790</td>\n",
       "      <td>-1.6500</td>\n",
       "      <td>-0.9420</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.6780</td>\n",
       "      <td>-1.360000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.947</td>\n",
       "      <td>1.030</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.728</td>\n",
       "      <td>0.869</td>\n",
       "      <td>1.030</td>\n",
       "      <td>0.958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.110</td>\n",
       "      <td>0.321</td>\n",
       "      <td>1.52000</td>\n",
       "      <td>0.883</td>\n",
       "      <td>-1.210</td>\n",
       "      <td>0.681</td>\n",
       "      <td>-1.0700</td>\n",
       "      <td>-0.9220</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.3740</td>\n",
       "      <td>0.113000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.756</td>\n",
       "      <td>1.360</td>\n",
       "      <td>0.987</td>\n",
       "      <td>0.838</td>\n",
       "      <td>1.130</td>\n",
       "      <td>0.872</td>\n",
       "      <td>0.808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.600</td>\n",
       "      <td>-0.608</td>\n",
       "      <td>0.00707</td>\n",
       "      <td>1.820</td>\n",
       "      <td>-0.112</td>\n",
       "      <td>0.848</td>\n",
       "      <td>-0.5660</td>\n",
       "      <td>1.5800</td>\n",
       "      <td>2.17</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.6540</td>\n",
       "      <td>-1.270000</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.824</td>\n",
       "      <td>0.938</td>\n",
       "      <td>0.972</td>\n",
       "      <td>0.789</td>\n",
       "      <td>0.431</td>\n",
       "      <td>0.961</td>\n",
       "      <td>0.958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.409</td>\n",
       "      <td>-1.880</td>\n",
       "      <td>-1.03000</td>\n",
       "      <td>1.670</td>\n",
       "      <td>-1.600</td>\n",
       "      <td>1.340</td>\n",
       "      <td>0.0554</td>\n",
       "      <td>0.0135</td>\n",
       "      <td>2.17</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0695</td>\n",
       "      <td>1.380000</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.869</td>\n",
       "      <td>1.220</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.545</td>\n",
       "      <td>0.699</td>\n",
       "      <td>0.977</td>\n",
       "      <td>0.829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.934</td>\n",
       "      <td>0.629</td>\n",
       "      <td>0.52800</td>\n",
       "      <td>0.238</td>\n",
       "      <td>-0.967</td>\n",
       "      <td>0.548</td>\n",
       "      <td>-0.0594</td>\n",
       "      <td>-1.7100</td>\n",
       "      <td>2.17</td>\n",
       "      <td>...</td>\n",
       "      <td>1.2900</td>\n",
       "      <td>-1.470000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.902</td>\n",
       "      <td>1.080</td>\n",
       "      <td>0.980</td>\n",
       "      <td>0.783</td>\n",
       "      <td>0.849</td>\n",
       "      <td>0.894</td>\n",
       "      <td>0.775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.410</td>\n",
       "      <td>0.537</td>\n",
       "      <td>0.69000</td>\n",
       "      <td>1.180</td>\n",
       "      <td>-0.110</td>\n",
       "      <td>3.200</td>\n",
       "      <td>-1.5300</td>\n",
       "      <td>-1.5800</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1510</td>\n",
       "      <td>1.160000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.670</td>\n",
       "      <td>4.040</td>\n",
       "      <td>1.180</td>\n",
       "      <td>1.050</td>\n",
       "      <td>1.540</td>\n",
       "      <td>3.530</td>\n",
       "      <td>2.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.180</td>\n",
       "      <td>0.104</td>\n",
       "      <td>1.40000</td>\n",
       "      <td>0.480</td>\n",
       "      <td>0.266</td>\n",
       "      <td>1.140</td>\n",
       "      <td>1.5300</td>\n",
       "      <td>-0.2530</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2690</td>\n",
       "      <td>0.530000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.774</td>\n",
       "      <td>0.986</td>\n",
       "      <td>1.100</td>\n",
       "      <td>0.849</td>\n",
       "      <td>0.937</td>\n",
       "      <td>0.812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.946</td>\n",
       "      <td>1.110</td>\n",
       "      <td>1.22000</td>\n",
       "      <td>0.908</td>\n",
       "      <td>0.822</td>\n",
       "      <td>1.150</td>\n",
       "      <td>-0.3650</td>\n",
       "      <td>-1.5700</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.4510</td>\n",
       "      <td>0.063700</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.829</td>\n",
       "      <td>0.981</td>\n",
       "      <td>0.994</td>\n",
       "      <td>0.908</td>\n",
       "      <td>0.776</td>\n",
       "      <td>0.783</td>\n",
       "      <td>0.725</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Label      a      b        c      d      e      f       g       h     i  \\\n",
       "0    1.0  0.908  0.329  0.35900  1.500 -0.313  1.100 -0.5580 -1.5900  2.17   \n",
       "1    1.0  0.799  1.470 -1.64000  0.454  0.426  1.100  1.2800  1.3800  0.00   \n",
       "2    0.0  1.340 -0.877  0.93600  1.990  0.882  1.790 -1.6500 -0.9420  0.00   \n",
       "3    1.0  1.110  0.321  1.52000  0.883 -1.210  0.681 -1.0700 -0.9220  0.00   \n",
       "4    0.0  1.600 -0.608  0.00707  1.820 -0.112  0.848 -0.5660  1.5800  2.17   \n",
       "5    1.0  0.409 -1.880 -1.03000  1.670 -1.600  1.340  0.0554  0.0135  2.17   \n",
       "6    1.0  0.934  0.629  0.52800  0.238 -0.967  0.548 -0.0594 -1.7100  2.17   \n",
       "7    1.0  1.410  0.537  0.69000  1.180 -0.110  3.200 -1.5300 -1.5800  0.00   \n",
       "8    1.0  1.180  0.104  1.40000  0.480  0.266  1.140  1.5300 -0.2530  0.00   \n",
       "9    1.0  0.946  1.110  1.22000  0.908  0.822  1.150 -0.3650 -1.5700  0.00   \n",
       "\n",
       "   ...       s         t    u     f1     f2     f3     f4     f5     f6     f7  \n",
       "0  ... -1.1400 -0.000819  0.0  0.302  0.833  0.986  0.978  0.780  0.992  0.798  \n",
       "1  ...  1.1300  0.900000  0.0  0.910  1.110  0.986  0.951  0.803  0.866  0.780  \n",
       "2  ... -0.6780 -1.360000  0.0  0.947  1.030  0.999  0.728  0.869  1.030  0.958  \n",
       "3  ... -0.3740  0.113000  0.0  0.756  1.360  0.987  0.838  1.130  0.872  0.808  \n",
       "4  ... -0.6540 -1.270000  3.1  0.824  0.938  0.972  0.789  0.431  0.961  0.958  \n",
       "5  ...  0.0695  1.380000  3.1  0.869  1.220  1.000  0.545  0.699  0.977  0.829  \n",
       "6  ...  1.2900 -1.470000  0.0  0.902  1.080  0.980  0.783  0.849  0.894  0.775  \n",
       "7  ... -0.1510  1.160000  0.0  1.670  4.040  1.180  1.050  1.540  3.530  2.740  \n",
       "8  ...  0.2690  0.530000  0.0  0.833  0.774  0.986  1.100  0.849  0.937  0.812  \n",
       "9  ... -0.4510  0.063700  3.1  0.829  0.981  0.994  0.908  0.776  0.783  0.725  \n",
       "\n",
       "[10 rows x 29 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#labeling the dataset\n",
    "data.columns = ['Label', 'a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','f1','f2','f3','f4','f5','f6','f7']\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting last 500k rows from data\n",
    "df = data.iloc[-500000:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 500000 entries, 548575 to 1048574\n",
      "Data columns (total 29 columns):\n",
      " #   Column  Non-Null Count   Dtype  \n",
      "---  ------  --------------   -----  \n",
      " 0   Label   500000 non-null  float64\n",
      " 1   a       500000 non-null  float64\n",
      " 2   b       500000 non-null  float64\n",
      " 3   c       500000 non-null  float64\n",
      " 4   d       500000 non-null  float64\n",
      " 5   e       500000 non-null  float64\n",
      " 6   f       500000 non-null  float64\n",
      " 7   g       500000 non-null  float64\n",
      " 8   h       500000 non-null  float64\n",
      " 9   i       500000 non-null  float64\n",
      " 10  j       500000 non-null  float64\n",
      " 11  k       500000 non-null  float64\n",
      " 12  l       500000 non-null  float64\n",
      " 13  m       500000 non-null  float64\n",
      " 14  n       500000 non-null  float64\n",
      " 15  o       500000 non-null  float64\n",
      " 16  p       500000 non-null  float64\n",
      " 17  q       500000 non-null  float64\n",
      " 18  r       500000 non-null  float64\n",
      " 19  s       500000 non-null  float64\n",
      " 20  t       500000 non-null  float64\n",
      " 21  u       500000 non-null  float64\n",
      " 22  f1      500000 non-null  float64\n",
      " 23  f2      500000 non-null  float64\n",
      " 24  f3      500000 non-null  float64\n",
      " 25  f4      500000 non-null  float64\n",
      " 26  f5      500000 non-null  float64\n",
      " 27  f6      500000 non-null  float64\n",
      " 28  f7      500000 non-null  float64\n",
      "dtypes: float64(29)\n",
      "memory usage: 110.6 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Label</th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>f</th>\n",
       "      <th>g</th>\n",
       "      <th>h</th>\n",
       "      <th>i</th>\n",
       "      <th>...</th>\n",
       "      <th>s</th>\n",
       "      <th>t</th>\n",
       "      <th>u</th>\n",
       "      <th>f1</th>\n",
       "      <th>f2</th>\n",
       "      <th>f3</th>\n",
       "      <th>f4</th>\n",
       "      <th>f5</th>\n",
       "      <th>f6</th>\n",
       "      <th>f7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>500000.000000</td>\n",
       "      <td>500000.000000</td>\n",
       "      <td>500000.000000</td>\n",
       "      <td>500000.000000</td>\n",
       "      <td>500000.000000</td>\n",
       "      <td>500000.000000</td>\n",
       "      <td>500000.000000</td>\n",
       "      <td>500000.000000</td>\n",
       "      <td>500000.000000</td>\n",
       "      <td>500000.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>500000.000000</td>\n",
       "      <td>500000.000000</td>\n",
       "      <td>500000.000000</td>\n",
       "      <td>500000.000000</td>\n",
       "      <td>500000.000000</td>\n",
       "      <td>500000.000000</td>\n",
       "      <td>500000.000000</td>\n",
       "      <td>500000.000000</td>\n",
       "      <td>500000.000000</td>\n",
       "      <td>500000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.529866</td>\n",
       "      <td>0.990204</td>\n",
       "      <td>0.002254</td>\n",
       "      <td>-0.001055</td>\n",
       "      <td>0.998281</td>\n",
       "      <td>-0.000121</td>\n",
       "      <td>0.991422</td>\n",
       "      <td>0.001087</td>\n",
       "      <td>0.000404</td>\n",
       "      <td>0.998112</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000552</td>\n",
       "      <td>-0.000571</td>\n",
       "      <td>0.999235</td>\n",
       "      <td>1.035067</td>\n",
       "      <td>1.025749</td>\n",
       "      <td>1.050189</td>\n",
       "      <td>1.009589</td>\n",
       "      <td>0.973340</td>\n",
       "      <td>1.033758</td>\n",
       "      <td>0.960130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.499108</td>\n",
       "      <td>0.564540</td>\n",
       "      <td>1.009146</td>\n",
       "      <td>1.005989</td>\n",
       "      <td>0.598587</td>\n",
       "      <td>1.006856</td>\n",
       "      <td>0.475389</td>\n",
       "      <td>1.010417</td>\n",
       "      <td>1.005639</td>\n",
       "      <td>1.026094</td>\n",
       "      <td>...</td>\n",
       "      <td>1.007341</td>\n",
       "      <td>1.006110</td>\n",
       "      <td>1.399361</td>\n",
       "      <td>0.680033</td>\n",
       "      <td>0.383375</td>\n",
       "      <td>0.163852</td>\n",
       "      <td>0.397727</td>\n",
       "      <td>0.525215</td>\n",
       "      <td>0.365905</td>\n",
       "      <td>0.313452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.275000</td>\n",
       "      <td>-2.430000</td>\n",
       "      <td>-1.740000</td>\n",
       "      <td>0.002250</td>\n",
       "      <td>-1.740000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>-2.970000</td>\n",
       "      <td>-1.740000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.500000</td>\n",
       "      <td>-1.740000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.101000</td>\n",
       "      <td>0.235000</td>\n",
       "      <td>0.202000</td>\n",
       "      <td>0.186000</td>\n",
       "      <td>0.066600</td>\n",
       "      <td>0.329000</td>\n",
       "      <td>0.351000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.590000</td>\n",
       "      <td>-0.736000</td>\n",
       "      <td>-0.873000</td>\n",
       "      <td>0.575000</td>\n",
       "      <td>-0.871000</td>\n",
       "      <td>0.679000</td>\n",
       "      <td>-0.687000</td>\n",
       "      <td>-0.868000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.714000</td>\n",
       "      <td>-0.871000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.791000</td>\n",
       "      <td>0.847000</td>\n",
       "      <td>0.986000</td>\n",
       "      <td>0.767000</td>\n",
       "      <td>0.674000</td>\n",
       "      <td>0.819000</td>\n",
       "      <td>0.770000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.852000</td>\n",
       "      <td>0.003840</td>\n",
       "      <td>-0.000796</td>\n",
       "      <td>0.892000</td>\n",
       "      <td>-0.000166</td>\n",
       "      <td>0.894000</td>\n",
       "      <td>0.002950</td>\n",
       "      <td>0.001820</td>\n",
       "      <td>1.090000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000372</td>\n",
       "      <td>-0.004700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.895000</td>\n",
       "      <td>0.951000</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>0.916000</td>\n",
       "      <td>0.874000</td>\n",
       "      <td>0.948000</td>\n",
       "      <td>0.872000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.240000</td>\n",
       "      <td>0.738000</td>\n",
       "      <td>0.868000</td>\n",
       "      <td>1.290000</td>\n",
       "      <td>0.871000</td>\n",
       "      <td>1.170000</td>\n",
       "      <td>0.691000</td>\n",
       "      <td>0.869000</td>\n",
       "      <td>2.170000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.714000</td>\n",
       "      <td>0.872000</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>1.020000</td>\n",
       "      <td>1.080000</td>\n",
       "      <td>1.020000</td>\n",
       "      <td>1.140000</td>\n",
       "      <td>1.140000</td>\n",
       "      <td>1.140000</td>\n",
       "      <td>1.060000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.810000</td>\n",
       "      <td>2.430000</td>\n",
       "      <td>1.740000</td>\n",
       "      <td>9.270000</td>\n",
       "      <td>1.740000</td>\n",
       "      <td>7.630000</td>\n",
       "      <td>2.970000</td>\n",
       "      <td>1.740000</td>\n",
       "      <td>2.170000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>1.740000</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>31.100000</td>\n",
       "      <td>15.600000</td>\n",
       "      <td>5.550000</td>\n",
       "      <td>10.800000</td>\n",
       "      <td>12.200000</td>\n",
       "      <td>8.780000</td>\n",
       "      <td>6.180000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Label              a              b              c  \\\n",
       "count  500000.000000  500000.000000  500000.000000  500000.000000   \n",
       "mean        0.529866       0.990204       0.002254      -0.001055   \n",
       "std         0.499108       0.564540       1.009146       1.005989   \n",
       "min         0.000000       0.275000      -2.430000      -1.740000   \n",
       "25%         0.000000       0.590000      -0.736000      -0.873000   \n",
       "50%         1.000000       0.852000       0.003840      -0.000796   \n",
       "75%         1.000000       1.240000       0.738000       0.868000   \n",
       "max         1.000000       7.810000       2.430000       1.740000   \n",
       "\n",
       "                   d              e              f              g  \\\n",
       "count  500000.000000  500000.000000  500000.000000  500000.000000   \n",
       "mean        0.998281      -0.000121       0.991422       0.001087   \n",
       "std         0.598587       1.006856       0.475389       1.010417   \n",
       "min         0.002250      -1.740000       0.140000      -2.970000   \n",
       "25%         0.575000      -0.871000       0.679000      -0.687000   \n",
       "50%         0.892000      -0.000166       0.894000       0.002950   \n",
       "75%         1.290000       0.871000       1.170000       0.691000   \n",
       "max         9.270000       1.740000       7.630000       2.970000   \n",
       "\n",
       "                   h              i  ...              s              t  \\\n",
       "count  500000.000000  500000.000000  ...  500000.000000  500000.000000   \n",
       "mean        0.000404       0.998112  ...      -0.000552      -0.000571   \n",
       "std         1.005639       1.026094  ...       1.007341       1.006110   \n",
       "min        -1.740000       0.000000  ...      -2.500000      -1.740000   \n",
       "25%        -0.868000       0.000000  ...      -0.714000      -0.871000   \n",
       "50%         0.001820       1.090000  ...       0.000372      -0.004700   \n",
       "75%         0.869000       2.170000  ...       0.714000       0.872000   \n",
       "max         1.740000       2.170000  ...       2.500000       1.740000   \n",
       "\n",
       "                   u             f1             f2             f3  \\\n",
       "count  500000.000000  500000.000000  500000.000000  500000.000000   \n",
       "mean        0.999235       1.035067       1.025749       1.050189   \n",
       "std         1.399361       0.680033       0.383375       0.163852   \n",
       "min         0.000000       0.101000       0.235000       0.202000   \n",
       "25%         0.000000       0.791000       0.847000       0.986000   \n",
       "50%         0.000000       0.895000       0.951000       0.990000   \n",
       "75%         3.100000       1.020000       1.080000       1.020000   \n",
       "max         3.100000      31.100000      15.600000       5.550000   \n",
       "\n",
       "                  f4             f5             f6             f7  \n",
       "count  500000.000000  500000.000000  500000.000000  500000.000000  \n",
       "mean        1.009589       0.973340       1.033758       0.960130  \n",
       "std         0.397727       0.525215       0.365905       0.313452  \n",
       "min         0.186000       0.066600       0.329000       0.351000  \n",
       "25%         0.767000       0.674000       0.819000       0.770000  \n",
       "50%         0.916000       0.874000       0.948000       0.872000  \n",
       "75%         1.140000       1.140000       1.140000       1.060000  \n",
       "max        10.800000      12.200000       8.780000       6.180000  \n",
       "\n",
       "[8 rows x 29 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500000, 29)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking dimention of data\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#to see any null value present in our dataset\n",
    "df.isnull().sum().max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### No null values present"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'no of samples')"
      ]
     },
     "execution_count": 10,
     "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": [
    "df['Label'].value_counts().plot.bar()\n",
    "# Add title and axis names\n",
    "plt.title('Label wise no of samples')\n",
    "plt.xlabel('Label')\n",
    "plt.ylabel('no of samples')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Label'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6,6), dpi=200)\n",
    "ax = plt.subplot(111)\n",
    "\n",
    "df['Label'].value_counts().plot(kind='pie', ax=ax, autopct='%1.1f%%', startangle=270, fontsize=17)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We can see Dataset is  quite balanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Label', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l',\n",
       "       'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'f1', 'f2', 'f3', 'f4',\n",
       "       'f5', 'f6', 'f7'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_data = df.drop('Label', axis=1)\n",
    "y = df['Label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize\n",
    "x = (x_data - np.min(x_data)) / (np.max(x_data) - np.min(x_data)).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#split train & test data\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size = 0.2,random_state=1)\n",
    "#transpose matrices\n",
    "x_train = x_train.T\n",
    "y_train = y_train.T\n",
    "x_test = x_test.T\n",
    "y_test = y_test.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy 64.12%\n"
     ]
    }
   ],
   "source": [
    "accuracies = {}\n",
    "\n",
    "lr = LogisticRegression()\n",
    "lr.fit(x_train.T,y_train.T)\n",
    "acc = lr.score(x_test.T,y_test.T)*100\n",
    "\n",
    "accuracies['Logistic Regression'] = acc\n",
    "print(\"Test Accuracy {:.2f}%\".format(acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "y_pred_logistic = lr.decision_function(x_test.T)\n",
    "\n",
    "logistic_fpr, logistic_tpr, threshold = roc_curve(y_test.T, y_pred_logistic)\n",
    "auc_logistic = auc(logistic_fpr, logistic_tpr)\n",
    "plt.figure(figsize=(5, 5), dpi=100)\n",
    "plt.plot(logistic_fpr, logistic_tpr, marker='.', label='Logistic (auc = %0.3f)' % auc_logistic)\n",
    "\n",
    "plt.xlabel('False Positive Rate -->')\n",
    "plt.ylabel('True Positive Rate -->')\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-Nearest Neighbour (KNN) Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 NN Score: 54.50%\n"
     ]
    }
   ],
   "source": [
    "# KNN Model\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors = 2)  # n_neighbors means k\n",
    "knn.fit(x_train.T, y_train.T)\n",
    "prediction = knn.predict(x_test.T)\n",
    "\n",
    "print(\"{} NN Score: {:.2f}%\".format(2, knn.score(x_test.T, y_test.T)*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Test Accuracy 63.85%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dtc = DecisionTreeClassifier()\n",
    "dtc.fit(x_train.T, y_train.T)\n",
    "\n",
    "acc = dtc.score(x_test.T, y_test.T)*100\n",
    "accuracies['Decision Tree'] = acc\n",
    "print(\"Decision Tree Test Accuracy {:.2f}%\".format(acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "predictions = dtc.predict_proba(x_test.T)\n",
    "\n",
    "# get false and true positive rates\n",
    "dtc_fpr, dtc_tpr, thresholds = roc_curve(y_test.T, predictions[:,0], pos_label=0)\n",
    "# get area under the curve\n",
    "dtc_roc_auc = auc(dtc_fpr, dtc_tpr)\n",
    "\n",
    "# PLOT ROC curve\n",
    "plt.figure(dpi=150)\n",
    "plt.plot(dtc_fpr, dtc_tpr, lw=1, color='green', label=f'AUC = {dtc_roc_auc:.3f}')\n",
    "plt.title('ROC Curve for Decision tree classifier')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate ')\n",
    "plt.xlim([-0.05, 1.05])\n",
    "plt.ylim([-0.05, 1.05])\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Algorithm Accuracy Score : 73.00%\n"
     ]
    }
   ],
   "source": [
    "# Random Forest Classification\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier()\n",
    "rf.fit(x_train.T, y_train.T)\n",
    "\n",
    "acc = rf.score(x_test.T,y_test.T)*100\n",
    "accuracies['Random Forest'] = acc\n",
    "print(\"Random Forest Algorithm Accuracy Score : {:.2f}%\".format(acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "probas = rf.predict_proba(x_test.T)\n",
    "\n",
    "# get false and true positive rates\n",
    "rf_fpr, rf_tpr, thresholds = roc_curve(y_test.T, probas[:,0], pos_label=0)\n",
    "# get area under the curve\n",
    "rf_roc_auc = auc(rf_fpr, rf_tpr)\n",
    "# PLOT ROC curve\n",
    "plt.figure(dpi=150)\n",
    "plt.plot(rf_fpr, rf_tpr, lw=1, color='green', label=f'AUC = {rf_roc_auc:.3f}')\n",
    "plt.title('ROC Curve for RF classifier')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate (Recall)')\n",
    "plt.xlim([-0.05, 1.05])\n",
    "plt.ylim([-0.05, 1.05])\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = [\"purple\", \"green\", \"orange\"]\n",
    "\n",
    "sns.set_style(\"whitegrid\")\n",
    "plt.figure(figsize=(16,5))\n",
    "plt.yticks(np.arange(0,100,10))\n",
    "plt.ylabel(\"Accuracy %\")\n",
    "plt.xlabel(\"Algorithms\")\n",
    "sns.barplot(x=list(accuracies.keys()), y=list(accuracies.values()), palette=colors)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicted values\n",
    "y_head_lr = lr.predict(x_test.T)\n",
    "y_head_dtc = dtc.predict(x_test.T)\n",
    "y_head_rf = rf.predict(x_test.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "cm_lr = confusion_matrix(y_test,y_head_lr)\n",
    "cm_dtc = confusion_matrix(y_test,y_head_dtc)\n",
    "cm_rf = confusion_matrix(y_test,y_head_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1728x864 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(24,12))\n",
    "\n",
    "plt.suptitle(\"Confusion Matrixes\",fontsize=24)\n",
    "plt.subplots_adjust(wspace = 0.4, hspace= 0.4)\n",
    "\n",
    "plt.subplot(2,3,1)\n",
    "plt.title(\"Logistic Regression Confusion Matrix\")\n",
    "sns.heatmap(cm_lr,annot=True,cmap=\"Blues\",fmt=\"d\",cbar=False, annot_kws={\"size\": 24})\n",
    "\n",
    "plt.subplot(2,3,5)\n",
    "plt.title(\"Decision Tree Classifier Confusion Matrix\")\n",
    "sns.heatmap(cm_dtc,annot=True,cmap=\"Blues\",fmt=\"d\",cbar=False, annot_kws={\"size\": 24})\n",
    "\n",
    "plt.subplot(2,3,6)\n",
    "plt.title(\"Random Forest Confusion Matrix\")\n",
    "sns.heatmap(cm_rf,annot=True,cmap=\"Blues\",fmt=\"d\",cbar=False, annot_kws={\"size\": 24})\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}