Naive Bayes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "0a571206",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: imbalanced-learn in c:\\users\\dell\\anaconda3\\lib\\site-packages (0.10.1)\n",
      "Requirement already satisfied: joblib>=1.1.1 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from imbalanced-learn) (1.2.0)\n",
      "Requirement already satisfied: numpy>=1.17.3 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from imbalanced-learn) (1.21.5)\n",
      "Requirement already satisfied: scikit-learn>=1.0.2 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from imbalanced-learn) (1.0.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from imbalanced-learn) (2.2.0)\n",
      "Requirement already satisfied: scipy>=1.3.2 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from imbalanced-learn) (1.7.3)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install -U imbalanced-learn\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 # for data visualization purposes\n",
    "import seaborn as sns # for statistical data visualization\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "3357723b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Student ID</th>\n",
       "      <th>Quiz01 [10]</th>\n",
       "      <th>Assignment01 [8]</th>\n",
       "      <th>Midterm Exam [20]</th>\n",
       "      <th>Assignment02 [12]</th>\n",
       "      <th>Assignment03 [25]</th>\n",
       "      <th>Final Exam [35]</th>\n",
       "      <th>Course Grade</th>\n",
       "      <th>Total [100]</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>student000000</td>\n",
       "      <td>95</td>\n",
       "      <td>91</td>\n",
       "      <td>70</td>\n",
       "      <td>90</td>\n",
       "      <td>84</td>\n",
       "      <td>64</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>student000001</td>\n",
       "      <td>85</td>\n",
       "      <td>76</td>\n",
       "      <td>65</td>\n",
       "      <td>61</td>\n",
       "      <td>73</td>\n",
       "      <td>64</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>student000002</td>\n",
       "      <td>85</td>\n",
       "      <td>41</td>\n",
       "      <td>73</td>\n",
       "      <td>61</td>\n",
       "      <td>73</td>\n",
       "      <td>61</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>student000003</td>\n",
       "      <td>80</td>\n",
       "      <td>78</td>\n",
       "      <td>80</td>\n",
       "      <td>79</td>\n",
       "      <td>79</td>\n",
       "      <td>57</td>\n",
       "      <td>80</td>\n",
       "      <td>79</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>student000004</td>\n",
       "      <td>85</td>\n",
       "      <td>91</td>\n",
       "      <td>78</td>\n",
       "      <td>80</td>\n",
       "      <td>84</td>\n",
       "      <td>67</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>G</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>student000481</td>\n",
       "      <td>90</td>\n",
       "      <td>85</td>\n",
       "      <td>93</td>\n",
       "      <td>87</td>\n",
       "      <td>81</td>\n",
       "      <td>77</td>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>student000482</td>\n",
       "      <td>80</td>\n",
       "      <td>68</td>\n",
       "      <td>93</td>\n",
       "      <td>70</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>student000483</td>\n",
       "      <td>80</td>\n",
       "      <td>86</td>\n",
       "      <td>93</td>\n",
       "      <td>95</td>\n",
       "      <td>90</td>\n",
       "      <td>83</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>student000484</td>\n",
       "      <td>75</td>\n",
       "      <td>68</td>\n",
       "      <td>80</td>\n",
       "      <td>76</td>\n",
       "      <td>88</td>\n",
       "      <td>50</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>student000485</td>\n",
       "      <td>85</td>\n",
       "      <td>81</td>\n",
       "      <td>88</td>\n",
       "      <td>86</td>\n",
       "      <td>96</td>\n",
       "      <td>60</td>\n",
       "      <td>88</td>\n",
       "      <td>88</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>486 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Student ID  Quiz01 [10]  Assignment01 [8]  Midterm Exam [20]  \\\n",
       "0    student000000           95                91                 70   \n",
       "1    student000001           85                76                 65   \n",
       "2    student000002           85                41                 73   \n",
       "3    student000003           80                78                 80   \n",
       "4    student000004           85                91                 78   \n",
       "..             ...          ...               ...                ...   \n",
       "481  student000481           90                85                 93   \n",
       "482  student000482           80                68                 93   \n",
       "483  student000483           80                86                 93   \n",
       "484  student000484           75                68                 80   \n",
       "485  student000485           85                81                 88   \n",
       "\n",
       "     Assignment02 [12]  Assignment03 [25]  Final Exam [35]  Course Grade  \\\n",
       "0                   90                 84               64            85   \n",
       "1                   61                 73               64            76   \n",
       "2                   61                 73               61            73   \n",
       "3                   79                 79               57            80   \n",
       "4                   80                 84               67            85   \n",
       "..                 ...                ...              ...           ...   \n",
       "481                 87                 81               77            92   \n",
       "482                 70                 83               83            90   \n",
       "483                 95                 90               83            96   \n",
       "484                 76                 88               50            78   \n",
       "485                 86                 96               60            88   \n",
       "\n",
       "     Total [100] Class  \n",
       "0             85     G  \n",
       "1             76     G  \n",
       "2             73     G  \n",
       "3             79     G  \n",
       "4             85     G  \n",
       "..           ...   ...  \n",
       "481           92     G  \n",
       "482           90     G  \n",
       "483           96     G  \n",
       "484           78     G  \n",
       "485           88     G  \n",
       "\n",
       "[486 rows x 10 columns]"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('Student Performance Prediction-Binary.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "4e3f3eef",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Student ID</th>\n",
       "      <th>Quiz01 [10]</th>\n",
       "      <th>Assignment01 [8]</th>\n",
       "      <th>Midterm Exam [20]</th>\n",
       "      <th>Assignment02 [12]</th>\n",
       "      <th>Assignment03 [25]</th>\n",
       "      <th>Final Exam [35]</th>\n",
       "      <th>Course Grade</th>\n",
       "      <th>Total [100]</th>\n",
       "      <th>Class</th>\n",
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       "  </thead>\n",
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       "      <td>student000000</td>\n",
       "      <td>95</td>\n",
       "      <td>91</td>\n",
       "      <td>70</td>\n",
       "      <td>90</td>\n",
       "      <td>84</td>\n",
       "      <td>64</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
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       "      <th>1</th>\n",
       "      <td>student000001</td>\n",
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       "      <td>76</td>\n",
       "      <td>65</td>\n",
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       "      <td>73</td>\n",
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       "      <td>76</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>student000002</td>\n",
       "      <td>85</td>\n",
       "      <td>41</td>\n",
       "      <td>73</td>\n",
       "      <td>61</td>\n",
       "      <td>73</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>student000003</td>\n",
       "      <td>80</td>\n",
       "      <td>78</td>\n",
       "      <td>80</td>\n",
       "      <td>79</td>\n",
       "      <td>79</td>\n",
       "      <td>57</td>\n",
       "      <td>80</td>\n",
       "      <td>79</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>student000004</td>\n",
       "      <td>85</td>\n",
       "      <td>91</td>\n",
       "      <td>78</td>\n",
       "      <td>80</td>\n",
       "      <td>84</td>\n",
       "      <td>67</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Student ID  Quiz01 [10]  Assignment01 [8]  Midterm Exam [20]  \\\n",
       "0  student000000           95                91                 70   \n",
       "1  student000001           85                76                 65   \n",
       "2  student000002           85                41                 73   \n",
       "3  student000003           80                78                 80   \n",
       "4  student000004           85                91                 78   \n",
       "\n",
       "   Assignment02 [12]  Assignment03 [25]  Final Exam [35]  Course Grade  \\\n",
       "0                 90                 84               64            85   \n",
       "1                 61                 73               64            76   \n",
       "2                 61                 73               61            73   \n",
       "3                 79                 79               57            80   \n",
       "4                 80                 84               67            85   \n",
       "\n",
       "   Total [100] Class  \n",
       "0           85     G  \n",
       "1           76     G  \n",
       "2           73     G  \n",
       "3           79     G  \n",
       "4           85     G  "
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "2ed701b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 2 categorical variables\n",
      "\n",
      "The categorical variables are :\n",
      "\n",
      " ['Student ID', 'Class']\n"
     ]
    }
   ],
   "source": [
    "categorical = [var for var in df.columns if df[var].dtype=='O']\n",
    "\n",
    "print('There are {} categorical variables\\n'.format(len(categorical)))\n",
    "\n",
    "print('The categorical variables are :\\n\\n', categorical)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "dcef16d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Student ID</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>student000000</td>\n",
       "      <td>G</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>student000001</td>\n",
       "      <td>G</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>student000002</td>\n",
       "      <td>G</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>student000003</td>\n",
       "      <td>G</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>student000004</td>\n",
       "      <td>G</td>\n",
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      ],
      "text/plain": [
       "      Student ID Class\n",
       "0  student000000     G\n",
       "1  student000001     G\n",
       "2  student000002     G\n",
       "3  student000003     G\n",
       "4  student000004     G"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[categorical].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "ec930230",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Student ID    0\n",
       "Class         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[categorical].isnull().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "4f44735a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "G    465\n",
       "W     21\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Class.value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "id": "67627e84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 8 numerical variables\n",
      "\n",
      "The numerical variables are : ['Quiz01 [10]', 'Assignment01 [8]', 'Midterm Exam [20]', 'Assignment02 [12]', 'Assignment03 [25]', 'Final Exam [35]', 'Course Grade', 'Total [100]']\n"
     ]
    }
   ],
   "source": [
    "numerical = [var for var in df.columns if df[var].dtype!='O']\n",
    "\n",
    "print('There are {} numerical variables\\n'.format(len(numerical)))\n",
    "\n",
    "print('The numerical variables are :', numerical)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "4fdffd26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Quiz01 [10]          0\n",
       "Assignment01 [8]     0\n",
       "Midterm Exam [20]    0\n",
       "Assignment02 [12]    0\n",
       "Assignment03 [25]    0\n",
       "Final Exam [35]      0\n",
       "Course Grade         0\n",
       "Total [100]          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[numerical].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "id": "3e353abd",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['Class'], axis=1)\n",
    "\n",
    "y = df['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "id": "80ad7278",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Student ID</th>\n",
       "      <th>Quiz01 [10]</th>\n",
       "      <th>Assignment01 [8]</th>\n",
       "      <th>Midterm Exam [20]</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>student000000</td>\n",
       "      <td>95</td>\n",
       "      <td>91</td>\n",
       "      <td>70</td>\n",
       "      <td>90</td>\n",
       "      <td>84</td>\n",
       "      <td>64</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>student000001</td>\n",
       "      <td>85</td>\n",
       "      <td>76</td>\n",
       "      <td>65</td>\n",
       "      <td>61</td>\n",
       "      <td>73</td>\n",
       "      <td>64</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>student000002</td>\n",
       "      <td>85</td>\n",
       "      <td>41</td>\n",
       "      <td>73</td>\n",
       "      <td>61</td>\n",
       "      <td>73</td>\n",
       "      <td>61</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>student000003</td>\n",
       "      <td>80</td>\n",
       "      <td>78</td>\n",
       "      <td>80</td>\n",
       "      <td>79</td>\n",
       "      <td>79</td>\n",
       "      <td>57</td>\n",
       "      <td>80</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>student000004</td>\n",
       "      <td>85</td>\n",
       "      <td>91</td>\n",
       "      <td>78</td>\n",
       "      <td>80</td>\n",
       "      <td>84</td>\n",
       "      <td>67</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>student000481</td>\n",
       "      <td>90</td>\n",
       "      <td>85</td>\n",
       "      <td>93</td>\n",
       "      <td>87</td>\n",
       "      <td>81</td>\n",
       "      <td>77</td>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>student000482</td>\n",
       "      <td>80</td>\n",
       "      <td>68</td>\n",
       "      <td>93</td>\n",
       "      <td>70</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>student000483</td>\n",
       "      <td>80</td>\n",
       "      <td>86</td>\n",
       "      <td>93</td>\n",
       "      <td>95</td>\n",
       "      <td>90</td>\n",
       "      <td>83</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>student000484</td>\n",
       "      <td>75</td>\n",
       "      <td>68</td>\n",
       "      <td>80</td>\n",
       "      <td>76</td>\n",
       "      <td>88</td>\n",
       "      <td>50</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>student000485</td>\n",
       "      <td>85</td>\n",
       "      <td>81</td>\n",
       "      <td>88</td>\n",
       "      <td>86</td>\n",
       "      <td>96</td>\n",
       "      <td>60</td>\n",
       "      <td>88</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>486 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Student ID  Quiz01 [10]  Assignment01 [8]  Midterm Exam [20]  \\\n",
       "0    student000000           95                91                 70   \n",
       "1    student000001           85                76                 65   \n",
       "2    student000002           85                41                 73   \n",
       "3    student000003           80                78                 80   \n",
       "4    student000004           85                91                 78   \n",
       "..             ...          ...               ...                ...   \n",
       "481  student000481           90                85                 93   \n",
       "482  student000482           80                68                 93   \n",
       "483  student000483           80                86                 93   \n",
       "484  student000484           75                68                 80   \n",
       "485  student000485           85                81                 88   \n",
       "\n",
       "     Assignment02 [12]  Assignment03 [25]  Final Exam [35]  Course Grade  \\\n",
       "0                   90                 84               64            85   \n",
       "1                   61                 73               64            76   \n",
       "2                   61                 73               61            73   \n",
       "3                   79                 79               57            80   \n",
       "4                   80                 84               67            85   \n",
       "..                 ...                ...              ...           ...   \n",
       "481                 87                 81               77            92   \n",
       "482                 70                 83               83            90   \n",
       "483                 95                 90               83            96   \n",
       "484                 76                 88               50            78   \n",
       "485                 86                 96               60            88   \n",
       "\n",
       "     Total [100]  \n",
       "0             85  \n",
       "1             76  \n",
       "2             73  \n",
       "3             79  \n",
       "4             85  \n",
       "..           ...  \n",
       "481           92  \n",
       "482           90  \n",
       "483           96  \n",
       "484           78  \n",
       "485           88  \n",
       "\n",
       "[486 rows x 9 columns]"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "id": "b02e2127",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quiz01 [10]</th>\n",
       "      <th>Assignment01 [8]</th>\n",
       "      <th>Midterm Exam [20]</th>\n",
       "      <th>Assignment02 [12]</th>\n",
       "      <th>Assignment03 [25]</th>\n",
       "      <th>Final Exam [35]</th>\n",
       "      <th>Course Grade</th>\n",
       "      <th>Total [100]</th>\n",
       "      <th>Class</th>\n",
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       "      <td>90</td>\n",
       "      <td>84</td>\n",
       "      <td>64</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>G</td>\n",
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       "      <th>1</th>\n",
       "      <td>85</td>\n",
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       "      <td>61</td>\n",
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       "      <td>85</td>\n",
       "      <td>41</td>\n",
       "      <td>73</td>\n",
       "      <td>61</td>\n",
       "      <td>73</td>\n",
       "      <td>61</td>\n",
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       "      <td>73</td>\n",
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       "      <td>79</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>85</td>\n",
       "      <td>91</td>\n",
       "      <td>78</td>\n",
       "      <td>80</td>\n",
       "      <td>84</td>\n",
       "      <td>67</td>\n",
       "      <td>85</td>\n",
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       "      <td>G</td>\n",
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       "      <td>81</td>\n",
       "      <td>77</td>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "      <td>G</td>\n",
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       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>80</td>\n",
       "      <td>68</td>\n",
       "      <td>93</td>\n",
       "      <td>70</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>80</td>\n",
       "      <td>86</td>\n",
       "      <td>93</td>\n",
       "      <td>95</td>\n",
       "      <td>90</td>\n",
       "      <td>83</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>75</td>\n",
       "      <td>68</td>\n",
       "      <td>80</td>\n",
       "      <td>76</td>\n",
       "      <td>88</td>\n",
       "      <td>50</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>85</td>\n",
       "      <td>81</td>\n",
       "      <td>88</td>\n",
       "      <td>86</td>\n",
       "      <td>96</td>\n",
       "      <td>60</td>\n",
       "      <td>88</td>\n",
       "      <td>88</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>486 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Quiz01 [10]  Assignment01 [8]  Midterm Exam [20]  Assignment02 [12]  \\\n",
       "0             95                91                 70                 90   \n",
       "1             85                76                 65                 61   \n",
       "2             85                41                 73                 61   \n",
       "3             80                78                 80                 79   \n",
       "4             85                91                 78                 80   \n",
       "..           ...               ...                ...                ...   \n",
       "481           90                85                 93                 87   \n",
       "482           80                68                 93                 70   \n",
       "483           80                86                 93                 95   \n",
       "484           75                68                 80                 76   \n",
       "485           85                81                 88                 86   \n",
       "\n",
       "     Assignment03 [25]  Final Exam [35]  Course Grade  Total [100] Class  \n",
       "0                   84               64            85           85     G  \n",
       "1                   73               64            76           76     G  \n",
       "2                   73               61            73           73     G  \n",
       "3                   79               57            80           79     G  \n",
       "4                   84               67            85           85     G  \n",
       "..                 ...              ...           ...          ...   ...  \n",
       "481                 81               77            92           92     G  \n",
       "482                 83               83            90           90     G  \n",
       "483                 90               83            96           96     G  \n",
       "484                 88               50            78           78     G  \n",
       "485                 96               60            88           88     G  \n",
       "\n",
       "[486 rows x 9 columns]"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(columns=['Student ID'],inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "id": "2b20daa3",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_20 =df.iloc[:, 0:2].values\n",
    "y_20= df.iloc[:, -1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "id": "bbd381b5",
   "metadata": {},
   "outputs": [
    {
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       "       [ 75,  65],\n",
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       "       [ 85,  77],\n",
       "       [ 95,  55],\n",
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       "       [ 75,  58],\n",
       "       [ 65,  62],\n",
       "       [  0,  39],\n",
       "       [ 75,  73],\n",
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       "       [100,   0],\n",
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       "       [ 80,  89],\n",
       "       [ 75,  78],\n",
       "       [  0,  56],\n",
       "       [ 75,  93],\n",
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       "       [ 70,  71],\n",
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       "       [ 75,  80],\n",
       "       [ 70,  89],\n",
       "       [ 70,  15],\n",
       "       [ 80,  76],\n",
       "       [100,  86],\n",
       "       [ 85,  83],\n",
       "       [ 85,  64],\n",
       "       [ 75,  83],\n",
       "       [ 85,  91],\n",
       "       [ 75,  63],\n",
       "       [ 80,  46],\n",
       "       [ 80,  74],\n",
       "       [ 90,  48],\n",
       "       [ 95,  91],\n",
       "       [ 70,  83],\n",
       "       [ 90,  81],\n",
       "       [ 85,  94],\n",
       "       [ 75,  70],\n",
       "       [ 80,  68],\n",
       "       [ 85,  81],\n",
       "       [ 85,  80],\n",
       "       [ 70,  61],\n",
       "       [ 15,  59],\n",
       "       [ 80,  74],\n",
       "       [ 85,  88],\n",
       "       [100,  70],\n",
       "       [ 90,  77],\n",
       "       [ 70,  74],\n",
       "       [ 75,  84],\n",
       "       [ 70,  72],\n",
       "       [ 70,  66],\n",
       "       [ 65,  74],\n",
       "       [100,  79],\n",
       "       [ 75,  88],\n",
       "       [ 70,  62],\n",
       "       [ 70,  87],\n",
       "       [ 85,  92],\n",
       "       [ 85,  79],\n",
       "       [ 70,  83],\n",
       "       [ 85,  81],\n",
       "       [ 90,  87],\n",
       "       [ 90,  89],\n",
       "       [ 85,  98],\n",
       "       [ 70,  38],\n",
       "       [ 80,  83],\n",
       "       [ 80,  84],\n",
       "       [ 65,  69],\n",
       "       [ 90,  76],\n",
       "       [ 65,  73],\n",
       "       [ 80,  46],\n",
       "       [ 75,  84],\n",
       "       [ 75,  83],\n",
       "       [ 70,  95],\n",
       "       [ 80,  78],\n",
       "       [ 80,  69],\n",
       "       [ 90,  85],\n",
       "       [ 80,  68],\n",
       "       [ 80,  86],\n",
       "       [ 75,  68],\n",
       "       [ 85,  81]], dtype=int64)"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "id": "ba350f3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'W', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'W', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'W', 'G',\n",
       "       'G', 'W', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'W', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'W', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'W', 'G', 'G', 'W', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'W', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'W', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'W', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'W', 'W', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'W',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'W', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'W', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'W', 'G', 'G', 'W', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'W', 'G', 'G', 'G', 'G', 'G', 'G', 'W', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'W', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G'], dtype=object)"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "0d621166",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "smote=SMOTE(sampling_strategy='minority')\n",
    "x_sm_1,y_sm_1 = smote.fit_resample(x_20,y_20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "id": "2bec5639",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'G': 465, 'W': 465}"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique, counts = np.unique(y_sm_1, return_counts=True)\n",
    "dict(zip(unique, counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "id": "f093d0a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train_20, X_test_20, Y_train_20, Y_test_20 = train_test_split(x_sm_1, y_sm_1, test_size = 0.25, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "id": "37e6d0e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((697, 2), (233, 2))"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_20.shape,X_test_20.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5369cc88",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "266585ad",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "id": "33ebf2c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "\n",
    "# instantiate the model\n",
    "gnb = GaussianNB()\n",
    "\n",
    "\n",
    "# fit the model\n",
    "gnb.fit(X_train_20, Y_train_20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "id": "3455575e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'W', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'W', 'W', 'W', 'G', 'W', 'W', 'G', 'G', 'G', 'W', 'G',\n",
       "       'G', 'W', 'G', 'W', 'G', 'W', 'G', 'G', 'W', 'G', 'G', 'W', 'G',\n",
       "       'G', 'G', 'G', 'W', 'W', 'G', 'G', 'G', 'W', 'G', 'W', 'W', 'G',\n",
       "       'W', 'G', 'W', 'G', 'G', 'G', 'G', 'W', 'G', 'G', 'G', 'W', 'G',\n",
       "       'G', 'G', 'W', 'W', 'G', 'W', 'W', 'W', 'W', 'W', 'G', 'W', 'W',\n",
       "       'G', 'G', 'G', 'G', 'W', 'G', 'W', 'G', 'W', 'W', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'W', 'W', 'W', 'G', 'G', 'G', 'W', 'W',\n",
       "       'G', 'W', 'W', 'W', 'G', 'G', 'W', 'W', 'W', 'G', 'W', 'G', 'W',\n",
       "       'G', 'G', 'W', 'W', 'G', 'W', 'W', 'G', 'G', 'G', 'G', 'G', 'W',\n",
       "       'W', 'W', 'W', 'G', 'G', 'W', 'W', 'G', 'G', 'G', 'W', 'G', 'W',\n",
       "       'G', 'W', 'G', 'G', 'G', 'G', 'W', 'G', 'G', 'W', 'W', 'W', 'G',\n",
       "       'G', 'W', 'G', 'G', 'G', 'G', 'G', 'G', 'W', 'G', 'W', 'G', 'G',\n",
       "       'G', 'W', 'G', 'G', 'G', 'G', 'G', 'G', 'W', 'G', 'W', 'W', 'G',\n",
       "       'G', 'W', 'G', 'G', 'W', 'W', 'G', 'W', 'G', 'W', 'W', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'W', 'W', 'W', 'W', 'G', 'G', 'G', 'W', 'W',\n",
       "       'W', 'G', 'G', 'G', 'G', 'G', 'W', 'G', 'W', 'W', 'G', 'G', 'G',\n",
       "       'W', 'G', 'W', 'G', 'G', 'G', 'G', 'G', 'W', 'G', 'G', 'G'],\n",
       "      dtype='<U1')"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = gnb.predict(X_test_20)\n",
    "\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "id": "725f8d7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy score at 20%: 0.8069\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "print('Model accuracy score at 20%: {0:0.4f}'. format(accuracy_score(Y_test_20, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "b535ddf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_50=df.iloc[:, 0:4].values\n",
    "Y_50=df.iloc[:, -1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "5e8bb172",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[95, 91, 70, 90],\n",
       "       [85, 76, 65, 61],\n",
       "       [85, 41, 73, 61],\n",
       "       ...,\n",
       "       [80, 86, 93, 95],\n",
       "       [75, 68, 80, 76],\n",
       "       [85, 81, 88, 86]], dtype=int64)"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "706de068",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'W', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'W', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'W', 'G',\n",
       "       'G', 'W', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'W', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'W', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'W', 'G', 'G', 'W', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'W', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'W', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'W', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'W', 'W', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'W',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'W', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'W', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'W', 'G', 'G', 'W', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'W', 'G', 'G', 'G', 'G', 'G', 'G', 'W', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'W', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G'], dtype=object)"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "7ccd9c42",
   "metadata": {},
   "outputs": [],
   "source": [
    "smote=SMOTE(sampling_strategy='minority')\n",
    "x_sm_2,y_sm_2 = smote.fit_resample(X_50,Y_50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "id": "69b72d41",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train_50, X_test_50, Y_train_50, Y_test_50 = train_test_split(x_sm_2, y_sm_2, test_size = 0.25, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "id": "b175b927",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import RobustScaler,StandardScaler\n",
    "\n",
    "scaler = RobustScaler()\n",
    "\n",
    "X_train_50 = scaler.fit_transform(X_train_50)\n",
    "\n",
    "X_test_50 = scaler.transform(X_test_50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "bcc1657c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gnb.fit(X_train_50, Y_train_50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "cb8a3dd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['G', 'W', 'G', 'G', 'G', 'W', 'G', 'G', 'W', 'G', 'G', 'W', 'W',\n",
       "       'G', 'G', 'W', 'W', 'W', 'G', 'W', 'W', 'W', 'G', 'W', 'W', 'G',\n",
       "       'G', 'W', 'G', 'W', 'G', 'W', 'G', 'W', 'W', 'G', 'G', 'W', 'G',\n",
       "       'G', 'G', 'W', 'W', 'W', 'G', 'G', 'W', 'W', 'G', 'W', 'W', 'W',\n",
       "       'W', 'G', 'W', 'G', 'W', 'G', 'G', 'W', 'G', 'G', 'G', 'W', 'G',\n",
       "       'G', 'G', 'W', 'W', 'G', 'W', 'W', 'W', 'W', 'W', 'G', 'W', 'W',\n",
       "       'W', 'G', 'W', 'G', 'W', 'W', 'W', 'G', 'W', 'W', 'W', 'W', 'G',\n",
       "       'G', 'G', 'G', 'G', 'G', 'W', 'W', 'G', 'G', 'W', 'G', 'W', 'W',\n",
       "       'W', 'W', 'W', 'W', 'G', 'G', 'W', 'W', 'W', 'G', 'G', 'G', 'W',\n",
       "       'G', 'G', 'W', 'W', 'G', 'W', 'G', 'G', 'G', 'G', 'G', 'W', 'W',\n",
       "       'W', 'G', 'W', 'G', 'W', 'W', 'W', 'G', 'G', 'G', 'W', 'G', 'G',\n",
       "       'G', 'W', 'G', 'G', 'G', 'G', 'W', 'G', 'G', 'W', 'W', 'W', 'G',\n",
       "       'G', 'W', 'G', 'G', 'G', 'W', 'W', 'G', 'W', 'W', 'W', 'W', 'G',\n",
       "       'G', 'W', 'G', 'G', 'W', 'G', 'G', 'W', 'W', 'G', 'W', 'W', 'W',\n",
       "       'G', 'W', 'G', 'G', 'W', 'W', 'G', 'W', 'G', 'W', 'W', 'G', 'W',\n",
       "       'G', 'G', 'G', 'G', 'W', 'W', 'G', 'W', 'G', 'W', 'G', 'W', 'W',\n",
       "       'W', 'G', 'G', 'W', 'G', 'W', 'W', 'W', 'W', 'W', 'G', 'G', 'G',\n",
       "       'W', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'W', 'G', 'W', 'W'],\n",
       "      dtype='<U1')"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = gnb.predict(X_test_50)\n",
    "\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "id": "a9c40a0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy score at 50%: 0.9142\n"
     ]
    }
   ],
   "source": [
    "print('Model accuracy score at 50%: {0:0.4f}'. format(accuracy_score(Y_test_50, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "id": "232bd954",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix\n",
      "\n",
      " [[105   9]\n",
      " [ 11 108]]\n",
      "\n",
      "True Positives(TP) =  105\n",
      "\n",
      "True Negatives(TN) =  108\n",
      "\n",
      "False Positives(FP) =  9\n",
      "\n",
      "False Negatives(FN) =  11\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "cm = confusion_matrix(Y_test_50, y_pred)\n",
    "\n",
    "print('Confusion matrix\\n\\n', cm)\n",
    "\n",
    "print('\\nTrue Positives(TP) = ', cm[0,0])\n",
    "\n",
    "print('\\nTrue Negatives(TN) = ', cm[1,1])\n",
    "\n",
    "print('\\nFalse Positives(FP) = ', cm[0,1])\n",
    "\n",
    "print('\\nFalse Negatives(FN) = ', cm[1,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "id": "95e42c22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize confusion matrix with seaborn heatmap\n",
    "\n",
    "cm_matrix = pd.DataFrame(data=cm, columns=['Actual Positive:1', 'Actual Negative:0'], \n",
    "                                 index=['Predict Positive:1', 'Predict Negative:0'])\n",
    "\n",
    "sns.heatmap(cm_matrix, annot=True, fmt='d', cmap='YlGnBu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89db9b2f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "403e52ea",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22bc2f13",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46f09c6c",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}