StudentPerf.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Machine Learning: A Practical Approach Using the Student Performance Dataset**\n",
    "### This project uses the Student Performance dataset to build and evaluate machine learning models, predict student grades, and visualize key relationships in the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Step 1. Loading Libraries and Data**\n",
    "### We start by loading essential libraries, including pandas for data handling, numpy for numerical operations, and matplotlib and seaborn for data visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing necessary libraries\n",
    "import pandas as pd  # For data manipulation\n",
    "import numpy as np  # For numerical operations\n",
    "import matplotlib.pyplot as plt  # For plotting\n",
    "import seaborn as sns  # For advanced visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **pd.read_csv():** \n",
    "### Reads data from a CSV file into a pandas DataFrame, enabling easy data manipulation.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the Student Performance dataset\n",
    "df = pd.read_csv(\"StudentsPerformance.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Step 2. Exploring the Dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset Head:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>gender</th>\n",
       "      <th>race/ethnicity</th>\n",
       "      <th>parental level of education</th>\n",
       "      <th>lunch</th>\n",
       "      <th>test preparation course</th>\n",
       "      <th>math score</th>\n",
       "      <th>reading score</th>\n",
       "      <th>writing score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>completed</td>\n",
       "      <td>69</td>\n",
       "      <td>90</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>master's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>90</td>\n",
       "      <td>95</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>male</td>\n",
       "      <td>group A</td>\n",
       "      <td>associate's degree</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>none</td>\n",
       "      <td>47</td>\n",
       "      <td>57</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>76</td>\n",
       "      <td>78</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender race/ethnicity parental level of education         lunch  \\\n",
       "0  female        group B           bachelor's degree      standard   \n",
       "1  female        group C                some college      standard   \n",
       "2  female        group B             master's degree      standard   \n",
       "3    male        group A          associate's degree  free/reduced   \n",
       "4    male        group C                some college      standard   \n",
       "\n",
       "  test preparation course  math score  reading score  writing score  \n",
       "0                    none          72             72             74  \n",
       "1               completed          69             90             88  \n",
       "2                    none          90             95             93  \n",
       "3                    none          47             57             44  \n",
       "4                    none          76             78             75  "
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying dataset head and checking its shape\n",
    "print(\"Dataset Head:\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset Tail:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>race/ethnicity</th>\n",
       "      <th>parental level of education</th>\n",
       "      <th>lunch</th>\n",
       "      <th>test preparation course</th>\n",
       "      <th>math score</th>\n",
       "      <th>reading score</th>\n",
       "      <th>writing score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td>male</td>\n",
       "      <td>group E</td>\n",
       "      <td>high school</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>completed</td>\n",
       "      <td>86</td>\n",
       "      <td>81</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>some high school</td>\n",
       "      <td>standard</td>\n",
       "      <td>completed</td>\n",
       "      <td>65</td>\n",
       "      <td>82</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>female</td>\n",
       "      <td>group D</td>\n",
       "      <td>associate's degree</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>none</td>\n",
       "      <td>55</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>female</td>\n",
       "      <td>group D</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>none</td>\n",
       "      <td>62</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>male</td>\n",
       "      <td>group A</td>\n",
       "      <td>high school</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>female</td>\n",
       "      <td>group E</td>\n",
       "      <td>master's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>completed</td>\n",
       "      <td>88</td>\n",
       "      <td>99</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>male</td>\n",
       "      <td>group C</td>\n",
       "      <td>high school</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>none</td>\n",
       "      <td>62</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>female</td>\n",
       "      <td>group C</td>\n",
       "      <td>high school</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>completed</td>\n",
       "      <td>59</td>\n",
       "      <td>71</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>female</td>\n",
       "      <td>group D</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>completed</td>\n",
       "      <td>68</td>\n",
       "      <td>78</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>female</td>\n",
       "      <td>group D</td>\n",
       "      <td>some college</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>none</td>\n",
       "      <td>77</td>\n",
       "      <td>86</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     gender race/ethnicity parental level of education         lunch  \\\n",
       "990    male        group E                 high school  free/reduced   \n",
       "991  female        group B            some high school      standard   \n",
       "992  female        group D          associate's degree  free/reduced   \n",
       "993  female        group D           bachelor's degree  free/reduced   \n",
       "994    male        group A                 high school      standard   \n",
       "995  female        group E             master's degree      standard   \n",
       "996    male        group C                 high school  free/reduced   \n",
       "997  female        group C                 high school  free/reduced   \n",
       "998  female        group D                some college      standard   \n",
       "999  female        group D                some college  free/reduced   \n",
       "\n",
       "    test preparation course  math score  reading score  writing score  \n",
       "990               completed          86             81             75  \n",
       "991               completed          65             82             78  \n",
       "992                    none          55             76             76  \n",
       "993                    none          62             72             74  \n",
       "994                    none          63             63             62  \n",
       "995               completed          88             99             95  \n",
       "996                    none          62             55             55  \n",
       "997               completed          59             71             65  \n",
       "998               completed          68             78             77  \n",
       "999                    none          77             86             86  "
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Dataset Tail:\")\n",
    "df.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset Shape:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1000, 8)"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Dataset Shape:\")\n",
    "df.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing Values per Column:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "gender                         0\n",
       "race/ethnicity                 0\n",
       "parental level of education    0\n",
       "lunch                          0\n",
       "test preparation course        0\n",
       "math score                     0\n",
       "reading score                  0\n",
       "writing score                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for missing values\n",
    "print(\"Missing Values per Column:\")\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Statistical Overview Of Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 8 columns):\n",
      " #   Column                       Non-Null Count  Dtype \n",
      "---  ------                       --------------  ----- \n",
      " 0   gender                       1000 non-null   object\n",
      " 1   race/ethnicity               1000 non-null   object\n",
      " 2   parental level of education  1000 non-null   object\n",
      " 3   lunch                        1000 non-null   object\n",
      " 4   test preparation course      1000 non-null   object\n",
      " 5   math score                   1000 non-null   int64 \n",
      " 6   reading score                1000 non-null   int64 \n",
      " 7   writing score                1000 non-null   int64 \n",
      "dtypes: int64(3), object(5)\n",
      "memory usage: 62.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Series name: gender\n",
      "Non-Null Count  Dtype \n",
      "--------------  ----- \n",
      "1000 non-null   object\n",
      "dtypes: object(1)\n",
      "memory usage: 7.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df[\"gender\"].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>math score</th>\n",
       "      <th>reading score</th>\n",
       "      <th>writing score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.00000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>66.08900</td>\n",
       "      <td>69.169000</td>\n",
       "      <td>68.054000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15.16308</td>\n",
       "      <td>14.600192</td>\n",
       "      <td>15.195657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>57.00000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>57.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>66.00000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>69.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>77.00000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>79.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.00000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       math score  reading score  writing score\n",
       "count  1000.00000    1000.000000    1000.000000\n",
       "mean     66.08900      69.169000      68.054000\n",
       "std      15.16308      14.600192      15.195657\n",
       "min       0.00000      17.000000      10.000000\n",
       "25%      57.00000      59.000000      57.750000\n",
       "50%      66.00000      70.000000      69.000000\n",
       "75%      77.00000      79.000000      79.000000\n",
       "max     100.00000     100.000000     100.000000"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>race/ethnicity</th>\n",
       "      <th>parental level of education</th>\n",
       "      <th>lunch</th>\n",
       "      <th>test preparation course</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>female</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>518</td>\n",
       "      <td>319</td>\n",
       "      <td>226</td>\n",
       "      <td>645</td>\n",
       "      <td>642</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        gender race/ethnicity parental level of education     lunch  \\\n",
       "count     1000           1000                        1000      1000   \n",
       "unique       2              5                           6         2   \n",
       "top     female        group C                some college  standard   \n",
       "freq       518            319                         226       645   \n",
       "\n",
       "       test preparation course  \n",
       "count                     1000  \n",
       "unique                       2  \n",
       "top                       none  \n",
       "freq                       642  "
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=\"O\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "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>gender</th>\n",
       "      <th>race/ethnicity</th>\n",
       "      <th>parental level of education</th>\n",
       "      <th>lunch</th>\n",
       "      <th>test preparation course</th>\n",
       "      <th>math score</th>\n",
       "      <th>reading score</th>\n",
       "      <th>writing score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000.00000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>female</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>518</td>\n",
       "      <td>319</td>\n",
       "      <td>226</td>\n",
       "      <td>645</td>\n",
       "      <td>642</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66.08900</td>\n",
       "      <td>69.169000</td>\n",
       "      <td>68.054000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.16308</td>\n",
       "      <td>14.600192</td>\n",
       "      <td>15.195657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.00000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>57.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66.00000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>69.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.00000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>79.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.00000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        gender race/ethnicity parental level of education     lunch  \\\n",
       "count     1000           1000                        1000      1000   \n",
       "unique       2              5                           6         2   \n",
       "top     female        group C                some college  standard   \n",
       "freq       518            319                         226       645   \n",
       "mean       NaN            NaN                         NaN       NaN   \n",
       "std        NaN            NaN                         NaN       NaN   \n",
       "min        NaN            NaN                         NaN       NaN   \n",
       "25%        NaN            NaN                         NaN       NaN   \n",
       "50%        NaN            NaN                         NaN       NaN   \n",
       "75%        NaN            NaN                         NaN       NaN   \n",
       "max        NaN            NaN                         NaN       NaN   \n",
       "\n",
       "       test preparation course  math score  reading score  writing score  \n",
       "count                     1000  1000.00000    1000.000000    1000.000000  \n",
       "unique                       2         NaN            NaN            NaN  \n",
       "top                       none         NaN            NaN            NaN  \n",
       "freq                       642         NaN            NaN            NaN  \n",
       "mean                       NaN    66.08900      69.169000      68.054000  \n",
       "std                        NaN    15.16308      14.600192      15.195657  \n",
       "min                        NaN     0.00000      17.000000      10.000000  \n",
       "25%                        NaN    57.00000      59.000000      57.750000  \n",
       "50%                        NaN    66.00000      70.000000      69.000000  \n",
       "75%                        NaN    77.00000      79.000000      79.000000  \n",
       "max                        NaN   100.00000     100.000000     100.000000  "
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Step 3. Data Visualization**\n",
    "### This step provides various plots to explore the distribution of scores and comparisons by gender, race/ethnicity, and parental education."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **1. Histogram of Math Scores**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5)) # Adjusting figure size\n",
    "sns.histplot(df['math score'], bins=10, kde=True,color=\"red\")#Creates a histogram with kernel density estimation (kde) for the 'math score' column.\n",
    "plt.title('Distribution of Math Scores')\n",
    "plt.xlabel('Math Score')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **2. Bar Plot of Writing Scores by Gender**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "sns.barplot(x='gender', y='writing score', data=df,hue=\"gender\")\n",
    "plt.title('Writing Scores by Gender')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **3. Bar Plot of Maths Scores by Gender**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "sns.barplot(x='gender', y='math score', data=df,hue=\"gender\")\n",
    "plt.title('Maths Scores by Gender')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **4. Bar Plot of Maths Scores by Gender**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.barplot(x='gender', y='reading score',hue=\"gender\", data=df,legend=[\"Female\",\"Male\"])\n",
    "plt.title('Reading Scores by Gender')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **5. Group vice comparison of writing score marks using bar plot**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.barplot(x=\"race/ethnicity\",y=\"writing score\",hue=\"race/ethnicity\",data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **6. Bar Plot of Average Scores by Parental Level of Education**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.barplot(x='parental level of education', y='math score', data=df,hue=\"parental level of education\")\n",
    "plt.title('Average Math Scores by Parental Level of Education')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Step 4: Data PreProcessing**\n",
    "- ### Removing unusuall columns\n",
    "- ### filling null values\n",
    "- ### removing duplicates\n",
    "- ### encoding categorical variables\n",
    "- ### scaling/normalizing data\n",
    "- ### Feature enginerring\n",
    "- ### Feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 8 columns):\n",
      " #   Column                       Non-Null Count  Dtype \n",
      "---  ------                       --------------  ----- \n",
      " 0   gender                       1000 non-null   object\n",
      " 1   race/ethnicity               1000 non-null   object\n",
      " 2   parental level of education  1000 non-null   object\n",
      " 3   lunch                        1000 non-null   object\n",
      " 4   test preparation course      1000 non-null   object\n",
      " 5   math score                   1000 non-null   int64 \n",
      " 6   reading score                1000 non-null   int64 \n",
      " 7   writing score                1000 non-null   int64 \n",
      "dtypes: int64(3), object(5)\n",
      "memory usage: 62.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Droping unusuall columns**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_to_remove=[\"gender\",\"race/ethnicity\",\"parental level of education\",\"lunch\",\"test preparation course\"]\n",
    "df.drop(columns_to_remove,inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "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>math score</th>\n",
       "      <th>reading score</th>\n",
       "      <th>writing score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>69</td>\n",
       "      <td>90</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90</td>\n",
       "      <td>95</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>57</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>76</td>\n",
       "      <td>78</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   math score  reading score  writing score\n",
       "0          72             72             74\n",
       "1          69             90             88\n",
       "2          90             95             93\n",
       "3          47             57             44\n",
       "4          76             78             75"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Finding null values in columns/features**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "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>math score</th>\n",
       "      <th>reading score</th>\n",
       "      <th>writing score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     math score  reading score  writing score\n",
       "0         False          False          False\n",
       "1         False          False          False\n",
       "2         False          False          False\n",
       "3         False          False          False\n",
       "4         False          False          False\n",
       "..          ...            ...            ...\n",
       "995       False          False          False\n",
       "996       False          False          False\n",
       "997       False          False          False\n",
       "998       False          False          False\n",
       "999       False          False          False\n",
       "\n",
       "[1000 rows x 3 columns]"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of null values in each column:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "math score       0\n",
       "reading score    0\n",
       "writing score    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Sum of null values in each column:\")\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Feature Enginerring**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "Average_marks=[]\n",
    "for math_mark,writing_mark,read_mark in df[[\"math score\",\"writing score\",\"reading score\"]].values:\n",
    "    average_mark=(math_mark+writing_mark+read_mark)/3\n",
    "    Average_marks.append(average_mark)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
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   "source": [
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   "execution_count": 188,
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       "     Average_marks\n",
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   "execution_count": 189,
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    "df[\"Average marks\"]=Average_marks # Adding new column named average marks in our dataset"
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   "cell_type": "code",
   "execution_count": 190,
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       "      <td>92.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>57</td>\n",
       "      <td>44</td>\n",
       "      <td>49.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>76</td>\n",
       "      <td>78</td>\n",
       "      <td>75</td>\n",
       "      <td>76.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   math score  reading score  writing score  Average marks\n",
       "0          72             72             74      72.666667\n",
       "1          69             90             88      82.333333\n",
       "2          90             95             93      92.666667\n",
       "3          47             57             44      49.333333\n",
       "4          76             78             75      76.333333"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Generating labels using average marks column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "Grades=[]\n",
    "for marks in df[\"Average marks\"].values:\n",
    "    if marks >= 90:\n",
    "        Grades.append('A')\n",
    "    elif marks >= 80:\n",
    "        Grades.append('B')\n",
    "    elif marks >= 70:\n",
    "        Grades.append('C')\n",
    "    elif marks >= 60:\n",
    "        Grades.append('D')\n",
    "    elif marks >= 50:\n",
    "        Grades.append('E')\n",
    "    else:\n",
    "        Grades.append('F')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
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       " 'E',\n",
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       " 'B',\n",
       " 'E',\n",
       " 'D',\n",
       " 'D',\n",
       " 'C',\n",
       " 'E',\n",
       " 'C',\n",
       " 'F',\n",
       " 'E',\n",
       " 'F',\n",
       " 'C',\n",
       " 'E',\n",
       " 'B',\n",
       " 'B',\n",
       " 'E',\n",
       " 'D',\n",
       " 'C',\n",
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       " 'D',\n",
       " 'B',\n",
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       " 'A',\n",
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       " 'F',\n",
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       " 'D',\n",
       " 'C',\n",
       " 'D',\n",
       " 'E',\n",
       " 'D',\n",
       " 'F',\n",
       " 'E',\n",
       " 'C',\n",
       " 'D',\n",
       " 'D',\n",
       " 'C',\n",
       " 'A',\n",
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       " 'C',\n",
       " 'C',\n",
       " 'C',\n",
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       " 'D',\n",
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       " 'E',\n",
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       " 'D',\n",
       " 'D',\n",
       " 'C',\n",
       " 'C',\n",
       " 'A',\n",
       " 'C',\n",
       " 'E',\n",
       " 'E',\n",
       " 'D',\n",
       " 'C',\n",
       " 'D',\n",
       " 'D',\n",
       " 'F',\n",
       " 'A',\n",
       " 'F',\n",
       " 'C',\n",
       " 'B',\n",
       " 'B',\n",
       " 'C',\n",
       " 'E',\n",
       " 'E',\n",
       " 'C',\n",
       " 'F',\n",
       " 'C',\n",
       " 'B',\n",
       " 'C',\n",
       " 'D',\n",
       " 'D',\n",
       " 'D',\n",
       " 'A',\n",
       " 'E',\n",
       " 'D',\n",
       " 'C',\n",
       " 'B']"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Grades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Grade\"]=Grades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>math score</th>\n",
       "      <th>reading score</th>\n",
       "      <th>writing score</th>\n",
       "      <th>Average marks</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>72</td>\n",
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       "      <td>74</td>\n",
       "      <td>72.666667</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>69</td>\n",
       "      <td>90</td>\n",
       "      <td>88</td>\n",
       "      <td>82.333333</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90</td>\n",
       "      <td>95</td>\n",
       "      <td>93</td>\n",
       "      <td>92.666667</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>57</td>\n",
       "      <td>44</td>\n",
       "      <td>49.333333</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>76</td>\n",
       "      <td>78</td>\n",
       "      <td>75</td>\n",
       "      <td>76.333333</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   math score  reading score  writing score  Average marks Grade\n",
       "0          72             72             74      72.666667     C\n",
       "1          69             90             88      82.333333     B\n",
       "2          90             95             93      92.666667     A\n",
       "3          47             57             44      49.333333     F\n",
       "4          76             78             75      76.333333     C"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Feature Mapping A Data PreProcessing Technique For Manually Encoding**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "categories={\n",
    "    \"A\":0,\n",
    "    \"B\":1,\n",
    "    \"C\":3,\n",
    "    \"D\":4,\n",
    "    \"E\":5,\n",
    "    \"F\":6,\n",
    "}\n",
    "df[\"Grade\"]=df[\"Grade\"].map(categories)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Using Prebuilt function named LabelEncoder for Automated Encoding**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>math score</th>\n",
       "      <th>reading score</th>\n",
       "      <th>writing score</th>\n",
       "      <th>Average marks</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "      <td>72.666667</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>69</td>\n",
       "      <td>90</td>\n",
       "      <td>88</td>\n",
       "      <td>82.333333</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90</td>\n",
       "      <td>95</td>\n",
       "      <td>93</td>\n",
       "      <td>92.666667</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>57</td>\n",
       "      <td>44</td>\n",
       "      <td>49.333333</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>76</td>\n",
       "      <td>78</td>\n",
       "      <td>75</td>\n",
       "      <td>76.333333</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   math score  reading score  writing score  Average marks  Grade\n",
       "0          72             72             74      72.666667      2\n",
       "1          69             90             88      82.333333      1\n",
       "2          90             95             93      92.666667      0\n",
       "3          47             57             44      49.333333      5\n",
       "4          76             78             75      76.333333      2"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Grade\"]=encoder.fit_transform(df[\"Grade\"])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Checking Coorelation b/w columns/features**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cor=df[[\"math score\",  \"reading score\",  \"writing score\",  \"Average marks\",\"Grade\"]].corr()\n",
    "sns.heatmap(cor,annot=True,cmap=\"coolwarm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In our project, the inverse correlation between average marks and grade might be occurring because grades in the dataset are mapped in reverse order, where a higher grade (e.g., 'A') is represented by a lower number (0) and a lower grade (e.g., 'F') is represented by a higher number (5). In such cases, the correlation between **Average marks** and **Grade** will be negative, as higher average marks correspond to lower numerical grades, and vice versa.\n",
    "\n",
    "### **Example Mapping:**\n",
    "| Grade | Numerical Value |\n",
    "|-------|-----------------|\n",
    "| A     | 0               |\n",
    "| B     | 1               |\n",
    "| C     | 2               |\n",
    "| D     | 3               |\n",
    "| E     | 4               |\n",
    "| F     | 5               |\n",
    "\n",
    "### **Explanation of Inverse Correlation**\n",
    "The correlation coefficient reflects the direction and strength of a linear relationship between two variables. In our case:\n",
    "- **Higher average marks** correspond to **lower grades (e.g., A or B)**.\n",
    "- **Lower average marks** correspond to **higher grades (e.g., F)**.\n",
    "\n",
    "This pattern results in a negative correlation because:\n",
    "1. When **average marks increase**, **grades decrease** in terms of numerical value (e.g., 1 to 0).\n",
    "2. Conversely, when **average marks decrease**, **grades increase** numerically (e.g., 0 to 5).\n",
    "\n",
    "### **Solution or Alternative Interpretation**\n",
    "To avoid this inverse correlation and represent grades in a way where higher grades align with higher scores, we can reverse the mapping so that an \"A\" grade gets a higher numeric value, or interpret the negative correlation as indicating that high marks correspond to high performance (i.e., a lower grade in the original mapping)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Feature Selection**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_to_keep=[\"Average marks\",\"Grade\"]\n",
    "df=df[columns_to_keep]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "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>Average marks</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>72.666667</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>82.333333</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>92.666667</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49.333333</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>76.333333</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>77.333333</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>91.666667</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>40.666667</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>65.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>49.333333</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Average marks  Grade\n",
       "0      72.666667      2\n",
       "1      82.333333      1\n",
       "2      92.666667      0\n",
       "3      49.333333      5\n",
       "4      76.333333      2\n",
       "5      77.333333      2\n",
       "6      91.666667      0\n",
       "7      40.666667      5\n",
       "8      65.000000      3\n",
       "9      49.333333      5"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Step 5: Model Preparation**\n",
    "### Before building models, we  split the data into training and testing sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting data into features (X) and target (y)\n",
    "X=df[[\"Average marks\"]].values \n",
    "y=df[\"Grade\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(800, 1)\n",
      "(800,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(y_train.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Step 6: Training and Evaluating Models**\n",
    "### We train two models—Naive Bayes and Decision Tree—and evaluate their accuracy and classification performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Naive Bayes : GaussionNB Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "gnb=GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GaussianNB<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.naive_bayes.GaussianNB.html\">?<span>Documentation for GaussianNB</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GaussianNB()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gnb.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=gnb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.985"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gnb.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm=confusion_matrix(y_test,y_pred)\n",
    "sns.heatmap(cm,annot=True,cmap=\"coolwarm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00         4\n",
      "           1       1.00      0.93      0.96        27\n",
      "           2       0.97      1.00      0.98        59\n",
      "           3       1.00      1.00      1.00        48\n",
      "           4       0.97      1.00      0.99        35\n",
      "           5       1.00      0.96      0.98        27\n",
      "\n",
      "    accuracy                           0.98       200\n",
      "   macro avg       0.99      0.98      0.99       200\n",
      "weighted avg       0.99      0.98      0.98       200\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cr=classification_report(y_test,y_pred)\n",
    "print(cr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **DecisionTreeClassifier Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtc=DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtc.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=dtc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtc.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dtc_cm=confusion_matrix(y_test,y_pred)\n",
    "sns.heatmap(dtc_cm,annot=True,cmap=\"coolwarm\")\n",
    "plt.xlabel(\"Actual Value\")\n",
    "plt.ylabel(\"Predicted Value\")\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00         4\n",
      "           1       1.00      1.00      1.00        27\n",
      "           2       1.00      1.00      1.00        59\n",
      "           3       1.00      1.00      1.00        48\n",
      "           4       1.00      1.00      1.00        35\n",
      "           5       1.00      1.00      1.00        27\n",
      "\n",
      "    accuracy                           1.00       200\n",
      "   macro avg       1.00      1.00      1.00       200\n",
      "weighted avg       1.00      1.00      1.00       200\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cr=classification_report(y_test,y_pred)\n",
    "print(cr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Step 7. Making Predictions with a Custom Function**\n",
    "### We define a function to predict grades based on average marks, with input validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(avg_marks):\n",
    "    if ((avg_marks>100) | (avg_marks<0)):\n",
    "        print(\"Plz Enter Between 0-100\")\n",
    "    else:\n",
    "        pred=dtc.predict([[avg_marks]])\n",
    "        return pred\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred=predict(89)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Predicted Grade is B\n"
     ]
    }
   ],
   "source": [
    "if pred:\n",
    "    if pred == 0:\n",
    "        print('The Predicted Grade is A')\n",
    "    elif pred == 1:\n",
    "        print('The Predicted Grade is B')\n",
    "    elif pred == 2:\n",
    "        print('The Predicted Grade is C')\n",
    "    elif pred == 3:\n",
    "        print('The Predicted Grade is D')\n",
    "    elif pred == 4:\n",
    "        print('The Predicted Grade is E')\n",
    "    else:\n",
    "        print('The Predicted Grade is F')\n",
    "else:\n",
    "    print(\"No value to predict\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Step 8. Saving the Model**\n",
    "### We save the trained Decision Tree model to reuse it without retraining."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to download write %pip or pip install joblib \n",
    "import joblib # using joblib library for saving the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **joblib.dump():** Saves the model as a .joblib file, making it easy to load for future predictions without retraining."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DTCmodel.joblib']"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(dtc,\"DTCmodel.joblib\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Conclusion:**\n",
    "\n",
    "### This project explored the **Student Performance** dataset to analyze patterns in student scores and predict grades based on their average marks using **Naive Bayes** and **Decision Tree** classifiers. Data visualizations helped uncover insights like score distributions across subjects and influences of gender and parental education on performance. An inverse correlation between average marks and grade values was identified, due to grade mapping where higher grades (e.g., \"A\") were assigned lower numerical values. This approach introduces students to data preprocessing, feature engineering, and the importance of interpreting model results in the context of data mappings, offering a foundational experience in machine learning."
   ]
  }
 ],
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   "display_name": "Python 3",
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