EDA-studentdata.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2b6ac688",
   "metadata": {},
   "source": [
    "# EDA\n",
    "\n",
    "-> EDA on student data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "549054e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7af90dee",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('Downloads/student.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ecd24bb4",
   "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>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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </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",
       "<p>1000 rows × 8 columns</p>\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",
       "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",
       "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  \n",
       "..                      ...         ...            ...            ...  \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  \n",
       "\n",
       "[1000 rows x 8 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "128c3ea7",
   "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": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "465c39fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['gender'].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6bfaf69f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['gender'].dtypes=='O'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6ed78256",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['gender', 'race/ethnicity', 'parental level of education', 'lunch',\n",
       "       'test preparation course', 'math score', 'reading score',\n",
       "       'writing score'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e25b047d",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_col = [ i for i in data.columns if data[i].dtype == 'O']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ac1847ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_col = [ i for i in data.columns if data[i].dtype != 'O']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5ee43786",
   "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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 5 columns</p>\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",
       "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  \n",
       "0                      none  \n",
       "1                 completed  \n",
       "2                      none  \n",
       "3                      none  \n",
       "4                      none  \n",
       "..                      ...  \n",
       "995               completed  \n",
       "996                    none  \n",
       "997               completed  \n",
       "998               completed  \n",
       "999                    none  \n",
       "\n",
       "[1000 rows x 5 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[cat_col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "60627dec",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c06bed1f",
   "metadata": {},
   "outputs": [
    {
     "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3b5e0643",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b84c3235",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender                          2\n",
       "race/ethnicity                  5\n",
       "parental level of education     6\n",
       "lunch                           2\n",
       "test preparation course         2\n",
       "math score                     81\n",
       "reading score                  72\n",
       "writing score                  77\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e7cff71d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['female', 'male'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['gender'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "005ffb90",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "460b228d",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>math score</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>66.089</td>\n",
       "      <td>15.163080</td>\n",
       "      <td>0.0</td>\n",
       "      <td>57.00</td>\n",
       "      <td>66.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reading score</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>69.169</td>\n",
       "      <td>14.600192</td>\n",
       "      <td>17.0</td>\n",
       "      <td>59.00</td>\n",
       "      <td>70.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>writing score</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>68.054</td>\n",
       "      <td>15.195657</td>\n",
       "      <td>10.0</td>\n",
       "      <td>57.75</td>\n",
       "      <td>69.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count    mean        std   min    25%   50%   75%    max\n",
       "math score     1000.0  66.089  15.163080   0.0  57.00  66.0  77.0  100.0\n",
       "reading score  1000.0  69.169  14.600192  17.0  59.00  70.0  79.0  100.0\n",
       "writing score  1000.0  68.054  15.195657  10.0  57.75  69.0  79.0  100.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0c0a1805",
   "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>math score</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.817580</td>\n",
       "      <td>0.802642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reading score</th>\n",
       "      <td>0.817580</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.954598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>writing score</th>\n",
       "      <td>0.802642</td>\n",
       "      <td>0.954598</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               math score  reading score  writing score\n",
       "math score       1.000000       0.817580       0.802642\n",
       "reading score    0.817580       1.000000       0.954598\n",
       "writing score    0.802642       0.954598       1.000000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0be5360e",
   "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>math score</th>\n",
       "      <td>229.918998</td>\n",
       "      <td>180.998958</td>\n",
       "      <td>184.939133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reading score</th>\n",
       "      <td>180.998958</td>\n",
       "      <td>213.165605</td>\n",
       "      <td>211.786661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>writing score</th>\n",
       "      <td>184.939133</td>\n",
       "      <td>211.786661</td>\n",
       "      <td>230.907992</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               math score  reading score  writing score\n",
       "math score     229.918998     180.998958     184.939133\n",
       "reading score  180.998958     213.165605     211.786661\n",
       "writing score  184.939133     211.786661     230.907992"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.cov()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d3f6d753",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "math score      -0.278935\n",
       "reading score   -0.259105\n",
       "writing score   -0.289444\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7fcfd4ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='math score', ylabel='Density'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data['math score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b6ecdfaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['gender', 'race/ethnicity', 'parental level of education', 'lunch',\n",
       "       'test preparation course', 'math score', 'reading score',\n",
       "       'writing score'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8f122beb",
   "metadata": {},
   "outputs": [],
   "source": [
    "avg = (data['math score']+data['reading score']+data['writing score'])/3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "40119b9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Average'] = avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c86fd18d",
   "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",
       "      <th>Average</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",
       "      <td>72.666667</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",
       "      <td>82.333333</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",
       "      <td>92.666667</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",
       "      <td>49.333333</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",
       "      <td>76.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>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",
       "      <td>94.000000</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",
       "      <td>57.333333</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",
       "      <td>65.000000</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",
       "      <td>74.333333</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",
       "      <td>83.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 9 columns</p>\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",
       "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",
       "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   \n",
       "..                      ...         ...            ...            ...   \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   \n",
       "\n",
       "       Average  \n",
       "0    72.666667  \n",
       "1    82.333333  \n",
       "2    92.666667  \n",
       "3    49.333333  \n",
       "4    76.333333  \n",
       "..         ...  \n",
       "995  94.000000  \n",
       "996  57.333333  \n",
       "997  65.000000  \n",
       "998  74.333333  \n",
       "999  83.000000  \n",
       "\n",
       "[1000 rows x 9 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "37f05fec",
   "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",
       "      <th>Average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>63.633205</td>\n",
       "      <td>72.608108</td>\n",
       "      <td>72.467181</td>\n",
       "      <td>69.569498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>68.728216</td>\n",
       "      <td>65.473029</td>\n",
       "      <td>63.311203</td>\n",
       "      <td>65.837483</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        math score  reading score  writing score    Average\n",
       "gender                                                     \n",
       "female   63.633205      72.608108      72.467181  69.569498\n",
       "male     68.728216      65.473029      63.311203  65.837483"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('gender').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7a4a1ce4",
   "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>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",
       "      <th>Average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>518</td>\n",
       "      <td>518</td>\n",
       "      <td>518</td>\n",
       "      <td>518</td>\n",
       "      <td>518</td>\n",
       "      <td>518</td>\n",
       "      <td>518</td>\n",
       "      <td>518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>482</td>\n",
       "      <td>482</td>\n",
       "      <td>482</td>\n",
       "      <td>482</td>\n",
       "      <td>482</td>\n",
       "      <td>482</td>\n",
       "      <td>482</td>\n",
       "      <td>482</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        race/ethnicity  parental level of education  lunch  \\\n",
       "gender                                                       \n",
       "female             518                          518    518   \n",
       "male               482                          482    482   \n",
       "\n",
       "        test preparation course  math score  reading score  writing score  \\\n",
       "gender                                                                      \n",
       "female                      518         518            518            518   \n",
       "male                        482         482            482            482   \n",
       "\n",
       "        Average  \n",
       "gender           \n",
       "female      518  \n",
       "male        482  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('gender').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fccd207c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(data['math score']<30).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "275f1695",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'p Value\\nif p > 0.05 then Data will be normal distributed'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''p Value\n",
    "if p > 0.05 then Data will be normal distributed'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1933d616",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_num = data[num_col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "cb3c29ee",
   "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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_num.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4674bec1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import normaltest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c7c07a4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04508029386993784"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normaltest(data_num['math score'])[1]*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0eebd86f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='math score', ylabel='Density'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data['math score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "41daeb77",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data is Left Skewed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "62f3a189",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='reading score', ylabel='Density'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data['reading score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "34df385a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Outlier\n",
    "\n",
    "sns.boxplot(data=data['reading score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d57f25b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAMvElEQVR4nO3dX4hc53mA8efVbBPLDabSaiXktakcVtRNCyVlCWmDS6kkKtxS+cagQOqlGHSTrral0CpXvgr4opTKghZEknZNQ4LrBiQXo1be1tSG4mYVG/xHTrQ4ji1pI23W5A+RarPS24s9brfKzGpmzkpn5uP5gTlzzsysXoz17MfxnDmRmUiSyrKp6QEkSRvPuEtSgYy7JBXIuEtSgYy7JBVopOkBALZt25a7du1qegxJGipnzpz5QWaOtXtuIOK+a9cu5ufnmx5DkoZKRHyv03OelpGkAhl3SSqQcZekAhl3SSqQcZekAt007hHxlYi4HBGvrTm2NSJOR8S5artlzXNfiIiFiPh2RPzurRpcktRZNyv3vwf233DsCDCXmbuBuWqfiPgEcBD4leo9fxMRrQ2bVpLUlZt+zj0z/yMidt1w+ADw29XjWeB54C+q41/PzPeB70bEAvAp4D83aF7dBseOHWNhYaHpMQbChQsXABgfH294ksEwMTHB9PR002OoC/2ec9+RmYsA1XZ7dXwceHfN685Xx35GRByKiPmImF9aWupzDOnWunr1KlevXm16DKlnG32FarQ51vZuIJl5HDgOMDk56R1DBogrs/8zMzMDwNGjRxueROpNvyv3SxGxE6DaXq6OnwfuXfO6e4CL/Y8nSepHv3E/CUxVj6eAE2uOH4yIj0bEfcBu4L/qjShJ6tVNT8tExNdY/Z+n2yLiPPAY8DjwVEQ8CrwDPAyQma9HxFPAG8AK8PnMvHaLZpckddDNp2U+2+GpPR1e/0Xgi3WGkiTV4xWqklQg4y5JBTLuklQg4y5JBTLuklQg4y5JBTLuklQg4y5JBTLuklQg4y5JBTLuklQg4y5JBTLuklQg4y5JBTLuklQg4y5JBTLuklQg4y5JBTLuklQg4y5JBTLuklQg4y5JBTLuklQg4y5JBTLuklQg4y5JBTLuklQg4y5JBTLuklQg4y5JBTLuklQg4y5JBTLuklQg4y5JBTLuklSgWnGPiD+NiNcj4rWI+FpE3BERWyPidEScq7ZbNmpYSVJ3+o57RIwDh4HJzPxVoAUcBI4Ac5m5G5ir9iVJt1Hd0zIjwOaIGAHuBC4CB4DZ6vlZ4KGaf4YkqUd9xz0zLwB/CbwDLAI/ysx/BXZk5mL1mkVge7v3R8ShiJiPiPmlpaV+x5AktTHS7xurc+kHgPuAHwL/GBGf6/b9mXkcOA4wOTmZ/c6xUY4dO8bCwkLTY2jAfPjfxMzMTMOTaNBMTEwwPT3d9Bgd9R13YC/w3cxcAoiIbwC/CVyKiJ2ZuRgRO4HLGzDnLbewsMArr53l2p1bmx5FA2TTB6vrjjNvXWp4Eg2S1pX3mh7hpurE/R3g0xFxJ3AV2APMAz8FpoDHq+2JukPeLtfu3MrV+x9segxJA27zm882PcJN9R33zHwpIp4GvgWsAC+zeprlY8BTEfEoq78AHt6IQSVJ3auzciczHwMeu+Hw+6yu4iVJDfEKVUkqkHGXpAIZd0kqkHGXpAIZd0kqkHGXpAIZd0kqkHGXpAIZd0kqkHGXpAIZd0kqkHGXpAIZd0kqkHGXpAIZd0kqkHGXpAIZd0kqUK07MZXkwoULtK78aCjujSipWa0ry1y4sNL0GOty5S5JBXLlXhkfH+f7749w9f4Hmx5F0oDb/OazjI/vaHqMdblyl6QCGXdJKpBxl6QCGXdJKpBxl6QCGXdJKpBxl6QCGXdJKpBxl6QCGXdJKpBxl6QCGXdJKpBxl6QCGXdJKpBxl6QC1Yp7RPxCRDwdEW9GxNmI+I2I2BoRpyPiXLXdslHDSpK6U3flfhQ4lZn3A78GnAWOAHOZuRuYq/YlSbdR33GPiLuA3wK+DJCZH2TmD4EDwGz1slngoXojSpJ6VWfl/nFgCfi7iHg5Ir4UET8P7MjMRYBqu73dmyPiUETMR8T80tJSjTEkSTeqE/cR4NeBv83MTwI/pYdTMJl5PDMnM3NybGysxhiSpBvVift54HxmvlTtP81q7C9FxE6Aanu53oiSpF71HffM/D7wbkT8UnVoD/AGcBKYqo5NASdqTShJ6tlIzfdPA1+NiI8AbwF/xOovjKci4lHgHeDhmn/GbdO68h6b33y26TE0QDb9948BuH7HXQ1PokHSuvIesKPpMdZVK+6Z+Qow2eapPXV+bhMmJiaaHkEDaGHhJwBMfHyw/yLrdtsx8M2ou3IvxvT0dNMjaADNzMwAcPTo0YYnkXrj1w9IUoGMuyQVyLhLUoGMuyQVyLhLUoGMuyQVyLhLUoGMuyQVyLhLUoGMuyQVyLhLUoGMuyQVyLhLUoGMuyQVyLhLUoGMuyQVyLhLUoGMuyQVyLhLUoGMuyQVyLhLUoGMuyQVyLhLUoGMuyQVyLhLUoGMuyQVyLhLUoGMuyQVyLhLUoGMuyQVyLhLUoGMuyQVyLhLUoGMuyQVqHbcI6IVES9HxD9X+1sj4nREnKu2W+qPKUnqxUas3GeAs2v2jwBzmbkbmKv2JUm3Ua24R8Q9wO8BX1pz+AAwWz2eBR6q82dIknpXd+X+18CfA9fXHNuRmYsA1XZ7uzdGxKGImI+I+aWlpZpjSJLW6jvuEfH7wOXMPNPP+zPzeGZOZubk2NhYv2NIktoYqfHezwB/EBEPAncAd0XEPwCXImJnZi5GxE7g8kYMKknqXt8r98z8Qmbek5m7gIPAv2Xm54CTwFT1singRO0pJUk9uRWfc38c2BcR54B91b4k6Taqc1rmf2Xm88Dz1eNlYM9G/FxJUn+8QlWSCmTcJalAxl2SCmTcJalAxl2SCmTcJalAxl2SCmTcJalAxl2SCmTcJalAxl2SCmTcJalAxl2SCmTcJalAxl2SCmTcJalAxl2SCmTcJalAxl2SCmTcJalAxl2SCmTcJalAI00PoMFz7NgxFhYWmh5jIHz472FmZqbhSQbDxMQE09PTTY+hLhh3aR2bN29uegSpL8ZdP8OVmTT8POcuSQUy7pJUIOMurWN5eZnDhw+zvLzc9ChST4y7tI7Z2VleffVVnnzyyaZHkXpi3KUOlpeXOXXqFJnJqVOnXL1rqBh3qYPZ2VmuX78OwLVr11y9a6gYd6mD5557jpWVFQBWVlY4ffp0wxNJ3TPuUgcPPPDAuvvSIDPuUgeZ2fQIUt+Mu9TBiy+++P/2X3jhhYYmkXrXd9wj4t6I+PeIOBsRr0fETHV8a0Scjohz1XbLxo0r3T579+6l1WoB0Gq12LdvX8MTSd2rs3JfAf4sM38Z+DTw+Yj4BHAEmMvM3cBctS8NnampKTZtWv0r0mq1eOSRRxqeSOpe33HPzMXM/Fb1+CfAWWAcOADMVi+bBR6qOaPUiNHRUcbHxwG4++67GR0dbXgiqXsbcs49InYBnwReAnZk5iKs/gIAtnd4z6GImI+I+aWlpY0YQ9pQy8vLXLx4EYCLFy96EZOGSu24R8THgH8C/iQzf9zt+zLzeGZOZubk2NhY3TGkDbf2Iqbr1697EZOGSq24R8TPsRr2r2bmN6rDlyJiZ/X8TuByvRGlZngRk4ZZnU/LBPBl4Gxm/tWap04CU9XjKeBE/+NJzdm7dy8jI6v3sxkZGfHTMhoqdVbunwH+EPidiHil+udB4HFgX0ScA/ZV+9LQ8dMyGmZ932YvM18EosPTe/r9udKgGB0dZf/+/TzzzDPs37/fT8toqHgPVWkdU1NTvP32267aNXT8+gFJKpBxl9bhnZg0rIy71IF3YtIwM+5SB96JScPMuEsdeBGThplxlzrwIiYNM+MudeBFTBpmxl3q4MOLmCLCi5g0dLyISVqHFzFpWBl3aR2jo6M88cQTTY8h9czTMpJUIOMurWN5eZnDhw97AZOGjnGX1uHXD2hYGXepA79+QMPMuEsd+PUDGmbGXerArx/QMDPuUgd+/YCGmXGXOvDrBzTMjLvUgV8/oGHmFarSOvz6AQ0r4y6tw68f0LDytIwkFci4S1KBjLskFci4S1KBIjObnoGIWAK+1/QcUgfbgB80PYTUxi9m5li7JwYi7tIgi4j5zJxseg6pF56WkaQCGXdJKpBxl27ueNMDSL3ynLskFciVuyQVyLhLUoGMu9RBROyPiG9HxEJEHGl6HqkXnnOX2oiIFvAdYB9wHvgm8NnMfKPRwaQuuXKX2vsUsJCZb2XmB8DXgQMNzyR1zbhL7Y0D767ZP18dk4aCcZfaizbHPIepoWHcpfbOA/eu2b8HuNjQLFLPjLvU3jeB3RFxX0R8BDgInGx4Jqlr3kNVaiMzVyLij4F/AVrAVzLz9YbHkrrmRyElqUCelpGkAhl3SSqQcZekAhl3SSqQcZekAhl3SSqQcZekAv0PDCwbrxQGyjcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=data['writing score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a6412ec6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=data['math score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "9f669cd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['math score'].quantile(0.10) # First Quartile(Q1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f5d3433f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['math score'].quantile(0.90) # Third Quantile (Q3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e65692ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['math score'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6d0d4412",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['math score'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "16c8395b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 72,  69,  90,  47,  76,  71,  88,  40,  64,  38,  58,  65,  78,\n",
       "        50,  18,  46,  54,  66,  44,  74,  73,  67,  70,  62,  63,  56,\n",
       "        97,  81,  75,  57,  55,  53,  59,  82,  77,  33,  52,   0,  79,\n",
       "        39,  45,  60,  61,  41,  49,  30,  80,  42,  27,  43,  68,  85,\n",
       "        98,  87,  51,  99,  84,  91,  83,  89,  22, 100,  96,  94,  48,\n",
       "        35,  34,  86,  92,  37,  28,  24,  26,  95,  36,  29,  32,  93,\n",
       "        19,  23,   8], dtype=int64)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['math score'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "7dd7a804",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100.0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['math score'].quantile(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "eedf62e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "q1 = data['math score'].quantile(0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "bd3d149c",
   "metadata": {},
   "outputs": [],
   "source": [
    "q3 = data['math score'].quantile(0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "3ef5f87b",
   "metadata": {},
   "outputs": [],
   "source": [
    "iqr = q3-q1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "319abb0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20.0"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iqr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "52a547b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "upper_limit = q3+1.5*iqr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "f3d8316d",
   "metadata": {},
   "outputs": [],
   "source": [
    "lower_limit = q1 - 1.5*iqr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "b54efcee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "107.0"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "upper_limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "cbfe7e0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27.0"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower_limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "f5661a7d",
   "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",
       "      <th>Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>some high school</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>none</td>\n",
       "      <td>18</td>\n",
       "      <td>32</td>\n",
       "      <td>28</td>\n",
       "      <td>26.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>female</td>\n",
       "      <td>group C</td>\n",
       "      <td>some high school</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>none</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>female</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>none</td>\n",
       "      <td>22</td>\n",
       "      <td>39</td>\n",
       "      <td>33</td>\n",
       "      <td>31.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>some high school</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>none</td>\n",
       "      <td>24</td>\n",
       "      <td>38</td>\n",
       "      <td>27</td>\n",
       "      <td>29.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</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>26</td>\n",
       "      <td>31</td>\n",
       "      <td>38</td>\n",
       "      <td>31.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>787</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>19</td>\n",
       "      <td>38</td>\n",
       "      <td>32</td>\n",
       "      <td>29.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>842</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>high school</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>completed</td>\n",
       "      <td>23</td>\n",
       "      <td>44</td>\n",
       "      <td>36</td>\n",
       "      <td>34.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>high school</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>none</td>\n",
       "      <td>8</td>\n",
       "      <td>24</td>\n",
       "      <td>23</td>\n",
       "      <td>18.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     gender race/ethnicity parental level of education         lunch  \\\n",
       "17   female        group B            some high school  free/reduced   \n",
       "59   female        group C            some high school  free/reduced   \n",
       "145  female        group C                some college  free/reduced   \n",
       "338  female        group B            some high school  free/reduced   \n",
       "466  female        group D          associate's degree  free/reduced   \n",
       "787  female        group B                some college      standard   \n",
       "842  female        group B                 high school  free/reduced   \n",
       "980  female        group B                 high school  free/reduced   \n",
       "\n",
       "    test preparation course  math score  reading score  writing score  \\\n",
       "17                     none          18             32             28   \n",
       "59                     none           0             17             10   \n",
       "145                    none          22             39             33   \n",
       "338                    none          24             38             27   \n",
       "466                    none          26             31             38   \n",
       "787                    none          19             38             32   \n",
       "842               completed          23             44             36   \n",
       "980                    none           8             24             23   \n",
       "\n",
       "       Average  \n",
       "17   26.000000  \n",
       "59    9.000000  \n",
       "145  31.333333  \n",
       "338  29.666667  \n",
       "466  31.666667  \n",
       "787  29.666667  \n",
       "842  34.333333  \n",
       "980  18.333333  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['math score']<lower_limit]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "df227f09",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can remove Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "9dbe6a3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['math score', 'reading score', 'writing score'], dtype='object')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_num.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "dca3c786",
   "metadata": {},
   "outputs": [],
   "source": [
    "def outlier_threshold(df,variable):\n",
    "    q1=df[variable].quantile(0.25)\n",
    "    q3=df[variable].quantile(0.75)\n",
    "    iqr=q3-q1\n",
    "    upper_limit=q3+1.5*iqr\n",
    "    lower_limit=q1-1.5*iqr\n",
    "    return lower_limit,upper_limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "a3e811d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def replace_with_threshold(data,numeric_col):\n",
    "    for variable in numeric_col:\n",
    "        lower_limit,upper_limit=outlier_threshold(data_num,variable)\n",
    "        data.loc[data[variable]<lower_limit,variable]=lower_limit\n",
    "        data.loc[data[variable]>upper_limit,variable]=upper_limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "25a10fa2",
   "metadata": {},
   "outputs": [],
   "source": [
    "replace_with_threshold(data_num,data_num.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "efb673aa",
   "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",
       "      <th>Average</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",
       "      <td>72.666667</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",
       "      <td>82.333333</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",
       "      <td>92.666667</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",
       "      <td>49.333333</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",
       "      <td>76.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>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",
       "      <td>94.000000</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",
       "      <td>57.333333</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",
       "      <td>65.000000</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",
       "      <td>74.333333</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",
       "      <td>83.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 9 columns</p>\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",
       "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",
       "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   \n",
       "..                      ...         ...            ...            ...   \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   \n",
       "\n",
       "       Average  \n",
       "0    72.666667  \n",
       "1    82.333333  \n",
       "2    92.666667  \n",
       "3    49.333333  \n",
       "4    76.333333  \n",
       "..         ...  \n",
       "995  94.000000  \n",
       "996  57.333333  \n",
       "997  65.000000  \n",
       "998  74.333333  \n",
       "999  83.000000  \n",
       "\n",
       "[1000 rows x 9 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "cd0f4afb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "math score       27.000\n",
       "reading score    29.000\n",
       "writing score    25.875\n",
       "dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_num.min()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7ca413f",
   "metadata": {},
   "source": [
    "## Graph Analysis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "8f025f7d",
   "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",
       "      <th>Average</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",
       "      <td>72.666667</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",
       "      <td>82.333333</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",
       "      <td>92.666667</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",
       "      <td>49.333333</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",
       "      <td>76.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>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",
       "      <td>94.000000</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",
       "      <td>57.333333</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",
       "      <td>65.000000</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",
       "      <td>74.333333</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",
       "      <td>83.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 9 columns</p>\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",
       "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",
       "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   \n",
       "..                      ...         ...            ...            ...   \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   \n",
       "\n",
       "       Average  \n",
       "0    72.666667  \n",
       "1    82.333333  \n",
       "2    92.666667  \n",
       "3    49.333333  \n",
       "4    76.333333  \n",
       "..         ...  \n",
       "995  94.000000  \n",
       "996  57.333333  \n",
       "997  65.000000  \n",
       "998  74.333333  \n",
       "999  83.000000  \n",
       "\n",
       "[1000 rows x 9 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "a535fe4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Univariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "f7d1df32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='gender', ylabel='count'>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data['gender'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "b50942d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='race/ethnicity', ylabel='count'>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data['race/ethnicity'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "2e282986",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=data.groupby('gender').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "9b8e150c",
   "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",
       "      <th>Average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>63.633205</td>\n",
       "      <td>72.608108</td>\n",
       "      <td>72.467181</td>\n",
       "      <td>69.569498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>68.728216</td>\n",
       "      <td>65.473029</td>\n",
       "      <td>63.311203</td>\n",
       "      <td>65.837483</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        math score  reading score  writing score    Average\n",
       "gender                                                     \n",
       "female   63.633205      72.608108      72.467181  69.569498\n",
       "male     68.728216      65.473029      63.311203  65.837483"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "aa91dcf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender\n",
       "female    69.569498\n",
       "male      65.837483\n",
       "Name: Average, dtype: float64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Average']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "519ea161",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "69.56949806949807"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Average'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "0b4a099e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "63.633204633204635"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['math score'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "68fa6c03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "X=['Total avg','math avg']\n",
    "female_score = df['Average'][0],df['math score'][0]\n",
    "male_score = df['Average'][1],df['math score'][1]\n",
    "X_axis = np.arange(len(X))\n",
    "plt.bar(X_axis-0.2,male_score,0.4,label='male')\n",
    "plt.bar(X_axis+0.2,female_score,0.4,label='female')\n",
    "\n",
    "plt.xticks(X_axis,X)\n",
    "plt.ylabel(\"Marks\")\n",
    "plt.title(\"Total Avg Vs Math Avg\", fontweight='bold')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "80c69ae1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data_num.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "263ba715",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data_num.corr(), annot=True,cmap='icefire',linewidths=0.3)\n",
    "fig=plt.gcf()\n",
    "fig.set_size_inches(15,10)\n",
    "plt.title('Corr Btw Variable',color='black',size=25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "3447d47a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x237d0505910>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 540x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "43f98446",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(data=data_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4eec43b2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}