EDA Analysis Demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   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",
      "5  female        group B          associate's degree      standard   \n",
      "6  female        group B                some college      standard   \n",
      "7    male        group B                some college  free/reduced   \n",
      "8    male        group D                 high school  free/reduced   \n",
      "9  female        group B                 high school  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",
      "5                    none          71             83             78  \n",
      "6               completed          88             95             92  \n",
      "7                    none          40             43             39  \n",
      "8               completed          64             64             67  \n",
      "9                    none          38             60             50  \n"
     ]
    }
   ],
   "source": [
    "#load data\n",
    "bd=pd.read_csv('StudentsPerformance.csv',sep=',')\n",
    "#Peek of data\n",
    "print(bd.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Descriptive Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000, 8)\n",
      "gender                         object\n",
      "race/ethnicity                 object\n",
      "parental level of education    object\n",
      "lunch                          object\n",
      "test preparation course        object\n",
      "math score                      int64\n",
      "reading score                   int64\n",
      "writing score                   int64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "#shape of data\n",
    "print(bd.shape)\n",
    "# data type\n",
    "print(bd.dtypes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['gender', 'race/ethnicity', 'parental level of education', 'lunch',\n",
      "       'test preparation course', 'math score', 'reading score',\n",
      "       'writing score'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(bd.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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\n"
     ]
    }
   ],
   "source": [
    "# Check whether there is missing value in any of the column \n",
    "#sum of all null values\n",
    "print(bd.isnull().sum())\n",
    "#there is no missing value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       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\n"
     ]
    }
   ],
   "source": [
    "# Mean,median and mode of data\n",
    "# descriptions\n",
    "print(bd.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grouping data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "parental level of education\n",
      "associate's degree    222\n",
      "bachelor's degree     118\n",
      "high school           196\n",
      "master's degree        59\n",
      "some college          226\n",
      "some high school      179\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# display  the count \n",
    "print(bd.groupby('parental level of education').size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "race/ethnicity\n",
      "group A     89\n",
      "group B    190\n",
      "group C    319\n",
      "group D    262\n",
      "group E    140\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(bd.groupby('race/ethnicity').size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender\n",
      "female    518\n",
      "male      482\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(bd.groupby('gender').size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lunch\n",
      "free/reduced    355\n",
      "standard        645\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(bd.groupby('lunch').size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test preparation course\n",
      "completed    358\n",
      "none         642\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(bd.groupby('test preparation course').size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">math score</th>\n",
       "      <th colspan=\"4\" halign=\"left\">reading score</th>\n",
       "      <th colspan=\"4\" halign=\"left\">writing score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>median</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>median</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>median</th>\n",
       "      <th>max</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",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>63.633205</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "      <td>100</td>\n",
       "      <td>72.608108</td>\n",
       "      <td>17</td>\n",
       "      <td>73</td>\n",
       "      <td>100</td>\n",
       "      <td>72.467181</td>\n",
       "      <td>10</td>\n",
       "      <td>74</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>68.728216</td>\n",
       "      <td>27</td>\n",
       "      <td>69</td>\n",
       "      <td>100</td>\n",
       "      <td>65.473029</td>\n",
       "      <td>23</td>\n",
       "      <td>66</td>\n",
       "      <td>100</td>\n",
       "      <td>63.311203</td>\n",
       "      <td>15</td>\n",
       "      <td>64</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       math score                 reading score                 writing score  \\\n",
       "             mean min median  max          mean min median  max          mean   \n",
       "gender                                                                          \n",
       "female  63.633205   0     65  100     72.608108  17     73  100     72.467181   \n",
       "male    68.728216  27     69  100     65.473029  23     66  100     63.311203   \n",
       "\n",
       "                        \n",
       "       min median  max  \n",
       "gender                  \n",
       "female  10     74  100  \n",
       "male    15     64  100  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Effect of gender on test performance\n",
    "bd.groupby(['gender']).agg(['mean','min','median','max'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Average writing and reading score of female students are higher than male students while average math score of male students are higher than female students.Minimum marks scored by girls is 0 in maths while minimum marks scored by boys is 27 in maths."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">math score</th>\n",
       "      <th colspan=\"4\" halign=\"left\">reading score</th>\n",
       "      <th colspan=\"4\" halign=\"left\">writing score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>median</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>median</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>median</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lunch</th>\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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">free/reduced</th>\n",
       "      <th>female</th>\n",
       "      <td>55.814815</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>93</td>\n",
       "      <td>67.386243</td>\n",
       "      <td>17</td>\n",
       "      <td>67.0</td>\n",
       "      <td>100</td>\n",
       "      <td>66.444444</td>\n",
       "      <td>10</td>\n",
       "      <td>68</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>62.457831</td>\n",
       "      <td>27</td>\n",
       "      <td>62</td>\n",
       "      <td>100</td>\n",
       "      <td>61.542169</td>\n",
       "      <td>23</td>\n",
       "      <td>61.0</td>\n",
       "      <td>100</td>\n",
       "      <td>59.126506</td>\n",
       "      <td>15</td>\n",
       "      <td>59</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">standard</th>\n",
       "      <th>female</th>\n",
       "      <td>68.124620</td>\n",
       "      <td>19</td>\n",
       "      <td>67</td>\n",
       "      <td>100</td>\n",
       "      <td>75.607903</td>\n",
       "      <td>29</td>\n",
       "      <td>75.0</td>\n",
       "      <td>100</td>\n",
       "      <td>75.927052</td>\n",
       "      <td>30</td>\n",
       "      <td>76</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>72.022152</td>\n",
       "      <td>30</td>\n",
       "      <td>72</td>\n",
       "      <td>100</td>\n",
       "      <td>67.537975</td>\n",
       "      <td>26</td>\n",
       "      <td>67.5</td>\n",
       "      <td>100</td>\n",
       "      <td>65.509494</td>\n",
       "      <td>22</td>\n",
       "      <td>67</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    math score                 reading score                  \\\n",
       "                          mean min median  max          mean min median  max   \n",
       "lunch        gender                                                            \n",
       "free/reduced female  55.814815   0     57   93     67.386243  17   67.0  100   \n",
       "             male    62.457831  27     62  100     61.542169  23   61.0  100   \n",
       "standard     female  68.124620  19     67  100     75.607903  29   75.0  100   \n",
       "             male    72.022152  30     72  100     67.537975  26   67.5  100   \n",
       "\n",
       "                    writing score                  \n",
       "                             mean min median  max  \n",
       "lunch        gender                                \n",
       "free/reduced female     66.444444  10     68  100  \n",
       "             male       59.126506  15     59   93  \n",
       "standard     female     75.927052  30     76  100  \n",
       "             male       65.509494  22     67  100  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Effect of lunch on test performance\n",
    "bd.groupby(['lunch','gender']).agg(['mean','min','median','max'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Students whether male or female having standard lunch have highest average marks in all 3 subjects . Minimum marks of females having free lunch in reading and writing is less than male while Minimum marks of females having standard lunch in reading and writing is more than males. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th colspan=\"4\" halign=\"left\">reading score</th>\n",
       "      <th colspan=\"4\" halign=\"left\">writing score</th>\n",
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       "    <tr>\n",
       "      <th>race/ethnicity</th>\n",
       "      <th>gender</th>\n",
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       "      <th rowspan=\"2\" valign=\"top\">group A</th>\n",
       "      <th>female</th>\n",
       "      <td>58.527778</td>\n",
       "      <td>34</td>\n",
       "      <td>57</td>\n",
       "      <td>92</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>43</td>\n",
       "      <td>67.5</td>\n",
       "      <td>100</td>\n",
       "      <td>67.861111</td>\n",
       "      <td>41</td>\n",
       "      <td>67.0</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>63.735849</td>\n",
       "      <td>28</td>\n",
       "      <td>63</td>\n",
       "      <td>100</td>\n",
       "      <td>61.735849</td>\n",
       "      <td>23</td>\n",
       "      <td>61.0</td>\n",
       "      <td>96</td>\n",
       "      <td>59.150943</td>\n",
       "      <td>19</td>\n",
       "      <td>60.0</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">group B</th>\n",
       "      <th>female</th>\n",
       "      <td>61.403846</td>\n",
       "      <td>8</td>\n",
       "      <td>62</td>\n",
       "      <td>97</td>\n",
       "      <td>71.076923</td>\n",
       "      <td>24</td>\n",
       "      <td>71.5</td>\n",
       "      <td>97</td>\n",
       "      <td>70.048077</td>\n",
       "      <td>23</td>\n",
       "      <td>71.5</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>65.930233</td>\n",
       "      <td>30</td>\n",
       "      <td>65</td>\n",
       "      <td>94</td>\n",
       "      <td>62.848837</td>\n",
       "      <td>24</td>\n",
       "      <td>62.0</td>\n",
       "      <td>96</td>\n",
       "      <td>60.220930</td>\n",
       "      <td>15</td>\n",
       "      <td>60.0</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">group C</th>\n",
       "      <th>female</th>\n",
       "      <td>62.033333</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>96</td>\n",
       "      <td>71.944444</td>\n",
       "      <td>17</td>\n",
       "      <td>73.0</td>\n",
       "      <td>100</td>\n",
       "      <td>71.777778</td>\n",
       "      <td>10</td>\n",
       "      <td>74.0</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>67.611511</td>\n",
       "      <td>27</td>\n",
       "      <td>68</td>\n",
       "      <td>98</td>\n",
       "      <td>65.424460</td>\n",
       "      <td>28</td>\n",
       "      <td>66.0</td>\n",
       "      <td>100</td>\n",
       "      <td>62.712230</td>\n",
       "      <td>27</td>\n",
       "      <td>62.0</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">group D</th>\n",
       "      <th>female</th>\n",
       "      <td>65.248062</td>\n",
       "      <td>26</td>\n",
       "      <td>65</td>\n",
       "      <td>98</td>\n",
       "      <td>74.046512</td>\n",
       "      <td>31</td>\n",
       "      <td>74.0</td>\n",
       "      <td>100</td>\n",
       "      <td>75.023256</td>\n",
       "      <td>32</td>\n",
       "      <td>76.0</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>69.413534</td>\n",
       "      <td>39</td>\n",
       "      <td>71</td>\n",
       "      <td>100</td>\n",
       "      <td>66.135338</td>\n",
       "      <td>34</td>\n",
       "      <td>68.0</td>\n",
       "      <td>97</td>\n",
       "      <td>65.413534</td>\n",
       "      <td>34</td>\n",
       "      <td>67.0</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">group E</th>\n",
       "      <th>female</th>\n",
       "      <td>70.811594</td>\n",
       "      <td>32</td>\n",
       "      <td>71</td>\n",
       "      <td>100</td>\n",
       "      <td>75.840580</td>\n",
       "      <td>34</td>\n",
       "      <td>76.0</td>\n",
       "      <td>100</td>\n",
       "      <td>75.536232</td>\n",
       "      <td>38</td>\n",
       "      <td>76.0</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>76.746479</td>\n",
       "      <td>30</td>\n",
       "      <td>78</td>\n",
       "      <td>100</td>\n",
       "      <td>70.295775</td>\n",
       "      <td>26</td>\n",
       "      <td>73.0</td>\n",
       "      <td>100</td>\n",
       "      <td>67.394366</td>\n",
       "      <td>22</td>\n",
       "      <td>70.0</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      math score                 reading score             \\\n",
       "                            mean min median  max          mean min median   \n",
       "race/ethnicity gender                                                       \n",
       "group A        female  58.527778  34     57   92     69.000000  43   67.5   \n",
       "               male    63.735849  28     63  100     61.735849  23   61.0   \n",
       "group B        female  61.403846   8     62   97     71.076923  24   71.5   \n",
       "               male    65.930233  30     65   94     62.848837  24   62.0   \n",
       "group C        female  62.033333   0     63   96     71.944444  17   73.0   \n",
       "               male    67.611511  27     68   98     65.424460  28   66.0   \n",
       "group D        female  65.248062  26     65   98     74.046512  31   74.0   \n",
       "               male    69.413534  39     71  100     66.135338  34   68.0   \n",
       "group E        female  70.811594  32     71  100     75.840580  34   76.0   \n",
       "               male    76.746479  30     78  100     70.295775  26   73.0   \n",
       "\n",
       "                           writing score                  \n",
       "                       max          mean min median  max  \n",
       "race/ethnicity gender                                     \n",
       "group A        female  100     67.861111  41   67.0   97  \n",
       "               male     96     59.150943  19   60.0   92  \n",
       "group B        female   97     70.048077  23   71.5   96  \n",
       "               male     96     60.220930  15   60.0   92  \n",
       "group C        female  100     71.777778  10   74.0  100  \n",
       "               male    100     62.712230  27   62.0   95  \n",
       "group D        female  100     75.023256  32   76.0  100  \n",
       "               male     97     65.413534  34   67.0   99  \n",
       "group E        female  100     75.536232  38   76.0  100  \n",
       "               male    100     67.394366  22   70.0  100  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Effect of race/ethnicity on test performance\n",
    "bd.groupby(['race/ethnicity','gender']).agg(['mean','min','median','max'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Students of group E have highest average marks in all 3 subjects ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>test preparation course</th>\n",
       "      <th>gender</th>\n",
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       "      <th rowspan=\"2\" valign=\"top\">completed</th>\n",
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       "      <td>72.339080</td>\n",
       "      <td>39</td>\n",
       "      <td>73</td>\n",
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       "      <td>37</td>\n",
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       "      <th>female</th>\n",
       "      <td>61.670659</td>\n",
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       "      <td>100</td>\n",
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       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>66.688312</td>\n",
       "      <td>27</td>\n",
       "      <td>67</td>\n",
       "      <td>97</td>\n",
       "      <td>62.795455</td>\n",
       "      <td>23</td>\n",
       "      <td>63</td>\n",
       "      <td>96</td>\n",
       "      <td>59.649351</td>\n",
       "      <td>15</td>\n",
       "      <td>60</td>\n",
       "      <td>92</td>\n",
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      ],
      "text/plain": [
       "                               math score                 reading score      \\\n",
       "                                     mean min median  max          mean min   \n",
       "test preparation course gender                                                \n",
       "completed               female  67.195652  23     67   99     77.375000  40   \n",
       "                        male    72.339080  39     73  100     70.212644  37   \n",
       "none                    female  61.670659   0     62  100     69.982036  17   \n",
       "                        male    66.688312  27     67   97     62.795455  23   \n",
       "\n",
       "                                           writing score                  \n",
       "                               median  max          mean min median  max  \n",
       "test preparation course gender                                            \n",
       "completed               female     78  100     78.793478  36     79  100  \n",
       "                        male       71  100     69.793103  38     70  100  \n",
       "none                    female     71  100     68.982036  10     70  100  \n",
       "                        male       63   96     59.649351  15     60   92  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Effect of test preparation course on test performance\n",
    "bd.groupby(['test preparation course','gender']).agg(['mean','min','median','max'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Students who have completed the test preparation course have more average marks in all 3 subjects than students who don't have  test preparation course"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>parental level of education</th>\n",
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       "      <th rowspan=\"2\" valign=\"top\">high school</th>\n",
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       "      <th rowspan=\"2\" valign=\"top\">master's degree</th>\n",
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       "      <th rowspan=\"2\" valign=\"top\">some college</th>\n",
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       "      <td>65.406780</td>\n",
       "      <td>19</td>\n",
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       "      <td>19</td>\n",
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       "      <td>99</td>\n",
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       "      <th rowspan=\"2\" valign=\"top\">some high school</th>\n",
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       "      <td>59.296703</td>\n",
       "      <td>0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>97</td>\n",
       "      <td>69.109890</td>\n",
       "      <td>17</td>\n",
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       "      <td>100</td>\n",
       "      <td>68.285714</td>\n",
       "      <td>10</td>\n",
       "      <td>72.0</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>67.840909</td>\n",
       "      <td>30</td>\n",
       "      <td>68.0</td>\n",
       "      <td>94</td>\n",
       "      <td>64.693182</td>\n",
       "      <td>26</td>\n",
       "      <td>66.0</td>\n",
       "      <td>88</td>\n",
       "      <td>61.375000</td>\n",
       "      <td>22</td>\n",
       "      <td>61.5</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   math score                 reading score  \\\n",
       "                                         mean min median  max          mean   \n",
       "parental level of education gender                                            \n",
       "associate's degree          female  65.250000  26   65.0  100     74.120690   \n",
       "                            male    70.764151  40   72.0  100     67.433962   \n",
       "bachelor's degree           female  68.349206  29   67.0  100     77.285714   \n",
       "                            male    70.581818  37   70.0  100     68.090909   \n",
       "high school                 female  59.351064   8   61.0   99     68.202128   \n",
       "                            male    64.705882  27   66.0   94     61.480392   \n",
       "master's degree             female  66.500000  40   64.5   94     76.805556   \n",
       "                            male    74.826087  46   79.0   95     73.130435   \n",
       "some college                female  65.406780  19   65.5  100     73.550847   \n",
       "                            male    69.009259  28   69.0  100     64.990741   \n",
       "some high school            female  59.296703   0   63.0   97     69.109890   \n",
       "                            male    67.840909  30   68.0   94     64.693182   \n",
       "\n",
       "                                                   writing score             \\\n",
       "                                   min median  max          mean min median   \n",
       "parental level of education gender                                            \n",
       "associate's degree          female  31   75.5  100     74.000000  38   74.0   \n",
       "                            male    37   68.0  100     65.405660  35   65.5   \n",
       "bachelor's degree           female  41   76.0  100     78.380952  38   80.0   \n",
       "                            male    42   67.0  100     67.654545  38   68.0   \n",
       "high school                 female  24   69.5   99     66.691489  23   68.0   \n",
       "                            male    24   61.5   91     58.539216  15   58.5   \n",
       "master's degree             female  53   74.0  100     77.638889  54   76.5   \n",
       "                            male    42   77.0   89     72.608696  46   74.0   \n",
       "some college                female  38   74.0  100     74.050847  32   74.5   \n",
       "                            male    23   63.5   97     63.148148  19   62.5   \n",
       "some high school            female  17   71.0  100     68.285714  10   72.0   \n",
       "                            male    26   66.0   88     61.375000  22   61.5   \n",
       "\n",
       "                                         \n",
       "                                    max  \n",
       "parental level of education gender       \n",
       "associate's degree          female  100  \n",
       "                            male     95  \n",
       "bachelor's degree           female  100  \n",
       "                            male    100  \n",
       "high school                 female  100  \n",
       "                            male     86  \n",
       "master's degree             female  100  \n",
       "                            male     90  \n",
       "some college                female   99  \n",
       "                            male     99  \n",
       "some high school            female  100  \n",
       "                            male     87  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Effect of parental level of education on test performance\n",
    "bd.groupby(['parental level of education','gender']).agg(['mean','min','median','max'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Girls whose parents have bachelor's degree score the highest average marks in all 3 subjects while boys whose parents have master's degree score the highest average marks in all 3 subjects."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pairwise correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               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\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26087386c08>"
      ]
     },
     "execution_count": 31,
     "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": [
    "correlation = bd.corr()\n",
    "print(correlation)\n",
    "sns.heatmap(correlation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As correlation between different colums are positive i.e with increase/decrease in one column the other one also increases/decreases.As magnitude increases , their dependency on each other increases. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x26087435248>"
      ]
     },
     "execution_count": 32,
     "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(bd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualisation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OUTLIERS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Identifying Outliers with Skewness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.27893514909431694\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    1000.00000\n",
       "mean       66.08900\n",
       "std        15.16308\n",
       "min         0.00000\n",
       "25%        57.00000\n",
       "50%        66.00000\n",
       "75%        77.00000\n",
       "max       100.00000\n",
       "Name: math score, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(bd['math score'].skew())\n",
    "bd['math score'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The skewness value of -0.27 shows that the variable 'math score' has a left-skewed distribution, indicating the presence of extreme lower values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.28944397150761897\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    1000.000000\n",
       "mean       68.054000\n",
       "std        15.195657\n",
       "min        10.000000\n",
       "25%        57.750000\n",
       "50%        69.000000\n",
       "75%        79.000000\n",
       "max       100.000000\n",
       "Name: writing score, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(bd['writing score'].skew())\n",
    "bd['writing score'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The skewness value of -0.289 shows that the variable 'writing score' has a left-skewed distribution, indicating the presence of extreme lower values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.25910451810923063\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    1000.000000\n",
       "mean       69.169000\n",
       "std        14.600192\n",
       "min        17.000000\n",
       "25%        59.000000\n",
       "50%        70.000000\n",
       "75%        79.000000\n",
       "max       100.000000\n",
       "Name: reading score, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(bd['reading score'].skew())\n",
    "bd['reading score'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The skewness value of -0.259 shows that the variable 'reading score' has a left-skewed distribution, indicating the presence of extreme lower values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Identifying Outliers with Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "bd.plot(kind='box', subplots=True,layout=(2,2))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x223d6fbaec8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#comparison of other with math score\n",
    "sns.boxplot(x='math score',y='test preparation course', data=bd)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Students who have not completed test preparation course have more marks that are numerically distant from rest of marks than students who have completed their course."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x223d6f3c688>"
      ]
     },
     "execution_count": 9,
     "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(x='math score',y='gender', data=bd)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Math scores of female students are more scattered as compared to male students."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x223d7157fc8>"
      ]
     },
     "execution_count": 12,
     "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(x='math score',y='race/ethnicity', data=bd)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Group B have more outliers as compared to other groups but Data points of group C are more numerically distant from rest of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x223d71fbd88>"
      ]
     },
     "execution_count": 13,
     "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(x='math score',y='lunch', data=bd)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Students having free/reduced lunch have more scattered math marks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x223d7278ec8>"
      ]
     },
     "execution_count": 14,
     "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(x='math score',y='parental level of education', data=bd)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Students whose parents are from some college have more outliers but data points of students whose parents are from high school and some high school are more distant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x223d7352d48>"
      ]
     },
     "execution_count": 15,
     "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": [
    "#comparison of other with reading score\n",
    "sns.boxplot(x='reading score',y='test preparation course', data=bd)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading score of students who completed their test preparation course have no outliers while Reading score of students who don't have their test preparation course are scattered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x223d728c408>"
      ]
     },
     "execution_count": 16,
     "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(x='reading score',y='gender', data=bd)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading scores of female students are more scattered as compared to male students."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x223d7445648>"
      ]
     },
     "execution_count": 17,
     "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(x='reading score',y='race/ethnicity', data=bd)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Large no of students of group C have  marks which are numerically distant from rest while group A and B have no outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x223d74fc348>"
      ]
     },
     "execution_count": 18,
     "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(x='reading score',y='lunch', data=bd)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Students having standard lunch have more scattered reading marks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x223d7650bc8>"
      ]
     },
     "execution_count": 20,
     "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(x='reading score',y='parental level of education', data=bd)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only students whose parents have degree from some college and some high school have outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x223d86d4848>"
      ]
     },
     "execution_count": 21,
     "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": [
    "#comparison of other with writing score\n",
    "sns.boxplot(x='writing score',y='test preparation course', data=bd)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Writing score of students who don't have test preparation course have more outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x223d8763688>"
      ]
     },
     "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.boxplot(x='writing score',y='gender', data=bd)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Writing scores of female students are more scattered as compared to male students."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x223d87dcb88>"
      ]
     },
     "execution_count": 23,
     "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(x='writing score',y='race/ethnicity', data=bd)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Students of group C and E have more scattered marks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x223d49a9508>"
      ]
     },
     "execution_count": 24,
     "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(x='writing score',y='lunch', data=bd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Students having free/reduced lunch have evenly scattered writing marks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x223d88fcf08>"
      ]
     },
     "execution_count": 25,
     "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(x='writing score',y='parental level of education', data=bd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Students whose parents have degree from some college , high school and some high school have outliers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inferential Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability of Students Scoring more than 50 marks in Maths : 85.0\n"
     ]
    }
   ],
   "source": [
    "#Probability of Students who score more than 50 percent marks in maths\n",
    "total = bd.shape[0]\n",
    "students_score = bd[bd['math score'] > 50].shape[0]\n",
    "\n",
    "probability_of_students = (students_score/total)*100\n",
    "print(\"Probability of Students Scoring more than 50 marks in Maths :\", probability_of_students)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability of Students Scoring more than 50 marks in Reading : 90.3\n"
     ]
    }
   ],
   "source": [
    "#Probability of Students who score more than 50 percent marks in reading\n",
    "total = bd.shape[0]\n",
    "students_score = bd[bd['reading score'] > 50].shape[0]\n",
    "\n",
    "probability_of_students = (students_score/total)*100\n",
    "print(\"Probability of Students Scoring more than 50 marks in Reading :\", probability_of_students)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability of Students Scoring more than 50 marks in Writing : 87.6\n"
     ]
    }
   ],
   "source": [
    "#Probability of Students who score more than 50 percent marks in writing\n",
    "total = bd.shape[0]\n",
    "students_score = bd[bd['writing score'] > 50].shape[0]\n",
    "\n",
    "probability_of_students = (students_score/total)*100\n",
    "print(\"Probability of Students Scoring more than 50 marks in Writing :\", probability_of_students)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability of Students Scoring more than 90 marks in Maths : 5.0\n"
     ]
    }
   ],
   "source": [
    "#Probability of Students who score more than 90 percent marks in maths\n",
    "total = bd.shape[0]\n",
    "students_score = bd[bd['math score'] > 90].shape[0]\n",
    "\n",
    "probability_of_students = (students_score/total)*100\n",
    "print(\"Probability of Students Scoring more than 90 marks in Maths :\", probability_of_students)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability of Students Scoring more than 90 marks in Maths : 6.2\n"
     ]
    }
   ],
   "source": [
    "#Probability of Students who score more than 90 percent marks in reading\n",
    "total = bd.shape[0]\n",
    "students_score = bd[bd['reading score'] > 90].shape[0]\n",
    "\n",
    "probability_of_students = (students_score/total)*100\n",
    "print(\"Probability of Students Scoring more than 90 marks in reading :\", probability_of_students)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability of Students Scoring more than 90 marks in writing : 6.800000000000001\n"
     ]
    }
   ],
   "source": [
    "#Probability of Students who score more than 90 percent marks in writing\n",
    "total = bd.shape[0]\n",
    "students_score = bd[bd['writing score'] > 90].shape[0]\n",
    "\n",
    "probability_of_students = (students_score/total)*100\n",
    "print(\"Probability of Students Scoring more than 90 marks in writing :\", probability_of_students)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability of Students Scoring more than 95 marks in 3 subjects : 1.0\n"
     ]
    }
   ],
   "source": [
    "#Probability of Students who score more than 95 percent marks in three subjects\n",
    "total = bd.shape[0]\n",
    "students_score = bd[(bd['math score'] > 95) &(bd['writing score'] > 95) & (bd['reading score'] >95)].shape[0]\n",
    "\n",
    "probability_of_students = (students_score/total)*100\n",
    "print(\"Probability of Students Scoring more than 95 marks in 3 subjects :\", probability_of_students)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability of Students passing all 3 subjects : 97.7\n"
     ]
    }
   ],
   "source": [
    "#Probability of Students passsing all three subjects\n",
    "total = bd.shape[0]\n",
    "students_score = bd[(bd['math score'] > 33) &(bd['writing score'] > 33) & (bd['reading score'] >33)].shape[0]\n",
    "\n",
    "probability_of_students = (students_score/total)*100\n",
    "print(\"Probability of Students passing all 3 subjects :\", probability_of_students)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability of girls Scoring more than 90 marks in 3 subjects : 1.7000000000000002\n"
     ]
    }
   ],
   "source": [
    "#Probability of girls who score more than 90 percent marks in three subjects\n",
    "total = bd.shape[0]\n",
    "students_score = bd[(bd['gender'] == 'female') &(bd['math score'] > 90) &(bd['writing score'] > 90) & (bd['reading score'] >90)].shape[0]\n",
    "\n",
    "probability_of_students = (students_score/total)*100\n",
    "print(\"Probability of girls Scoring more than 90 marks in 3 subjects :\", probability_of_students)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability of girls passing all the 3 subjects : 50.2\n"
     ]
    }
   ],
   "source": [
    "#Probability of girls passing all the  three subjects\n",
    "total = bd.shape[0]\n",
    "students_score = bd[(bd['gender'] == 'female') &(bd['math score'] > 33) &(bd['writing score'] > 33) & (bd['reading score'] >33)].shape[0]\n",
    "\n",
    "probability_of_students = (students_score/total)*100\n",
    "print(\"Probability of girls passing all the 3 subjects :\", probability_of_students)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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