DA_Python_Proj.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "fc738f49",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "175a41d6",
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Gender</th>\n",
       "      <th>EthnicGroup</th>\n",
       "      <th>ParentEduc</th>\n",
       "      <th>LunchType</th>\n",
       "      <th>TestPrep</th>\n",
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       "      <th>IsFirstChild</th>\n",
       "      <th>NrSiblings</th>\n",
       "      <th>TransportMeans</th>\n",
       "      <th>WklyStudyHours</th>\n",
       "      <th>MathScore</th>\n",
       "      <th>ReadingScore</th>\n",
       "      <th>WritingScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>married</td>\n",
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       "      <td>school_bus</td>\n",
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       "      <td>71</td>\n",
       "      <td>74</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>NaN</td>\n",
       "      <td>married</td>\n",
       "      <td>sometimes</td>\n",
       "      <td>yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5 - 10</td>\n",
       "      <td>69</td>\n",
       "      <td>90</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\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>single</td>\n",
       "      <td>sometimes</td>\n",
       "      <td>yes</td>\n",
       "      <td>4.0</td>\n",
       "      <td>school_bus</td>\n",
       "      <td>&lt; 5</td>\n",
       "      <td>87</td>\n",
       "      <td>93</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\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>married</td>\n",
       "      <td>never</td>\n",
       "      <td>no</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5 - 10</td>\n",
       "      <td>45</td>\n",
       "      <td>56</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>male</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>married</td>\n",
       "      <td>sometimes</td>\n",
       "      <td>yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>school_bus</td>\n",
       "      <td>5 - 10</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",
       "      <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>30636</th>\n",
       "      <td>816</td>\n",
       "      <td>female</td>\n",
       "      <td>group D</td>\n",
       "      <td>high school</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>single</td>\n",
       "      <td>sometimes</td>\n",
       "      <td>no</td>\n",
       "      <td>2.0</td>\n",
       "      <td>school_bus</td>\n",
       "      <td>5 - 10</td>\n",
       "      <td>59</td>\n",
       "      <td>61</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30637</th>\n",
       "      <td>890</td>\n",
       "      <td>male</td>\n",
       "      <td>group E</td>\n",
       "      <td>high school</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>single</td>\n",
       "      <td>regularly</td>\n",
       "      <td>no</td>\n",
       "      <td>1.0</td>\n",
       "      <td>private</td>\n",
       "      <td>5 - 10</td>\n",
       "      <td>58</td>\n",
       "      <td>53</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30638</th>\n",
       "      <td>911</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>high school</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>completed</td>\n",
       "      <td>married</td>\n",
       "      <td>sometimes</td>\n",
       "      <td>no</td>\n",
       "      <td>1.0</td>\n",
       "      <td>private</td>\n",
       "      <td>5 - 10</td>\n",
       "      <td>61</td>\n",
       "      <td>70</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30639</th>\n",
       "      <td>934</td>\n",
       "      <td>female</td>\n",
       "      <td>group D</td>\n",
       "      <td>associate's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>completed</td>\n",
       "      <td>married</td>\n",
       "      <td>regularly</td>\n",
       "      <td>no</td>\n",
       "      <td>3.0</td>\n",
       "      <td>school_bus</td>\n",
       "      <td>5 - 10</td>\n",
       "      <td>82</td>\n",
       "      <td>90</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30640</th>\n",
       "      <td>960</td>\n",
       "      <td>male</td>\n",
       "      <td>group B</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>married</td>\n",
       "      <td>never</td>\n",
       "      <td>no</td>\n",
       "      <td>1.0</td>\n",
       "      <td>school_bus</td>\n",
       "      <td>5 - 10</td>\n",
       "      <td>64</td>\n",
       "      <td>60</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30641 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  Gender EthnicGroup          ParentEduc     LunchType  \\\n",
       "0               0  female         NaN   bachelor's degree      standard   \n",
       "1               1  female     group C        some college      standard   \n",
       "2               2  female     group B     master's degree      standard   \n",
       "3               3    male     group A  associate's degree  free/reduced   \n",
       "4               4    male     group C        some college      standard   \n",
       "...           ...     ...         ...                 ...           ...   \n",
       "30636         816  female     group D         high school      standard   \n",
       "30637         890    male     group E         high school      standard   \n",
       "30638         911  female         NaN         high school  free/reduced   \n",
       "30639         934  female     group D  associate's degree      standard   \n",
       "30640         960    male     group B        some college      standard   \n",
       "\n",
       "        TestPrep ParentMaritalStatus PracticeSport IsFirstChild  NrSiblings  \\\n",
       "0           none             married     regularly          yes         3.0   \n",
       "1            NaN             married     sometimes          yes         0.0   \n",
       "2           none              single     sometimes          yes         4.0   \n",
       "3           none             married         never           no         1.0   \n",
       "4           none             married     sometimes          yes         0.0   \n",
       "...          ...                 ...           ...          ...         ...   \n",
       "30636       none              single     sometimes           no         2.0   \n",
       "30637       none              single     regularly           no         1.0   \n",
       "30638  completed             married     sometimes           no         1.0   \n",
       "30639  completed             married     regularly           no         3.0   \n",
       "30640       none             married         never           no         1.0   \n",
       "\n",
       "      TransportMeans WklyStudyHours  MathScore  ReadingScore  WritingScore  \n",
       "0         school_bus            < 5         71            71            74  \n",
       "1                NaN         5 - 10         69            90            88  \n",
       "2         school_bus            < 5         87            93            91  \n",
       "3                NaN         5 - 10         45            56            42  \n",
       "4         school_bus         5 - 10         76            78            75  \n",
       "...              ...            ...        ...           ...           ...  \n",
       "30636     school_bus         5 - 10         59            61            65  \n",
       "30637        private         5 - 10         58            53            51  \n",
       "30638        private         5 - 10         61            70            67  \n",
       "30639     school_bus         5 - 10         82            90            93  \n",
       "30640     school_bus         5 - 10         64            60            58  \n",
       "\n",
       "[30641 rows x 15 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv(r\"G:\\Expanded_data_with_more_features.csv\")\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "ea73104f",
   "metadata": {},
   "outputs": [
    {
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>group C</td>\n",
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       "      <td>2</td>\n",
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       "      <td>group B</td>\n",
       "      <td>master's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>single</td>\n",
       "      <td>sometimes</td>\n",
       "      <td>yes</td>\n",
       "      <td>4.0</td>\n",
       "      <td>school_bus</td>\n",
       "      <td>&lt; 5</td>\n",
       "      <td>87</td>\n",
       "      <td>93</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\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>married</td>\n",
       "      <td>never</td>\n",
       "      <td>no</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5 - 10</td>\n",
       "      <td>45</td>\n",
       "      <td>56</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>male</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>married</td>\n",
       "      <td>sometimes</td>\n",
       "      <td>yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>school_bus</td>\n",
       "      <td>5 - 10</td>\n",
       "      <td>76</td>\n",
       "      <td>78</td>\n",
       "      <td>75</td>\n",
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      ],
      "text/plain": [
       "   Unnamed: 0  Gender EthnicGroup          ParentEduc     LunchType TestPrep  \\\n",
       "0           0  female         NaN   bachelor's degree      standard     none   \n",
       "1           1  female     group C        some college      standard      NaN   \n",
       "2           2  female     group B     master's degree      standard     none   \n",
       "3           3    male     group A  associate's degree  free/reduced     none   \n",
       "4           4    male     group C        some college      standard     none   \n",
       "\n",
       "  ParentMaritalStatus PracticeSport IsFirstChild  NrSiblings TransportMeans  \\\n",
       "0             married     regularly          yes         3.0     school_bus   \n",
       "1             married     sometimes          yes         0.0            NaN   \n",
       "2              single     sometimes          yes         4.0     school_bus   \n",
       "3             married         never           no         1.0            NaN   \n",
       "4             married     sometimes          yes         0.0     school_bus   \n",
       "\n",
       "  WklyStudyHours  MathScore  ReadingScore  WritingScore  \n",
       "0            < 5         71            71            74  \n",
       "1         5 - 10         69            90            88  \n",
       "2            < 5         87            93            91  \n",
       "3         5 - 10         45            56            42  \n",
       "4         5 - 10         76            78            75  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "68803106",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>NrSiblings</th>\n",
       "      <th>MathScore</th>\n",
       "      <th>ReadingScore</th>\n",
       "      <th>WritingScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30641.000000</td>\n",
       "      <td>29069.000000</td>\n",
       "      <td>30641.000000</td>\n",
       "      <td>30641.000000</td>\n",
       "      <td>30641.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>499.556607</td>\n",
       "      <td>2.145894</td>\n",
       "      <td>66.558402</td>\n",
       "      <td>69.377533</td>\n",
       "      <td>68.418622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>288.747894</td>\n",
       "      <td>1.458242</td>\n",
       "      <td>15.361616</td>\n",
       "      <td>14.758952</td>\n",
       "      <td>15.443525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>249.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>58.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>500.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>69.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>750.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>79.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>999.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0    NrSiblings     MathScore  ReadingScore  WritingScore\n",
       "count  30641.000000  29069.000000  30641.000000  30641.000000  30641.000000\n",
       "mean     499.556607      2.145894     66.558402     69.377533     68.418622\n",
       "std      288.747894      1.458242     15.361616     14.758952     15.443525\n",
       "min        0.000000      0.000000      0.000000     10.000000      4.000000\n",
       "25%      249.000000      1.000000     56.000000     59.000000     58.000000\n",
       "50%      500.000000      2.000000     67.000000     70.000000     69.000000\n",
       "75%      750.000000      3.000000     78.000000     80.000000     79.000000\n",
       "max      999.000000      7.000000    100.000000    100.000000    100.000000"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "902b67c0",
   "metadata": {},
   "source": [
    "# checking null values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "2ffe3bbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0                0\n",
       "Gender                    0\n",
       "EthnicGroup            1840\n",
       "ParentEduc             1845\n",
       "LunchType                 0\n",
       "TestPrep               1830\n",
       "ParentMaritalStatus    1190\n",
       "PracticeSport           631\n",
       "IsFirstChild            904\n",
       "NrSiblings             1572\n",
       "TransportMeans         3134\n",
       "WklyStudyHours          955\n",
       "MathScore                 0\n",
       "ReadingScore              0\n",
       "WritingScore              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "103b0fbf",
   "metadata": {},
   "source": [
    "# Drop Unnamed column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "b7cc985d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gender EthnicGroup          ParentEduc     LunchType TestPrep  \\\n",
      "0  female         NaN   bachelor's degree      standard     none   \n",
      "1  female     group C        some college      standard      NaN   \n",
      "2  female     group B     master's degree      standard     none   \n",
      "3    male     group A  associate's degree  free/reduced     none   \n",
      "4    male     group C        some college      standard     none   \n",
      "\n",
      "  ParentMaritalStatus PracticeSport IsFirstChild  NrSiblings TransportMeans  \\\n",
      "0             married     regularly          yes         3.0     school_bus   \n",
      "1             married     sometimes          yes         0.0            NaN   \n",
      "2              single     sometimes          yes         4.0     school_bus   \n",
      "3             married         never           no         1.0            NaN   \n",
      "4             married     sometimes          yes         0.0     school_bus   \n",
      "\n",
      "  WklyStudyHours  MathScore  ReadingScore  WritingScore  \n",
      "0            < 5         71            71            74  \n",
      "1         5 - 10         69            90            88  \n",
      "2            < 5         87            93            91  \n",
      "3         5 - 10         45            56            42  \n",
      "4         5 - 10         76            78            75  \n"
     ]
    }
   ],
   "source": [
    "dataset= dataset.drop(\"Unnamed: 0\", axis=1)\n",
    "print(dataset.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "378340fc",
   "metadata": {},
   "source": [
    "# Gender distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "ba7a43f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (5,5))\n",
    "a = sns.countplot(data = dataset, x=\"Gender\")\n",
    "a.bar_label(a.containers[0])\n",
    "plt.title(\"Gender Distribution\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea6364bb",
   "metadata": {},
   "source": [
    "#### From the above chart we get the insight about gender column that the number of females in the data is more that the males"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "ecdd8a53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    MathScore  ReadingScore  WritingScore\n",
      "ParentEduc                                               \n",
      "associate's degree  68.365586     71.124324     70.299099\n",
      "bachelor's degree   70.466627     73.062020     73.331069\n",
      "high school         64.435731     67.213997     65.421136\n",
      "master's degree     72.336134     75.832921     76.356896\n",
      "some college        66.390472     69.179708     68.501432\n",
      "some high school    62.584013     65.510785     63.632409\n"
     ]
    }
   ],
   "source": [
    "gb = dataset.groupby(\"ParentEduc\").agg({\"MathScore\": 'mean', \"ReadingScore\": 'mean', \"WritingScore\": 'mean'})\n",
    "print(gb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "b055af19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (5,5))\n",
    "sns.heatmap(gb, annot = True)\n",
    "plt.title(\"Relationship between Parent's education and Student's score\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "499fdc50",
   "metadata": {},
   "source": [
    "#### We check the relationship between Parents education and students scores and got the result that education of the parents have a good impact on students score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "40bb24eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     MathScore  ReadingScore  WritingScore\n",
      "ParentMaritalStatus                                       \n",
      "divorced             66.691197     69.655011     68.799146\n",
      "married              66.657326     69.389575     68.420981\n",
      "single               66.165704     69.157250     68.174440\n",
      "widowed              67.368866     69.651438     68.563452\n"
     ]
    }
   ],
   "source": [
    "gb1 = dataset.groupby(\"ParentMaritalStatus\").agg({\"MathScore\":'mean', \"ReadingScore\":'mean',\"WritingScore\": 'mean'})\n",
    "print(gb1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "c86fd8fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (5,5))\n",
    "sns.heatmap(gb1, annot = True)\n",
    "plt.title(\"Relation between parent's marital status and student's score\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7594b25c",
   "metadata": {},
   "source": [
    "#### from the above heatmap we have got the conclusion that there is no impact on the student's score due to parent's marital status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "bfb424b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data= dataset, x= \"MathScore\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "7d3921cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data = dataset, x=\"ReadingScore\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "79c57569",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data = dataset, x=\"WritingScore\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "9b438fbd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nan 'group C' 'group B' 'group A' 'group D' 'group E']\n"
     ]
    }
   ],
   "source": [
    "print(dataset[\"EthnicGroup\"].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22a6c504",
   "metadata": {},
   "source": [
    "# Ditribution of ethnic groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "7ac36656",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "groupA = dataset.loc[(dataset[\"EthnicGroup\"] == \"group A\")].count()\n",
    "groupB = dataset.loc[(dataset[\"EthnicGroup\"] == \"group B\")].count()\n",
    "groupC = dataset.loc[(dataset[\"EthnicGroup\"] == \"group C\")].count()\n",
    "groupD = dataset.loc[(dataset[\"EthnicGroup\"] == \"group D\")].count()\n",
    "groupE = dataset.loc[(dataset[\"EthnicGroup\"] == \"group E\")].count()\n",
    "\n",
    "mylist = [groupA[\"EthnicGroup\"], groupB[\"EthnicGroup\"], groupC[\"EthnicGroup\"], groupD[\"EthnicGroup\"], groupE[\"EthnicGroup\"]]\n",
    "\n",
    "l = [\"group A\", \"group B\", \"group C\", \"group D\", \"group E\"]\n",
    "\n",
    "plt.pie(mylist, labels=l, autopct=\"%1.2f%%\")\n",
    "plt.title(\"Distribution of Ethnic groups\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06ac271a",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}