Student's-performance.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# To Understand The Students Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>race/ethnicity</th>\n",
       "      <th>parental level of education</th>\n",
       "      <th>lunch</th>\n",
       "      <th>test preparation course</th>\n",
       "      <th>math score</th>\n",
       "      <th>reading score</th>\n",
       "      <th>writing score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>completed</td>\n",
       "      <td>69</td>\n",
       "      <td>90</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>master's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>90</td>\n",
       "      <td>95</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>male</td>\n",
       "      <td>group A</td>\n",
       "      <td>associate's degree</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>none</td>\n",
       "      <td>47</td>\n",
       "      <td>57</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>76</td>\n",
       "      <td>78</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender race/ethnicity parental level of education         lunch  \\\n",
       "0  female        group B           bachelor's degree      standard   \n",
       "1  female        group C                some college      standard   \n",
       "2  female        group B             master's degree      standard   \n",
       "3    male        group A          associate's degree  free/reduced   \n",
       "4    male        group C                some college      standard   \n",
       "\n",
       "  test preparation course  math score  reading score  writing score  \n",
       "0                    none          72             72             74  \n",
       "1               completed          69             90             88  \n",
       "2                    none          90             95             93  \n",
       "3                    none          47             57             44  \n",
       "4                    none          76             78             75  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Reading the dataset\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd \n",
    "st_data=pd.read_csv(r\"C:\\Users\\sandeep\\Documents\\StudentsPerformance.csv\")\n",
    "st_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": [
    "st_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categories in 'gender' variable:      ['female' 'male']\n",
      "Categories in 'race/ethnicity' variable:   ['group B' 'group C' 'group A' 'group D' 'group E']\n",
      "Categories in'parental level of education' variable: [\"bachelor's degree\" 'some college' \"master's degree\" \"associate's degree\"\n",
      " 'high school' 'some high school']\n",
      "Categories in 'lunch' variable:      ['standard' 'free/reduced']\n",
      "Categories in 'test preparation course' variable:      ['none' 'completed']\n"
     ]
    }
   ],
   "source": [
    "# Various categories present in the different categorical column\n",
    "\n",
    "\n",
    "print(\"Categories in 'gender' variable:     \",end=\" \" )\n",
    "print(st_data['gender'].unique())\n",
    "\n",
    "print(\"Categories in 'race/ethnicity' variable:  \",end=\" \")\n",
    "print(st_data['race/ethnicity'].unique())\n",
    "\n",
    "print(\"Categories in'parental level of education' variable:\",end=\" \" )\n",
    "print(st_data['parental level of education'].unique())\n",
    "\n",
    "print(\"Categories in 'lunch' variable:     \",end=\" \" )\n",
    "print(st_data['lunch'].unique())\n",
    "\n",
    "print(\"Categories in 'test preparation course' variable:     \",end=\" \" )\n",
    "print(st_data['test preparation course'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>math score</th>\n",
       "      <th>reading score</th>\n",
       "      <th>writing score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.00000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>66.08900</td>\n",
       "      <td>69.169000</td>\n",
       "      <td>68.054000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15.16308</td>\n",
       "      <td>14.600192</td>\n",
       "      <td>15.195657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>57.00000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>57.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>66.00000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>69.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>77.00000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>79.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.00000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       math score  reading score  writing score\n",
       "count  1000.00000    1000.000000    1000.000000\n",
       "mean     66.08900      69.169000      68.054000\n",
       "std      15.16308      14.600192      15.195657\n",
       "min       0.00000      17.000000      10.000000\n",
       "25%      57.00000      59.000000      57.750000\n",
       "50%      66.00000      70.000000      69.000000\n",
       "75%      77.00000      79.000000      79.000000\n",
       "max     100.00000     100.000000     100.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Describing the datasets\n",
    "\n",
    "st_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#data.isnull()\n",
    "st_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of unique item present in categorical columns\n",
    "\n",
    "st_data.select_dtypes('object').nunique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['group B', 'group C', 'group A', 'group D', 'group E'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st_data['race/ethnicity'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Race/Ethnicity ')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import xticks\n",
    "plt.rcParams['figure.figsize'] = (11,6)\n",
    "sns.countplot(x=\"race/ethnicity\",  data = st_data)\n",
    "plt.xlabel('Race/Ethnicity')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Race/Ethnicity ')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([\"bachelor's degree\", 'some college', \"master's degree\",\n",
       "       \"associate's degree\", 'high school', 'some high school'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st_data['parental level of education'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5]), <a list of 6 Text xticklabel objects>)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import xticks\n",
    "\n",
    "sns.countplot(x=\"parental level of education\",hue=\"gender\",  data = st_data)\n",
    "plt.xlabel('levels of Education')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Parental Level of Education')\n",
    "xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['standard', 'free/reduced'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st_data[\"lunch\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1]), <a list of 2 Text xticklabel objects>)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import xticks\n",
    "\n",
    "sns.countplot(x=\"lunch\",hue=\"gender\",  data = st_data)\n",
    "plt.xlabel('lunch')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Lunch based on parental level of education')\n",
    "xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Lunch based on parental level of education')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.pyplot import xticks\n",
    "sns.countplot(x=\"lunch\",hue = \"parental level of education\", data= st_data)\n",
    "xticks(rotation=90)\n",
    "plt.xlabel('lunch')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Lunch based on parental level of education')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.pyplot import xticks\n",
    "sns.countplot(x=\"test preparation course\",hue= \"lunch\", data= st_data)\n",
    "xticks(rotation=90)\n",
    "plt.xlabel('preparation course')\n",
    "plt.ylabel('Frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Comparison of Math score')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.pyplot import xticks\n",
    "sns.countplot(x=\"math score\",data = st_data)\n",
    "plt.xlabel('score')\n",
    "plt.ylabel('Number of student')\n",
    "plt.title('Comparison of Math score')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Comparison of Reading score')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.pyplot import xticks\n",
    "sns.countplot(x=\"reading score\",data = st_data)\n",
    "plt.xlabel('score')\n",
    "plt.ylabel('Number of student')\n",
    "plt.title('Comparison of Reading score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Comparison of writing score')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.pyplot import xticks\n",
    "sns.countplot(x=\"writing score\",data = st_data)\n",
    "plt.xlabel('score')\n",
    "plt.ylabel('Number of student')\n",
    "plt.title('Comparison of writing score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'writing Score Distribution')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Comparison of Score Distribution.\n",
    "\n",
    "\n",
    "plt.figure(figsize=(16,6))\n",
    "plt.subplot(1,3,1)\n",
    "sns.distplot(st_data['math score'], kde = False, color='c', bins = 30)\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Math Score Distribution')\n",
    "\n",
    "plt.subplot(1,3,2)\n",
    "sns.distplot(st_data['reading score'], kde = False, color='c', bins = 30)\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('reading Score Distribution')\n",
    "\n",
    "plt.subplot(1,3,3)\n",
    "sns.distplot(st_data['writing score'], kde = False, color='c', bins = 30)\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('writing Score Distribution')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Total score = math score + reading score + writing score\n",
    "\n",
    "st_data['Total Score']=st_data['math score']+st_data['reading score']+st_data['writing score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Criterion for getting a passing grade.\n",
    "\n",
    "def result(TS,MS,WS,RS ):\n",
    "    if(TS>120 and MS>40 and WS>40 and RS>40):\n",
    "        return 'P'\n",
    "    else:\n",
    "        return 'F'\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Total Score</th>\n",
       "      <th>Pass/Fail</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>218</td>\n",
       "      <td>P</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>247</td>\n",
       "      <td>P</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>278</td>\n",
       "      <td>P</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>148</td>\n",
       "      <td>P</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>229</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender race/ethnicity parental level of education         lunch  \\\n",
       "0  female        group B           bachelor's degree      standard   \n",
       "1  female        group C                some college      standard   \n",
       "2  female        group B             master's degree      standard   \n",
       "3    male        group A          associate's degree  free/reduced   \n",
       "4    male        group C                some college      standard   \n",
       "\n",
       "  test preparation course  math score  reading score  writing score  \\\n",
       "0                    none          72             72             74   \n",
       "1               completed          69             90             88   \n",
       "2                    none          90             95             93   \n",
       "3                    none          47             57             44   \n",
       "4                    none          76             78             75   \n",
       "\n",
       "   Total Score Pass/Fail  \n",
       "0          218         P  \n",
       "1          247         P  \n",
       "2          278         P  \n",
       "3          148         P  \n",
       "4          229         P  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st_data['Pass/Fail']=st_data.apply(lambda x: result(x['Total Score'],x['math score'],x['writing score'],x['reading score']),axis = 1 )  \n",
    "    \n",
    "    \n",
    "    \n",
    "st_data.head()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "P    939\n",
       "F     61\n",
       "Name: Pass/Fail, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total pass/fail student\n",
    "\n",
    "st_data['Pass/Fail'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Comparison of Total score')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.pyplot import xticks\n",
    "sns.countplot(x=\"Total Score\",data = st_data)\n",
    "plt.xlabel('score')\n",
    "plt.ylabel('Number of student')\n",
    "plt.title('Comparison of Total score')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Total Pass/Fail Student')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.pyplot import xticks\n",
    "sns.countplot(x=\"Pass/Fail\",hue= \"gender\", data= st_data)\n",
    "xticks(rotation=90)\n",
    "plt.xlabel('Pass/Fail')\n",
    "plt.ylabel('Number of Student')\n",
    "plt.title('Total Pass/Fail Student')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Percentage of Total Score\n",
    "\n",
    "st_data['percentage']=st_data['Total Score']/3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grading of marks\n",
    "\n",
    "         # 0  - 40 marks : grade E\n",
    "         # 41 - 60 marks : grade D\n",
    "         # 60 - 70 marks : grade C\n",
    "         # 70 - 80 marks : grade B\n",
    "         # 80 - 90 marks : grade A\n",
    "         # 90 - 100 marks : grade O\n",
    "\n",
    "def getgrade(percentage,status):\n",
    "  if status == 'Fail':\n",
    "    return 'E'\n",
    "  if(percentage >= 90):\n",
    "    return 'O'\n",
    "  if(percentage >= 80):\n",
    "    return 'A'\n",
    "  if(percentage >= 70):\n",
    "    return 'B'\n",
    "  if(percentage >= 60):\n",
    "    return 'C'\n",
    "  if(percentage >= 40):\n",
    "    return 'D'\n",
    "  else :\n",
    "    return 'E'\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "st_data['grades'] = st_data.apply(lambda x: getgrade(x['percentage'], x['Pass/Fail']), axis = 1 )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Total Score</th>\n",
       "      <th>Pass/Fail</th>\n",
       "      <th>percentage</th>\n",
       "      <th>grades</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>218</td>\n",
       "      <td>P</td>\n",
       "      <td>72.666667</td>\n",
       "      <td>B</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>247</td>\n",
       "      <td>P</td>\n",
       "      <td>82.333333</td>\n",
       "      <td>A</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>278</td>\n",
       "      <td>P</td>\n",
       "      <td>92.666667</td>\n",
       "      <td>O</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>148</td>\n",
       "      <td>P</td>\n",
       "      <td>49.333333</td>\n",
       "      <td>D</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>229</td>\n",
       "      <td>P</td>\n",
       "      <td>76.333333</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender race/ethnicity parental level of education         lunch  \\\n",
       "0  female        group B           bachelor's degree      standard   \n",
       "1  female        group C                some college      standard   \n",
       "2  female        group B             master's degree      standard   \n",
       "3    male        group A          associate's degree  free/reduced   \n",
       "4    male        group C                some college      standard   \n",
       "\n",
       "  test preparation course  math score  reading score  writing score  \\\n",
       "0                    none          72             72             74   \n",
       "1               completed          69             90             88   \n",
       "2                    none          90             95             93   \n",
       "3                    none          47             57             44   \n",
       "4                    none          76             78             75   \n",
       "\n",
       "   Total Score Pass/Fail  percentage grades  \n",
       "0          218         P   72.666667      B  \n",
       "1          247         P   82.333333      A  \n",
       "2          278         P   92.666667      O  \n",
       "3          148         P   49.333333      D  \n",
       "4          229         P   76.333333      B  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B    261\n",
       "C    256\n",
       "D    255\n",
       "A    146\n",
       "O     52\n",
       "E     30\n",
       "Name: grades, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st_data['grades'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Grades of Student')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.pyplot import xticks\n",
    "sns.countplot(x=\"grades\", data= st_data)\n",
    "plt.xlabel('Grade')\n",
    "plt.ylabel('Number of Student')\n",
    "plt.title('Grades of Student')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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