Untitled1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('StudentsPerformance.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quick glance of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>race/ethnicity</th>\n",
       "      <th>parental level of education</th>\n",
       "      <th>lunch</th>\n",
       "      <th>test preparation course</th>\n",
       "      <th>math score</th>\n",
       "      <th>reading score</th>\n",
       "      <th>writing score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>completed</td>\n",
       "      <td>69</td>\n",
       "      <td>90</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>master's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>90</td>\n",
       "      <td>95</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>male</td>\n",
       "      <td>group A</td>\n",
       "      <td>associate's degree</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>none</td>\n",
       "      <td>47</td>\n",
       "      <td>57</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>76</td>\n",
       "      <td>78</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>math score</th>\n",
       "      <th>reading score</th>\n",
       "      <th>writing score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 8 columns):\n",
      " #   Column                       Non-Null Count  Dtype \n",
      "---  ------                       --------------  ----- \n",
      " 0   gender                       1000 non-null   object\n",
      " 1   race/ethnicity               1000 non-null   object\n",
      " 2   parental level of education  1000 non-null   object\n",
      " 3   lunch                        1000 non-null   object\n",
      " 4   test preparation course      1000 non-null   object\n",
      " 5   math score                   1000 non-null   int64 \n",
      " 6   reading score                1000 non-null   int64 \n",
      " 7   writing score                1000 non-null   int64 \n",
      "dtypes: int64(3), object(5)\n",
      "memory usage: 62.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visual Representation of Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24bd5ce0700>"
      ]
     },
     "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.countplot(x='gender',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24bd5d86a60>"
      ]
     },
     "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": [
    "sns.countplot(x='race/ethnicity',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-11-dd4e147a04d6>:3: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,6))\n",
    "sns.countplot(x='parental level of education',data=df)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24bd5fe0d90>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEGCAYAAACKB4k+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAATtElEQVR4nO3dfZBfV33f8ffHsrHBYLCitSssE5mMWiIXzINqIO5QJ2ZiQxLkAmaUwUUhnrqdODzMNM3YfQgQopZMaac0YBqHYAtC6qoQYsWZOHYFJk9gWcKPkjFWsWOrVqzFPBpSU6vf/vE7e/hJWkkrW3d35X2/Znbuued3zt3v7tzdz957f/duqgpJkgCOmesCJEnzh6EgSeoMBUlSZyhIkjpDQZLUHTvXBTwVS5YsqeXLl891GZJ0VNm6devXq2piuteO6lBYvnw5W7ZsmesyJOmokuSvD/Sap48kSZ2hIEnqDAVJUmcoSJI6Q0GS1BkKkqTOUJAkdYaCJKkzFCRJ3VF9R/OR8Ip/+Ym5LkHz0Nb/8La5LkGaEx4pSJI6Q0GS1BkKkqTOUJAkdYaCJKkzFCRJnaEgSeoMBUlSZyhIkjpDQZLUDRoKSZ6X5NNJvpLkniSvTrI4yU1J7mvLk8fGX5FkR5J7k5w/ZG2SpP0NfaTwIeCGqnoRcBZwD3A5sKmqVgCb2jpJVgJrgDOBC4ArkywauD5J0pjBQiHJScBrgN8FqKofVNW3gNXA+jZsPXBha68Grq2qx6vqfmAHcPZQ9UmS9jfkkcILgUng6iS3JflYkhOBU6tqF0BbntLGnwY8NDZ/Z+vbS5JLk2xJsmVycnLA8iVp4RkyFI4FXg58tKpeBnyPdqroADJNX+3XUXVVVa2qqlUTExNHplJJEjBsKOwEdlbVLW3904xC4pEkSwHacvfY+NPH5i8DHh6wPknSPgYLhar6G+ChJH+vdZ0HbAc2Amtb31rgutbeCKxJcnySM4AVwOah6pMk7W/o/7z2DuBTSZ4BfA14O6Mg2pDkEuBB4CKAqtqWZAOj4HgCuKyq9gxcnyRpzKChUFW3A6umeem8A4xfB6wbsiZJ0oF5R7MkqTMUJEmdoSBJ6gwFSVJnKEiSOkNBktQZCpKkzlCQJHWGgiSpMxQkSZ2hIEnqDAVJUmcoSJI6Q0GS1BkKkqTOUJAkdYaCJKkzFCRJnaEgSeoMBUlSZyhIkjpDQZLUGQqSpG7QUEjyQJK7ktyeZEvrW5zkpiT3teXJY+OvSLIjyb1Jzh+yNknS/mbjSOEnq+qlVbWqrV8ObKqqFcCmtk6SlcAa4EzgAuDKJItmoT5JUjMXp49WA+tbez1w4Vj/tVX1eFXdD+wAzp6D+iRpwRo6FAq4McnWJJe2vlOrahdAW57S+k8DHhqbu7P17SXJpUm2JNkyOTk5YOmStPAcO/D2z6mqh5OcAtyU5CsHGZtp+mq/jqqrgKsAVq1atd/rkqQnb9Ajhap6uC13A59ldDrokSRLAdpydxu+Ezh9bPoy4OEh65Mk7W2wUEhyYpLnTLWBnwbuBjYCa9uwtcB1rb0RWJPk+CRnACuAzUPVJ0na35Cnj04FPptk6vP8flXdkORWYEOSS4AHgYsAqmpbkg3AduAJ4LKq2jNgfZKkfQwWClX1NeCsafofBc47wJx1wLqhapIkHZx3NEuSOkNBktQZCpKkzlCQJHWGgiSpMxQkSZ2hIEnqDAVJUmcoSJI6Q0GS1BkKkqTOUJAkdYaCJKkzFCRJnaEgSeoMBUlSZyhIkjpDQZLUGQqSpM5QkCR1hoIkqTMUJEmdoSBJ6gYPhSSLktyW5Pq2vjjJTUnua8uTx8ZekWRHknuTnD90bZKkvc3GkcK7gHvG1i8HNlXVCmBTWyfJSmANcCZwAXBlkkWzUJ8kqRk0FJIsA34G+NhY92pgfWuvBy4c67+2qh6vqvuBHcDZQ9YnSdrb0EcK/xn4VeD/jfWdWlW7ANrylNZ/GvDQ2LidrW8vSS5NsiXJlsnJyWGqlqQFarBQSPKzwO6q2jrTKdP01X4dVVdV1aqqWjUxMfGUapQk7e3YAbd9DvCGJK8HTgBOSvJ7wCNJllbVriRLgd1t/E7g9LH5y4CHB6xPkrSPwY4UquqKqlpWVcsZXUD+XFVdDGwE1rZha4HrWnsjsCbJ8UnOAFYAm4eqT5K0vyGPFA7kA8CGJJcADwIXAVTVtiQbgO3AE8BlVbVnDuqTpAVrVkKhqm4Gbm7tR4HzDjBuHbBuNmqSJO3PO5olSZ2hIEnqDAVJUjejUEiyaSZ9kqSj20EvNCc5AXgWsKQ9uG7qBrOTgOcPXJskaZYd6t1H/wx4N6MA2MoPQ+E7wEcGrEuSNAcOGgpV9SHgQ0neUVW/NUs1SZLmyIzuU6iq30ryE8Dy8TlV9YmB6pIkzYEZhUKSTwI/BtwOTN1lXIChIElPIzO9o3kVsLKq9ntqqaRhPPjrL57rEjQPveDX7hp0+zO9T+Fu4O8MWYgkae7N9EhhCbA9yWbg8anOqnrDIFVJkubETEPhvUMWIUmaH2b67qMvDF2IJGnuzfTdR9/lh/8a8xnAccD3quqkoQqTJM2+mR4pPGd8PcmFwNmDVCRJmjNP6impVfWHwE8d4VokSXNspqeP3ji2egyj+xa8Z0GSnmZm+u6jnxtrPwE8AKw+4tVIkubUTK8pvH3oQiRJc2+m/2RnWZLPJtmd5JEkn0mybOjiJEmza6YXmq8GNjL6vwqnAX/U+iRJTyMzDYWJqrq6qp5oH9cAEwPWJUmaAzMNha8nuTjJovZxMfDowSYkOSHJ5iR3JNmW5H2tf3GSm5Lc15Ynj825IsmOJPcmOf/Jf1mSpCdjpqHwi8BbgL8BdgFvBg518flx4Keq6izgpcAFSV4FXA5sqqoVwKa2TpKVwBrgTOAC4Mokiw7vy5EkPRUzDYX3A2uraqKqTmEUEu892IQaeaytHtc+itFbWde3/vXAha29Gri2qh6vqvuBHXjXtCTNqpmGwkuq6ptTK1X1DeBlh5rUTjXdDuwGbqqqW4BTq2pX284u4JQ2/DTgobHpO1vfvtu8NMmWJFsmJydnWL4kaSZmGgrH7HPufzEzuMehqvZU1UuBZcDZSf7+QYZnuk1Ms82rqmpVVa2amPBatyQdSTO9o/k/An+V5NOMflG/BVg3009SVd9KcjOjawWPJFlaVbuSLGV0FAGjI4PTx6YtAx6e6eeQJD11MzpSqKpPAG8CHgEmgTdW1ScPNifJRJLntfYzgdcCX2F0v8PaNmwtcF1rbwTWJDk+yRnACmDz4X05kqSnYqZHClTVdmD7YWx7KbC+vYPoGGBDVV2f5IvAhiSXAA8CF7Xtb0uyoX2OJ4DLqmrPYXw+SdJTNONQOFxVdSfTXIyuqkeB8w4wZx2HcVpKknRkPan/pyBJenoyFCRJnaEgSeoMBUlSZyhIkjpDQZLUGQqSpM5QkCR1hoIkqTMUJEmdoSBJ6gwFSVJnKEiSOkNBktQZCpKkzlCQJHWGgiSpMxQkSZ2hIEnqDAVJUmcoSJI6Q0GS1BkKkqRusFBIcnqSzye5J8m2JO9q/YuT3JTkvrY8eWzOFUl2JLk3yflD1SZJmt6QRwpPAP+iqn4ceBVwWZKVwOXApqpaAWxq67TX1gBnAhcAVyZZNGB9kqR9DBYKVbWrqr7c2t8F7gFOA1YD69uw9cCFrb0auLaqHq+q+4EdwNlD1SdJ2t+sXFNIshx4GXALcGpV7YJRcACntGGnAQ+NTdvZ+iRJs2TwUEjybOAzwLur6jsHGzpNX02zvUuTbEmyZXJy8kiVKUli4FBIchyjQPhUVf1B634kydL2+lJgd+vfCZw+Nn0Z8PC+26yqq6pqVVWtmpiYGK54SVqAhnz3UYDfBe6pqv809tJGYG1rrwWuG+tfk+T4JGcAK4DNQ9UnSdrfsQNu+xzgnwB3Jbm99f0r4APAhiSXAA8CFwFU1bYkG4DtjN65dFlV7RmwPknSPgYLhar6C6a/TgBw3gHmrAPWDVWTJOngvKNZktQZCpKkzlCQJHWGgiSpMxQkSZ2hIEnqDAVJUmcoSJI6Q0GS1BkKkqTOUJAkdYaCJKkzFCRJnaEgSeoMBUlSZyhIkjpDQZLUGQqSpM5QkCR1hoIkqTMUJEmdoSBJ6gwFSVJnKEiSusFCIcnHk+xOcvdY3+IkNyW5ry1PHnvtiiQ7ktyb5Pyh6pIkHdiQRwrXABfs03c5sKmqVgCb2jpJVgJrgDPbnCuTLBqwNknSNAYLhar6M+Ab+3SvBta39nrgwrH+a6vq8aq6H9gBnD1UbZKk6c32NYVTq2oXQFue0vpPAx4aG7ez9e0nyaVJtiTZMjk5OWixkrTQzJcLzZmmr6YbWFVXVdWqqlo1MTExcFmStLDMdig8kmQpQFvubv07gdPHxi0DHp7l2iRpwZvtUNgIrG3ttcB1Y/1rkhyf5AxgBbB5lmuTpAXv2KE2nOS/AecCS5LsBN4DfADYkOQS4EHgIoCq2pZkA7AdeAK4rKr2DFWbJGl6g4VCVf38AV467wDj1wHrhqpHknRo8+VCsyRpHjAUJEmdoSBJ6gwFSVJnKEiSOkNBktQZCpKkzlCQJHWGgiSpMxQkSZ2hIEnqDAVJUmcoSJI6Q0GS1BkKkqTOUJAkdYaCJKkzFCRJnaEgSeoMBUlSZyhIkjpDQZLUGQqSpG7ehUKSC5Lcm2RHksvnuh5JWkjmVSgkWQR8BHgdsBL4+SQr57YqSVo45lUoAGcDO6rqa1X1A+BaYPUc1yRJC8axc13APk4DHhpb3wm8cnxAkkuBS9vqY0nunaXaFoIlwNfnuoj5IB9cO9claG/um1PekyOxlR890AvzLRSm+2prr5Wqq4CrZqechSXJlqpaNdd1SPty35w98+300U7g9LH1ZcDDc1SLJC048y0UbgVWJDkjyTOANcDGOa5JkhaMeXX6qKqeSPLLwJ8Ci4CPV9W2OS5rIfG0nOYr981Zkqo69ChJ0oIw304fSZLmkKEgSeoMhaeJJO9O8qwjuL0Hkix5CvN/IcmHj1Q9mh+SvDPJPUk+dYS2t7W9qeTJzB18H0vy2JDbn48MhaePdwNHLBQOV3tEiZ7+fgl4fVW9daojyZN6w0qS5cD/bk8vGO93X5pDhsJRKMmJSf44yR1J7k7yHuD5wOeTfL6N+WiSLUm2JXnf2NwHkrwvyZeT3JXkRa3/R5LcmOS2JL/N2I2ESf6w/UW3rd1RPtX/WJJfT3IL8Ookb0/y1SRfAM6ZpW+HZkmS/wq8ENiY5NtJrkpyI/CJJBNJPpPk1vZxTptzYpKPt77bkow/tuZ1wA1t3L770sVJNie5PclvTwXFgfaxJNckefPY+mNj7V9t+/odST7Q+n4syQ1tv/7zsZ+DM5J8sdX7/oG+lfNbVflxlH0AbwJ+Z2z9ucADwJKxvsVtuQi4GXhJW38AeEdr/xLwsdb+L8CvtfbPMLqTfMk+23omcDfwI229gLe09lLgQWACeAbwl8CH5/p75ccR3/ceYPTIifcCW4Fntv7fB/5ha78AuKe1/x1wcWs/D/gqcGJbvw544TT70o8DfwQc19avBN52sH0MuAZ481idj7Xl64C/Ap7V1qf25U3AitZ+JfC51t4IvK21L5vazkL6mFf3KWjG7gI+mOQ3geur6s+T/Z4Q8pb2V/2xjH6YVgJ3ttf+oC23Am9s7ddMtavqj5N8c2xb70zyj1v7dGAF8CiwB/hM638lcHNVTQIk+e/A332qX6jmtY1V9bet/Vpg5dh+eFKS5wA/Dbwhya+0/hOAFyT5X8Cyqvpa6x/fl84DXgHc2rb3TGA3T24fey1wdVV9H6CqvpHk2cBPAP9jrN7j2/IcRn90AXwS+M2ZfCOeTgyFo1BVfTXJK4DXA/++HcJ3Sc4AfgX4B1X1zSTXMPphnPJ4W+5h731gv5tWkpzL6Afr1VX1/SQ3j23r/1TVnoPN19Pa98baxzDaR/52fEBGv3XfVFX37tN/HvAXY13j+1KA9VV1xT5zLuTA+9gTrYapzzl18TrTzDkG+FZVvfQA21rQ+7HXFI5CSZ4PfL+qfg/4IPBy4LvAc9qQkxj9wH47yamMDqEP5c+At7btvw44ufU/F/hmC4QXAa86wPxbgHPbtYnjgIsO/yvTUexG4JenVpJM/cL9U+Ad7Rc1SV7W+i8A/uQA29oEvDnJKW3O4iQ/ysH3sQcYHV3A6HH7x43V9Ytp78xLsriqvgPcn+Si1pckZ7Xxf8no8TrQfh4WGkPh6PRiYHOS24F/DfwGo8cA/EmSz1fVHcBtwDbg44x29EN5H/CaJF9mdMj/YOu/ATg2yZ3A+4EvTTe5qnYxOs/8ReB/Al9+cl+ajlLvBFYluTPJduCft/73M/oFfWeSu9s6wLnAF6bbUFVtB/4NcGPb724Clh5iH/sd4B8l2czoNNP32rZuYHSdYEv7eZk6jfVW4JIkdzD6OZm6AP4u4LIktzL6g2jB8TEXkmZVkmWM3igxkyNYzTJDQZLUefpIktQZCpKkzlCQJHWGgiSpMxSkGcgAT8scYpvSU2UoSJI6Q0E6DEnOTXL92PqHk/xCax/oCbTPTnJ167szyZvG5q9rT+/8Urv7XJpThoJ0ZH29ql4OfJQf3j37b4FvV9WLq+olwOda/4nAl6rqLEaPGfmns16ttA9DQTqyxp9Au7y1Xwt8ZGpAVU09gfYHwPXTjJfmjKEgHZ7+NM7mhH1en+4JtNM9qRPg/9YPHymw7xNrpTlhKEiH568Z/d+A45M8l9Gz/w9l3yeInnyQsdKcMhSkw1BVDwEbGP3Dok8xehrtofwGcHJG/zr1DuAnByxRekp8IJ4kqfNIQZLUGQqSpM5QkCR1hoIkqTMUJEmdoSBJ6gwFSVL3/wGq1Znn3gcMvQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='lunch',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24bd5e72250>"
      ]
     },
     "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.countplot('test preparation course',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distribution of target variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24bd5ead910>"
      ]
     },
     "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.distplot(df['math score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24bd5f4ba00>"
      ]
     },
     "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": [
    "sns.distplot(df['reading score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24bd5fb4af0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vozEzK4FTzV30eXx2h6LQBKDUmDhS28a+0y3cOCuT/Ix4u8MJWoXZCXh9hhNN2gwUDDQBKHWZuvu8vLi/lpzkGFbODv8x/i9Hfno8LodQrtcBgoImAKUu08uH6ujs8/CxRXk4Hdrl81KinA4KMuL1foAgoQlAqctQXt/OvtMtXF+UGdbTO46lwuxEmjp6dbL4IKAJQKlR8nh9/PlALRkJ0ayclWV3OCHjQnfQnRVNNkeiNAEoNUpvVzXT3NnHP83PweXUn1KgshKjSY6NYkd5o92hRDz91io1Cu09/Ww71sCs7ESKshPtDiekiDUsxJuVTXi82h3UTpoAlBqFLUfq6ff6InaM/8tVmJVAe4+HA9VtdocS0TQBKDVCDe097D3VwjXT08lIjLY7nJA0MysBEbQZyGaaAJQaoTeONRDldHCDXvgdtTi3i/l5Keyo0ARgJ00ASo1Aw/keDla3sWx6OgnRLrvDCWk3FGZw4EwrrV19docSsTQBKDUC28r8R//XFmbYHUrIu2FWFj4DO7Q7qG00ASgVoIbzPRzSo/8xs3BKCilxUWwva7A7lIilCUCpAO2sbMLlFD36HyNOh3B9YSY7yhvx+XSWMDsElABEZLWIlIlIpYg8MMR6EZEfW+sPisiiAeseFZEGETk8aJtviUiNiOy3HmsvvzpKjY+OXg8HzrRy1ZRUPfofQzfOyqSpo4/Dtdod1A7DJgARcQI/A9YAc4H1IjJ3ULE1QKH1uBfYOGDdY8Dqi7z9D4wxC63HKyOMXakJs7uqGY/PsHxmut2hhJXrizIB2F6mvYHsEMgZwBKg0hhTZYzpA54B1g0qsw54wvjtAlJEJAfAGLMDODeWQSs1kfq9PnadOMes7ESyEmPsDiesZCREsyAvWa8D2CSQBJALnBnwutpaNtIyQ7nfajJ6VERShyogIveKSImIlDQ26lGCmngHq1vp7PWwYqa2/Y+HG2Zl8e6ZVlo6tTvoRAskAQw1wPngKzaBlBlsIzADWAjUAQ8NVcgY87AxptgYU5yZmTlcrEqNKWMMbx1vJjspmhmZOtPXeLhxVibGoDeF2SCQBFANTBnwOg+oHUWZ9zDG1BtjvMYYH/Ar/E1NSgWV6pZu6tp6WDY9HRGd7GU8LMhLIT3ezdaj2gw00QJJAHuAQhEpEBE3cAewaVCZTcBdVm+gZUCbMabuUm964RqB5aPA4YuVVcoue06ew+10sCAvxe5QwpbTIdw0O4vtZQ306+igE2rYBGCM8QD3A5uBo8CzxphSEdkgIhusYq8AVUAl/qP5L17YXkSeBt4GZolItYjcY636rogcEpGDwE3Av45VpZQaCz39Xg5UtzI/L5mYKKfd4YS1W+Zkcb7HQ8nJFrtDiSgBdWi2umi+MmjZLwY8N8B9F9l2/UWWfybwMJWaePvPtNLvNSwpSLM7lLB3XWEmbqeDrUfruWaGdrWdKHonsFJDMMaw5+Q5cpJjyNW5fsddfLSLZTPS2XpMrwNMJE0ASg3hUE0bdW09XJ2fphd/J8gtc7I40dTJ8cYOu0OJGJoAlBrCc3urcTlEL/5OoJvnZAOw9Wi9zZFEDk0ASg3S6/Hy4v5a5k5OItatF38nSm5KLHNyknj9iDYDTRRNAEoNsvVoA23d/SyaOuTN6WocfWBuNntOnaOxvdfuUCKCJgClBnl+bzXZSdHMzEqwO5SIs2beJIyBLUe0GWgiaAJQaoCG9h62lzdy26I8HHrxd8LNyk4kPz2Ovxy+5H2kaoxoAlBqgBffrcXrM3xsUZ7doUQkEeHWKyfx9vFm2rr67Q4n7GkCUMpijOG5vdUsnJKizT82Wn3FJDw+w+vaG2jcaQJQylJae56y+nY+vliP/u20IC+FnOQYXi09a3coYU/ntlPK8tzeatwuBx+aP9nuUCLCU7tPX3Rdfno8bxxr4Dd/O0G0y8mnlk6dwMgih54BKAX0eXy8uL+GVXOzSY6LsjuciHdlbjIen+FYXbvdoYQ1TQBKAduONdDS1a/NP0FiWnocSTEuDlS32h1KWNMEoBT+5p/MxGiu02kfg4JDhPl5KVTUd9DV57E7nLClCUBFvMb2XraXNXDbVbm4nPqTCBbz85LxGkNp7Xm7Qwlb+m1XEe+P71bj8Rk+UTxl+MJqwuSmxJIW79ZmoHGkCUBFNGMMv99zhsXTUrXvf5ARERbkJXOisZOG8z12hxOWNAGoiLbvdCvHGzu5vVgv/gaj+XkpGOClgzo0xHjQBKAi2rN7zhDndvJB7fsflLKTYshJjuGP79bYHUpY0gSgIlZnr4eXDtbywXk5JETrPZHBatHUVA7VtHHsrF4MHmuaAFTEevlQHZ19Xj55tV78DWYLpqTgcgjP7622O5SwowlARaw/lJxhemY8i6fpxC/BLCHaxcrZWfzx3Vr6vT67wwkrmgBURDre2MGeky3cXjxFJ30PAR9fnEdTRy87yhvtDiWsaAJQEekPJdU4HcJti3LtDkUF4KbZWaTHu3lOm4HGlCYAFXE8Xh/P76vmpllZZCXG2B2OCkCU08G6hbm8frSepg6dL3isaAJQEWd7WSON7b168TfErF8yhX6v0bOAMaQJQEWcZ/acISMhmhtnZdodihqBwuxEluSn8fQ7p/H5jN3hhAVNACqi1LR2s+1YPbcX5xGlA7+FnE8tncqp5i7eOt5sdyhhQX8BKqI8uesUAHcum2ZzJGo0Vl85idS4KJ5655TdoYQFTQAqYvT0e3lmzxlWzc0mNyXW7nDUKMREOfnYojxeK62noV0HiLtcASUAEVktImUiUikiDwyxXkTkx9b6gyKyaMC6R0WkQUQOD9omTUS2iEiF9VfvxlHj6uWDdZzr7OOua/LtDkVdhvVLp+LxGZ5554zdoYS8YROAiDiBnwFrgLnAehGZO6jYGqDQetwLbByw7jFg9RBv/QCw1RhTCGy1Xis1bp54+yQzMuNZPiPd7lDUZZiRmcANRZn8dtcpej1eu8MJaYGcASwBKo0xVcaYPuAZYN2gMuuAJ4zfLiBFRHIAjDE7gHNDvO864HHr+ePAR0ZTAaUCsfdUCweq27jrmny98zcM3HNtAY3tvbx0QIeJvhyBDIGYCww816oGlgZQJhe41P9OtjGmDsAYUyciWUMVEpF78Z9VMHXq1ADCVer9fr2ziqQYl076HqKe2n36Pa+NMWQlRvPQa2X09Hv/ntQ/tVT3ESMRyBnAUIdLgzvhBlJmVIwxDxtjio0xxZmZ2m9bjdyp5k5eLT3Lp5dNI16HfQ4LIsKKGRnUtvVwornT7nBCViAJoBoYeMtkHlA7ijKD1V9oJrL+NgQQi1Ij9uibJ3A5hM8uz7c7FDWGFk5NIc7t5K1KvSdgtAJJAHuAQhEpEBE3cAewaVCZTcBdVm+gZUDbheadS9gE3G09vxt4cQRxKxWQls4+ni2p5iMLc8lK0nF/wkmU08GSgjSO1p2nWccHGpVhE4AxxgPcD2wGjgLPGmNKRWSDiGywir0CVAGVwK+AL17YXkSeBt4GZolItYjcY616EFglIhXAKuu1UmPqd7tO0d3v5Z+vn253KGocLCtIxyHC21V6FjAaATWIGmNewb+TH7jsFwOeG+C+i2y7/iLLm4GbA45UqRHq6PXwyN9OsHJ2FkXZiXaHo8ZBUmwU8/KSKTnVwi1zsu0OJ+ToncAqbP327VO0dvXz5ZsL7Q5FjaMVMzPo8/goOTlUb3N1KdolQoWFwd0E+zw+frKtgqLsBI7UnudIrU4oHq5yU2LJT4/nrapmPF4fLh3kL2D6L6XC0u4TzXT1eVk5a8jbS1SYuXZmOq1d/fzl8Fm7QwkpmgBU2Onz+NhZ0cTMzASmpsfbHY6aALNzkshMjOZnb1TivySpAqEJQIWdPSfP0dHr4abZevQfKRwi3FiUybGz7bxRprcUBUoTgAor/V4fOyoaKciIpyBDj/4jyfy8FPJSY/npNj0LCJQmABVWSk610N7jYaUe/Uccp0P4lxtmsO90K7tPaI+gQGgCUGHD4/Wxo7yRaelxTNej/4j0icV5ZCT4rwWo4WkCUGGj5FQLbd39rJyVpUM+R6iYKCf/fF0BOyuaOFjdanc4QU8TgAoL/V4f28samJYWx8ysBLvDUTa6c9k0kmJcehYQAE0AKizsqmrmfI+HVVdk69F/hEuIdvHZFQVsLq2nor7d7nCCmiYAFfI6ej38tbyRmVkJTM/Qo38Fn1ueT5zbycbtx+0OJajpUBAq5D365gm6+rys0sHAIt7AIUEWTU3lT/tryM+IJyMh+n1ldfYwPQNQIa61q49f7ahiTk4SU9Li7A5HBZHrCjNwOoQ3jumNYRejCUCFtF/uqKKjz6NH/+p9EmOiWDY9nf1nWmlo77E7nKCkCUCFrIb2Hh7720k+NH8yk5J1ti/1ftcXZhLlcrBNzwKGpAlAhayN24/T5/Xxr6uK7A5FBan4aBfLp6dzqLqNs+f1LGAwTQAqJNW2dvPkrtN8fFGejvmjLunawgzcLgdbj9bbHUrQ0QSgQtJPtlVgMHzp5pl2h6KCXJzbxYqZGZTWnqe2tdvucIKKJgAVck42dfJsSTWfWjKVvFTt+aOGt2JGBjFRDl7Xs4D30ASgQs4PXy8nyinct1KP/lVgYt1Oriv0zxdQ3dJldzhBQxOACillZ9t58UAtdy/PJytRe/6owC2fnk5slJMtR/Qs4AK9E1iFlO9vKSPB7WLD9TPsDkWFmOgoJzfOyuQvh89S2dDxnruGhxOudw3rGYAKGQerW9lcWs891xWQGu+2OxwVgq6Znk5qXBSvHq7Dp7OGaQJQoeN7r5WTGhfFPdcW2B2KClEup4NVcydR29bDgTM6X4AmABUS3jlxjh3ljWy4YQaJMVF2h6NC2Py8ZCanxLDlSD39Xp/d4dhKE4AKesYYvre5jMzEaO66Jt/ucFSIc4iw5socWrv72VXVbHc4ttIEoILetmMNvHPyHF9eOZNYt9PucFQYmJGZQFF2Am+UNdDV57E7HNtoAlBBzeP18eBfjlGQEc8dS8KzJ4ayx+orc+jt97G9rNHuUGwTUAIQkdUiUiYilSLywBDrRUR+bK0/KCKLhttWRL4lIjUist96rB2bKqlw8tzeaioaOvjG6llEOfV4RY2dSUkxLJqWyttVzbR09tkdji2G/UWJiBP4GbAGmAusF5G5g4qtAQqtx73AxgC3/YExZqH1eOVyK6PCS1efh+9vKWfxtFRuvWKS3eGoMHTLnGwcAq+WnrU7FFsEcki1BKg0xlQZY/qAZ4B1g8qsA54wfruAFBHJCXBbpYb0650naGjv5ZtrZ+tE72pcJMdGcV1hJodq2jjR1Gl3OBMukASQC5wZ8LraWhZImeG2vd9qMnpURFKH+nARuVdESkSkpLExctvqIk1jey+//OtxVl8xicXT0uwOR4Wx6wszSYmN4qWDtRF3c1ggQ0EMdeg1+F/pYmUute1G4NvW628DDwGff19hYx4GHgYoLi6OrP+dCPajreV093uZm5M0olv2lRopt8vBmnk5PP3OafacPMfSgnS7Q5owgZwBVANTBrzOA2oDLHPRbY0x9cYYrzHGB/wKf3ORUhxv7ODpd86wpCCNjMRou8NREeDKyUkUZMSz5Ug93X1eu8OZMIEkgD1AoYgUiIgbuAPYNKjMJuAuqzfQMqDNGFN3qW2tawQXfBQ4fJl1UWHiwb8cI8blYOVsnehdTQwR4Z/m59Dd542oOQOGbQIyxnhE5H5gM+AEHjXGlIrIBmv9L4BXgLVAJdAFfO5S21pv/V0RWYi/Cegk8C9jWTEVmraXNbDlSD1fXz2LhGgdrFZNnJzkWJYUpLH7RDNXF6QxKSn8hxsXE0IXPYqLi01JSYndYahx0uvxcusPduAQ4dWvXs9ze6vtDklFmK5eDw9tKWdySgyfX1Hw995noT4ctIjsNcYUD16ud9aooPHrnSc42dzFtz58BW6XfjXVxIuLdnHLnCyON3ZSWnve7nDGnf7KVFA4c66Ln26rZPUVk7i+KNPucFQEW1KQTk5yDC8fqqPPE96jhWoCULYzxvDNPx7CIfC/PzT4JnOlJpbTIXx4wWTauvvZdqzB7nDGlSYAZbvn99Wws6KJB9bMJjcl1ry9bScAAA61SURBVO5wlGJaejyLp6byZmUjDed77A5n3GgCULZqaO/h2y8d4er8VO5cOs3ucJT6u1uvnITb5WDTgVpCqbPMSGgCULYxxvDNFw7R3e/lwY/Nx+HQ8X5U8EiIdvGBuZOoaupk04HB976GB00Ayja/23WK14828O9rZjMjM8HucJR6nyUFaeSmxPJfLx+lvaff7nDGnCYAZYvy+na+8/JRbpyVyWeX59sdjlJDcoiwbuFkGjt6+cGWCrvDGXN6q6WaUE/tPk2fx8fGv1bicjq4Zno6T79zZvgNlbJJXmoc65dM5fG3T3LbolyuzE22O6Qxo2cAakIZY3h+XzUN53u5fXEeiTFRdoek1LC+cetsUuPcPPDCQTze8Lk3QBOAmlA7K5o4VNPGB66YRGF2ot3hKBWQ5Lgo/nPdFRyuOc+jfzthdzhjRhOAmjDbjtWzufQsV+Ymc31hht3hKDUia66cxKq52Xx/SzmnmsNj9jBNAGpC7Dvdwhef3EdOSgwfW5SrUzyqkCMifHvdlUQ5HHzzj4fC4t4ATQBq3FU2dPD5x/YwKSmGzy4vINrltDskpUZlUnIM31gzm79VNofFaLWaANS4qmzo4M5f78LlcPDE55fqGP8q5H1qyVSuzk/lOy8fpbG91+5wLosmADVuys62c8fDu/D6DE9+YSlT0+PsDkmpy+ZwCP/3tvl093n51qbS4TcIYpoA1LjYe6qF9b/ahUPgmXuvYdYk7fGjwsfMrAS+fPNMXj5Ux4v7a+wOZ9Q0Aagx9+L+Gtb/aheJMS6e/ZdrmJmlwzyo8LPhhhksmprC//rTYWpau+0OZ1Q0Aagx0+/18d+vHuMrz+xn4ZQU/vTFFeRnxNsdllLjwuV08MNPXoXPZ/i33+/H6wu9XkGaANSYOHOui0/+8m02bj/O+iVT+O09S0iNd9sdllLjamp6HN/68BXsPnGOn26rtDucEdMuGeqyeH2Gr/5+P6+VngXgjqunMC83hef3hm67qFIj8fHFebx9vJkfbi1n/pRkbpqVZXdIAdMzADVq755u4WMb3+LPB2qZkhbHl1YWMj8vxe6wlJpQIsJ/fXQesycl8ZWn3+V0c5fdIQVME4AasYr6djb8di8f/flbVLd0cXtxHp9bnk+aNvmoCBXrdvLLTy9GRPjCE3to7eqzO6SAaBOQGtJTu0+/57UxhqqmTt6saKKsvh23y8HNc7K4dmaG3tmrFP7rARs/vYjPPrqHex4v4Xf3LCXWHdy/DU0A6pLOdfZxsLqVvadaaO7sI97t5JY5WSwtSCde7+pV6j2Wz8jgh3cs5L6n9nHfU/vY+OlFQX2ApL9g9R4+n+FAdSuvlZ7l6Nnz1J/33+pekBHPTbOymJeXTJRTWw6Vupi183L4zkeu5D/+eJjPP7aHX3x6cdDOe6EJIMJ5vD6OnW2n5OQ59pxqYXfVOZo6enEITEuPZ+28NObmJGn7vlIjcOfSacS4nHz9+YPc8fAufvPZq8lKirE7rPfRBBBB+r0+qho7OVLXxtG6dkpr29h/upXOPi8Ak5NjWD4jnZWzs2jq6CXOrV8PpUbrY4vzSEtw88Xf7WPNj3by/z4xn5Wzs+0O6z30Fx5mfD5Dc2cfZ1q6ONHYyYkm/6OqqZPjDR30WdPZuV0OirITuG1RHsX5qRTnp5GbEvv39xl8EVgpNXI3zcpi0/0r+PIz+/n8YyWsXzKFf1s1i8zEaLtDAzQBBD1jDD39Ptp7+jnf4+F8Tz/tPR7Od/fT1NHLtmMNtHX309bdz/nufs53e/AOmKjCIZAa5yYjIZrPrchnTk4ScycnUZARr235Sk2AwuxE/nTfch56rZxH3jzBi/tr+fyKAu66ZprtzUIBJQARWQ38CHACvzbGPDhovVjr1wJdwGeNMfsuta2IpAG/B/KBk8DtxpiWy69ScPF4fbT3ePw77Z5+/6PbQ/uFHfmAHfrA1xd2+O09/fR7Lz7GiMshJMVGkRwbxbT0eJJiokiOdZES5yYzIZrUeDdOh3/2rU8tnTpR1VZKDRDtcvLNtXNYv2QqD71Wxk/fqGTjX49zQ1Ema+flsHxGOpMHnIFPFBluWjMRcQLlwCqgGtgDrDfGHBlQZi3wJfwJYCnwI2PM0kttKyLfBc4ZYx4UkQeAVGPMNy4VS3FxsSkpKRlxJfs8Prw+w4VZCEVAkL8/B/B4Df0+Hx6vweP10e8z9Ht8eHw+uvq8dPZ66e73+P/2eens89DV533/TnvQjrzLal+/FLfLQYzLQUyU03r843lslNO/zu0kxvXedQnRLuLcTp1eUalxNtYHT1WNHTy3t5oX9tVw9nwPAHmpscyelMiMrARykmJIjXeTFu8mNc7/NyMhGrdrdGftIrLXGFM8eHkgZwBLgEpjTJX1Rs8A64AjA8qsA54w/myyS0RSRCQH/9H9xbZdB9xobf84sB24ZAIYrW+/dITf7jo1Hm9NlFNIiokiKTaKxBgXiTEuspNiSIxxkRQTRWJMFEmxLkprzr9n531hRx/tcv79CF0pFRmmZybw9dWz+R8fmMWxs+28XdXMvlMtVDZ08NfyxiHP+h+5u5ib54ztReRAEkAucGbA62r8R/nDlckdZttsY0wdgDGmTkSGHEFJRO4F7rVedohIWQAxj0QG0DTG72k3rVPoCMd6hV2d7gyCOt3y35e1+bShFgaSAIY6PB2cni5WJpBtL8kY8zDw8Ei2GQkRKRnq1CiUaZ1CRzjWS+sUOgJpUKoGpgx4nQfUBljmUtvWW81EWH8bAg9bKaXU5QokAewBCkWkQETcwB3ApkFlNgF3id8yoM1q3rnUtpuAu63ndwMvXmZdlFJKjcCwTUDGGI+I3A9sxt+V81FjTKmIbLDW/wJ4BX8PoEr83UA/d6ltrbd+EHhWRO4BTgOfGNOaBW7cmpdspHUKHeFYL61TiBi2G6hSSqnwpLeCKqVUhNIEoJRSESpiE4CIrBaRMhGptO5EDjkiMkVE3hCRoyJSKiJfsZanicgWEamw/qbaHetIiYhTRN4VkZes1+FQpxQReU5Ejln/Z9eEer1E5F+t795hEXlaRGJCsU4i8qiINIjI4QHLLloPEfl3a99RJiK32hP15YvIBGANUfEzYA0wF1gvInPtjWpUPMDXjDFzgGXAfVY9HgC2GmMKga3W61DzFeDogNfhUKcfAa8aY2YDC/DXL2TrJSK5wJeBYmPMlfg7etxBaNbpMWD1oGVD1sP6jd0BXGFt83NrnxJyIjIBMGB4C2NMH3BhiIqQYoypuzDonjGmHf8OJRd/XR63ij0OfMSeCEdHRPKADwK/HrA41OuUBFwPPAJgjOkzxrQS4vXC35MwVkRcQBz++3xCrk7GmB3AuUGLL1aPdcAzxpheY8wJ/L0fl0xIoGMsUhPAxYauCFkikg9cBexm0DAbwJDDbASxHwJfB3wDloV6naYDjcBvrKatX4tIPCFcL2NMDfA9/N246/Df//MaIVynQS5Wj7DZf0RqArjsISqCiYgkAM8DXzXGnLc7nsshIv8ENBhj9todyxhzAYuAjcaYq4BOQqNp5KKsNvF1QAEwGYgXkU/bG9WECJv9R6QmgECGtwgJIhKFf+f/pDHmBWtxKA+zsQL4sIicxN80t1JEfkdo1wn837lqY8xu6/Vz+BNCKNfrFuCEMabRGNMPvAAsJ7TrNNDF6hE2+49ITQCBDG8R9KyJeB4Bjhpjvj9gVcgOs2GM+XdjTJ4xJh///8s2Y8ynCeE6ARhjzgJnRGSWtehm/MOih3K9TgPLRCTO+i7ejP86VCjXaaCL1WMTcIeIRItIAVAIvGNDfJfPGBORD/xDV5QDx4H/sDueUdbhWvynngeB/dZjLZCOv9dChfU3ze5YR1m/G4GXrOchXydgIVBi/X/9CUgN9XoB/wc4BhwGfgtEh2KdgKfxX8fox3+Ef8+l6gH8h7XvKAPW2B3/aB86FIRSSkWoSG0CUkqpiKcJQCmlIpQmAKWUilCaAJRSKkJpAlBKqQilCUBFLGs4hrnW828OWveWPVEpNXG0G6iKSCLiNMZ4B7zuMMYk2BlTIETEZYzx2B2HCg96BqDCgoh8XUS+bD3/gYhss57fbA0lgYh0iMh/ishu4BoR2S4ixSLyIP4RLfeLyJMXylp/b7TKXRjH/0nrrldEZK217E0R+fGFuQsGxXWFiLxjvfdBESm0lt9lvT4gIr+1lk0Tka3W8q0iMtVa/piIfF9E3gD+W0RmiMirIrJXRHaKyOxx/udVYUoTgAoXO4DrrOfFQII1TtK1wE5reTxw2Biz1Bjz5oUNjTEPAN3GmIXGmDuHeO+rgK/inztiOrBCRGKAX+K/C/RaIPMicW0AfmSMWWjFVS0iV+C/k3SlMWYB/rkPAH4KPGGMmQ88Cfx4wPsUAbcYY76Gf4LyLxljFgP/A/h5AP8+Sr2Py+4AlBoje4HFIpII9AL78O9wr8M/aQmAF//AeSP1jjGmGkBE9gP5QAdQZfzjwYN/KIF7h9j2beA/rDkOXjDGVIjISuA5Y0wTgDHmwjj01wC3Wc9/C3x3wPv8wRjjtUZ+XQ78wToRAf/wC0qNmCYAFRaMMf3WCKKfA97CP97OTcAM/jGzWM/Adv8R6B3w3Iv/dzPUkMBDxfWU1eT0QWCziHzB2jaQi28Dy3Rafx1Aq3VGodRl0SYgFU524G8S2YG/2WcDsN8E1tOh32oyCtQxYLo1EQ/AJ4cqJCLT8Z8p/Bj/KJLz8Q8sdruIpFtl0qzib+EfARXgTuDNQW+H8c/3cEJEPmFtKyKyYARxK/V3mgBUONkJ5ABvG2PqgR7+0f4/nIeBgxcuAg/HGNMNfBF4VUTeBOqBtiGKfhI4bDUdzcbfxl8K/BfwVxE5AFwYyvvLwOdE5CDwGf5xbWCwO4F7rG1LCcHpTFVw0G6gSo2SiCQYYzqsXkE/AyqMMT+wOy6lAqVnAEqN3j9bR/alQDL+XkFKhQw9A1BKqQilZwBKKRWhNAEopVSE0gSglFIRShOAUkpFKE0ASikVof4/EX6LdBgzuDEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['writing score'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Relation of target variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24bd5dd12e0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(),annot=True,cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Student performing good on one subject is expected to score good in the remaining too subjects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24bd62b0f70>"
      ]
     },
     "execution_count": 19,
     "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.scatterplot(x='math score',y='reading score',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24bd63009a0>"
      ]
     },
     "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.scatterplot(x='reading score',y='writing score',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24bd6367610>"
      ]
     },
     "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": [
    "sns.scatterplot(x='math score',y='writing score',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Per column analysis to be continued"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparision of marks based on various factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>math score</th>\n",
       "      <th>reading score</th>\n",
       "      <th>writing score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>63.633205</td>\n",
       "      <td>72.608108</td>\n",
       "      <td>72.467181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>68.728216</td>\n",
       "      <td>65.473029</td>\n",
       "      <td>63.311203</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        math score  reading score  writing score\n",
       "gender                                          \n",
       "female   63.633205      72.608108      72.467181\n",
       "male     68.728216      65.473029      63.311203"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(by='gender').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24bd63fd4f0>"
      ]
     },
     "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": [
    "df.groupby(by='gender').mean().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Female students tend to score more marks in reading and writing while male students score more in maths."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>math score</th>\n",
       "      <th>reading score</th>\n",
       "      <th>writing score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race/ethnicity</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>group A</th>\n",
       "      <td>61.629213</td>\n",
       "      <td>64.674157</td>\n",
       "      <td>62.674157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group B</th>\n",
       "      <td>63.452632</td>\n",
       "      <td>67.352632</td>\n",
       "      <td>65.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group C</th>\n",
       "      <td>64.463950</td>\n",
       "      <td>69.103448</td>\n",
       "      <td>67.827586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group D</th>\n",
       "      <td>67.362595</td>\n",
       "      <td>70.030534</td>\n",
       "      <td>70.145038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group E</th>\n",
       "      <td>73.821429</td>\n",
       "      <td>73.028571</td>\n",
       "      <td>71.407143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                math score  reading score  writing score\n",
       "race/ethnicity                                          \n",
       "group A          61.629213      64.674157      62.674157\n",
       "group B          63.452632      67.352632      65.600000\n",
       "group C          64.463950      69.103448      67.827586\n",
       "group D          67.362595      70.030534      70.145038\n",
       "group E          73.821429      73.028571      71.407143"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(by='race/ethnicity').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24bd5e682b0>"
      ]
     },
     "execution_count": 26,
     "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": [
    "df.groupby(by=['race/ethnicity']).mean().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Students of race/ethnicity of group E tend to score more marks in all subjects than students of other groups."
   ]
  },
  {
   "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>math score</th>\n",
       "      <th>reading score</th>\n",
       "      <th>writing score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>parental level of education</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>associate's degree</th>\n",
       "      <td>67.882883</td>\n",
       "      <td>70.927928</td>\n",
       "      <td>69.896396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bachelor's degree</th>\n",
       "      <td>69.389831</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>73.381356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high school</th>\n",
       "      <td>62.137755</td>\n",
       "      <td>64.704082</td>\n",
       "      <td>62.448980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>master's degree</th>\n",
       "      <td>69.745763</td>\n",
       "      <td>75.372881</td>\n",
       "      <td>75.677966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>some college</th>\n",
       "      <td>67.128319</td>\n",
       "      <td>69.460177</td>\n",
       "      <td>68.840708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>some high school</th>\n",
       "      <td>63.497207</td>\n",
       "      <td>66.938547</td>\n",
       "      <td>64.888268</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             math score  reading score  writing score\n",
       "parental level of education                                          \n",
       "associate's degree            67.882883      70.927928      69.896396\n",
       "bachelor's degree             69.389831      73.000000      73.381356\n",
       "high school                   62.137755      64.704082      62.448980\n",
       "master's degree               69.745763      75.372881      75.677966\n",
       "some college                  67.128319      69.460177      68.840708\n",
       "some high school              63.497207      66.938547      64.888268"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(by='parental level of education').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24bd64e9130>"
      ]
     },
     "execution_count": 28,
     "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": [
    "df.groupby(by=['parental level of education']).mean().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Students score tend to increase directly as per their parent's education. Parents having master's degree have a significant\n",
    "impact on students to score more than those parents who only have a high school degree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>math score</th>\n",
       "      <th>reading score</th>\n",
       "      <th>writing score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lunch</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>free/reduced</th>\n",
       "      <td>58.921127</td>\n",
       "      <td>64.653521</td>\n",
       "      <td>63.022535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>standard</th>\n",
       "      <td>70.034109</td>\n",
       "      <td>71.654264</td>\n",
       "      <td>70.823256</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              math score  reading score  writing score\n",
       "lunch                                                 \n",
       "free/reduced   58.921127      64.653521      63.022535\n",
       "standard       70.034109      71.654264      70.823256"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(by='lunch').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24bd658c340>"
      ]
     },
     "execution_count": 30,
     "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": [
    "df.groupby(by=['lunch']).mean().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Students scoring more who pay standard fee for their lunch can be reasoned by the fact that they might be capable of having better study environment due to their financial status. This relates directly with the parental level of education as higher educations usually leads to higher salary and financial stability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>math score</th>\n",
       "      <th>reading score</th>\n",
       "      <th>writing score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test preparation course</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>completed</th>\n",
       "      <td>69.695531</td>\n",
       "      <td>73.893855</td>\n",
       "      <td>74.418994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>none</th>\n",
       "      <td>64.077882</td>\n",
       "      <td>66.534268</td>\n",
       "      <td>64.504673</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         math score  reading score  writing score\n",
       "test preparation course                                          \n",
       "completed                 69.695531      73.893855      74.418994\n",
       "none                      64.077882      66.534268      64.504673"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(by=['test preparation course']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24bd75c3a00>"
      ]
     },
     "execution_count": 32,
     "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": [
    "df.groupby(by=['test preparation course']).mean().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, students completing test preparation course score more than students who do not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#End of Explorary Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Machine Learning using Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier,RandomForestRegressor\n",
    "from sklearn.metrics import mean_absolute_error,mean_squared_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preparing data for Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.get_dummies(data=df,drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>math score</th>\n",
       "      <th>reading score</th>\n",
       "      <th>writing score</th>\n",
       "      <th>gender_male</th>\n",
       "      <th>race/ethnicity_group B</th>\n",
       "      <th>race/ethnicity_group C</th>\n",
       "      <th>race/ethnicity_group D</th>\n",
       "      <th>race/ethnicity_group E</th>\n",
       "      <th>parental level of education_bachelor's degree</th>\n",
       "      <th>parental level of education_high school</th>\n",
       "      <th>parental level of education_master's degree</th>\n",
       "      <th>parental level of education_some college</th>\n",
       "      <th>parental level of education_some high school</th>\n",
       "      <th>lunch_standard</th>\n",
       "      <th>test preparation course_none</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>69</td>\n",
       "      <td>90</td>\n",
       "      <td>88</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90</td>\n",
       "      <td>95</td>\n",
       "      <td>93</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>57</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>76</td>\n",
       "      <td>78</td>\n",
       "      <td>75</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   math score  reading score  writing score  gender_male  \\\n",
       "0          72             72             74            0   \n",
       "1          69             90             88            0   \n",
       "2          90             95             93            0   \n",
       "3          47             57             44            1   \n",
       "4          76             78             75            1   \n",
       "\n",
       "   race/ethnicity_group B  race/ethnicity_group C  race/ethnicity_group D  \\\n",
       "0                       1                       0                       0   \n",
       "1                       0                       1                       0   \n",
       "2                       1                       0                       0   \n",
       "3                       0                       0                       0   \n",
       "4                       0                       1                       0   \n",
       "\n",
       "   race/ethnicity_group E  parental level of education_bachelor's degree  \\\n",
       "0                       0                                              1   \n",
       "1                       0                                              0   \n",
       "2                       0                                              0   \n",
       "3                       0                                              0   \n",
       "4                       0                                              0   \n",
       "\n",
       "   parental level of education_high school  \\\n",
       "0                                        0   \n",
       "1                                        0   \n",
       "2                                        0   \n",
       "3                                        0   \n",
       "4                                        0   \n",
       "\n",
       "   parental level of education_master's degree  \\\n",
       "0                                            0   \n",
       "1                                            0   \n",
       "2                                            1   \n",
       "3                                            0   \n",
       "4                                            0   \n",
       "\n",
       "   parental level of education_some college  \\\n",
       "0                                         0   \n",
       "1                                         1   \n",
       "2                                         0   \n",
       "3                                         0   \n",
       "4                                         1   \n",
       "\n",
       "   parental level of education_some high school  lunch_standard  \\\n",
       "0                                             0               1   \n",
       "1                                             0               1   \n",
       "2                                             0               1   \n",
       "3                                             0               0   \n",
       "4                                             0               1   \n",
       "\n",
       "   test preparation course_none  \n",
       "0                             1  \n",
       "1                             0  \n",
       "2                             1  \n",
       "3                             1  \n",
       "4                             1  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Separating and Splitting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df1.drop(['math score','reading score','writing score'],axis=1)\n",
    "y_maths = df1['math score']\n",
    "y_reading = df1['reading score']\n",
    "y_writing = df1['writing score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train_maths, y_test_maths = train_test_split(X, y_maths, test_size=0.33, random_state=42)\n",
    "X_train, X_test, y_train_reading, y_test_reading = train_test_split(X, y_reading, test_size=0.33, random_state=42)\n",
    "X_train, X_test, y_train_writing, y_test_writing = train_test_split(X, y_writing, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfr = RandomForestRegressor(min_samples_leaf=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For maths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.1991433841052\n"
     ]
    }
   ],
   "source": [
    "rfr.fit(X_train,y_train_maths)\n",
    "pred_maths = rfr.predict(X_test)\n",
    "print(mean_absolute_error(y_test_maths,pred_maths))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For reading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.949988835973853\n"
     ]
    }
   ],
   "source": [
    "rfr.fit(X_train,y_train_reading)\n",
    "pred_reading = rfr.predict(X_test)\n",
    "print(mean_absolute_error(y_test_reading,pred_reading))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For writing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.662669946226027\n"
     ]
    }
   ],
   "source": [
    "rfr.fit(X_train,y_train_writing)\n",
    "pred_writing = rfr.predict(X_test)\n",
    "print(mean_absolute_error(y_test_writing,pred_writing))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "End of Machine Learning Section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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