Student-Performance-EDA.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fab7589b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Necessary Libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "52a606b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the data\n",
    "data = pd.read_csv('StudentsPerformance.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4a4066a0",
   "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>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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prints the first 5 rows\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d5a9558a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 8)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checks the rows and column\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cad66c5a",
   "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": [
    "# Gives the information about the dataframe\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1d180416",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender                         0\n",
       "race/ethnicity                 0\n",
       "parental level of education    0\n",
       "lunch                          0\n",
       "test preparation course        0\n",
       "math score                     0\n",
       "reading score                  0\n",
       "writing score                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checks the null values in the dataframe\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e37806bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Statistical Measure\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0d0cbae7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>math score</th>\n",
       "      <th>reading score</th>\n",
       "      <th>writing score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>math score</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.817580</td>\n",
       "      <td>0.802642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reading score</th>\n",
       "      <td>0.817580</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.954598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>writing score</th>\n",
       "      <td>0.802642</td>\n",
       "      <td>0.954598</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "               math score  reading score  writing score\n",
       "math score       1.000000       0.817580       0.802642\n",
       "reading score    0.817580       1.000000       0.954598\n",
       "writing score    0.802642       0.954598       1.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checks the correlation \n",
    "data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5c333ea1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.title('Correlation Matrix')\n",
    "sns.heatmap(data.corr(), annot=True, fmt='.1f', cbar=True, cmap='Wistia');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "80742845",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['female' 'male']\n",
      "['group B' 'group C' 'group A' 'group D' 'group E']\n",
      "[\"bachelor's degree\" 'some college' \"master's degree\" \"associate's degree\"\n",
      " 'high school' 'some high school']\n",
      "['standard' 'free/reduced']\n",
      "['none' 'completed']\n"
     ]
    }
   ],
   "source": [
    "# Prints the unique values\n",
    "print(data['gender'].unique())\n",
    "print(data['race/ethnicity'].unique())\n",
    "print(data['parental level of education'].unique())\n",
    "print(data['lunch'].unique())\n",
    "print(data['test preparation course'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "860cf8ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65    36\n",
       "62    35\n",
       "69    32\n",
       "59    32\n",
       "61    27\n",
       "      ..\n",
       "24     1\n",
       "28     1\n",
       "33     1\n",
       "18     1\n",
       "8      1\n",
       "Name: math score, Length: 81, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['math score'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "839eec53",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.figure(figsize=(12,6))\n",
    "plt.title('Math Score Distribution')\n",
    "sns.histplot(data=data, \n",
    "             x='math score', \n",
    "             color='red', \n",
    "             kde=True)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "befac2e0",
   "metadata": {},
   "source": [
    "## Insight\n",
    "Data is skewed to the left, that means mean is less than the median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d20ede36",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.figure(figsize=(12,6))\n",
    "plt.title('Math Score Distribution')\n",
    "sns.boxplot(data=data, x='math score', palette='Pastel1')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3631e67",
   "metadata": {},
   "source": [
    "## Insight\n",
    "As you can see in math score have some outlires."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6536d4b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72    34\n",
       "74    33\n",
       "64    32\n",
       "67    30\n",
       "73    30\n",
       "      ..\n",
       "28     1\n",
       "26     1\n",
       "17     1\n",
       "32     1\n",
       "40     1\n",
       "Name: reading score, Length: 72, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['reading score'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "13fb6b42",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.figure(figsize=(12,6))\n",
    "plt.title('Reading Score Distribution')\n",
    "sns.histplot(data=data, \n",
    "             x='reading score', \n",
    "             color='blue', \n",
    "             kde=True)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32345ca9",
   "metadata": {},
   "source": [
    "## Insight\n",
    "Data is skewed to the left in reading score, that means mean is less than the median."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fea5a3cb",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.figure(figsize=(12,6))\n",
    "plt.title('Reading Score Distribution')\n",
    "sns.boxplot(data=data, x='reading score', palette='viridis')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d71bf31",
   "metadata": {},
   "source": [
    "## Insight\n",
    "As you can see in Reading score have some outlires."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0c113400",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "74    35\n",
       "70    33\n",
       "68    31\n",
       "73    28\n",
       "80    27\n",
       "      ..\n",
       "28     1\n",
       "35     1\n",
       "19     1\n",
       "22     1\n",
       "23     1\n",
       "Name: writing score, Length: 77, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['writing score'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5fc053fe",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.figure(figsize=(12,6))\n",
    "plt.title('Writing Score Distribution')\n",
    "sns.histplot(data=data, \n",
    "             x='writing score', \n",
    "             color='orange', \n",
    "             kde=True)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b463739e",
   "metadata": {},
   "source": [
    "## Insight\n",
    "Data is skewed to the left in writing score, that means mean is less than the median."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6dc3200c",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.figure(figsize=(12,6))\n",
    "plt.title('Writing Score Distribution')\n",
    "sns.boxplot(data=data, x='writing score', palette='icefire')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c178d9c",
   "metadata": {},
   "source": [
    "## Insight\n",
    "As you can see in writing score have some outlires."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "07cc670b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "female    518\n",
       "male      482\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "30946910",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.figure(figsize=(12,6))\n",
    "plt.title('Gender')\n",
    "sns.countplot(data=data, x='gender', palette='Accent')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdd12113",
   "metadata": {},
   "source": [
    "## Insight\n",
    "Female's are more than Male's."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1e015a56",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.figure(figsize=(12,6))\n",
    "plt.title('Gender VS Math Score')\n",
    "sns.barplot(data=data, \n",
    "            x='gender',  \n",
    "            y='math score', \n",
    "            palette='Accent_r')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4e89560",
   "metadata": {},
   "source": [
    "## Insight\n",
    "Male have more score in math's than female."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "644d31b1",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.figure(figsize=(12,6))\n",
    "plt.title('Gender VS Reading Score')\n",
    "sns.barplot(data=data, \n",
    "            x='gender',  \n",
    "            y='reading score', \n",
    "            palette='PRGn')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0a3a7d1",
   "metadata": {},
   "source": [
    "## Insight\n",
    "Female have more score in reading than male."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b189d5ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ykp9P8oUkJyc5etjt6CQnzasGAADYEeY5PWP/JG+vqs3neUN3v6eqPpnkzVV1TJILkjxijjUAAMC1NrfQ3N3nJTlsifavJbnfvM4LAAA7micCAgDACKEZAABGCM0AADBCaAYAgBFCMwAAjBCaAQBghNAMAAAjhGYAABghNAMAwAihGQAARgjNAAAwQmgGAIARo6G5qu5QVadW1ReG9TtX1f+Zf2kAALAYZhlp/ockv5/ke0nS3Z9L8sh5FgUAAItkltB8o+7+xBZtV82jGAAAWESzhOavVtVtk3SSVNXDk1w816oAAGCB7DbDPr+Z5Pgkh1TVhiTnJ3n0XKsCAIAFstXQXFW7Jnlyd9+/qvZKskt3X7EypQEAwGLYamju7u9X1b2G5StXpiQAAFgss0zP+HRVnZzkxCQ/CM7d/ba5VQUAAAtkltC8Z5KvJbnvVFsnEZoBALheGA3N3f2ElSgEAAAW1SxPBDyoqt5eVZcOr7dW1UErURwAACyCWe7T/JokJye5xfB6x9AGAADXC7OE5jXd/Zruvmp4nZBkzZzrAgCAhTFLaP5aVT2mqnYdXo/J5MJAAAC4XpglND8xySOSbMzk8dkPT+LiQAAArjdmuXvGBUkesgK1AADAQprl7hnrqmrfqfWbVNWr51oVAAAskFmmZ9y5uy/bvNLd30hy17lVBAAAC2aW0LxLVd1k80pV7ZfZniQIAADXCbOE35ckOb2qTkxSmVwI+Ly5VgUAAAtklgsBX1tV65Pcd2h6aHefNd+yAABgccxyIeBtk3y5u1+R5AtJ7j99YeAMx+9aVZ+uqncO67epqo9X1Zeq6k1Vtcf2Fg8AACthljnNb03y/aq6XZK/T3KrJG/YhnM8LcnZU+svSPJX3X27JN9Icsw29AUAACtultB8dXdfleShSV7R3b+X5MBZOq+qg5I8KMk/DuuVyTSPtwy7rEty1DbWDAAAK2qW0Py9qnpUkscleefQtvuM/b80yTOTXD2s3zTJZUMIT5KLktxyqQOr6tiqWl9V6zdt2jTj6QAAYMebJTQ/Ick9kjyvu8+vqtsked3YQVX1y0ku7e4ztqew7j6+u9d299o1a9ZsTxcAALBDzHL3jLOS/NbU+vmZzEsec0SSh1TVLyXZM8mPJHlZkn2rardhtPmgJBu2p3AAAFgps4w0b5fu/v3uPqi7D07yyCTv6+5HJ3l/Jvd6TpKjk5w0rxoAAGBHmFto3opnJfndqvpSJnOcX7UKNQAAwMxW5HHY3f2BJB8Yls9LcveVOC8AAOwIo6G5qt6RpLdo/s8k65P8fXd/ex6FAQDAophlesZ5Sb6Z5B+G1+VJrkhyh2EdAACu02aZnnHP7j58av0dVfXJ7j68qs6cV2EAALAoZhlp3ruqbr15ZVjee1j97lyqAgCABTLLSPPTk3y4qr6cpJLcJsmTq2qvTB6DDQAA12mzPNzkXVV1+ySHDE3nTF3899J5FQYAAIti1lvO3S3JwcP+h1VVuvu1c6sKAAAWyCy3nHtdktsm+UyS7w/NnURoBgDgemGWkea1SQ7t7i3v1QwAANcLs9w94wtJDph3IQAAsKhmGWm+WZKzquoTSb6zubG7HzK3qgAAYIHMEpqfPe8iAABgkc1yy7kPrkQhAACwqJYNzVX14e6+V1VdkcndMn6wKUl394/MvToAAFgAy4bm7r7X8HOflSsHAAAWz+jdM4b7NI+2AQDAddUst5z7iemVqtotkycEAgDA9cKyobmqfn+Yz3znqrp8eF2R5JIkJ61YhQAAsMqWDc3d/RfDfOYXdfePDK99uvum3f37K1gjAACsqq3dPeOQ7v5ikhOr6ie33N7dn5prZQAAsCC2dp/m301ybJKXLLGtk9x3LhUBAMCC2dot546tql2S/J/u/sgK1gQAAAtlq3fP6O6rk7xihWoBAICFNMst506tqodVVc29GgAAWECzhOb/leTEJN/ZfNu5qrp8znUBAMDC2NqFgEk8RhsAAGZ6jHZV/UZVHbISBQEAwKKZZXrGq5McmOTlVXVeVb21qp4257oAAGBhzDI94/1VdVqSw5PcJ8mTkvxEkpfNuTYAAFgIo6G5qk5NsleS05N8KMnh3X3pvAsDAIBFMcv0jM8l+W6SOyW5c5I7VdUN51oVAAAskFmmZ/xOklTVPkken+Q1SQ5IcoO5VgYAAAtilukZT0nyM0nuluQrmVwY+KH5lgUAAItjNDQn2TPJXyY5o7uvmrXjqtozyWmZjEjvluQt3f0nVXWbJP+U5KZJzkjy2O7+7jZXDgAAK2R0TnN3v7i7P74tgXnwnST37e7DktwlyS9W1U8neUGSv+ru2yX5RpJjtrFfAABYUbNcCLhdeuKbw+ruw6uT3DfJW4b2dUmOmlcNAACwI8wtNCdJVe1aVZ9JcmmSU5J8OcllU6PWFyW55TLHHltV66tq/aZNm+ZZJgAAbNVcQ3N3f7+775LkoCR3TzLzo7i7+/juXtvda9esWTOvEgEAYNRcQ/Nm3X1ZkvcnuUeSfatq8wWIByXZsBI1AADA9ppbaK6qNVW177B8wyQPSHJ2JuH54cNuRyc5aV41AADAjjDLLee214FJ1lXVrpmE8zd39zur6qwk/1RVz03y6SSvmmMNAABwrc0tNHf355LcdYn28zKZ3wwAADuFFZnTDAAAOzOhGQAARgjNAAAwQmgGAIARQjMAAIwQmgEAYITQDAAAI4RmAAAYITQDAMAIoRkAAEYIzQAAMEJoBgCAEUIzAACMEJoBAGCE0AwAACOEZgAAGCE0AwDACKEZAABGCM0AADBCaAYAgBFCMwAAjBCaAQBghNAMAAAjhGYAABghNAMAwAihGQAARgjNAAAwQmgGAIARQjMAAIwQmgEAYITQDAAAI4RmAAAYITQDAMCIuYXmqrpVVb2/qs6qqjOr6mlD+35VdUpVnTv8vMm8agAAgB1hniPNVyV5encfmuSnk/xmVR2a5Lgkp3b37ZOcOqwDAMDCmlto7u6Lu/tTw/IVSc5OcsskRyZZN+y2LslR86oBAAB2hBWZ01xVBye5a5KPJ9m/uy8eNm1Msv9K1AAAANtr7qG5qvZO8tYkv93dl09v6+5O0sscd2xVra+q9Zs2bZp3mQAAsKy5huaq2j2TwPz67n7b0HxJVR04bD8wyaVLHdvdx3f32u5eu2bNmnmWCQAAWzXPu2dUklclObu7/3Jq08lJjh6Wj05y0rxqAACAHWG3OfZ9RJLHJvl8VX1maPuDJM9P8uaqOibJBUkeMccaAADgWptbaO7uDyepZTbfb17nBQCAHc0TAQEAYITQDAAAI4RmAAAYITQDAMAIoRkAAEYIzQAAMEJoBgCAEUIzAACMEJoBAGCE0AwAACOEZgAAGCE0AwDACKEZAABGCM0AADBCaAYAgBFCMwAAjBCaAQBghNAMAAAjhGYAABghNAMAwAihGQAARgjNAAAwQmgGAIARQjMAAIwQmgEAYITQDAAAI4RmAAAYITQDAMAIoRkAAEYIzQAAMEJoBgCAEUIzAACMEJoBAGDE3EJzVb26qi6tqi9Mte1XVadU1bnDz5vM6/wAALCjzHOk+YQkv7hF23FJTu3u2yc5dVgHAICFNrfQ3N2nJfn6Fs1HJlk3LK9LctS8zg8AADvKSs9p3r+7Lx6WNybZf4XPDwAA22zVLgTs7k7Sy22vqmOran1Vrd+0adMKVgYAANe00qH5kqo6MEmGn5cut2N3H9/da7t77Zo1a1asQAAA2NJKh+aTkxw9LB+d5KQVPj8AAGyzed5y7o1JTk9yx6q6qKqOSfL8JA+oqnOT3H9YBwCAhbbbvDru7kcts+l+8zonAADMgycCAgDACKEZAABGCM0AADBCaAYAgBFCMwAAjBCaAQBghNAMAAAjhGYAABghNAMAwAihGQAARgjNAAAwQmgGAIARQjMAAIwQmgEAYITQDAAAI4RmAAAYITQDAMAIoRkAAEYIzQAAMEJoBgCAEUIzAACMEJoBAGCE0AwAACOEZgAAGCE0AwDACKEZAABGCM0AADBCaAYAgBFCMwAAjBCaAQBghNAMAAAjhGYAABghNAMAwIhVCc1V9YtVdU5VfamqjluNGgAAYFYrHpqratckf5PkgUkOTfKoqjp0pesAAIBZrcZI892TfKm7z+vu7yb5pyRHrkIdAAAwk9UIzbdMcuHU+kVDGwAALKTdVruA5VTVsUmOHVa/WVXnrGY9sIybJfnqahfBgnlOrXYFsOh8d/JD6rcW4rvzR5fbsBqheUOSW02tHzS0XUN3H5/k+JUqCrZHVa3v7rWrXQfAzsR3Jzuj1Zie8ckkt6+q21TVHkkemeTkVagDAABmsuIjzd19VVU9Jcl7k+ya5NXdfeZK1wEAALNalTnN3f2uJO9ajXPDDmYKEcC2893JTqe6e7VrAACAheYx2gAAMEJo5nqtqn6rqs6uqtfPqf9nV9Uz5tE3wHVFVf1cVb1zteuArVnY+zTDCnlykvt390WrXQgAsLiMNHO9VVWvTPJjSd5dVX9YVa+uqk9U1aer6shhn8dX1T9X1SlV9ZWqekpV/e6wz8eqar9hv9+oqk9W1Wer6q1VdaMlznfbqnpPVZ1RVR+qqkNW9h0DzE9VHVxVX6yqE6rq36rq9VV1/6r6SFWdW1V3H16nD9+hH62qOy7Rz15LfR/DahOaud7q7icl+Y8k90myV5L3dffdh/UXVdVew653SvLQJIcneV6Sb3X3XZOcnuRxwz5v6+7Du/uwJGcnOWaJUx6f5Kndfbckz0jyt/N5ZwCr5nZJXpLkkOH160nulcl33h8k+WKSnxm+Q/84yZ8v0ccfZvnvY1g1pmfAxM8necjU/OM9k9x6WH5/d1+R5Iqq+s8k7xjaP5/kzsPynarquUn2TbJ3Jvch/4Gq2jvJPZOcWPWDx4TeYA7vA2A1nd/dn0+Sqjozyand3VX1+SQHJ7lxknVVdfsknWT3JfpY7vv47HkXD1sjNMNEJXlYd59zjcaqn0rynammq6fWr85//zd0QpKjuvuzVfX4JD+3Rf+7JLmsu++yQ6sGWCxj35d/lslAxK9U1cFJPrBEH0t+H8NqMz0DJt6b5Kk1DANX1V238fh9klxcVbsnefSWG7v78iTnV9WvDv1XVR12LWsG2NncOMmGYfnxy+xzbb+PYS6EZpj4s0x+Tfi54VeKf7aNx/9Rko8n+Ugmc/aW8ugkx1TVZ5OcmcTFLcD1zQuT/EVVfTrL/7b72n4fw1x4IiAAAIww0gwAACOEZgAAGCE0AwDACKEZAABGCM0AADBCaAa4nqiqE6rq4atdB8DOSGgGYElV5amxAAOhGWABVdUfVdU5VfXhqnpjVT2jqm5bVe+pqjOq6kNVdciw7wlV9ddV9dGqOm/zaPLw5MlXDP38a5KbT/V/t6r64NDXe6vqwKH9A1X10qpan+Rpq/HeARaRUQSABVNVhyd5WJLDMnky2qeSnJHk+CRP6u5zq+qnkvxtkvsOhx2Y5F5JDklycpK3JPmVJHdMcmiS/ZOcleTVw+PeX57kyO7eVFW/luR5SZ449LVHd6+d+xsF2IkIzQCL54gkJ3X3t5N8u6rekWTPJPdMcmJVbd7vBlPH/HN3X53krKraf2i7d5I3dvf3k/xHVb1vaL9jkjslOWXoa9ckF0/19aY5vCeAnZrQDLBz2CXJZd19l2W2f2dquZbZZ3r7md19j2W2X7mNtQFc55nTDLB4PpLkwVW1Z1XtneSXk3wryflV9avJD+YrHzbSz2lJfq2qdh3mLN9naD8nyZqqusfQ1+5V9RNzeScA1xFCM8CC6e5PZjIv+XNJ3p3k80n+M8mjkxxTVZ9NcmaSI0e6enuSczOZy/zaJKcP/X83ycOTvGDo6zOZTP0AYBnV3atdAwBbqKq9u/ubVXWjTEaMj+3uT612XQDXV+Y0Ayym46vq0EwuAFwnMAOsLiPNAAAwwpxmAAAYITQDAMAIoRkAAEYIzQAAMEJoBgCAEUIzAACM+P9ZiLDnv/M0QgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.title('Gender VS Writing Score')\n",
    "sns.barplot(data=data, \n",
    "            x='gender',  \n",
    "            y='writing score', \n",
    "            palette='Paired_r')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ec4cdf2",
   "metadata": {},
   "source": [
    "## Insight\n",
    "Female have score more in writing than male."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9be5a14d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "group C    319\n",
       "group D    262\n",
       "group B    190\n",
       "group E    140\n",
       "group A     89\n",
       "Name: race/ethnicity, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['race/ethnicity'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ef22e7b1",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.figure(figsize=(12,6))\n",
    "plt.title('Ethnicity')\n",
    "sns.countplot(data=data, x='race/ethnicity', palette='RdYlBu')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f51264ca",
   "metadata": {},
   "source": [
    "## Insight\n",
    "Based on ethnicity Group C have more people."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "75f08695",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.figure(figsize=(12,6))\n",
    "plt.title('Ethnicity Based On Gender')\n",
    "sns.countplot(data=data, \n",
    "              x='race/ethnicity', \n",
    "              hue='gender', \n",
    "              palette='gist_earth')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b74de63",
   "metadata": {},
   "source": [
    "## Insight\n",
    "Group A, D and E have more male than female.\n",
    "Group B and C have more female than male."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "aacbe5ad",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.figure(figsize=(12,6))\n",
    "plt.title('Ethnicity VS Math Score')\n",
    "sns.barplot(data=data, \n",
    "            x='race/ethnicity', \n",
    "            y='math score', \n",
    "            palette='Dark2_r')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f8a4e7f",
   "metadata": {},
   "source": [
    "## Insight\n",
    "Group E scores more in math's as compared to other groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f60aabef",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.figure(figsize=(12,6))\n",
    "plt.title('Ethnicity VS Reading Score')\n",
    "sns.barplot(data=data, \n",
    "            x='race/ethnicity', \n",
    "            y='reading score', \n",
    "            palette='Purples')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f13d14eb",
   "metadata": {},
   "source": [
    "## Insight\n",
    "Group E scores more in reading as compared to other groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f5e464b4",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.figure(figsize=(12,6))\n",
    "plt.title('Ethnicity VS Writing Score')\n",
    "sns.barplot(data=data, \n",
    "            x='race/ethnicity', \n",
    "            y='writing score', \n",
    "            palette='pink')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1822e71",
   "metadata": {},
   "source": [
    "## Insight\n",
    "Group E and D scores more in writing as compared to other groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e76d173a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "some college          226\n",
       "associate's degree    222\n",
       "high school           196\n",
       "some high school      179\n",
       "bachelor's degree     118\n",
       "master's degree        59\n",
       "Name: parental level of education, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['parental level of education'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e7cbe062",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.figure(figsize=(12,6))\n",
    "plt.title('Parental Education')\n",
    "sns.countplot(data=data, x='parental level of education', palette='vlag')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b977857c",
   "metadata": {},
   "source": [
    "## Insight\n",
    "Parental Education of student have more college degree and associate degree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "41ea39dc",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.figure(figsize=(12,6))\n",
    "plt.title('Parental Education Based On Gender')\n",
    "sns.countplot(data=data, \n",
    "              x='parental level of education', \n",
    "              hue='gender', \n",
    "              palette='seismic')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bc7b7f2",
   "metadata": {},
   "source": [
    "## Insight\n",
    "Female student parent's are more educated than male student."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "961ef473",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "standard        645\n",
       "free/reduced    355\n",
       "Name: lunch, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['lunch'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5e07a1ce",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.figure(figsize=(12,6))\n",
    "plt.title('Lunch')\n",
    "sns.countplot(data=data, x='lunch', palette='coolwarm_r')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdf44c4e",
   "metadata": {},
   "source": [
    "## Insight\n",
    "Student's prefer standard lunch as compared to free lunch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f28fd103",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.figure(figsize=(12,6))\n",
    "plt.title('Lunch Based On Gender')\n",
    "sns.countplot(data=data, \n",
    "              x='lunch', \n",
    "              palette='cubehelix_r', \n",
    "              hue='gender', \n",
    "              dodge=True)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49e59862",
   "metadata": {},
   "source": [
    "## Insight\n",
    "More female student prefer both standard and free lunch as compared to male student. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d7dd1bc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "none         642\n",
       "completed    358\n",
       "Name: test preparation course, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['test preparation course'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a12c3b0d",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.figure(figsize=(12,6))\n",
    "plt.title('Test Prepration Course')\n",
    "sns.countplot(data=data, x='test preparation course', palette='PuOr')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36d0a5a9",
   "metadata": {},
   "source": [
    "## Insight\n",
    "Majortiy of students are not completed there test prepration course."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b837464d",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.figure(figsize=(12,6))\n",
    "plt.title('Test Prepration Course Based On Gender')\n",
    "sns.countplot(data=data, \n",
    "              x='test preparation course', \n",
    "              hue='gender', \n",
    "              palette='bone')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9b1a348",
   "metadata": {},
   "source": [
    "## Insight\n",
    "Majority of female students are more who have completed or not completed there test prepration course."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "af93baa3",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.figure(figsize=(12,6))\n",
    "plt.title('Test Prepration Course VS Math Score')\n",
    "sns.barplot(data=data, \n",
    "            x='test preparation course', \n",
    "            y='math score', \n",
    "            hue='gender', \n",
    "            palette='magma')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9be3ebb4",
   "metadata": {},
   "source": [
    "## Insight\n",
    "Majority of male student are score more in maths as compared to female student, if there test prepration course is complete or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a612d2ed",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.figure(figsize=(12,6))\n",
    "plt.title('Test Prepration Course VS Reading Score')\n",
    "sns.barplot(data=data, \n",
    "            x='test preparation course', \n",
    "            y='reading score', \n",
    "            hue='gender', \n",
    "            palette='inferno')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb698341",
   "metadata": {},
   "source": [
    "## Insight\n",
    "Majority of female student are score more in reading as compared to female student, if there test prepration course is complete or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b302d90a",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.figure(figsize=(12,6))\n",
    "plt.title('Test Prepration Course VS Writing Score')\n",
    "sns.barplot(data=data, \n",
    "            x='test preparation course', \n",
    "            y='writing score', \n",
    "            hue='gender', \n",
    "            palette='copper')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e606883",
   "metadata": {},
   "source": [
    "## Insight\n",
    "Majority of female student are score more in Writing as compared to female student,  if there test prepration course is complete or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1552846a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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