Institutional Performance Analysis in Board Exams 2023

Dataset

Files

Analysis Overview

1. Exploratory Data Analysis (EDA)

2. Clustering Analysis

Installation and Setup

pip install pandas matplotlib seaborn scikit-learn

Usage

Code Highlights

EDA Code

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

data = pd.read_csv('Institutional Result of Board Exams.csv')
print(data.head())

grades = ['Grade A+', 'A', 'B', 'C', 'D', 'E']
grade_counts = data[grades].sum()

plt.figure(figsize=(10, 6))
sns.barplot(x=grade_counts.index, y=grade_counts.values, palette='viridis')
plt.title('Total Students Achieving Each Grade')
plt.xlabel('Grade')
plt.ylabel('Number of Students')
plt.show()

print(data.isnull().sum())
data.fillna(data.mean(), inplace=True)
print(data.isnull().sum())

Clustering Code

from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans

scaler = StandardScaler()
X_scaled = scaler.fit_transform(data[['Pass%', 'Appeared']])

kmeans = KMeans(n_clusters=3, random_state=42)
kmeans.fit(X_scaled)

data['Cluster'] = kmeans.labels_

plt.figure(figsize=(10, 6))
sns.scatterplot(x='Pass%', y='Appeared', hue='Cluster', data=data, palette='viridis')
plt.title('K-Means Clustering of Institutions')
plt.xlabel('Passing Percentage')
plt.ylabel('Number of Students Appeared')
plt.legend(title='Cluster')
plt.show()

Results

Contributing

License