analysis.py

# Student Performance Analysis
# Simple data analysis project for beginners

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

# Load the dataset
print("Loading student data...")
df = pd.read_csv('student_performance.csv')

print("\n" + "="*50)
print("STEP 1: DATA CLEANING")
print("="*50)

# Check the data
print(f"\nTotal students: {len(df)}")
print(f"\nColumns in dataset: {list(df.columns)}")

# Check for missing values
print("\nMissing values in each column:")
print(df.isnull().sum())

# Clean the data - remove rows with missing final_score
print("\nRemoving rows with missing final scores...")
df_clean = df.dropna(subset=['final_score'])

# Fill missing values in other columns with median
print("Filling missing values with median...")
for col in df_clean.columns:
    if df_clean[col].isnull().any():
        df_clean[col].fillna(df_clean[col].median(), inplace=True)

# Remove any negative or invalid values
print("Removing invalid data (negative values)...")
df_clean = df_clean[(df_clean['internet_usage'] >= 0) & 
                    (df_clean['sleep_hours'] >= 0) &
                    (df_clean['final_score'] >= 0) &
                    (df_clean['final_score'] <= 100)]

print(f"\nCleaned dataset: {len(df_clean)} students")

# Save cleaned data
df_clean.to_csv('student_performance_cleaned.csv', index=False)
print("Cleaned data saved!")

print("\n" + "="*50)
print("STEP 2: DESCRIPTIVE ANALYSIS")
print("="*50)

# Calculate statistics
avg_score = df_clean['final_score'].mean()
max_score = df_clean['final_score'].max()
min_score = df_clean['final_score'].min()
avg_study_hours = df_clean['hours_studied'].mean()

# Pass/Fail count (assuming 60 is passing grade)
df_clean['pass_fail'] = df_clean['final_score'].apply(lambda x: 'Pass' if x >= 60 else 'Fail')
pass_count = (df_clean['pass_fail'] == 'Pass').sum()
fail_count = (df_clean['pass_fail'] == 'Fail').sum()

print(f"\nKey Statistics:")
print(f"   Average Exam Score: {avg_score:.2f}")
print(f"   Highest Score: {max_score:.2f}")
print(f"   Lowest Score: {min_score:.2f}")
print(f"   Average Study Hours: {avg_study_hours:.2f} hours/day")
print(f"\n   Pass Count: {pass_count} students")
print(f"   Fail Count: {fail_count} students")
print(f"   Pass Rate: {(pass_count/len(df_clean)*100):.1f}%")

print("\n" + "="*50)
print("STEP 3: DATA VISUALIZATION")
print("="*50)

# Create figure with 4 subplots
fig, axes = plt.subplots(2, 2, figsize=(14, 10))
fig.suptitle('Student Performance Analysis', fontsize=16, fontweight='bold')

# 1. Study Hours vs Exam Score (Scatter Plot)
axes[0, 0].scatter(df_clean['hours_studied'], df_clean['final_score'], 
                   alpha=0.5, color='blue', edgecolors='black')
axes[0, 0].set_xlabel('Study Hours per Day')
axes[0, 0].set_ylabel('Final Score')
axes[0, 0].set_title('Study Hours vs Exam Score')
axes[0, 0].grid(True, alpha=0.3)

# 2. Pass vs Fail (Bar Chart)
pass_fail_counts = df_clean['pass_fail'].value_counts()
colors = ['green', 'red']
axes[0, 1].bar(pass_fail_counts.index, pass_fail_counts.values, color=colors, alpha=0.7)
axes[0, 1].set_xlabel('Result')
axes[0, 1].set_ylabel('Number of Students')
axes[0, 1].set_title('Pass vs Fail Distribution')
for i, v in enumerate(pass_fail_counts.values):
    axes[0, 1].text(i, v + 2, str(v), ha='center', fontweight='bold')

# 3. Attendance vs Score (Scatter Plot)
axes[1, 0].scatter(df_clean['attendance'], df_clean['final_score'], 
                   alpha=0.5, color='orange', edgecolors='black')
axes[1, 0].set_xlabel('Attendance (%)')
axes[1, 0].set_ylabel('Final Score')
axes[1, 0].set_title('Attendance vs Score')
axes[1, 0].grid(True, alpha=0.3)

# 4. Score Distribution (Histogram)
axes[1, 1].hist(df_clean['final_score'], bins=20, color='purple', alpha=0.7, edgecolor='black')
axes[1, 1].set_xlabel('Final Score')
axes[1, 1].set_ylabel('Number of Students')
axes[1, 1].set_title('Score Distribution')
axes[1, 1].axvline(avg_score, color='red', linestyle='--', linewidth=2, label=f'Average: {avg_score:.1f}')
axes[1, 1].legend()

plt.tight_layout()
plt.savefig('student_analysis_charts.png', dpi=300, bbox_inches='tight')
print("\nCharts saved as 'student_analysis_charts.png'")

print("\n" + "="*50)
print("STEP 4: INSIGHTS & FINDINGS")
print("="*50)

# Calculate insights
high_study = df_clean[df_clean['hours_studied'] > 3]['final_score'].mean()
low_study = df_clean[df_clean['hours_studied'] <= 3]['final_score'].mean()

high_attendance = df_clean[df_clean['attendance'] > 75]['final_score'].mean()
low_attendance = df_clean[df_clean['attendance'] <= 75]['final_score'].mean()

high_attendance_pass_rate = (df_clean[df_clean['attendance'] > 75]['pass_fail'] == 'Pass').sum() / len(df_clean[df_clean['attendance'] > 75]) * 100
low_attendance_pass_rate = (df_clean[df_clean['attendance'] <= 75]['pass_fail'] == 'Pass').sum() / len(df_clean[df_clean['attendance'] <= 75]) * 100

print("\nKey Insights:")
print(f"\n1. Study Time Impact:")
print(f"   - Students studying >3 hours/day: Average score = {high_study:.2f}")
print(f"   - Students studying ≤3 hours/day: Average score = {low_study:.2f}")
print(f"   - Difference: {high_study - low_study:.2f} points")

print(f"\n2. Attendance Impact:")
print(f"   - Students with >75% attendance: Average score = {high_attendance:.2f}")
print(f"   - Students with ≤75% attendance: Average score = {low_attendance:.2f}")
print(f"   - Difference: {high_attendance - low_attendance:.2f} points")

print(f"\n3. Pass Rate by Attendance:")
print(f"   - >75% attendance: {high_attendance_pass_rate:.1f}% pass rate")
print(f"   - ≤75% attendance: {low_attendance_pass_rate:.1f}% pass rate")

print(f"\n4. Sleep Impact:")
sleep_score_corr = df_clean['sleep_hours'].corr(df_clean['final_score'])
print(f"   - Correlation between sleep and score: {sleep_score_corr:.3f}")

print("\n" + "="*50)
print("ANALYSIS COMPLETE!")
print("="*50)
print("\nGenerated files:")
print("  1. student_performance_cleaned.csv - Cleaned dataset")
print("  2. student_analysis_charts.png - All visualizations")
print("\nNext: Check the charts and review the insights above!")