Student Performance Analytics

On This Page

• C14.1  Project Overview
• C14.2  Business Problem
• C14.3  Dataset Description
• C14.4  Objectives
• C14.5  Step-by-Step Solution
• C14.6  Complete Python Code
• C14.7  Expected Output
• C14.8  Visualizations
• C14.9  Key Insights
• C14.10  Business Recommendations

Capstone Project 14: Student Performance Analytics

C14.1  Project Overview

This project analyzes student study habits and subject-wise performance data — an ideal project for the Samantus Institute itself to run on its own student data.

C14.2  Business Problem

An educational institute wants to understand whether study hours meaningfully predict exam performance, and which subjects students are consistently struggling with most.

C14.3  Dataset Description

Sample scope: 150 students across 5 subjects.

C14.4  Objectives

• Analyze the relationship between study hours and marks
• Compare average marks across different subjects
• Identify the weakest-performing subject overall
• Recommend interventions to improve outcomes

C14.5  Step-by-Step Solution

• Step 1: Load the student performance CSV into Pandas
• Step 2: Clean missing marks/study-hour values
• Step 3: Create a scatter plot of study hours vs marks
• Step 4: Calculate the correlation between study hours and marks
• Step 5: Calculate average marks per subject and rank them
• Step 6: Summarize academic-improvement recommendations

C14.6  Complete Python Code

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv("student_performance.csv")
df = df.dropna(subset=['marks','study_hours'])

correlation = df[['study_hours','marks']].corr()
print("Study Hours - Marks Correlation:\n", correlation)

avg_marks_by_subject = df.groupby('subject')['marks'].mean().sort_values(ascending=False)
print("Average Marks by Subject:\n", avg_marks_by_subject.round(1))

plt.scatter(df["study_hours"], df["marks"], alpha=0.5)
plt.title("Study Hours vs Marks")
plt.savefig("study_marks.png")

C14.7  Expected Output

C14.8  Visualizations

C14.9  Key Insights

• Study hours show a clear positive relationship with marks — more study time consistently correlates with better performance across the sample.
• Computer has the highest average marks (85%), while English is the weakest subject overall (68%) — a 17-point gap.
• The scatter shows some students achieve high marks with relatively few study hours, suggesting differences in study efficiency, not just quantity, also matter.

C14.10  Business Recommendations

• Introduce a focused English improvement track (extra practice sessions, writing workshops) given it's the clear weak point across the student body.
• Share the study-hours-to-marks relationship directly with students as motivation — concrete data often persuades better than general advice to 'study more.'
• Study the habits of high-efficiency students (high marks, moderate study hours) and share their techniques as best practices with the broader student group.