📊 Analyzing Academic Trends Among VIT Vellore Students Using Python This project explores academic performance trends among students at VIT Vellore using Python-based data analysis techniques. The aim is to gain insights into CGPA distributions across different departments and academic years, identify top-performing branches, visualize overall student performance, and detect anomalies in the data.

🔍 Key Features: Data Cleaning & Preprocessing using Pandas and NumPy

Exploratory Data Analysis (EDA) to uncover patterns in student CGPAs

Visualizations with Matplotlib and Seaborn to represent trends and outliers

Branch-wise and Year-wise Analysis to compare performance across departments

Insightful Findings that could support academic planning and strategy

📁 Technologies Used: Python

Pandas

NumPy

Matplotlib

Seaborn

📌 Project Goals: Understand CGPA distribution patterns

Identify high- and low-performing departments

Visualize student performance over time

Detect outliers and anomalies in the academic data