Bashifu/EDA_Assignment

🎓 Student Dropout Prediction Dataset — EDA Assignment

By Tomer Bash | Data Science Course — Assignment #1

📹 Presentation Video

Presentation Video link - https://youtu.be/KyafBx9W7Qg

<video src="presentation.mp4" controls="controls" style="max-width: 720px;"></video>

📌 Dataset Overview

The dataset contains demographic, financial, academic, and application-related information about students at a Portuguese higher education institution. The primary goal is to understand and predict student dropout before first-year academic results are available.

🎯 Research Goal

Can we predict whether a student will drop out — early, before first-year grades are available?

🧹 Part 1: Data Cleaning & Preparation

• —No missing values — df.isnull().sum() returned 0 for all columns.
• —No duplicate rows — df.duplicated().sum() returned 0.
• —All 35 features are numeric (int/float) — verified with df.info().
• —Target column contains exactly 3 classes: Graduate, Enrolled, Dropout.
• —Class imbalance noted:
• —Graduate: 2,209
• —Dropout: 1,421
• —Enrolled: 794
• —Outlier detection: Age at enrollment shows right-skewed distribution with outliers at high ages — identified using a boxplot.
• —Grade anomaly detected: Significant spike at grade = 0 in 1st semester grades — investigated and documented below.

📊 Part 2: Exploratory Data Analysis

📈 Target Distribution

The dataset is imbalanced. Graduate is the majority class, followed by Dropout, and Enrolled is the smallest group. This imbalance must be handled carefully in any downstream modeling.

👤 Age at Enrollment

• —Distribution is right-skewed — most students enroll young, but a long tail of older students exists.
• —Boxplot reveals several outliers at high ages (up to ~70).
• —The Dropout group has a higher average age than the Graduate group — age at enrollment is a risk factor.

📚 1st Semester Grades — The 0-Grade Anomaly

• —Grades follow a near-normal distribution overall.
• —A massive spike at grade = 0 was detected — far more students received exactly 0 than expected.
• —Upon investigation: some students with grade = 0 still graduated.
• —Conclusion: The 0-grade entries likely represent administrative records (late withdrawals, course deregistrations), not pure academic failure. This is a data quality issue to flag for modeling.
• —The anomaly is concentrated in Course 2 (Animation degree), though the proportion is consistent with class sizes.

💰 Financial Factors — Primary Predictor

Financial health is the strongest non-academic predictor of student outcomes.

Students not keeping up with tuition fees show the single highest dropout signal in the entire dataset.

⚧ Gender

• —Female students (0) show a slightly higher graduation rate and lower dropout rate than male students (1).
• —The difference is modest but consistent across the dataset.

🏛️ Application Mode

• —Top 5 most common application modes were analyzed.
• —Application Mode 12 (Over 23 years old) has the highest dropout rate — consistent with the age finding above.
• —Standard routes (1st phase general contingent) perform best overall.

🔥 Correlation Heatmap

Features analyzed: Age at enrollment, Tuition fees up to date, Scholarship holder, 1st & 2nd semester grades, GDP, Target (encoded 0=Dropout, 1=Enrolled, 2=Graduate).

Key findings:

• —✅ Grades and financial stability are the strongest positive predictors of graduation.
• —❌ Age at enrollment correlates negatively with graduation.
• —📉 GDP shows almost no direct linear impact — individual finances matter more than the macro economy.

🏁 Conclusion

1. 💰 Financial Stability — Primary Predictor

Scholarships drastically reduce dropout. Unpaid tuition and university debt dramatically raise it. Crucially, these signals exist before first-year grades are available — enabling early identification of at-risk students.

2. 📚 Grades & the 0-Grade Anomaly

High grades are a strong positive predictor of graduation. The spike at grade = 0 is likely administrative, not academic — some 0-grade students still graduated.

3. 🎂 Older Students Are at Higher Risk

Age at enrollment correlates negatively with graduation. Students admitted via Mode 12 ("Over 23") have the highest dropout rates — likely due to work and family pressures.

4. 🌍 Macro Economy Has No Direct Linear Effect

National GDP shows near-zero correlation with the target. Individual financial circumstances matter far more.

📁 Repository Contents

🛠️ Technologies Used

Python 3 · pandas · numpy · matplotlib · seaborn · Jupyter Notebook

Dataset originally sourced from Kaggle. Uploaded to HuggingFace as part of Data Science Course Assignment #1.