Bashifu/EDA_Assignment

🎓 Student Dropout Prediction Dataset — EDA Assignment

By Tomer Bash | Data Science Course — Assignment #1

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📹 Presentation Video

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

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📌 Dataset Overview

Property	Details
Source	Kaggle
Rows	4,424 students
Features	35 columns
Target Variable	Target — Graduate, Enrolled, Dropout
Task Type	Multi-class Classification

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.

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🎯 Research Goal

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

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🧹 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.

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📊 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.

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👤 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.

Group	Avg Age at Enrollment
Graduate	~22
Enrolled	~23
Dropout	~26

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📚 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.

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💰 Financial Factors — Primary Predictor

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

Financial Factor	Effect on Dropout
Tuition fees not up to date	Overwhelmingly high dropout rate
Scholarship holder	Drastically reduces dropout risk
University debtor	Significantly increases dropout risk

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

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⚧ 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.

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🏛️ 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.

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🔥 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.

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🏁 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.

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📁 Repository Contents

File	Description
dataset.csv	The full student dataset (4,424 × 35)
Assignment_1_EDA_Tomer_Bash.ipynb	Full EDA notebook with code & visualizations
README.md	This file
presentation.mp4	Video walkthrough (2–3 min)

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🛠️ Technologies Used

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

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