# 🎓 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

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

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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.*