🎓 Students Retention Analysis: Mitigating Attrition and Revenue Loss

Project Overview

This project aims to identify and quantify the primary factors driving first-year student attrition (non-retention) and translate these risks into concrete business costs (potential revenue loss). The analysis provides data-driven insights and actionable recommendations for academic advisors and university administration to intervene effectively, specifically targeting financial, academic, and demographic high-risk cohorts.

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🚀 Key Findings (The Bottom Line)

The comprehensive analysis identified a $8.67 Million total potential revenue loss due to student attrition. Key risk drivers include:

• Financial Stress: The Extreme Unmet Need cohort has a retention rate of 72.8%. Over 77% of all students report High Financial Stress.
• Academic Barriers: Killer Courses like ASTR 1305 and CPSC 1301L exhibited failure rates near 100%, acting as critical academic bottlenecks.
• Momentum Loss: Students earning less than 12 credit hours in their first term have significantly lower retention (75.7%).
• Highest Attrition Group: Students in Background Category 5 (BGD 5) have the lowest retention rate at 50.0%.

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🛠️ Recommendations for Intervention

The project advocates for a tiered intervention model focusing resources on the High and Critical Risk categories (which account for 25% of the total revenue loss risk).

1. Financial Aid: Establish an Emergency Micro-Grant Fund targeted specifically at students flagged with Extreme Unmet Need.
2. Academic Support: Implement mandatory, high-intensity Tutoring/Restructuring for high D/F rate courses (e.g., ASTR 1305).
3. Advising: Mandate a First-Term Advising Checkpoint to ensure all students are on track to earn at least 12 credits.
4. Targeted Support: Assign Dedicated Success Coaches to the highest-risk demographic groups (BGD 5/7).

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📁 Repository Structure

File/Directory	Description
Preprocessing.ipynb	Contains data cleaning, feature engineering, handling missing values, and normalization steps.
Analytics.ipynb	The core analytical notebook featuring all visualizations (Demographic, Academic, Financial, Risk Impact) and the final conclusion/recommendations.
student_retention_DASHBOARD.csv	The primary, clean dataset used for all in-depth analysis and modeling.
data_dictionary_dashboard.csv	Metadata describing the columns and variables found in the primary dataset.
Student.xlsx	The raw, original data file (unprocessed).
student_retention_processed_FULL.csv	Intermediate file used after initial cleaning steps.
summary_*.csv files	Pre-aggregated summary tables used to generate key visualizations (e.g., summary_by_risk.csv, summary_gateway_courses.csv).

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💻 Setup and Usage

This project uses Python and common data science libraries.

1. Clone the Repository:

   git clone [https://github.com/0Abanoub/Students-Retention-Analysis.git](https://github.com/0Abanoub/Students-Retention-Analysis.git)
   cd Students-Retention-Analysis

2. Install Dependencies: (Assumes use of Anaconda/Jupyter environment)

   # Example dependencies
   pip install pandas numpy matplotlib seaborn jupyter

3. Run Analysis:
   • Start with Preprocessing.ipynb to understand the data preparation steps.
   • Proceed to Analytics.ipynb to view the comprehensive analysis, visualizations, and final findings.

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✍️ Author

• Author Name: [Abanoub\Seyam]
• Course: [Data Analysis - Year 3]