🎓 Jamboree Education – Linear Regression Analysis for Admissions Strategy

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📘 Overview

This project focuses on analyzing historical student data from Jamboree Education, a leading institute for test prep and admissions consulting.
The goal is to build a Linear Regression model to understand how different student attributes influence admission chances and to guide data-driven decisions in marketing, outreach, and course design.

Using regression modeling, we quantify how key factors (such as test scores, CGPA, and research experience) affect admission probability and derive strategic insights that can help improve student conversion rates.

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📁 Dataset Overview

Feature	Description
GRE_Score	GRE score of the student
TOEFL_Score	TOEFL score of the student
University_Rating	Rating of the university (1 to 5)
SOP	Strength of Statement of Purpose (1 to 5)
LOR	Strength of Letter of Recommendation (1 to 5)
CGPA	CGPA of the student
Research	Binary indicator if the student has research experience (0/1)
Chance_of_Admit	🎯 Target variable – Probability of admission (0 to 1)

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🎯 Objectives

• Perform Exploratory Data Analysis (EDA) to understand student characteristics
• Build and interpret a Multiple Linear Regression model
• Identify which features most significantly impact the chance of admission
• Evaluate model performance using metrics like R², RMSE, and residual plots
• Translate analytical results into actionable insights for admissions strategy

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📊 Key Insights

1️⃣ CGPA is the Strongest Predictor

• CGPA has the highest positive correlation with admission probability.
• Students with CGPA above 8.5 have a significantly higher chance of admission.

2️⃣ Research Adds an Edge

• Candidates with research experience perform better even with moderate GRE/TOEFL scores.
• Indicates that universities value research exposure during evaluation.

3️⃣ GRE & TOEFL Impact is Linear but Moderate

• Both scores contribute positively but plateau after a certain threshold.
• Focused improvement helps, but returns diminish beyond a score range.

4️⃣ SOP and LOR Matter for Borderline Cases

• SOP and LOR scores are key differentiators when academic metrics are average.
• Subjective elements can influence decisions in competitive cases.

5️⃣ Model Performance

• The Linear Regression model explains about 80% of the variance (R² ≈ 0.8).
• Residual analysis shows minimal heteroscedasticity → model assumptions hold true.

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✅ Business Recommendations

• 🎯 Prioritize high-CGPA students for programs with strict academic cutoffs.
• 🔬 Encourage research projects to enhance student profiles and admission chances.
• 🧠 Offer GRE/TOEFL booster programs for students with potential but low test scores.
• ✍️ Conduct SOP/LOR workshops to help borderline applicants strengthen their profiles.
• 📊 Integrate model insights into a dashboard to assist counselors in personalized guidance.

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📈 Conclusion

By applying Linear Regression, Jamboree Education can predict admission probabilities and optimize strategies for recruitment, counseling, and student success.
This project empowers data-backed decision-making, ensuring higher conversion rates, better guidance, and improved outcomes for both the institute and its students.

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🧠 Tech Stack

Python, Pandas, NumPy, Matplotlib, Seaborn, scikit-learn, Jupyter Notebook

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🏆 Author

Ankit Verma
📍 Data Analyst | Machine Learning Enthusiast
📫 Gmail.com | 🌐 LinkedIn

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