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Getting Admission in College Prediction/README.md

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+<div align="center">
+
+# 🎓 Getting Admission in College Prediction
+
+[![Python](https://img.shields.io/badge/Python-3.7+-3776AB?style=for-the-badge&logo=python&logoColor=white)](https://www.python.org/)
+[![scikit-learn](https://img.shields.io/badge/scikit--learn-F7931E?style=for-the-badge&logo=scikit-learn&logoColor=white)](https://scikit-learn.org/)
+[![Jupyter](https://img.shields.io/badge/Jupyter-Notebook-F37626?style=for-the-badge&logo=jupyter&logoColor=white)](https://jupyter.org/)
+[![Dataset](https://img.shields.io/badge/Dataset-Kaggle-20BEFF?style=for-the-badge&logo=kaggle&logoColor=white)](https://www.kaggle.com/mohansacharya/graduate-admissions)
+[![Best R²](https://img.shields.io/badge/Best%20R²-0.821-brightgreen?style=for-the-badge)]()
+[![License](https://img.shields.io/badge/License-MIT-1abc9c?style=for-the-badge)](../LICENSE.md)
+
+> Predicts a student's **probability of graduate college admission** (as a continuous value between 0 and 1) from 7 academic and profile features — using a `GridSearchCV`-powered model comparison across 6 regression algorithms.
+
+[🔙 Back to Main Repository](https://github.com/shsarv/Machine-Learning-Projects)
+
+</div>
+
+---
+
+## 📌 Table of Contents
+
+- [About the Project](#-about-the-project)
+- [Dataset](#-dataset)
+- [Features](#-features)
+- [Methodology](#-methodology)
+- [Model Comparison Results](#-model-comparison-results)
+- [Final Model Performance](#-final-model-performance)
+- [Sample Predictions](#-sample-predictions)
+- [Project Structure](#-project-structure)
+- [Getting Started](#-getting-started)
+- [Tech Stack](#-tech-stack)
+
+---
+
+## 🔬 About the Project
+
+Getting into a good graduate program is one of the most competitive processes for students worldwide. This project builds a **regression model** that predicts the probability of admission based on a student's GRE score, TOEFL score, CGPA, university rating, SOP, LOR, and research experience.
+
+Six regression algorithms are trained and compared using **GridSearchCV with 5-fold cross-validation** via a custom `find_best_model()` function. The best-performing model is then evaluated on a held-out test set.
+
+**What this project covers:**
+- Exploratory data analysis on 500 graduate applicant profiles
+- Custom `find_best_model()` with GridSearchCV across 6 regressors
+- Feature importance and correlation analysis
+- Linear Regression selected as the final model with **R² = 0.821** on test set
+
+---
+
+## 📊 Dataset
+
+| Property | Details |
+|----------|---------|
+| **File** | `admission_predict.csv` |
+| **Source** | [Kaggle — Graduate Admissions](https://www.kaggle.com/mohansacharya/graduate-admissions) |
+| **Rows** | 500 student records |
+| **Columns** | 9 (including Serial No. and target) |
+| **Task** | Regression — predict `Chance of Admit` ∈ [0, 1] |
+| **Missing Values** | None |
+
+---
+
+## 🔬 Features
+
+| Column | Type | Range | Description |
+|--------|------|:-----:|-------------|
+| `GRE Score` | Integer | 290–340 | Graduate Record Examination score |
+| `TOEFL Score` | Integer | 92–120 | Test of English as a Foreign Language score |
+| `University Rating` | Integer | 1–5 | Prestige rating of undergraduate university |
+| `SOP` | Float | 1.0–5.0 | Strength of Statement of Purpose |
+| `LOR` | Float | 1.0–5.0 | Strength of Letter of Recommendation |
+| `CGPA` | Float | 6.8–9.92 | Undergraduate GPA (out of 10) |
+| `Research` | Binary | 0 / 1 | Research experience (0 = No, 1 = Yes) |
+| `Chance of Admit` ⭐ | Float | 0.34–0.97 | **Target variable** — probability of admission |
+
+> `Serial No.` is dropped before training as it carries no predictive information.
+
+---
+
+## ⚙️ Methodology
+
+```
+Load admission_predict.csv (500 × 9)
+          │
+          ▼
+EDA + Correlation Analysis
+(heatmap, pairplots, distributions)
+          │
+          ▼
+Drop 'Serial No.' column
+Define X (7 features) and y ('Chance of Admit')
+          │
+          ▼
+find_best_model(X, y)
+└── GridSearchCV (cv=5) over 6 models
+          │
+          ▼
+Select best model → Linear Regression (normalize=True)
+          │
+          ▼
+Train/Test Split (80/20, random_state=5)
+→ 400 train samples, 100 test samples
+          │
+          ▼
+Fit LinearRegression(normalize=True)
+Evaluate on test set → R² = 0.821
+          │
+          ▼
+Sample Predictions
+```
+
+---
+
+## 📈 Model Comparison Results
+
+All 6 models evaluated using `GridSearchCV(cv=5)` via the custom `find_best_model()` function:
+
+| Model | Best Parameters | CV R² Score |
+|-------|----------------|:-----------:|
+| **Linear Regression** ✅ | `{'normalize': True}` | **0.8108** |
+| Random Forest | `{'n_estimators': 15}` | 0.7689 |
+| KNN | `{'n_neighbors': 20}` | 0.7230 |
+| SVR | `{'gamma': 'scale'}` | 0.6541 |
+| Decision Tree | `{'criterion': 'mse', 'splitter': 'random'}` | 0.5868 |
+| Lasso | `{'alpha': 1, 'selection': 'random'}` | 0.2151 |
+
+> ✅ **Linear Regression** selected as the final model — highest cross-validation R² score of **0.8108**.
+
+> Lasso performed poorly (R² = 0.2151) because L1 regularization shrinks coefficients aggressively, which is harmful here where all 7 features are genuinely correlated with admission probability.
+
+---
+
+## 🏆 Final Model Performance
+
+| Metric | Value |
+|--------|:-----:|
+| Model | `LinearRegression(normalize=True)` |
+| 5-Fold Cross-Validation Score | **81.0%** |
+| Train samples | 400 |
+| Test samples | 100 |
+| **Test R² Score** | **0.8215** |
+
+---
+
+## 🔮 Sample Predictions
+
+```python
+# Input: [GRE, TOEFL, Univ Rating, SOP, LOR, CGPA, Research]
+
+model.predict([[337, 118, 4, 4.5, 4.5, 9.65, 0]])
+# → Chance of getting into UCLA is 92.855%
+
+model.predict([[320, 113, 2, 2.0, 2.5, 8.64, 1]])
+# → Chance of getting into UCLA is 73.627%
+```
+
+---
+
+## 📁 Project Structure
+
+```
+Getting Admission in College Prediction/
+│
+├── Admission_prediction.ipynb      # Main notebook — EDA, model comparison, training
+├── admission_predict.csv           # Dataset (500 student records)
+├── requirements.txt                # Python dependencies
+└── README.md                       # You are here
+```
+
+---
+
+## 🚀 Getting Started
+
+### 1. Clone the repository
+
+```bash
+git clone https://github.com/shsarv/Machine-Learning-Projects.git
+cd "Machine-Learning-Projects/Getting Admission in College Prediction"
+```
+
+### 2. Set up environment
+
+```bash
+python -m venv venv
+source venv/bin/activate        # Linux / macOS
+venv\Scripts\activate           # Windows
+
+pip install -r requirements.txt
+```
+
+### 3. Launch the notebook
+
+```bash
+jupyter notebook Admission_prediction.ipynb
+```
+
+---
+
+## 🛠️ Tech Stack
+
+| Layer | Technology |
+|-------|-----------|
+| Language | Python 3.7.4 |
+| ML Library | scikit-learn |
+| Model Selection | `GridSearchCV`, `cross_val_score` |
+| Models | `LinearRegression`, `Lasso`, `SVR`, `DecisionTreeRegressor`, `RandomForestRegressor`, `KNeighborsRegressor` |
+| Data Processing | Pandas, NumPy |
+| Visualization | Matplotlib |
+| Notebook | Jupyter |
+
+---
+
+<div align="center">
+
+Part of the [Machine Learning Projects](https://github.com/shsarv/Machine-Learning-Projects) collection by [Sarvesh Kumar Sharma](https://github.com/shsarv)
+
+⭐ Star the main repo if this helped you!
+
+</div>