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+classification_confusion_matrices.png filter=lfs diff=lfs merge=lfs -text
+classification_distribution.png filter=lfs diff=lfs merge=lfs -text
+eda_boxplots.png filter=lfs diff=lfs merge=lfs -text
+eda_correlation_heatmap.png filter=lfs diff=lfs merge=lfs -text
+eda_price_distribution.png filter=lfs diff=lfs merge=lfs -text
+global_house_purchase_dataset.csv filter=lfs diff=lfs merge=lfs -text
+regression_actual_vs_predicted.png filter=lfs diff=lfs merge=lfs -text
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README.MD

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+---
+license: mit
+tags:
+  - sklearn
+  - random-forest
+  - gradient-boosting
+  - regression
+  - classification
+  - house-price-prediction
+  - tabular-data
+datasets:
+  - global-house-purchase-dataset
+metrics:
+  - r2
+  - mae
+  - rmse
+  - accuracy
+  - f1
+---
+
+# 🏠 House Price Prediction - Regression & Classification Models
+
+## 📹 Presentation Video
+
+[![Watch the video](https://img.shields.io/badge/Watch-Presentation-red?style=for-the-badge&logo=youtube)](YOUR_VIDEO_LINK_HERE)≈
+
+> **Replace `YOUR_VIDEO_LINK_HERE` with your YouTube/Loom/Vimeo link**
+
+---
+
+## 📋 Project Overview
+
+This project predicts house prices using machine learning, implementing both **regression** (predicting exact price) and **classification** (predicting price category: Low/Medium/High) approaches.
+
+### Dataset
+- **Name**: Global House Purchase Decision Dataset
+- **Size**: 200,000 entries, 27 features
+- **Target**: Property price prediction
+
+### Main Goals
+1. Predict house prices using regression models
+2. Classify properties into price categories (Low/Medium/High)
+3. Compare multiple ML algorithms and select the best performers
+
+---
+
+## 🔍 Exploratory Data Analysis (EDA)
+
+### Price Distribution
+![Price Distribution](eda_price_distribution.png)
+
+**Key Insights:**
+- Price distribution is slightly right-skewed
+- Most properties fall in the low-to-medium price range
+- Outliers exist in the high-price segment
+
+### Correlation Analysis
+![Correlation Heatmap](eda_correlation_heatmap.png)
+
+**Key Findings:**
+- Property size has the strongest correlation with price
+- Location features (country, city) significantly impact price
+- Customer salary shows moderate correlation with purchase decisions
+
+### Feature Distributions
+![Boxplots](eda_boxplots.png)
+
+---
+
+## 🛠️ Feature Engineering
+
+### New Features Created (9 total):
+1. **property_age** = 2025 - constructed_year
+2. **rooms_to_bathrooms_ratio** = rooms / (bathrooms + 1)
+3. **total_amenities** = garage + garden
+4. **size_category** = Small/Medium/Large bins
+5. **safety_score** = 1 / (crime_cases + legal_cases + 1)
+6. **financial_capacity** = customer_salary - monthly_expenses
+7. **is_new_property** = 1 if property_age <= 5
+8. **high_satisfaction** = 1 if satisfaction >= median
+9. **location_quality** = neighbourhood_rating + connectivity_score
+
+### One-Hot Encoding
+- Encoded: country, city, property_type, furnishing_status, size_category
+- Created ~60 binary columns
+
+### Polynomial Features
+- property_size_sqft², customer_salary², property_age²
+- Interaction terms (e.g., property_size × customer_salary)
+
+### Final Feature Count: 78 features
+
+---
+
+## 🎯 K-Means Clustering
+
+### Elbow Method for Optimal K
+![Elbow Method](clustering_elbow.png)
+
+**Selected K = 4** based on the elbow curve
+
+### Cluster Visualization (PCA)
+![PCA Clusters](clustering_pca.png)
+
+### Cluster Interpretation:
+| Cluster | Property Age | Salary | Purchase Rate | Characteristics |
+|---------|--------------|--------|---------------|-----------------|
+| 0 | Older (50 yrs) | Low (~$29k) | 18% | Budget buyers, older properties |
+| 1 | Older (33 yrs) | High (~$79k) | 22% | Affluent buyers |
+| 2 | Newer (17 yrs) | Low (~$29k) | 18% | First-time buyers, new builds |
+| 3 | Older (33 yrs) | Medium (~$50k) | 35% | Sweet spot - highest purchase rate |
+
+---
+
+## 📈 Part 1: Regression Models
+
+### Baseline Model (Linear Regression - Part 3)
+- **R²**: 0.1945 (19.45%)
+- **MAE**: 0.4168
+- **RMSE**: 0.5345
+
+### Improved Models with Engineered Features (Part 5)
+
+![Model Comparison](regression_comparison.png)
+
+| Model | Train R² | Test R² | Test MAE | Test RMSE | Improvement |
+|-------|----------|---------|----------|-----------|-------------|
+| Baseline (Part 3) | 0.1919 | 0.1945 | 0.4168 | 0.5345 | - |
+| Linear Regression | 0.9847 | 0.9845 | 0.0919 | 0.1237 | +406% |
+| Random Forest | 0.9999 | 1.0000 | 0.0054 | 0.0063 | +414% |
+| Gradient Boosting | 0.9995 | 0.9994 | 0.0177 | 0.0236 | +414% |
+
+### Actual vs Predicted
+![Actual vs Predicted](regression_actual_vs_predicted.png)
+
+### Feature Importance
+![Feature Importance](regression_feature_importance.png)
+
+**Top 5 Most Important Features (Random Forest):**
+1. property_size_sqft × property_age (0.249)
+2. country_uae (0.197)
+3. country_usa (0.114)
+4. country_singapore (0.093)
+5. city_singapore (0.088)
+
+### 🏆 Regression Winner: Random Forest
+- **Test R²**: 0.9999 (99.99%)
+- **Test MAE**: 0.0054
+- **Improvement**: 414% over baseline
+
+---
+
+## 📊 Part 2: Classification Models
+
+### Target Conversion Strategy
+**Quantile Binning (3 Classes):**
+- Class 0 (Low): Price < 33rd percentile
+- Class 1 (Medium): 33rd - 66th percentile
+- Class 2 (High): Price ≥ 66th percentile
+
+### Class Distribution
+![Class Distribution](classification_distribution.png)
+
+| Class | Label | Count | Percentage |
+|-------|-------|-------|------------|
+| 0 | Low | 20,398 | 33.0% |
+| 1 | Medium | 20,398 | 33.0% |
+| 2 | High | 21,016 | 34.0% |
+
+### Model Performance
+
+![Classification Comparison](classification_comparison.png)
+
+| Model | Accuracy | Precision | Recall | F1-Score |
+|-------|----------|-----------|--------|----------|
+| Logistic Regression | 99.50% | 0.9949 | 0.9949 | 0.9949 |
+| Random Forest | 98.58% | 0.9855 | 0.9855 | 0.9854 |
+| Gradient Boosting | 99.56% | 0.9956 | 0.9956 | 0.9956 |
+
+### Confusion Matrices
+![Confusion Matrices](classification_confusion_matrices.png)
+
+### 🏆 Classification Winner: Gradient Boosting
+- **Accuracy**: 99.56%
+- **F1-Score**: 0.9956
+- **Total Errors**: 55 out of 12,363 (0.44%)
+
+---
+
+## 🧠 Key Insights & Learnings
+
+### Precision vs Recall Analysis
+- **Precision is more important** for house price classification
+- False Positives (overvaluation) are more critical than False Negatives
+- Overpricing can lead to: lost buyers, investor losses, legal issues
+
+### Challenges Faced
+1. **Data Leakage**: Initial model showed R² = 1.0 due to leaky features (loan_amount, down_payment). Removed them for valid results.
+2. **Training Time**: Gradient Boosting was slow; optimized hyperparameters for faster training.
+3. **Feature Engineering**: Creating meaningful features required domain knowledge about real estate.
+
+### Lessons Learned
+1. Always check for data leakage before trusting "perfect" results
+2. Feature engineering has massive impact (+400% improvement)
+3. Ensemble methods (Random Forest, Gradient Boosting) outperform linear models
+4. Location is the most important factor in house pricing
+
+---
+
+## 📁 Repository Contents
+
+| File | Description |
+|------|-------------|
+| `random_forest_house_price_model.pkl` | Regression model (Random Forest) |
+| `classification_model_winner.pkl` | Classification model (Gradient Boosting) |
+| `house_purchase_dataset.csv` | Engineered dataset |
+| `README.md` | This documentation |
+
+---
+
+## 🚀 Usage
+
+### Load Regression Model
+```python
+import pickle
+
+# Load model
+with open('random_forest_house_price_model.pkl', 'rb') as f:
+    regression_model = pickle.load(f)
+
+# Predict (features must be scaled and in same format as training)
+predictions = regression_model.predict(X_scaled)
+```
+
+### Load Classification Model
+```python
+import pickle
+
+# Load model
+with open('classification_model_winner.pkl', 'rb') as f:
+    classification_model = pickle.load(f)
+
+# Predict price class (0=Low, 1=Medium, 2=High)
+price_class = classification_model.predict(X_scaled)
+```
+
+---
+
+## 📊 Model Specifications
+
+### Regression Model (Random Forest)
+```
+Algorithm: RandomForestRegressor
+n_estimators: 50
+max_depth: 10
+min_samples_split: 20
+min_samples_leaf: 10
+random_state: 42
+```
+
+### Classification Model (Gradient Boosting)
+```
+Algorithm: GradientBoostingClassifier
+n_estimators: 50
+max_depth: 3
+learning_rate: 0.2
+subsample: 0.8
+random_state: 42
+```
+
+---
+
+## 👨‍💻 Author
+
+Created as part of a Data Science course assignment.
+
+---
+
+## 📜 License
+
+MIT License

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