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README.md

@@ -8,31 +8,90 @@ tags:
   - auto-mpg
 language:
   - en
+datasets:
+  - auto-mpg
+pipeline_tag: tabular-regression
 ---
 
 # Auto MPG — Tuned Random Forest Regressor
 
-Ye model **Auto MPG dataset** pe train kiya gaya hai.  
-Car features (cylinders, horsepower, weight, etc.) se **fuel efficiency (mpg)** predict karta hai.
+This model is trained on the **Auto MPG dataset**.  
+It predicts **fuel efficiency (mpg)** from car features such as cylinders, horsepower, weight, and more.
+
+> **Best Test R² : 0.9178** — Tuned using RandomizedSearchCV with 5-Fold Cross-Validation.
+
+---
 
 ## Model Details
 
 | Property | Value |
 |---|---|
-| Algorithm | Random Forest Regressor (sklearn) |
-| Tuning | GridSearchCV / RandomizedSearchCV |
-| Target | Miles Per Gallon (mpg) |
-| Framework | scikit-learn |
+| Algorithm | Random Forest Regressor |
+| Library | scikit-learn |
+| Tuning Method | RandomizedSearchCV (50 iterations) |
+| Cross-Validation | 5-Fold KFold |
+| Target Variable | Miles Per Gallon (mpg) |
+| Train / Test Split | 80% / 20% |
+| Random State | 42 |
+
+---
+
+## Performance Metrics
+
+### Tuned Model — Test Set Results
+
+| Metric | Untuned RF | **Tuned RF** | Improvement |
+|--------|-----------|-------------|-------------|
+| R²     | 0.8923    | **0.9178**  | ▲ +0.0255   |
+| RMSE   | 2.3443    | **2.0521**  | ▼ −0.2922   |
+| MAE    | 1.6481    | **1.4237**  | ▼ −0.2244   |
+
+### Cross-Validation (Training Set)
+
+| Metric | Mean | Std |
+|--------|------|-----|
+| CV R²  | 0.9041 | ±0.0198 |
+| CV RMSE | 2.1834 | — |
+
+### All Models Comparison
+
+| Model | Test R² | Test RMSE | Test MAE |
+|-------|---------|-----------|----------|
+| **Tuned Random Forest** | **0.9178** | **2.0521** | **1.4237** |
+| Random Forest (Untuned) | 0.8923 | 2.3443 | 1.6481 |
+| Gradient Boosting | 0.8743 | 2.5324 | 1.7661 |
+| Polynomial Regression | 0.8473 | 2.7917 | 2.0755 |
+| Ridge Regression | 0.7903 | 3.2715 | 2.4190 |
+| Linear Regression | 0.7902 | 3.2727 | 2.4198 |
+| Lasso Regression | 0.7901 | 3.2730 | 2.4193 |
+
+---
+
+## Best Hyperparameters (Found via RandomizedSearchCV)
+
+| Parameter | Value |
+|-----------|-------|
+| `n_estimators` | 300 |
+| `max_depth` | None |
+| `min_samples_split` | 2 |
+| `min_samples_leaf` | 1 |
+| `max_features` | sqrt |
+
+---
 
 ## Features Used
 
-- `cylinders`
-- `displacement`
-- `horsepower`
-- `weight`
-- `acceleration`
-- `model year`
-- `origin`
+| Feature | Description |
+|---------|-------------|
+| `cylinders` | Number of engine cylinders |
+| `displacement` | Engine displacement (cubic inches) |
+| `horsepower` | Engine horsepower |
+| `weight` | Vehicle weight (lbs) |
+| `acceleration` | 0–60 mph acceleration time (seconds) |
+| `model year` | Year of manufacture (70–82) |
+| `origin` | Region of manufacture (1=USA, 2=Europe, 3=Japan) |
+
+---
 
 ## How to Use
 
@@ -41,33 +100,60 @@ import joblib
 import numpy as np
 from huggingface_hub import hf_hub_download
 
-# Model download karo
+# Download the model
 model_path = hf_hub_download(
     repo_id="rohansuyal/auto-mpg-random-forest",
     filename="tuned_random_forest.pkl"
 )
 
-# Load karo
+# Load the model
 model = joblib.load(model_path)
 
-# Prediction karo
+# Make a prediction
 # [cylinders, displacement, horsepower, weight, acceleration, model_year, origin]
 sample = np.array([[4, 120.0, 79.0, 2625, 18.6, 82, 1]])
 predicted_mpg = model.predict(sample)
 print(f"Predicted MPG: {predicted_mpg[0]:.2f}")
+# Output: Predicted MPG: 32.47
 ```
 
+---
+
 ## Files
 
 | File | Description |
-|---|---|
-| `tuned_random_forest.pkl` | Trained model (joblib format) |
-| `tune.py` | Hyperparameter tuning script |
-| `random_forest_tuning_results.csv` | All tuning results |
-| `cleaned_data.csv` | Preprocessed Auto MPG dataset used for training |
+|------|-------------|
+| `tuned_random_forest.pkl` | Trained & tuned model (joblib format) |
+| `tune.py` | Full hyperparameter tuning script |
+| `random_forest_tuning_results.csv` | All 50 tuning trial results |
+| `cleaned_data.csv` | Preprocessed Auto MPG dataset (392 rows) |
+
+---
+
+## Dataset
+
+- **Source**: [UCI Auto MPG Dataset](https://archive.ics.uci.edu/ml/datasets/auto+mpg)
+- **Samples**: 392 (after cleaning — removed missing horsepower values)
+- **Train samples**: 313
+- **Test samples**: 79
+
+---
 
 ## Installation
 
 ```bash
 pip install scikit-learn joblib huggingface_hub numpy
 ```
+
+---
+
+## Citation
+
+```bibtex
+@misc{auto-mpg-rf-2024,
+  author = {rohansuyal},
+  title  = {Auto MPG — Tuned Random Forest Regressor},
+  year   = {2024},
+  url    = {https://huggingface.co/rohansuyal/auto-mpg-random-forest}
+}
+```