Mobile Price Range Classifier

Deployment Link

TRYYYY MEEEE!

Description

The above referenced project predicts the price range of mobile phones based on hardware specifications using machine learning.

This project takes input features like RAM, battery life, camera specs, screen, and connectivity attributes to build models that classify mobiles into one of four price categories:

• 0 – low cost
• 1 – medium cost
• 2 – high cost
• 3 – very high cost

---

🚀 Features

• Supports multiple ML algorithms: Logistic Regression, KNN, SVM, Decision Trees, Random Forest
• Includes EDA, preprocessing, and feature engineering
• Generates model evaluation reports: accuracy, confusion matrix, classification report
• Saves the best-performing model for reuse/prediction

---

🧰 Technologies Used

• Python 3.x
• pandas, NumPy
• scikit-learn
• Jupyter Notebook
• matplotlib / seaborn

---

📦 Installation

Prerequisites

• Python 3.8+
• Git
• pip

Steps

git clone https://github.com/Ogutu01/Mobile-Price-Classifier.git
cd Mobile-Price-Classifier

# (Optional) create & activate virtual environment
python -m venv venv
source venv/bin/activate   # On Windows: venv\Scripts\activate

pip install -r requirements.txt

If requirements.txt is missing, manually install:

pip install pandas numpy scikit-learn matplotlib seaborn jupyter

---

🧪 Usage

1. Run the Jupyter notebook

jupyter notebook

Open notebooks/Mobile_Price_Classifier.ipynb to explore EDA, preprocessing steps, model training, and evaluation.

2. Train & evaluate models

Inside the notebook:

• Preprocess dataset (handle missing values, scale features)
• Split into train/test
• Train multiple classifiers
• Evaluate using metrics & select the best model
• Save the winning model as model.pkl

3. Load model & predict

from src.predict import load_model, predict_price

model = load_model("model.pkl")

features = {
  "battery_power": 1500,
  "ram": 2048,
  "pc": 12,
  "fc": 5,
  # Add all required features...
}

print("Predicted price range:", predict_price(model, features))

---

📂 Project Structure

Mobile-Price-Classifier/
├── data/
│   └── images
    └──test_data_with_oredictions.csv
    └──test.csv
    └──train.csv
├── notebooks/
│   └── index.ipynb
├── model.pkl
├── requirements.txt
└── GEN_AI(Updated)_README.md

🛠️ Troubleshooting

• Jupyter won't run: Ensure jupyter is installed and you're in the project root.
• Module import errors: Activate your virtual environment before running scripts/notebooks.
• Error loading model: Make sure model.pkl exists and paths in app.py are correct.
• Poor model accuracy: Tune hyperparameters, try different test/train splits, or add more features.

🤝 Contributing

Contributions are welcome!

1. Fork the repository
2. Create a feature branch (git checkout -b feature/YourFeature)
3. Make changes and add tests
4. Commit with clear messages (git commit -m "Add XYZ")
5. Push to your branch and open a PR

Please follow existing code style and document new features.

---

📄 License

This project is open-source and distributed under the MIT License. See the LICENSE file for more details.

---

🙋 Support & Contact

If you've got any questions or found a bug, please open an issue or contact via email.