Problem statement: Our task is to develop a model that predicts the optimal price for a product based on various factors. This prediction would enable us to make an informed decision when pricing a product, leading to maximized sales and customer satisfaction.

solution: The machine learning model used in the provided code is Gradient Boosted Decision Trees,uses gradient boosting, where trees are built sequentially, and each tree focuses on reducing the residual errors of the ensemble.The model optimizes a loss function (in this case, squared error) using gradient descent to determine how to adjust predictions.

.
├── frontend/               React Native frontend application
│   ├── .expo/              Expo-specific project files
│   ├── assets/             Static assets (images, fonts, etc.)
│   ├── components/         Reusable UI components
│   ├── node_modules/       Node.js packages (auto-generated)
│   ├── .gitignore          Git ignore rules
│   ├── App.js              Main app entry point
│   ├── app.json            Expo configuration
│   ├── eas.json            EAS (Expo Application Services) config
│   ├── index.js            React Native entry point
│   ├── package.json        Project metadata and dependencies
│   └── package-lock.json   Exact dependency tree
│
├── ml-api/                 Backend ML API
│   ├── app.py              Python API code (Flask/FastAPI)
│   ├── label_encoders.pkl  Encoders for categorical variables
│   ├── original_values.pkl Original labels for decoding predictions
│   ├── requirements.txt    Python dependencies
│   ├── xgb_model.json      XGBoost model in JSON format
│   └── xgb_model.pkl       Pickled XGBoost model
│
├── model/                  Data and notebooks for modeling
│   ├── retail_price.csv    Dataset
│   └── Untitled-1.ipynb    Jupyter notebook (EDA/modeling)
│
├── notebook/               Additional notebooks
│   └── Untitled-1.ipynb    Notebook for analysis or testing
│
└── Readme.md               # Project documentation

setup

Backend

cd ml-api
pip install -r requirements.txt

Frontend

cd frontend
npm install
npx expo start

Tools and Libraries

• Python Libraries: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, SciPy
• Environment: Jupyter Notebook for interactive analysis
• Backend:Flask for api render for deploying api
• Frontend: React Native , Expo

apk video