README.md

---
title: DataSynthis ML JobTask
emoji: 🐢
colorFrom: green
colorTo: gray
sdk: gradio
sdk_version: 5.48.0
app_file: app.py
pinned: false
license: apache-2.0
short_description: Stock price forecasting ML demo for DataSynthis internship
---

# 📈 DataSynthis ML JobTask  
Stock Price Forecasting with Baseline, Statistical, and ML Models  

## 🚀 Project Overview  
This project demonstrates a complete **time-series forecasting pipeline** using daily stock price data (2010–2024). It was developed as part of the **DataSynthis ML Internship Task**.  

We cover the full workflow:  
1. **Baseline Models** → Naïve Forecast, Simple Exponential Smoothing (SES)  
2. **Statistical Model** → ARIMA  
3. **ML / DL Models** → Prophet, LSTM  
4. **Evaluation** → Rolling-window accuracy metrics (RMSE, MAPE)  
5. **Deployment** → Interactive demo with Gradio (via Hugging Face Spaces)  

## 🛠️ Features  
- Data preprocessing & feature engineering (lags, volatility, RSI, MACD, Bollinger Bands, etc.)  
- Feature validation & pruning (correlation, VIF, outlier checks)  
- Unified comparison of models with a performance summary table  
- Visualizations: trends, normalized comparisons, total returns  
- Exportable datasets for reproducibility  

## 📊 Deliverables  
- **Notebook**: End-to-end workflow (data → models → evaluation)  
- **Models**: Naïve, SES, ARIMA, Prophet, LSTM  
- **Visualizations**: stock trends, indicators, correlations, performance plots  
- **Deployment**: Hugging Face Space with Gradio app  

## 📂 Repository Structure  
📁 DataSynthis_ML_JobTask
├── app.py # Gradio demo app
├── data/ # Preprocessed & engineered datasets
├── notebooks/ # Jupyter notebooks with full pipeline
├── models/ # Trained ARIMA / Prophet / LSTM models
├── outputs/ # Plots, summary tables, feature files
├── README.md # This file