README.md

metadata

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