DataSynthis_ML_JobTask - Stock Price Forecasting (Deliverables)

This bundle contains a ready-to-run Jupyter notebook, a small synthetic sample dataset, and helper scripts for training ARIMA, LSTM, and Prophet models; evaluating them with rolling-window evaluation; and steps to deploy model artifacts to Hugging Face Hub (you must run that step locally or from an environment with internet access).

Files included

stock_forecasting_notebook.ipynb — The full notebook with preprocessing, ARIMA, LSTM, Prophet, rolling-window evaluation, plots, and saving models.
sample_stock.csv — A synthetic daily closing-price CSV (2020-01-01 to 2021-12-31, business days) to run the notebook offline.
requirements.txt — Python dependencies.
upload_to_hf.py — Example script to upload model files to Hugging Face Hub (requires huggingface_hub and HF token).
README.md — This file.

Quick start

Create and activate a python environment (recommended: conda or venv)

python -m venv venv
source venv/bin/activate   # Linux/macOS
venv\Scripts\activate    # Windows
pip install -r requirements.txt

Start Jupyter and open the notebook:

jupyter notebook stock_forecasting_notebook.ipynb

The notebook contains cells to download real stock data via yfinance (if you have internet) or use the included sample_stock.csv for an offline demo.

Hugging Face deployment (notes)

You cannot deploy directly from this notebook in an environment without internet.
Use upload_to_hf.py to push saved model files to the HF repo DataSynthis_ML_JobTask after creating it on the Hugging Face website (or the script will create the repo for you if you provide a valid token).
Create a HF token at https://huggingface.co/settings/tokens and set environment variable HF_TOKEN or pass --token to the script.

About results

The notebook runs quick examples and shows how to compute RMSE and MAPE, and how to perform rolling-window evaluation.
For production-quality training and evaluation (e.g., longer LSTM training), run on a machine with a GPU and more data.