The goal is to turn long-term Reddit chatter into actionable forecasts for the equity market. Here’s the flow I need built and documented: • Data assembly – Pull every post and comment that mentions publicly traded tickers over the past 3-5 years, using Reddit’s API (PRAW or Pushshift). – Filter & rank tickers by daily trading volume, then lock in the top ten companies for the study. – Store raw text, basic metadata, and a timestamp-aligned sentiment score in a clean, queryable format (Parquet or CSV). • Market reference set – Fetch matching historical OHLCV data for those ten tickers from Yahoo Finance so each trading day can be paired with Reddit sentiment features. • Modelling & evaluation – Train two different machine-learning models of your choice on the combined dataset to predict next-day closing prices (or percentage moves). – Present a clear comparison of their performance using MAE, RMSE, and directional accuracy, and outline strengths or weaknesses you observe. – Include all notebooks / scripts in Python with reproducible environments (requirements.txt or Conda YAML). Acceptance criteria 1. End-to-end code runs without manual tweaks. 2. Dataset covers a continuous 3-5 year span. 3. Top-10 selection is demonstrably based on trading-volume rankings.