# robertmartin8/MachineLearningStocks

Using python and scikit-learn to make stock predictions

Repository: https://github.com/robertmartin8/MachineLearningStocks
Canonical: https://ross.abutalabs.com/products/machinelearningstocks
Language: Python
License: MIT
License Family: permissive
Topics: stock-prediction, machine-learning, scikit-learn, python, yahoo-finance, stock, historical-stock-fundamentals, quantitative-finance, algorithmic-trading, trading, sklearn, tutorial, guide, data-science, stock-prices
Last push: 2024-06-17T22:43:10+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3489, "days_push": 807, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1961, forks 539 (observed 2026-08-28T04:05:59.519352+00:00)

## What it is
A Python tutorial project and template applying scikit-learn classifiers to historical stock prices and fundamentals to predict which stocks will outperform. It walks through data cleaning with pandas, feature engineering, backtesting, and generating predictions on current data.

## Use cases
- learn how to apply machine learning to stock prediction
- scrape and clean historical stock fundamentals with pandas
- backtest an ML-based stock selection strategy
- build a starter template for algorithmic trading research
- understand the ML workflow for predicting stock price movements
- classify stocks by fundamentals like PE ratio and debt/equity

## When to choose
- you want an educational, extensible walkthrough of ML for stock prediction
- you are learning pandas and scikit-learn on real financial data
- you need a starting template for a quantitative trading research project

## When to avoid
- you need actively maintained production trading software
- you want reliable live-trading signals without your own improvements
- you need portfolio optimization rather than stock selection

## Facets
- artifact type: learning-resource
- maturity: abandoned
- function: machine-learning, data-science, etl
- domain: machine-learning, fintech, data-science, tutorials
- platform: python, cross-platform
- tags: stock-prediction, scikit-learn, quantitative-finance, algorithmic-trading, tutorial, backtesting, educational

## Member repositories
- robertmartin8/MachineLearningStocks (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:59.519352+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:05:39.692605+00:00, confidence not recorded.
  - readme: https://github.com/robertmartin8/MachineLearningStocks (fetched 2026-08-28T04:05:59.519352+00:00, sha 0f61710e77d1)
- Data as of 2026-08-30T08:39:29.467469+00:00.