https://github.com/eastonarcher/python-machine-learning
⚡Explore machine learning concepts through Python scripts and Jupyter notebooks showcasing algorithms and applications
https://github.com/eastonarcher/python-machine-learning
jupyter-notebook llm machine-learning machine-learning-algorithms python-machine-learning
Last synced: 2 months ago
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⚡Explore machine learning concepts through Python scripts and Jupyter notebooks showcasing algorithms and applications
- Host: GitHub
- URL: https://github.com/eastonarcher/python-machine-learning
- Owner: EastonArcher
- Created: 2024-01-30T05:15:13.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-03-14T18:27:03.000Z (about 1 year ago)
- Last Synced: 2025-01-12T08:33:28.015Z (4 months ago)
- Topics: jupyter-notebook, llm, machine-learning, machine-learning-algorithms, python-machine-learning
- Language: Python
- Homepage:
- Size: 75.2 KB
- Stars: 2
- Watchers: 2
- Forks: 1
- Open Issues: 0
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Machine Learning Repository 📈
This repository contains a collection of Python scripts and Jupyter notebooks that demonstrate various machine learning algorithms, techniques, and applications.
## Projects 📂
### Music Recommendation 🎧
* This script utilizes a Decision Tree Classifier to build a music recommender system. The model is trained on a dataset (music.csv) containing user information (age and gender) and their preferred music genres. The trained model generates a decision tree visualized in the music-recommender.dot file.
#### ❔How to Use
1. Ensure you have the required dependencies installed (pandas and scikit-learn).
2. Run the script MusicRecommendationMachineLearning.py.
3. Explore the generated decision tree in the music-recommender.dot file.
### Video Game Sales Predictor 🎮
* This script predicts global video game sales using a Decision Tree Classifier. The model is trained on a dataset (vgsales.csv) containing various features related to video games. The script preprocesses the data, handles missing values, encodes categorical variables, and evaluates the model's accuracy.
#### ❔How to Use
1. Install the required dependencies (pandas, scikit-learn).
2. Run the script VideoGameMachineLearning.py.
3. Check the accuracy of the model printed in the console.
### Sports Predictions 🏀
* This script predicts the
#### ❔How to Use
1.