Public Machine Learning Projects

This repository contains a collection of machine learning projects, each utilizing different types of models applied to publicly accessible datasets. The goal is to learn and implement various machine learning techniques while creating valuable and practical applications.

Project List

1. Linear Regression: Predicting Car Prices
2. Logistic Regression: Titanic Survival Prediction
3. Decision Trees and Random Forests: Iris Flower Classification
4. Support Vector Machines (SVM): Handwritten Digit Recognition
5. K-Nearest Neighbors (KNN): Movie Recommendation System
6. Neural Networks: Predicting House Prices
7. Convolutional Neural Networks (CNNs): Object Detection in COCO Dataset
8. Recurrent Neural Networks (RNNs) and LSTMs: Stock Price Prediction
9. Transformer Models: Text Summarization with News Articles

Project Descriptions

Linear Regression: Predicting Car Prices

• Description: Predict car prices based on features like mileage, year, make, and model.
• Data Source: Kaggle's Car Price dataset.
• Tools and Libraries: scikit-learn, pandas, matplotlib
• Status: Planned

Logistic Regression: Titanic Survival Prediction

• Description: Predict whether a passenger survived the Titanic disaster based on various features.
• Data Source: Kaggle's Titanic dataset.
• Tools and Libraries: scikit-learn, pandas, matplotlib
• Status: Planned

Decision Trees and Random Forests: Iris Flower Classification

• Description: Classify species of Iris flowers based on petal and sepal measurements.
• Data Source: UCI Machine Learning Repository (Iris dataset).
• Tools and Libraries: scikit-learn, pandas, matplotlib
• Status: Planned

Support Vector Machines (SVM): Handwritten Digit Recognition

• Description: Recognize handwritten digits using the MNIST dataset.
• Data Source: MNIST dataset.
• Tools and Libraries: scikit-learn, tensorflow, keras
• Status: Planned

K-Nearest Neighbors (KNN): Movie Recommendation System

• Description: Recommend movies to users based on their ratings.
• Data Source: MovieLens dataset.
• Tools and Libraries: scikit-learn, pandas, numpy
• Status: Planned

Neural Networks: Predicting House Prices

• Description: Predict house prices based on various features like location, size, and year built.
• Data Source: Kaggle's House Prices dataset.
• Tools and Libraries: tensorflow, keras, pandas
• Status: Planned

Convolutional Neural Networks (CNNs): Object Detection in COCO Dataset

• Description: Detect and identify objects in images using the COCO dataset.
• Data Source: COCO dataset.
• Tools and Libraries: tensorflow, keras, OpenCV
• Status: Planned

Recurrent Neural Networks (RNNs) and LSTMs: Stock Price Prediction

• Description: Forecast stock prices based on historical data.
• Data Source: Yahoo Finance.
• Tools and Libraries: tensorflow, keras, pandas
• Status: Planned

Transformer Models: Text Summarization with News Articles

• Description: Generate summaries of news articles using transformer models.
• Data Source: News API or any public news dataset.
• Tools and Libraries: transformers (Hugging Face), tensorflow, pandas
• Status: Planned

Contributing

Feel free to fork this repository and submit pull requests. Contributions are welcome!

License

This project is licensed under the MIT License - see the LICENSE file for details.