MLProjects

A collection of small machine learning experiments built with Python and Jupyter notebooks. I added small inline explanations about the aim of the code in project files.

Projects

Project	Description
Spam Mail Prediction	Classifies emails as spam or ham using logistic regression
Wine Quality	Predicts wine quality using Random Forest
Diabetes Prediction	Binary classification to predict diabetes onset
Movie Recommender System	Content-based movie recommendations
Credit Card Fraud	Anomaly detection for fraudulent transactions
Rainfall Prediction	Predicts rainfall using weather features
Computer Engineers Salary Prediction	Salary estimation for engineering graduates
YouTube Trending Videos	EDA on US YouTube trending video data

Getting Started

1. Clone

git clone https://github.com/eisenheiim/MLProjects.git
cd MLProjects

2. Create a virtual environment (recommended)

python -m venv .venv
# macOS/Linux:
source .venv/bin/activate
# Windows:
.venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt

4. Launch Jupyter

jupyter notebook

Open any .ipynb file from the project folders to explore.

Notes

• Notebooks contain inline comments explaining each step.
• Datasets are included in their respective folders where file size permits.
• Experiments are exploratory; model results may vary with different random seeds.