Welcome to Classification-Models — a curated collection of practical classification models built for learning, experimentation, and real-world application.
This repository is dedicated to exploring various classification algorithms and techniques using interactive Jupyter Notebooks.
Whether you're a beginner getting started with machine learning or a seasoned practitioner looking to sharpen your skills, you'll find useful resources, code snippets, and hands-on examples here.
- Model Implementations: Logistic Regression, Decision Trees, Random Forests, SVM, KNN, and more.
- Dataset Exploration: Data preprocessing, visualization, and feature engineering.
- Evaluation Metrics: Accuracy, precision, recall, F1-score, ROC curves, and more.
- Guided Notebooks: Step-by-step explanations for better understanding.
- Jupyter Notebook (100%)
- Clone this repo:
git clone https://github.com/HammadAli08/Classification-Models.git - Open notebooks in Jupyter or compatible environment.
- Explore, run, and modify the code to suit your needs!
Contributions, suggestions, and feedback are always welcome!
Feel free to fork the repo, open issues, or submit pull requests.
Distributed under the MIT License.
“Classification Models for Practice”
Enjoy exploring and practicing classification models!