A curated collection of machine learning projects built with Python and popular data science libraries.
Projects cover a range of topics including supervised and unsupervised learning, deep learning, and interactive applications.
This repository grows as I explore new techniques and tools in the ML ecosystem.
| Project Name | Problem Type | Description | Open in Colab |
|---|---|---|---|
| Rock vs Mine Prediction | Classification | Classifies sonar signals as rock or mine |  |
| Fake News Detection | Binary Classification | Detects whether a news article is real or fake using NLP and Logistic Regression | |
| Diabetes Prediction | Binary Classification | Predicts whether a person has diabetes using health indicators and SVM | |
| Customer Segmentation | Unsupervised Learning | Segments mall customers based on income and spending using KMeans clustering | |
| House Price Prediction | Supervised Learning | Predicts residential property prices in Houston using Random Forest on Zillow-based data | |
Each project is contained in its own folder with a dedicated README.md explaining the dataset, approach, and usage.
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Clone this repository:
git clone https://github.com/Toshaksha/Machine-Learning.git
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Navigate to a project folder:
cd Machine-Learning/rock_vs_mine_prediction -
Install the required dependencies:
pip install -r requirements.txt
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Follow the instructions in the project’s
README.mdto run the code.
- Python 3.x
- Libraries:
- NumPy, Pandas
- Matplotlib, Seaborn
- Scikit-learn
- Jupyter Notebooks
- Build hands-on ML projects using real-world datasets
- Practice classification, regression, and clustering techniques
- Improve model evaluation, feature engineering, and data visualization skills
This project is licensed under the MIT License.
Use it freely for learning, building, and growing.
Feel free to connect or reach out:
- GitHub: @Toshaksha
- LinkedIn: Toshaksha
⭐ Thanks for exploring! More ML projects coming soon — stay tuned 🚀