This repository contains my machine learning models from my various assignments/ mini projects, organized into subdirectories for each task. Each subdirectory includes:
- A Jupyter Notebook (
.ipynb) showcasing the implementation and results of the assignment. - A
README.mdfile providing a detailed explanation of the task, approach, and findings.
The goal of this repository is to demonstrate my progress and understanding of various machine learning concepts and techniques through practical assignments.
📂 ML-Models-Repository
├── 📂 Fraud detection
│ ├── Fraud_detection.ipynb
│ ├── README.md
├── 📂 Content
│ ├── transcribinator.ipynb
│ ├── README.md
└── README.md
##These Include
- Fraud Detection
- Task: Create a prediction Model for fraudulent transactions
- Focus Areas: Used RandomForestClassifier and ensemble method with early stopping for this.
-
Clone the repository:
git clone [https://github.com/PhoenixAlpha23/ML-models.git](https://github.com/PhoenixAlpha23/ML-models)
-
Navigate to the desired assignment folder:
cd Fraud detection -
Open the Jupyter Notebook to explore the code and results:
jupyter notebook Fraud_detection.ipynb
Ensure you have the following installed:
- Python 3.x
- Jupyter Notebook
- Required libraries (specified in each notebook or
requirements.txt)
Install dependencies using pip:
pip install -r requirements.txt- Add more assignments covering advanced topics.
- Incorporate detailed visualizations and discussions for each task.
- Implement additional metrics and benchmarks to evaluate models.
This repository is a personal project for learning and showcasing my progress. If you have suggestions or ideas for improvement, feel free to raise an issue or submit a pull request.
This repository is licensed under the Apache 2.0 License. See the LICENSE file for more details.
Thank you for exploring my work! Feel free to reach out if you have any questions or feedback.