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https://github.com/raihan4520/ml
A collection of machine learning projects showcasing various algorithms and techniques, including a final project for the Machine Learning course at AIUB.
https://github.com/raihan4520/ml
data-preprocessing jupyter-notebook machine-learning model-evaluation numpy pandas python scikit-learn
Last synced: 6 days ago
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A collection of machine learning projects showcasing various algorithms and techniques, including a final project for the Machine Learning course at AIUB.
- Host: GitHub
- URL: https://github.com/raihan4520/ml
- Owner: Raihan4520
- Created: 2024-09-28T23:30:19.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2024-09-30T12:41:08.000Z (about 1 month ago)
- Last Synced: 2024-10-28T03:56:55.989Z (9 days ago)
- Topics: data-preprocessing, jupyter-notebook, machine-learning, model-evaluation, numpy, pandas, python, scikit-learn
- Language: Jupyter Notebook
- Homepage: https://www.aiub.edu/faculties/fst/ug-course-catalog
- Size: 833 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Machine Learning (ML) Projects - AIUB
This repository contains a collection of machine learning projects developed as part of the **Machine Learning** course at **American International University - Bangladesh (AIUB)**. It includes various Jupyter Notebooks demonstrating the implementation of machine learning algorithms and techniques, with a final project housed in the `/Final/Project` folder.
### Course Information
For more details on the course, refer to the [AIUB Undergraduate Course Catalog](https://www.aiub.edu/faculties/fst/ug-course-catalog).
*Note: Search for "Machine Learning" for specific course information.*
## Project Overview
The repository explores a range of machine learning concepts from basic models to more advanced techniques. The projects are implemented using popular libraries like **scikit-learn**, **pandas**, **NumPy**, and **Matplotlib** for model training, evaluation, and visualization.
### Final Project:
The `/Final/Project` folder contains the capstone project for this course, which involves the implementation of a machine learning pipeline to solve a real-world problem using appropriate algorithms, data preprocessing, and model evaluation metrics.
## Repository Structure
- 📂`Final`:
- 📂`Codes`:
- `ANN_MNIST.ipynb`
- `CNN_MNIST.ipynb`
- 📂`Project`:
- `ML-Project-Report.pdf`
- `ML_Project.ipynb`
- `README.txt`
- `qt_dataset.csv`
- 📂`Mid`:
- 📂`Codes`:
- `Naive_Bayes.ipynb`
- 📂`Linear Regression using Gradient Descent`:
- `Linear_Regression_using_Gradient_Descent.ipynb`
- `data.csv`
- `README.md`
## Key Topics Covered
- Data Preprocessing
- Model Evaluation
- Feature Selection
- Hyperparameter Tuning
- Cross-validation
## How to Use
1. **Clone the repository**:
```bash
git clone https://github.com/Raihan4520/ML.git
2. **Install dependencies**: Ensure you have Python installed along with the required libraries. You can install the dependencies using the following command:
```bash
pip install
3. **Run Jupyter Notebooks**: Open Jupyter Notebook and explore the individual notebooks or the final project.
```bash
jupyter notebook
4. **Final Project**: Navigate to the `/Final/Project` folder and open `ML_Project.ipynb` to explore the final project implementation.
## Technologies Used
- **Python** (Programming Language)
- **Jupyter Notebook** (Interactive Environment)
- **scikit-learn** (Machine Learning Library)
- **pandas** (Data Manipulation)
- **NumPy** (Numerical Computing)
- **Matplotlib** (Data Visualization)
## Contact
If you have any questions or suggestions, feel free to reach out through the repository's issues or contact me directly.