saleamlakw/Ethiopian_athletes_image_classifier

# Ethiopian Athletes Image Classifier In this project, I developed an image classifier for Ethiopian athletes using scikit-learn. I utilized matplotlib for data visualization and used Haar cascade to detect and select images with facial features such as the face, eyes, nose, and mouth. I also used wavelet transformation for feature engineering. This project was built on Google Colab. ## Features - Classifies images of Ethiopian athletes - Uses Haar cascade for facial feature detection - Employs wavelet transformation for feature engineering - Visualizes data using matplotlib ## Technologies Used - Python - Scikit-learn - Matplotlib - OpenCV (for Haar cascade) - PyWavelets (for wavelet transformation) - Google Colab ## Installation 1. Clone the repository: ```bash git clone github.com cd Ethiopian_athletes_image_classifier ``` 2. Open the project in Google Colab by uploading the notebook file (`ethiopian_athletes_classifier.ipynb`). ## Usage 1. Upload your dataset to Google Colab or use the provided dataset. 2. Run the cells in the Colab notebook to execute the code step-by-step. 3. The notebook includes data visualization, image preprocessing, feature engineering, and model training. 4. After training, the model can classify new images of Ethiopian athletes. ## Data Preprocessing - **Image Selection**: Used Haar cascade to detect and select images with prominent facial features such as the face, eyes, nose, and mouth. - **Feature Engineering**: Applied wavelet transformation to extract relevant features from the images. ## Model Training - **Algorithm**: Utilized various algorithms available in scikit-learn. - **Evaluation**: Evaluated the model using appropriate metrics and visualized the results using matplotlib. ## Visualization - **Matplotlib**: Used for plotting graphs and visualizing data distributions and model performance. ## Dependencies The project requires the following Python p …