The BTS Member Classifier is a machine learning project designed to identify BTS member from images. It takes an image as input and classify the member. This project is an excellent example of a classification problem solved using advanced computer vision techniques and machine learning algorithms.
Used Logistic regression for classification
Used OpenCV Haarcascades for preliminary face detection, ensuring that the classifier focuses on the relevant parts of an image.
Used Wavelet transforms are applied to the detected faces to extract meaningful features from the images as preprocessing
The model predict the output with highest probabilities image.
Used Fatkun extension for image collection for each members.
- Python
- Numpy and OpenCV for data cleaning
- Matplotlib & Seaborn for data visualization
- Sklearn for model building
- Jupyter notebook, visual studio code as IDE
- Python flask for http server
- HTML/CSS/Javascript for UI
