Malaria Detection

Project Overview

Compare Naive Bayes, SVM, XGBoost, Bagging, AdaBoost, K-NN, etc. for Malaria Cells classification
Used different feature extraction techniques like HOG, LBP, SIFT, SURF, pixel values
Feature reduction techniques PCA, LDA
Normalization techniques such as z-score and min-max
Classifiers such as Naive Bayes, SVM XGBoost, Bagging, AdaBoost, K-Nearest Neighbors, Random Forests
Metrics such as Accuracy, Precision, Recall, F1 score, and ROC

Name		Name	Last commit message	Last commit date
Latest commit History 3 Commits
output		output
.gitignore		.gitignore
Malaria-Detection-CNN.ipynb		Malaria-Detection-CNN.ipynb
Malaria_Detection_Project.ipynb		Malaria_Detection_Project.ipynb
README.md		README.md
my_ml_lib.py		my_ml_lib.py
requirements.txt		requirements.txt