Clothing-Category-prediction-

Adopted Fashion-MNIST dataset to train a model to predict the category of a clothing using labelled image of varities of clothing, used Scikit learn to apply machine learning technique (Decision Tree, random Forset Esemble method, Gradient Boosting, Logistic Regression, KNN, Multi Layer Perceptron) compare the accuracy of each model, then apply the model with best accuracy on test data to precdict the category of a clothing.

Build-Naive-Bayes-Classification-Model-and-Evaluate-Accuracy

The dataset I have used for this project is the Breast Cancer Wisconsin Diagnostic Database. The dataset includes various information about breast cancer tumors, as well as classification labels of malignant or benign. The dataset has 569 instances, or data, on 569 tumors and includes information on 30 attributes, or features, such as the radius of the tumor, texture, smoothness, and area. Using this dataset, I have build a machine learning model to use tumor information to predict whether or not a tumor is malignant or benign.