Here are some of my machine learning projects. Each folder has a .ipynb file of my work and a README.md file to introduce the structure and the goal of the proejct. Notice that some projects do not include datasets, for some datasets you can download from the link in my work.
Here are the links to some famous machine learning tasks.
A personalized movie recommender system by using SVD to perform Latent Semantic Analysis (LSA), and use it to construct a Latent Factor Model (LFM) for personalized recommendation. Using k-nearest neighbors to predict latitude and longitude coordinates of images from their CLIP embeddings.
Implemented and modified a Convolutional Neural Network (CNN) to an AlexNetwork to achieve better accuracy on CIFAR-10 classification.
Spam classification and Titanic survivors prediction by decision trees and random forests.
Using Support Vector Machines (SVMs) and Gaussian Discriminant Analysis (GDA) for MNSIT digit classification and Spam classification.
Classifying wine by logistic regression with Batch Gradient Descent (BGD) and Stochastic Gradient Descent (SGD) update methods.
Performing Exploratory Data Analysis (EDA) including feature engineering to clean out, and understand the data of housing information. Applying linear regression method to predict the accurate housing prices for houses in Cook County, Chicago, IL.