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Introduction The goal of this project is to train a model that can predict house prices accurately with minimal errors for house buyers and house sellers. The value of a house is usually determined by several factors which include location, square footage, number of bedroom, number of baths and so on.

The goal of this assignment is to learn about the Naive Bayes Classifier (NBC). In this assignment we’re going to implement the NBC from scratch without using NBC library in python and perform sentiment analysis using text dataset about amazon reviews.

The aim of this project is to demonstrate the problems of underfitting and overfitting. We will also illustrate on how to use linear regression with polynomial features to approximate nonlinear functions.

The goal of this project is to build a CIFAR-10 image classifier using pytorch and tensorflow. In this project, different classifiers are built for the purposes of comparisons. To compare the performances among the fitted models, we will use the predicted accuracy and the estimated loss of each model as comparison metric.

The primary objective of this project is to build a predictive Machine Learning model that predicts which passengers survived the Titanic shipwreck in April 15, 1912 using passengers data. Also, we will perform a variable or feature importance test in this study which will help us to better improve our predictive model and gain more insights to the given data.