This repository contains my coursework from the Practical Machine Learning course (MSDS 422) at Northwestern University. Brief descriptions of the nine Python assignments are provided below.
- Assignment 1: Exploratory Data Analysis (EDA) and Regression Using Kaggle "Ames Housing" Data
- Assignment 2: Piecewise, ElasticNet, Ridge, and Polynomial Regression Using Kaggle "Ames Housing" Data
- Assignment 3: Principal Components Analysis (PCA), Lasso, Ridge, and ElasticNet Regression Using Kaggle "Ames Housing" Data
- Assignment 4: Logistic Regression, Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), and K-Nearest Neighbors Modeling Using Kaggle "Titanic" Data
- Assignment 5: Random Forest, Gradient Boosted Trees, and Extra Trees Models Using Kaggle "Titanic" Data
- Assignment 6: Principal Components Analysis (PCA), Random Forest, and K-Means Clustering Using Kaggle "Digit Recognizer" Data
- Assignment 7: Neural Networks Using Kaggle "Digit Recognizer" Data
- Assignment 8: Convolutional Neural Networks Using Kaggle "Dogs Vs Cats" Data
- Assignment 9: Long Short-Term Memory Recurrent Neural Networks Using Kaggle "Natural Language Processing with Disaster Tweets" Data