Machine Learning Model Implementation (CSE - 6363 Assignment)
Project Overview: Developed and executed multiple machine learning models including Linear Regression, Logistic Regression, and Linear Discriminant Analysis using Python. The project involved the training and evaluation of these models on different datasets to analyze their performance and generate insights.
Key Components:
Linear Regression Models: Implemented and trained four distinct linear regression models using train_regression1.py, train_regression2.py, train_regression3.py, and train_regression4.py. Each model training script plotted relevant graphs and saved the trained model. Evaluation scripts (eval_regression1.py, eval_regression2.py, eval_regression3.py, and eval_regression4.py) were used to compute and display the Mean Squared Error (MSE) values for the respective models.
Logistic Regression: Developed a logistic regression model (LogisticRegression.py) to classify data and predict outcomes based on input features.
Linear Discriminant Analysis: Implemented a Linear Discriminant Analysis model (LinearDiscriminantAnalysis.py) for dimensionality reduction and classification tasks.
Outcomes and Deliverables:
Successfully trained and evaluated multiple regression models, generating plots and saving models for future use. Calculated and compared the performance metrics (MSE) for different linear regression models. Built robust logistic regression and LDA models to handle classification problems.