• Particle swarm optimization and grey wolf optimization algorithms were learned, and these two algorithms were compared with each other in terms of unimodal, multimodal, and fixed-dimension multimodal functions.
• Data preprocessing was conducted, and missing data was filled using the SimpleImputer function with the "mean" strategy. Dependent and independent variables were converted into numerical format using label encoder and one-hot encoder functions. Feature scaling was applied to prepare the data for machine learning algorithms.
• Popular Regression Analysis, Decision Trees, Classification, and Clustering algorithms were learned. Using three different datasets at a small to medium scale, results and model performance were evaluated using confusion matrix, r2 score, accuracy score, and Python visualization functions.
The work I have done is included in two separate PDF files. There are two different work periods: midterm and final.