do any of you doing thesis using machine learning?

Start by cleaning it through handling missing values, removing duplicates, and normalizing or standardizing numerical features using tools like pandas in Python. When unsure which machine learning model to choose, begin with simpler models like linear regression or decision trees and gradually move to more complex ones such as random forests, gradient boosting, or neural networks, using cross-validation to evaluate performance. To tackle overfitting, where your model performs well on training data but poorly on test data, try regularization techniques like L1 or L2 regularization, pruning for decision trees, or dropout for neural networks. Conversely, if your model is underfitting, performing poorly on both training and test data, consider using more complex models or adding more features to your dataset. For feature selection, if you have too many or irrelevant features, use techniques like Recursive Feature Elimination (RFE), Lasso (L1 regularization), or feature importance scores from tree-based models to identify and keep only the most relevant features. Talking about machine learning are you looking for washing machine repair in Tampa appkiance fix is your go to destination