Data Exploration: Exploring the general situation of the data ( head ) , exploring data types ( object , float, int, etc. ) , understanding
Data Exploration: Exploring the general situation of the data head exploring data types object float, int, etc. understanding the data shape shape and examining data distribution describe Data Preprocessing and Feature Engineering: Removing features with too many NaN values, filling in missing values, normalizing the data, selecting appropriate features, performing one hot encoding on nonnumerical data, and splitting the dataset into training and validation sets using sklearn's traintestsplit Model Selection: Choosing supervised learning models learned in class, such as Linear Regression, Decision Tree Regression, Random Forest Regression, etc., to train a suitable regression model. Model Validation: Selecting appropriate evaluation metrics such as MSE, RMSE, MAE, etc. to horizontally compare the performance of different models and analyzing the reasons for their performance.
