DeepsDataScience

Below is the first machine learning pipeline I implemented after completing the machine learning section of Kaggle learn. It uses XGBRegressor with Simple Imputer to impute the missing values and One-Hot encoding to convert the categorical data (like SaleCondition) into binary columns. There are many data analysis techniques and visualization methods that can be applied to the dataset to get the best results. Due to my limited knowledge of those methods for now, I will only be implementing the methods taught in the Kaggle learn course. Steps: Extract and modify the data from the .csv file to suit our model's requirement. Impute and Encode the training and test set if needed. Define the machine learning model. Train or fit the model with the training data. Make predictions on the testing data. Evaluate the model, if any improvement possible then apply it and refit the model. First, import the train and test data then convert it to dataframe using pandas. ...