All our Data Science projects include bite-sized activities to test your knowledge and practice in an environment with constant feedback.
All our activities include solutions with explanations on how they work and why we chose them.
Calculate the percentage of missing values.
Drop the columns that you have previously identified as independent. Perform your drop in place, modifying the df variable. If you have made a mistake, restart your notebook from the beginning.
Store the data frame in the variable df.
Store the features in X and the target y.
Store the values in the variables in X_train, X_test,y_train, y_test, and random_state.
Train a Random Forest Classifier using the training data, and store the model in rf. You can specify the model parameters such as the maximum depth of the tree or the minimum number of samples required to split an internal node.
Calculate the accuracy of both the training and testing sets and run the code in a Jupyter Notebook.
Store the results in the variables train_accuracy and test_accuracy.
The expected accuracy for a simple problem varies depending on the specifics of the problem and data. However, for a well-defined and simple problem with a large and diverse training dataset, a well-trained machine learning model could achieve an accuracy of over 80% in some cases.
The two models that present the best performance in terms of the evaluation metrics (Highest accuracy and AUC).
