- There is a prediction about which model you expect to do better. (5 points)
- The labels set (y) is created from the “spam” column. (5 points)
- The features DataFrame (X) is created from the remaining columns. (5 points)
- The value_counts function is used to check the balance of the labels variable (y). (5 points)
- The data is correctly split into training and testing datasets by using train_test_split. (10 points)
- An instance of StandardScaler is created. (5 points)
- The Standard Scaler instance is fit with the training data. (5 points)
- The training features DataFrame is scaled using the transform function. (5 points)
- The testing features DataFrame is scaled using the transform function. (5 points)
- A logistic regression model is created with a random_state of 1. (5 points)
- The logistic regression model is fitted to the scaled training data (X_train_scaled and y_train). (5 points)
- Predictions are made for the testing data labels by using the testing feature data (X_test_scaled) and the fitted model, and saved to a variable. (5 points)
- The model’s performance is evaluated by calculating the accuracy score of the model with the accuracy_score function. (5 points)
- A random forest model is created with a random_state of 1. (5 points)
- The random forest model is fitted to the scaled training data (X_train_scaled and y_train). (5 points)
- Predictions are made for the testing data labels by using the testing feature data (X_test_scaled) and the fitted model, and saved to a variable. (5 points)
- The model’s performance is evaluated by calculating the accuracy score of the model with the accuracy_score function. (5 points)
- The following questions are answered accurately:
- Which model performed better? (5 points)
- How does that compare to your prediction? (5 points)
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent