ML Model Training Workflow Tool

Here's a Python code snippet that uses a linear regression model to predict the global box office of a movie based on its cast, year of release, and production budget: import pandas as pd from sklearn.linear_model import LinearRegression # Load the dataset into a Pandas DataFrame movie_data = pd.read_csv('movie_dataset.csv') # Split the data into training and testing sets X_train = movie_data[['Cast', 'Year of Release', 'Production Budget']].values y_train = movie_data['Global Box Office'].values # Train a linear regression model on the training data lr_model = LinearRegression() lr_model.fit(X_train, y_train) # Predict the global box office for a new movie new_movie = [[['Tom Hanks', 'Emma Watson', 'John Boyega'], 2021, 100000000]] predicted_box_office = lr_model.predict(new_movie) # Print the predicted global box office for the new movie print('Predicted global box office:', predicted_box_office) In this code, we first load the movie dataset into a Pandas DataFrame. We then split the data into training and testing sets, where the training set consists of the cast, year of release, and production budget for each movie, and the corresponding global box office. Next, we train a linear regression model on the training data using the scikit-learn library. This model will be able to predict the global box office for a new movie based on its cast, year of release, and production budget. To demonstrate how the model works, we create a new movie with a cast that includes Tom Hanks, Emma Watson, and John Boyega, a year of release of 2021, and a production budget of $100 million. We then use the trained model to predict the global box office for this movie. Finally, we print out the predicted global box office value.