<gh_stars>0
from tutorial.functions import *
from tutorial.utils import *

# This is the task for Tutorial 2
# It does an end-to-end job, from generating the dataset
# to making the predictions and scoring the model

def generate_train_step(dataset_name, model_name):
    # Reading the data
    data = generate_dataset(dataset_name)

    # Processing the data
    features, labels = preprocess_data(data)
    
    # Training the model
    model = train_model(features, labels, model_name)
    
    # Making predictions
    predictions = make_predictions(model, features)
    
    # Calculating the accuracy of the model
    accuracy = calculate_accuracy(predictions, labels)
    
    # Saving the prediction and score as results
    result = {}
    result['predictions'] = predictions.tolist()
    result['accuracy'] = accuracy
    save_json(result, 'result.json')

from tutorial.functions import *
from tutorial.utils import *

# This is the task for Tutorial 2
# It does an end-to-end job, from generating the dataset
# to making the predictions and scoring the model

def generate_train_step(dataset_name, model_name):
    # Reading the data
    data = generate_dataset(dataset_name)

    # Processing the data
    features, labels = preprocess_data(data)
    
    # Training the model
    model = train_model(features, labels, model_name)
    
    # Making predictions
    predictions = make_predictions(model, features)
    
    # Calculating the accuracy of the model
    accuracy = calculate_accuracy(predictions, labels)
    
    # Saving the prediction and score as results
    result = {}
    result['predictions'] = predictions.tolist()
    result['accuracy'] = accuracy
    save_json(result, 'result.json')
# This is the task for Tutorial 2 # It does an end-to-end job, from generating the dataset # to making the predictions and scoring the model # Reading the data # Processing the data # Training the model # Making predictions # Calculating the accuracy of the model # Saving the prediction and score as results