Model Training
Now we will separate the features and target variables and split them into training and testing data by using which we will select the model which is performing best on the validation data.
Python3
features = df.drop(['User_ID', 'Calories'], axis=1) target = df['Calories'].values X_train, X_val,\ Y_train, Y_val = train_test_split(features, target, test_size=0.1, random_state=22) X_train.shape, X_val.shape |
Output:
((13500, 5), (1500, 5))
Now, let’s normalize the data to obtain stable and fast training.
Python3
# Normalizing the features for stable and fast training. scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_val = scaler.transform(X_val) |
Now let’s train some state-of-the-art machine learning models and compare them which fit better with our data.
Python3
from sklearn.metrics import mean_absolute_error as mae models = [LinearRegression(), XGBRegressor(), Lasso(), RandomForestRegressor(), Ridge()] for i in range(5): models[i].fit(X_train, Y_train) print(f'{models[i]} : ') train_preds = models[i].predict(X_train) print('Training Error : ', mae(Y_train, train_preds)) val_preds = models[i].predict(X_val) print('Validation Error : ', mae(Y_val, val_preds)) print() |
Output:
LinearRegression() : Training Error : 17.893463692619434 Validation Error : 18.007896272831253 XGBRegressor() : Training Error : 10.110870876925963 Validation Error : 10.16210130894184 Lasso() : Training Error : 17.915089584958036 Validation Error : 17.995033362288662 RandomForestRegressor() : Training Error : 3.982735208112875 Validation Error : 10.472395222222223 Ridge() : Training Error : 17.893530494767777 Validation Error : 18.00781790803129
Out of all the above models, we have trained RandomForestRegressor and the XGB model’s performance is the same as their MAE for the validation data is same.
Calories Burnt Prediction using Machine Learning
In this article, we will learn how to develop a machine learning model using Python which can predict the number of calories a person has burnt during a workout based on some biological measures.