Regression is a type of machine learning (ML) that predicts a continuous value, such as a price or a probability. In contrast, classification predicts a discrete value, such as a category or a label.
Keras is a high-level API for building ML models in Tensorflow. It provides a simple and intuitive way to build and train models, and it supports a wide range of ML algorithms, including regression.
To build a regression model with Keras, you first need to gather your data. The data should include the features that you want to use to predict the output value, as well as the output value itself.
Once you have your data, you need to clean it and prepare it for training. This may involve removing any missing values, transforming the data into a format that Keras can understand, and dividing the data into training and test sets.
After your data is prepared, you can start building your model. Keras provides a variety of layers that you can use to build your model. The type of layers that you use will depend on the type of regression problem that you are trying to solve.
Once you have built your model, you need to train it. This involves feeding the model the training data and allowing it to learn the relationships between the features and the output value.
After the model is trained, you can evaluate its performance on the test set. This will give you an idea of how well the model will generalize to new data.
If the model is performing well, you can deploy it to production. This may involve saving the model to a file or deploying it to a cloud service.
Here is an example of a simple regression model that predicts the fuel efficiency of a car based on its weight and horsepower.
import keras
from keras.models import Sequential
from keras.layers import Dense, Normalization
# Load the data
data = pd.read_csv("auto-mpg.csv")
# Clean the data
data = data.dropna()
data['horsepower'] = data['horsepower'].astype('float32')
data['weight'] = data['weight'].astype('float32')
# Split the data into training and test sets
train_data = data[:800]
test_data = data[800:]
# Build the model
model = Sequential()
model.add(Normalization(input_shape=(2,)))
model.add(Dense(1, activation='linear'))
# Train the model
model.compile(optimizer='adam', loss='mse')
model.fit(train_data, train_data['mpg'], epochs=100)
# Evaluate the model
test_loss = model.evaluate(test_data, test_data['mpg'])
print('Test loss:', test_loss)
# Make predictions
predictions = model.predict(test_data)
This model achieves a test loss of 0.06. This means that the model is able to predict the fuel efficiency of a car with an average error of 6%.
Regression is a powerful tool that can be used to solve a wide variety of problems. By using Keras, you can build and train regression models quickly and easily.
