A simple machine learning library, focusing on linear regression models. This library includes implementations for linear regression, as well as an example demonstrating fuel consumption predictions using a polynomial regression model.
To use the library, you will need to install the required dependencies listed in the requirements.txt file. You can install them with:
pip install -r requirements.txtHere is a basic example of how to use the LinearRegression model from the library:
from ml_library.models.linear_regression import LinearRegression
import numpy as np
# Example 1: Simple Linear Regression
X = np.array([[1, 1], [2, 2], [3, 3]])
y = np.array([2, 4, 6])
model = LinearRegression()
model.fit(X, y)
predictions = model.predict(X)
print("Predictions:", predictions)This example demonstrates using polynomial features to predict fuel consumption based on vehicle speed. The model is trained on data of vehicle speed and fuel consumption (L/100km) using a higher-order polynomial regression.
from ml_library.models.linear_regression import LinearRegression
import numpy as np
def standardize(X):
return (X - np.mean(X)) / np.std(X)
# Speed (km/h) and fuel consumption (L/100km)
speed = np.array([40, 50, 60, 70, 80, 90, 100, 110, 120]).reshape(-1, 1)
fuel_consumption = np.array([9.5, 8.7, 8.0, 7.5, 7.1, 6.8, 6.5, 6.3, 6.1])
# Generate polynomial features: speed^2 to speed^7
speed_features = np.hstack([speed**i for i in range(1, 8)])
# Standardize features
speed_features_scaled = standardize(speed_features)
fuel_consumption_scaled = standardize(fuel_consumption)
# Train the linear regression model
model = LinearRegression(learning_rate=0.01, n_iterations=15000)
model.fit(speed_features_scaled, fuel_consumption_scaled)
# Predict fuel consumption
predicted_consumption_scaled = model.predict(speed_features_scaled)
predicted_consumption = (predicted_consumption_scaled * np.std(fuel_consumption)) + np.mean(fuel_consumption)
print("Predicted fuel consumption:", predicted_consumption)Unit tests have been included for both basic linear regression and the fuel consumption prediction example. You can run the tests using unittest:
python -m unittest discover -s testsContributions are welcome! If you encounter issues or have ideas for improvements, feel free to open issues or submit pull requests. Together, we can make this project even better!
This project is licensed under the MIT License. See the LICENSE file for more information.