mathdatech/Python
1
0
Fork 0
mirror of https://github.com/TheAlgorithms/Python.git synced 2025-01-31 06:33:44 +00:00
Code Issues Packages Projects Releases Wiki Activity

Resolved ruff checks

Browse Source
This commit is contained in:
Harmanaya Sharma 2024-10-23 00:14:37 +05:30
parent a2d07af8c1
commit 8f1f091aa4
1 changed files with 9 additions and 9 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

18
machine_learning/ridge_regression.py
View File

@ -68,7 +68,7 @@ class RidgeRegression:
m, n = features_scaled.shape
self.theta = np.zeros(n) # Initialize weights to zeros
for i in range(self.iterations):
for _ in range(self.iterations):
predictions = features_scaled.dot(self.theta)
error = predictions - target
@ -149,21 +149,21 @@ if __name__ == "__main__":
data = pd.read_csv(
"https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/master/Week1/ADRvsRating.csv"
)
x = data[["Rating"]].to_numpy() # Feature: Rating
y = data["ADR"].to_numpy() # Target: ADR
y = (y - np.mean(y)) / np.std(y)
data_x = data[["Rating"]].to_numpy() # Feature: Rating
data_y = data["ADR"].to_numpy() # Target: ADR
data_y = (data_y - np.mean(data_y)) / np.std(data_y)
# Add bias term (intercept) to the feature matrix
x = np.c_[np.ones(X.shape[0]), x] # Add intercept term
data_x = np.c_[np.ones(data_x.shape[0]), data_x] # Add intercept term
# Initialize and train the Ridge Regression model
model = RidgeRegression(alpha=0.01, lambda_=0.1, iterations=1000)
model.fit(x, y)
model.fit(data_x, data_y)
# Predictions
predictions = model.predict(x)
predictions = model.predict(data_x)
# Results
print("Optimized Weights:", model.theta)
print("Cost:", model.compute_cost(x, y))
print("Mean Absolute Error:", model.mean_absolute_error(y, predictions))
print("Cost:", model.compute_cost(data_x, data_y))
print("Mean Absolute Error:", model.mean_absolute_error(data_y, predictions))