Ridge Regression Algorithm Added to Regression files and Also updated readme

3_Regression/README.md

@@ -12,6 +12,8 @@
 
 [6. Random Forest Regression](Random_Forest_Regression)
 
+[7. Ridge Regression](Ridge_Regression)
+
 ## Comparing Regression Models and their performance
 
 ### R Squared Intution for Simple Linear Regression

3_Regression/Ridge_Regression/python/Salary_Data.csv

@@ -0,0 +1,31 @@
+YearsExperience,Salary
+1.1,39343
+1.3,46205
+1.5,37731
+2,43525
+2.2,39891
+2.9,56642
+3,60150
+3.2,54445
+3.2,64445
+3.7,57189
+3.9,63218
+4,55794
+4,56957
+4.1,57081
+4.5,61111
+4.9,67938
+5.1,66029
+5.3,83088
+5.9,81363
+6,93940
+6.8,91738
+7.1,98273
+7.9,101302
+8.2,113812
+8.7,109431
+9,105582
+9.5,116969
+9.6,112635
+10.3,122391
+10.5,121872

3_Regression/Ridge_Regression/python/python-scratch/RidgeRegression_scratch.py

@@ -0,0 +1,109 @@
+# Importing libraries
+
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split
+import matplotlib.pyplot as plt
+
+
+# Ridge Regression
+
+class RidgeRegression():
+
+    def __init__(self, learning_rate, iterations, l2_penality):
+        self.learning_rate = learning_rate
+        self.iterations = iterations
+        self.l2_penality = l2_penality
+
+    # Function for model training
+    def fit(self, X, Y):
+        # no_of_training_examples, no_of_features
+        self.m, self.n = X.shape
+
+        # weight initialization
+        self.W = np.zeros(self.n)
+
+        self.b = 0
+        self.X = X
+        self.Y = Y
+
+        # gradient descent learning
+
+        for i in range(self.iterations):
+            self.update_weights()
+        return self
+
+    # Helper function to update weights in gradient descent
+
+    def update_weights(self):
+        Y_pred = self.predict(self.X)
+
+        # calculate gradients
+        dW = (- (2 * (self.X.T).dot(self.Y - Y_pred)) +
+              (2 * self.l2_penality * self.W)) / self.m
+        db = - 2 * np.sum(self.Y - Y_pred) / self.m
+
+        # update weights
+        self.W = self.W - self.learning_rate * dW
+        self.b = self.b - self.learning_rate * db
+        return self
+
+    # Hypothetical function h( x )
+    def predict(self, X):
+        return X.dot(self.W) + self.b
+
+def train(X,Y):
+    # Splitting dataset into train and test set
+    X_train, X_test, Y_train, Y_test = train_test_split(X, Y,
+                                                        test_size=1 / 3, random_state=0)
+    # Model training
+    model = RidgeRegression(iterations=1000,
+                            learning_rate=0.01, l2_penality=1)
+    model.fit(X_train, Y_train)
+    return model, X_test, Y_test
+
+
+def visualize(X, Y, Y_pred, col):
+    # Splitting dataset into train and test set
+    X_train, X_test, Y_train, Y_test = train_test_split(X, Y,
+                                                        test_size=1 / 3, random_state=0)
+    fig, axs = plt.subplots(2)
+    axs[0].scatter(X_train, Y_train, color='blue')
+    axs[0].plot(X_train, Y_train, color='orange')
+    axs[0].set_title('training')
+
+    axs[1].scatter(X_test, Y_pred, color='blue')
+    axs[1].plot(X_test, Y_pred, color='orange')
+    axs[1].set_title('validation')
+
+    for ax in axs.flat:
+        ax.set(xlabel=col[0], ylabel=col[1])
+
+    fig.tight_layout()
+    #plt.savefig('output.png')
+    plt.show()
+
+# Driver code
+
+def main():
+    # Importing dataset
+    df = pd.read_csv("../Salary_Data.csv")
+    X = df.iloc[:, :-1].values
+    Y = df.iloc[:, 1].values
+    col = df.columns
+    #training the regression model
+    model, X_test, Y_test = train(X, Y)
+
+    # Prediction on test set
+    Y_pred = model.predict(X_test)
+    print("Predicted values ", np.round(Y_pred[:3], 2))
+    print("Real values	 ", Y_test[:3])
+    print("Trained W	 ", round(model.W[0], 2))
+    print("Trained b	 ", round(model.b, 2))
+
+    # Visualization on test set
+    visualize(X, Y, Y_pred, col)
+
+
+if __name__ == "__main__":
+    main()

3_Regression/Ridge_Regression/python/ridge_regression.py

@@ -0,0 +1,53 @@
+""" Ridge Regression
+"""
+
+# Importing the libraries
+import matplotlib.pyplot as plt
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import Ridge
+
+
+def main():
+    # Importing the dataset
+    dataset = pd.read_csv('Salary_Data.csv')
+    X = dataset.iloc[:, :-1].values
+    y = dataset.iloc[:, 1].values
+
+    # Splitting the dataset into the Training set and Test set
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1 / 3, random_state=0)
+
+    # Feature Scaling
+    """from sklearn.preprocessing import StandardScaler
+    sc_X = StandardScaler()
+    X_train = sc_X.fit_transform(X_train)
+    X_test = sc_X.transform(X_test)
+    sc_y = StandardScaler()
+    y_train = sc_y.fit_transform(y_train)"""
+
+    # Fitting Simple Linear Regression to the Training set
+    regressor = Ridge()
+    regressor.fit(X_train, y_train)
+
+    # Predicting the Test set results
+    y_pred = regressor.predict(X_test)
+
+    # Visualising the Training set results
+    plt.scatter(X_train, y_train, color='red')
+    plt.plot(X_train, regressor.predict(X_train), color='blue')
+    plt.title('Salary vs Experience (Training set)')
+    plt.xlabel('Years of Experience')
+    plt.ylabel('Salary')
+    plt.show()
+
+    # Visualising the Test set results
+    plt.scatter(X_test, y_test, color='red')
+    plt.plot(X_train, regressor.predict(X_train), color='blue')
+    plt.title('Salary vs Experience (Test set)')
+    plt.xlabel('Years of Experience')
+    plt.ylabel('Salary')
+    plt.show()
+
+
+if __name__ == '__main__':
+    main()

README.md

@@ -20,6 +20,7 @@
  * [Support Vector Regression](3_Regression/Support_Vector_Regression)
  * [Decision Tree Regression](3_Regression/Decision_Tree_Regression)
  * [Random Forest Regression](3_Regression/Random_Forest_Regression)
+ * [Ridge Regression](3_Regression/Ridge_Regression)
 
 [**3. Classification**](4_Classification/README.md)
  * [Logistic Regression](4_Classification/Logistic_Regression)
@@ -109,4 +110,4 @@ Check the official MIT License [here](LICENSE).
 - **[@pragyakapoor](https://github.com/pragyakapoor)**
 
 <!-- DO NOT REMOVE - contributor_list:end -->
-<!-- prettier-ignore-end -->
+<!-- prettier-ignore-end -->