Update README.md

README.md

@@ -6,34 +6,94 @@ To write a python program to implement multivariate linear regression and predic
 2.	Anaconda – Python 3.7 Installation / Moodle-Code Runner
 ## Algorithm:
 ### Step1
-<br>
+Import the required libraries and load the California Housing dataset for linear regression.
 
-### Step2
-<br>
 
+### Step2
+Split the dataset into training and testing sets using train_test_split().
 ### Step3
-<br>
-
+Create and train the Linear Regression model using the training data.
 ### Step4
-<br>
+Predict outputs and plot residual errors for both training and testing data using Matplotlib.
 
-### Step5
-<br>
 
 ## Program:
 ```
+import matplotlib.pyplot as plt
+import numpy as np
+
+from sklearn.datasets import fetch_california_housing
+from sklearn import linear_model
+from sklearn.model_selection import train_test_split
+
+# Load the California Housing dataset
+housing = fetch_california_housing()
+
+# Defining feature matrix (X) and response vector (y)
+X = housing.data
+y = housing.target
+
+# Splitting X and y into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.4, random_state=1
+)
+
+# Create linear regression object
+reg = linear_model.LinearRegression()
+
+# Train the model using the training sets
+reg.fit(X_train, y_train)
+
+# Regression coefficients
+print("Coefficients:\n", reg.coef_)
+
+# Variance score
+print("Variance score: {:.2f}".format(reg.score(X_test, y_test)))
+
+# Setting plot style
+plt.style.use('fivethirtyeight')
+
+# Plotting residual errors in training data
+plt.scatter(
+    reg.predict(X_train),
+    reg.predict(X_train) - y_train,
+    color="green",
+    s=10,
+    label="Train data"
+)
+
+# Plotting residual errors in test data
+plt.scatter(
+    reg.predict(X_test),
+    reg.predict(X_test) - y_test,
+    color="blue",
+    s=10,
+    label="Test data"
+)
+
+# Plotting line for zero residual error
+plt.hlines(y=0, xmin=0, xmax=6, linewidth=2)
+
+# Plotting legend
+plt.legend(loc="upper right")
+
+# Plot title
+plt.title("Residual Errors")
 
+# Show plot
+plt.show()
 
 
 
 
 
 ```
 ## Output:
+<img width="642" height="558" alt="image" src="https://github.com/user-attachments/assets/0530ba96-6e60-442f-9766-8244271652d6" />
 
 ### Insert your output
 
 <br>
 
 ## Result
-Thus the multivariate linear regression is implemented and predicted the output using python program.
+Thus the multivariate linear regression is implemented and predicted the output using python program.