Linear Regression Concept
Linear regression is a supervised learning algorithm used to model the relationship between a dependent variable and one or more independent variables. It aims to fit a linear equation (y = mx + b) to the data, predicting the value of the dependent variable based on the independent variables.
Implementation in Python (Scikit-learn)
Step 1: Import the Necessary Libraries
pythonCopier le codefrom sklearn.linear_model import LinearRegressionfrom sklearn.model_selection import train_test_split
Step 2: Prepare Data and Split into Training/Testing Sets
pythonCopier le codeX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
Step 3: Train the Model
pythonCopier le codemodel = LinearRegression()model.fit(X_train, y_train)
Step 4: Make Predictions
pythonCopier le codepredictions = model.predict(X_test)
Implementation in Java (Weka)
Step 1: Load the Dataset
javaCopier le codeInstances data = new Instances(new BufferedReader(new FileReader(“data.arff”)));data.setClassIndex(data.numAttributes() – 1);
Step 2: Build the Linear Regression Model
javaCopier le codeLinearRegression model = new LinearRegression();model.buildClassifier(data);
Step 3: Evaluate the Model
javaCopier le codeEvaluation eval = new Evaluation(data);eval.crossValidateModel(model, data, 10, new Random(1));