How to Implement Multiple Linear Regression Using Scikit-Learn in Python?
Share
Condition for Implement Multiple Linear Regression using Sklearn Library
Description: Multiple Linear Regression (MLR) is a statistical technique that models the relationship between a dependent variable and two or more independent variables. The objective is to predict the dependent variable by fitting a linear relationship with multiple independent variables. scikit-learn, a powerful Python library, provides tools for implementing this model efficiently.
Why Should We Use Multiple Linear Regression?
Prediction: MLR allows for predicting the value of a dependent variable using multiple input features(independent variables).
Relationships between Variables: Helps in understanding how each feature (independent variable) affects the dependent variable, assuming a linear relationship.
Simplicity and Interpretability: Unlike complex models, MLR is easy to interpret, and coefficients represent the impact of each feature on the target.
Efficiency: Suitable for datasets where there is a linear correlation between the variables.
Step-by-Step Process
Data Collection: Collect or load the dataset containing both the target variable and multiple features(independent variables).
Data Preprocessing: Clean the dataset by handling missing values, removing outliers, and scaling features(if necessary).
Splitting the Dataset: Split the dataset into training and testing sets to evaluate the model’s performance.
Model Building: Instantiate the LinearRegression model from scikit-learn.Fit the model on the training data.
Model Evaluation: Predict the target variable using the test set.Evaluate the model using performance metrics such as R-squared, Mean Squared Error (MSE), or Root Mean Squared Error (RMSE)
Interpretation: Model Analyze the coefficients to understand the relationship between the features and the target variable.
Sample Code
# Step 1: Import necessary libraries
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.datasets import fetch_california_housing # Import the California housing dataset
# Step 2: Load the dataset
california = fetch_california_housing()
# Convert dataset to DataFrame
df = pd.DataFrame(california.data, columns=california.feature_names)
df['TARGET'] = california.target
# Step 3: Preprocess the data (check for null values, etc.)
# In this case, no preprocessing is needed, but in general, check for missing values
# Step 4: Split the dataset into features (X) and target (y)
X = df.drop('TARGET', axis=1)
y = df['TARGET']
# Step 5: Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Step 6: Instantiate and train the model
model = LinearRegression()
model.fit(X_train, y_train)
# Step 7: Make predictions on the test set
y_pred = model.predict(X_test)
# Step 8: Evaluate the model performance
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
print("Mean Squared Error:", mse)
print("R-squared:", r2)
# Step 9: Model interpretation (coefficients)
print("Coefficients:", model.coef_)
Research Breakthrough Possible @S-Logix
