Understanding Linear Regression in Machine Learning

Linear regression is one of the simplest and most widely used algorithms in machine learning. It is used for predictive analysis and is a statistical approach for modeling the relationship between a dependent and independent variable. In this blog, we will take a deeper dive into the mathematical intuition behind linear regression, the different types of linear regression, and how to implement it using Python.

Mathematical Intuition:

The basic idea behind linear regression is to find a line that best fits the given data points. This line is called the regression line and is represented mathematically as y = mx + b, where y is the dependent variable, m is the slope of the line, x is the independent variable, and b is the y-intercept. The goal is to find the values of m and b that minimize the difference between the actual and predicted values of y.

You can get the complete math intuition in pdf format from here https://github.com/saikamal3344/Linear-Regression-Machine-Learning-

Implementation in Python:

To implement linear regression in Python, we will use the popular scikit-learn library. The first step is to load the required libraries and the dataset.

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

Next, we load the dataset and split it into training and test sets.

data = pd.read_csv("data.csv")
X = data[['Independent_Variable_1', 'Independent_Variable_2', ...]]
y = data['Dependent_Variable']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

Now, we can create an instance of the linear regressionmodel and fit it to the training data.

regressor = LinearRegression()
regressor.fit(X_train, y_train)

Finally, we can make predictions on the test data and evaluate the model performance.

y_pred = regressor.predict(X_test)
r2_score = regressor.score(X_test, y_test)
print("R2 Score: ", r2_score)

Conclusion:

Linear regression is a powerful tool in machine learning that can be used to model the relationship between a dependent and independent variable. In this blog, we have gone through the mathematical intuition behind linear regression, the different types of linear regression, and how to implement it in Python using scikit-learn. With a good understanding of linear regression, you can now start solving real-world problems and making predictions with confidence.

If you want to do more on Linear Regression code implementation pls follow:

https://github.com/saikamal3344/Linear-Regression-Machine-Learning-