Introduction
Linear regression is a fundamental statistical and machine learning technique used to model the relationship between a dependent variable and one or more independent variables. Python, with libraries such as scikit-learn, statsmodels, and pandas, makes performing linear regression simple and efficient.

Types of Linear Regression

1. Simple Linear Regression – Analyzes the relationship between one independent variable and one dependent variable.
   Example: Predicting fuel efficiency from engine size.

2. Multiple Linear Regression – Uses two or more independent variables to predict the dependent variable.
   Example: Predicting house prices using location, size, and number of bedrooms.

Steps to Perform Linear Regression in Python

1. Load Data – Import data using libraries like pandas.

2. Prepare Data – Clean data, select dependent and independent variables.

3. Fit the Model – Use LinearRegression from scikit-learn or ols() from statsmodels.

4. Evaluate the Model – Check R-squared, p-values, coefficients, and residual plots.

5. Make Predictions – Use the trained model to predict outcomes for new data.

Applications of Linear Regression in Python

• Predicting sales based on advertising expenditure.

• Estimating exam performance from study hours and attendance.

• Forecasting housing prices in real estate.

• Modeling the effect of temperature on electricity consumption.

Strengths of Linear Regression

• Easy to implement with Python libraries.

• Provides interpretable coefficients and relationships.

• Good starting point for predictive modeling.

Limitations of Linear Regression

• Assumes linearity, which may not always hold true.

• Prone to errors from multicollinearity and outliers.

• Cannot capture complex nonlinear patterns without transformation.

Conclusion
Python makes linear regression highly accessible for both beginners and professionals. With a few lines of code, you can analyze data, build models, and make accurate predictions.