Linear regression is a statistical method for predicting a dependent variable based on independent variables by fitting a straight line to the data. It can be simple (one independent variable) or multiple (multiple independent variables) and is guided by assumptions such as linearity and independence. Common applications include price prediction, sales forecasting, and trend analysis, with evaluation metrics like mean squared error and R-squared to assess model performance.

1. Understanding Linear Regression
A Simple Guide to Linear Regression
in Machine Learning

2. What is Linear Regression?
• Linear regression is a statistical method for
modeling the relationship between a
dependent variable (target) and one or more
independent variables (features) by fitting a
straight line to the data.

3. Equation of Linear Regression
• The equation for linear regression is:
• y = wX + b
• Where:
• • y: Dependent variable (target)
• • X: Independent variable(s) (features)
• • w: Weight (slope)
• • b: Bias (intercept)

4. Types of Linear Regression
• 1. Simple Linear Regression:
• - Involves one independent variable.
• - Example: Predicting house price based on
size.
• 2. Multiple Linear Regression:
• - Involves multiple independent variables.
• - Example: Predicting house price based on
size, location, and number of bedrooms.

5. Assumptions of Linear Regression
• 1. Linearity: The relationship between features
and target is linear.
• 2. Independence: Observations are
independent of each other.
• 3. Homoscedasticity: Constant variance of
residuals.
• 4. Normality: Residuals are normally
distributed.
• 5. No Multicollinearity: Features are not highly
correlated.

6. Steps in Building a Linear
Regression Model
• 1. Understand and prepare the data.
• 2. Split data into training and testing sets.
• 3. Train the model on the training set.
• 4. Evaluate the model using metrics like MSE,
R².
• 5. Optimize the model if necessary.

7. Applications of Linear Regression
• • Predicting housing prices
• • Forecasting sales and demand
• • Analyzing trends in data
• • Estimating risk factors in insurance
• • Predicting stock prices

8. Common Evaluation Metrics
• 1. Mean Squared Error (MSE): Measures
average squared difference between predicted
and actual values.
• 2. Mean Absolute Error (MAE): Measures
average absolute difference between
predicted and actual values.
• 3. R-squared (R²): Represents the proportion
of variance explained by the model.

9. Advantages and Limitations
• Advantages:
• • Simple and easy to interpret.
• • Fast to compute.
• • Works well for linearly separable data.
• Limitations:
• • Assumes linearity between variables.
• • Sensitive to outliers.
• • Not suitable for complex or non-linear