Regression analysis is a statistical technique used to establish a relationship between a dependent variable and one or more independent variables.
Linear Regression: It highlights the relationship between the independent and dependent variables. Non-Linear Regression: It highlights relationship between the independent and dependent variables is not linear.
– Predicting future sales – Forecasting stock prices – Analyzing customer behavior – Identifying trends in data Recommending products to customers
1. Data Collection 2. Data Exploration and Cleaning 3. Feature Engineering 4. Model Selection and Training 5. Model Evaluation
– Overfitting: This occurs when a regression model is too complex and fits the training data too closely, but does not perform well on unseen data. Underfitting: This occurs when a regression model is too simple and does not capture the underlying relationships in the data.
– Collect high-quality data – Clean your data – Explore your data – Select the right regression model – Train your model on a large dataset – Regularize your model – Evaluate your model