Regression: 100 Tips and Strategies for Optimizing Prediction Precision

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Regression involves predicting a continuous outcome based on input features. Here are 100 tips for working with linear regression:

1. Basics of Linear Regression:

1. Understand the basic concept of linear regression: modeling the relationship between a dependent variable and one or more independent variables.
2. Familiarize yourself with the linear regression equation: y=mx+b, where y is the dependent variable, x is the independent variable, m is the slope, and b is the intercept.

2. Data Preparation:

1. Ensure that the assumptions of linear regression are met: linearity, independence, homoscedasticity, and normality of residuals.
2. Handle missing data appropriately, considering imputation or removal of missing values.

3. Exploratory Data Analysis:

1. Visualize the relationship between the independent and dependent variables using scatter plots.
2. Check for outliers and influential data points that might affect the regression model.

4. Data Scaling: