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How can I use MLR to fill gap data?

The ability of Multiple Linear Regression (MLR) to fill gap data depends on various factors, including the characteristics of the dataset and how well the chosen predictors capture the relationships in the data. Using Multiple Linear Regression (MLR) to fill gap data involves the following steps:

Data Preparation:

Identify the variables: Select the relevant variables (features) that will be used in the regression model. Split the data: Divide your dataset into two subsets—one with complete data (training set) and another with gaps (testing set). Build the MLR Model:

Build and Formulate the MLR Model:

Define the MLR model equation based on the selected variables. For example, if 'Y' is the target variable and 'X1', 'X2', etc., are predictor variables, the model may look like: Y = b0 + b1X1 + b2X2 + ... + bn*Xn.

Train the model:

Use the training set to train the MLR model, allowing it to learn the relationships between variables.

Apply the model and Predict Missing Values:

Use the trained MLR model to predict the missing values in the testing set.

Insert predictions: Substitute the predicted values into the gaps in your original dataset, effectively filling the missing data.

Evaluate the accuracy of the MLR predictions by comparing them to actual values in the testing set.

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