Multiple Input LR Exercise

Programming for Data Science is a subject we’ve designed to explore the various programming components of data science.

Keywords

data science, data analysis, programming, dataidea

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Week 4: Multiple Input Linear Regression

1. Making Predictions in Multiple Linear Regression
   • Exercise: Implement a multiple input linear regression model to predict a target variable based on two or more input features. Train the model on synthetic data and evaluate its performance.
2. Training Multiple Linear Regression Models
   • Exercise: Train multiple linear regression models with different feature sets and compare their performance. Analyze how the inclusion or exclusion of features affects the model’s predictions.
3. Multi-Target Linear Regression
   • Exercise: Extend your linear regression model to predict multiple target variables simultaneously. Create a dataset with multiple targets and train the model to predict all targets.

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4. Training Multiple Output Linear Regression Models
   • Exercise: Implement a model to handle multiple outputs (e.g., predicting multiple continuous variables). Train the model on a dataset with multiple output variables and assess its performance.

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