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Module 9 Lab
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Juan David Pérez
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Linear Regression and Logistic Regression Models
We began by cleaning the data, handling missing values, removing outliers, and formatting categorical variables. Through exploratory data analysis, we used visualizations and summary statistics to understand variable distributions and relationships. We fit a linear regression model to predict a continuous outcome and evaluated it using metrics like R-squared . For the logistic regression model, we predicted a binary outcome, interpreting odds ratios
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DBCA TJU 01210332002
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