## Conclusions

In this blog post I will be discussing my final model and how it could potentially be used; whether it is good enough to use or not.

## Interactions

In this post I will be discussing how to add interactions to try to improve my linear model.

## Outliers

In this post I will be showing how I remove outliers from my dataset and seeking out if this improves my models fit.

## Overfitting & Variable Selection

In this post I will be using backwards stepwise selection using my training data to build the best possible linear regression model for my dataset.

## Logistic Regression Prediction

In this post I will be assessing my logistic regression model’s stability and accuracy.

## Linear Regression Prediction

In this post I will be dividing my dataset into a training set and test set to assess my model’s prediction capability.

## My Tableau Dashboard

In this post I will be going over my interactive tableau dashboard.

## Logistic Regression Assumptions

In this post I will be testing my model for logistic regression assumptions.

## Logistic Regression

In this post I will be discussing logistic regression and applying it to my Sephora Website Dataset.

## Testing Assumptions of Linear Regression

In this post I will be testing the 4 assumptions of Linear Regression.

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