EDA, linear, logistic regression model, Decision tree, Random Forest, GBM/Xgboost and Neural Network model

After you've gone through the steps in the practice exercise with the code provided perform the EDA on the dataset of your choice. Prepare a paper and articulate your findings. Make sure you cover the steps you would need to explain this to a non-technical person or someone you are reporting to. Look for datasets on Data.World and Kaggle to use for this analysis.

Where did the data come from?

Why did you choose this data?

What did you do with the data in the context of exploration?

What did you find? Why does that matter?

What would your proposed next steps be?

Be sure to include R code and screen shots.

1. Generate a linear and a logistic regression model to predict an outcome. The outcomes can be different from each other.

2. Explain the results of the models using performance metrics such as Coefficient of Determination, QQ plots, Confusion matrix etc.

3. Improve the model using regularization and address multicollinearity in the data

4. Explain your modeling conclusions.

Note: Try to engineer features to extract non-linear relationships in the data. You will be using the same dataset that you had used for Week 2  Assignment (to build the regression models) for Week 3 Assignment as well. In this week's Assignment, you are required to predict an outcome using the tree based models that we discussed in the class today. 

You will be asked to prepare a paper and articulate your findings. This should include:

Types of models you ran and compare them with each other for accuracy, run time etc.

Model Optimization techniques you used for hyper-parameter tuning

Discuss pros and cons of each model

Note: Ensure you have understood the concepts behind regression and tree based model classification. For week 6 group presentation, you will be asked to demonstrate all of the models you have built thus far and do a comparison between the models.

Build a Neural Network model to predict an outcome of choice (either regression or classification) from your dataset.

Discuss pros and cons using Neural Networks as opposed to other ML models

Optional: do hyper-parameter tuning on hidden layer sizes, number of epochs, activation functions, optimization function, learning rate etc.

Use Google Colab environment if needed