Dataset Source

http://archive.ics.uci.edu/ml/datasets/Las+Vegas+Strip

Deliverables:

1. Analysis Write Up

Save this as a .pdf file under the following naming convention: FIRSTNAME_LASTNAME_A1_Write_Up.pdf

Present your best TWO insights (maximum 100 words per insight)

Make one actionable recommendation based on your analysis and offer recommendations for business implementation (maximum 200 words)

State your final model’s highest R-Square value, rounded to three decimal places

2. Data Analysis and Code

Save this as a Jupyter Notebook (.ipynb) file under the following naming convention: FIRSTNAME_LASTNAME_A1_Analysis.ipynb

Tell the story of your analysis through:

exploratory data analysis

• feature treatment and engineering

utilizing appropriate modeling techniques

• 3. Final Model

Save this as a Python (.py) script under the following naming convention: FIRSTNAME_LASTNAME_A1_Model.py

Model will be assessed on:

• R-Square value on unseen data (randomly seeded)

Processing speed (see coding requirements below)

Appropriateness for the problem at hand

Being submitted as a .py script (Note: coding files submitted as anything other than a .py script will receive a one-letter grade deduction)

Coding Requirements

Both the analysis code and the final model must meet the following requirements:

runs from start to finish in under one minute (based on Prof. Chase’s computer)

• be coded in Python or R (R may only be used if it runs within your Python script)
• be well commented
• avoid “data dumping” (i.e. avoid any unnecessary output or graphics)