Course Case: Apprentice Chef
Regression-Based Analysis (Individual)
In this assignment, you are tasked with using the information in our course case to build a regression-based predictive model and provide insights. This assignment encompasses feature engineering, variable selection, and model development.
Deliverables:
1. Analysis Write Up
Present your best TWO insights (maximum 100 words per insight).
Make one actionable recommendation 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
Tell the story of your analysis through:
- Exploratory data analysis
- Feature treatment and engineering
- Utilizing appropriate modeling techniques
3. Final Model
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)
- run without errors or bugs.
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Attachment:- Machine Learning.rar