learn-co-students/ds-skills_midterm_solution_starters-nyc-ds-091018

Repository Overview

In this repository you will find 3 notebooks, one for each of the midterm dataset options. While your model tuning is apt to differ (maybe even be better) use these as a general guidelines and ideas in data exploration, feature engineering, and model tuning. Key highlights and notes are included throughout. In general, remember that your process should generally include the following:

• Define X and y
  • y should be a continuous numeric variable
  • X should be a number of numeric features *X features may need substantial preproccesing including: * Transforming datetime values * Normalizing features ranges/distributions * Creating dummy variables * Ensuring there are no categorical variables coded (misleadingly) as numbers
• Train / Test Split
• Fit algorithm on training data
  • Cross Validation
  • Feature Engineering
    • Synthetic Polynomial Features / Polynomial Regression
• Evaluate Model Performance on Test Set
  • Repeat process with seperate train/test split; do results hold?
• Continue feature engineering, tuning, etc.

Cheers!