QMCBT-JustinEvans / regression-exercises

Regression Exercises

Goal:

• Learn how to apply Regression Models while practicing the repetitve skills of:
  • Acquire
  • Prepare
  • Split
  • Explore
  • Feature Engineer
  • Hypothesis Creation
  • Hypothesis Testing
  • Feature Selection
  • Model Creation
  • Model Evaluation
  • Model Selection
  • Model Test
  • Summary & Conclusion of Analaysis

Acquisition and Preperation Lesson

• Create regression-exercises repository
  • Create this README.md file
  • Create wrangle.ipynb Jupyter Notebook to show work
  • Create wrangle.py file to run custom Functions
  • Create function wrangle_zillow to perform all Acquire and Prepare tasks

Scaling Numeric Data Lesson

• Create scaling.ipynb Jupyter Notebook to show work
• Create a Function in your prepare.py to scale the zillow DataFrame
  • I wrote the function scale_data into my QMCBT_wrangle.py file
    • scale_data takes in arguments (train, test, validate, columns_to_scale, scaler, return_scaler=False)
    • train = Assign the train DataFrame
    • validate = Assign the validate DataFrame
    • test = Assign the test DataFrame
    • columns_to_scale = Assign the Columns that you want to scale
    • scaler = Assign the scaler to use MinMaxScaler(), StandardScaler(), RobustScaler(), or QuantileTransformer()
    • return_scaler = False by default and will not return scaler data True will return the scaler data before displaying the _scaled data

Exploration Lesson

• Create explore.ipynb Jupyter Notebook to show work
• Create explore.py file to hold custom functions
  • I wrote my functions into a combined QMCBT_explore_evaluate.py file
  • Create a function named plot_variable_pairs that accepts a dataframe as input and plots all of the pairwise relationships along with the regression line for each pair.
  • Create a function named plot_categorical_and_continuous_vars that accepts your dataframe and the name of the columns that hold the continuous and categorical features and outputs 3 different plots for visualizing a categorical variable and a continuous variable

Evaluating Regression Models Lesson

• Create evaluate.ipynb Jupyter Notebook to show work
• Create evaluate.py file to hold custom functions
  • I wrote my functions into a combined QMCBT_explore_evaluate.py file
  • Create the following Fuctions:
    • plot_residuals(y, yhat): creates a residual plot
    • regression_errors(y, yhat): returns the following values: sum of squared errors (SSE) explained sum of squares (ESS) total sum of squares (TSS) mean squared error (MSE) root mean squared error (RMSE)
    • baseline_mean_errors(y): computes the SSE, MSE, and RMSE for the baseline model
    • better_than_baseline(y, yhat): returns true if your model performs better than the baseline, otherwise false

Feature Engineering Lesson

• Create feature_engineering.ipynb Jupyter Notebook to show work

Modeling Lesson

• Create modeling.ipynb Jupyter Notebook to show work