- 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
- 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
- Create this
- 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 myQMCBT_wrangle.py
filescale_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
- I wrote the function
- 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
- I wrote my functions into a combined
- 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 plotregression_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 modelbetter_than_baseline(y, yhat)
: returns true if your model performs better than the baseline, otherwise false
- I wrote my functions into a combined
- Create
feature_engineering.ipynb
Jupyter Notebook to show work
- Create
modeling.ipynb
Jupyter Notebook to show work