Reusable Python Functions in my repo to quickly develop any Machine Learning Models

Build once use many

While executing any end-to-end data science project, any data science professional or a student has to mainly focus on problem definition, data collection, data investigation, cleansing, statistical and visual analysis, feature engineering, decision making, and model building. In the entire Lifecycle of a Data Science project, one step where one should not invest much time is writing codes for building any ML models.

Note: All the feature engineering and feature selection processes should be performed before the below-given steps.

To implement any ML model I keep the below functions in my code repository which I reuse to develop any classifier or regressor model.

Initialize the data frames to store and compare the performance metrics of the models.

I have this below code snippet to store the model performance scores in a data frame to compare the different models after the predictions.

Function to fit and predict the model:

Function to calculate and print the performance metrics of train and test dataset

There it is!

Now I can develop any ML model and I can do the prediction, calculate the scores and compare the model performance by just giving the right model and parameters to the above functions.

4. Append the results to the dataframe to compare all the built model performance

4. Append the results to the dataframe to compare all the built model performance

Now we can use these functions to develop any model by passing the model specific parameters as done for Linear Regression and Logistic Regression in the example above .

Yay….Happy model building !!!

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