Review

[eaedk]

Improve your readme and include data description, links (article, contact, etc), etc; Add interpretation/observation to your plots; Use sklearn encoder to encode the features; Please remove the useless viz of cell #44; Use your numerical imputer for all the numerical features at once instead of using a loop, watch thinkific tuto, split your dataset before to impute it, during EDA do not impute missing values, ignore them; Improve your hypothesis testing; Balance the dataset after splitting it, balance just the train subset obtained after splitting; Cell #103 check the variable that you append

# Add the results to the dataframe
results_tuned_df = results_tuned_df.append({'Model': 'Random Forest (Tuned)',
                                'Accuracy': accuracy_rf,
                                'Precision': precision_rf,
                                'F1 Score': f1_rf}, ignore_index=True)

results_tuned_df = results_tuned_df.append({'Model': 'LightGBM (Tuned)',
                                'Accuracy': accuracy_rf, # have a look
                                'Precision': precision_rf, # have a look
                                'F1 Score': f1_rf}, ignore_index=True)
...

Don't export smote; You may export lists of features by type.

Interesting work done, share this review with your team.

[eaedk]

https://github.com/matiassingers/awesome-readme, have a look then make a more attractive readme.