Will This Customer Churn?

Walkthrough

1. Load the table

   Read the public CSV and drop the ID column, which tells a model nothing about churn.

   import pandas as pd
   from welt import Client
   
   url = "https://raw.githubusercontent.com/IBM/telco-customer-churn-on-icp4d/master/data/Telco-Customer-Churn.csv"
   customers = pd.read_csv(url).drop(columns="customerID")
   customers["TotalCharges"] = pd.to_numeric(customers["TotalCharges"], errors="coerce")
   print(customers.shape, customers["Churn"].value_counts().to_dict())

2. Pick the customers

   Set aside 20 customers to score at the end, and keep the rest for the models to learn from.

   new_rows = customers.sample(20, random_state=1)
   table = customers.drop(new_rows.index)

3. Compare the models and score

   TabICLv2 and Kumo Tabular Medium compete on the same folds against a simple guess, then the top-ranked one is fitted on the whole table. ROC AUC runs from 0.5, a coin toss, to 1, a perfect ranking, and a lower Brier score means the probabilities can be trusted more. The last table puts each customer's chance of leaving next to what they did.

   with Client() as client:
       evaluation = client.evaluate(table, "Churn", task="classification", seed=7,
                                    models=["tabicl-v2", "kumo-medium"])
       model = evaluation.fit()
   
   print(evaluation.summary()[["name", "rank", "roc_auc_mean", "brier_mean"]])
   features = new_rows.drop(columns="Churn")
   print(pd.DataFrame({"actual": new_rows["Churn"].values,
                       "chance of leaving": model.predict_proba(features)[:, 1]}))

What you get