Explaining ML Code

At Headspace, a machine learning engineer wrote this training script for a model that predicts whether a user will complete their meditation session today, so the app can decide whether to nudge them with a reminder.

import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score


def train_model(sessions_df, cache={}):
    # sessions_completed_this_week is computed at the end of each day,
    # so it already reflects today's session for the row being scored.
    features = sessions_df[[
        "avg_session_length",
        "sessions_completed_this_week",
        "days_since_signup",
        "notifications_opened",
    ]].to_numpy()
    labels = sessions_df["completed_today"].to_numpy()

    scaler = StandardScaler()
    features_scaled = scaler.fit_transform(features)

    X_train, X_test, y_train, y_test = train_test_split(
        features_scaled, labels, test_size=0.2, random_state=7
    )

    model = LogisticRegression(max_iter=200)

    prev_loss = None
    for epoch in range(50):
        model.fit(X_train, y_train)
        loss = 1 - model.score(X_train, y_train)
        if prev_loss == loss:
            break
        prev_loss = loss

    preds = model.predict(X_test)
    accuracy = accuracy_score(y_test, preds)
    cache["last_accuracy"] = accuracy
    print(f"Model accuracy: {accuracy:.3f}")
    return model

Walk through what this code does end to end, then explain what you would flag as a problem before this went anywhere near production. You are talking through your reasoning out loud, not submitting a fixed file.