هندسة الـ Features للتنبؤ بانقطاع العملاء

import numpy as np
import pandas as pd
from sklearn.preprocessing import FunctionTransformer

churn = pd.read_csv("churn.csv")
features = ["tenure_days", "orders", "total_spent", "avg_order_value",
            "days_since_last_order", "used_coupon", "support_tickets", "mobile_share"]

def add_features(df):
    df = df.copy()
    months = df["tenure_days"] / 30.4
    df["orders_per_month"] = df["orders"] / months
    df["typical_gap"] = df["tenure_days"] / df["orders"]
    df["overdue"] = df["days_since_last_order"] / df["typical_gap"]
    df["log_spent"] = np.log1p(df["total_spent"])
    return df

engineered = FunctionTransformer(add_features).fit_transform(churn[features])
print(engineered[["orders", "tenure_days", "orders_per_month", "overdue", "log_spent"]].head().round(2))
print("skew of total_spent:", round(churn["total_spent"].skew(), 2),
      "| after log1p:", round(np.log1p(churn["total_spent"]).skew(), 2))

import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import StratifiedKFold, cross_val_score, train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import FunctionTransformer, StandardScaler
from sklearn.tree import DecisionTreeClassifier

churn = pd.read_csv("churn.csv")
features = ["tenure_days", "orders", "total_spent", "avg_order_value",
            "days_since_last_order", "used_coupon", "support_tickets", "mobile_share"]
X_train, X_test, y_train, y_test = train_test_split(
    churn[features], churn["churned"], test_size=0.25, random_state=42,
    stratify=churn["churned"])
cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)

def add_features(df):
    df = df.copy()
    df["orders_per_month"] = df["orders"] / (df["tenure_days"] / 30.4)
    df["typical_gap"] = df["tenure_days"] / df["orders"]
    df["overdue"] = df["days_since_last_order"] / df["typical_gap"]
    df["log_spent"] = np.log1p(df["total_spent"])
    return df

models = {"tree": DecisionTreeClassifier(min_samples_leaf=20, random_state=42),
          "logistic": make_pipeline(StandardScaler(), LogisticRegression())}
for name, model in models.items():
    plain = cross_val_score(model, X_train, y_train, cv=cv, scoring="roc_auc")
    richer = cross_val_score(make_pipeline(FunctionTransformer(add_features), model),
                             X_train, y_train, cv=cv, scoring="roc_auc")
    print(f"{name:8} plain {plain.mean():.3f} ± {plain.std():.3f}   "
          f"engineered {richer.mean():.3f} ± {richer.std():.3f}")

import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler

churn = pd.read_csv("churn.csv")
features = ["tenure_days", "orders", "total_spent", "avg_order_value",
            "days_since_last_order", "used_coupon", "support_tickets", "mobile_share"]
X_train, X_test, y_train, y_test = train_test_split(
    churn[features], churn["churned"], test_size=0.25, random_state=42,
    stratify=churn["churned"])

champion = make_pipeline(StandardScaler(), LogisticRegression()).fit(X_train, y_train)
print("test ROC AUC:", round(roc_auc_score(y_test, champion.predict_proba(X_test)[:, 1]), 3))
print("test accuracy:", champion.score(X_test, y_test))