Tahminleme

import pandas as pd
import lightgbm as lgb
from sklearn.metrics import mean_absolute_percentage_error

df = pd.read_csv("orders.csv", parse_dates=["date"]).sort_values("date")

# Lag özellikleri (geçmişten sızıntı yok!)
for lag in [1, 7, 14, 30, 365]:
    df[f"lag_{lag}"] = df["orders"].shift(lag)
df["dow"] = df["date"].dt.dayofweek
df["month"] = df["date"].dt.month
df = df.dropna()

# Zaman tabanlı bölme — rastgele DEĞİL
cutoff = "2025-09-01"
train = df[df["date"] < cutoff]
test = df[df["date"] >= cutoff]

features = [c for c in df.columns if c not in ["date", "orders"]]
model = lgb.LGBMRegressor(n_estimators=500, learning_rate=0.05)
model.fit(train[features], train["orders"])

preds = model.predict(test[features])
mape = mean_absolute_percentage_error(test["orders"], preds)
print(f"Test MAPE: {mape:.3f}")

from prophet import Prophet
import pandas as pd

df = pd.DataFrame({"ds": dates, "y": orders})

m = Prophet(
    yearly_seasonality=True,
    weekly_seasonality=True,
    daily_seasonality=False,
    seasonality_mode="multiplicative",
)
m.add_country_holidays(country_name="TR")
m.fit(df)

future = m.make_future_dataframe(periods=30)
forecast = m.predict(future)

# yhat: nokta tahmin, yhat_lower / yhat_upper: %80 güven aralığı
print(forecast[["ds", "yhat", "yhat_lower", "yhat_upper"]].tail(10))