File size: 2,560 Bytes
5cbc1e9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 |
import pandas as pd
import numpy as np
from sklearn.metrics import mean_squared_error
from lightgbm import LGBMRegressor
from sklearn.model_selection import train_test_split
# Load data
sales = pd.read_csv("./input/sales_train.csv")
test = pd.read_csv("./input/test.csv")
# Convert date to datetime and extract year and month
sales["date"] = pd.to_datetime(sales["date"], format="%d.%m.%Y")
sales["year"] = sales["date"].dt.year
sales["month"] = sales["date"].dt.month
# Aggregate data to monthly level
monthly_sales = (
sales.groupby(["year", "month", "shop_id", "item_id"])
.agg({"item_cnt_day": "sum"})
.reset_index()
)
monthly_sales.rename(columns={"item_cnt_day": "item_cnt_month"}, inplace=True)
# Create lag features
for lag in [1, 2, 3]:
shifted = monthly_sales.copy()
shifted["month"] += lag
shifted["year"] += shifted["month"] // 12
shifted["month"] %= 12
shifted.rename(
columns={"item_cnt_month": f"item_cnt_month_lag_{lag}"}, inplace=True
)
monthly_sales = pd.merge(
monthly_sales, shifted, on=["year", "month", "shop_id", "item_id"], how="left"
)
# Mean encoded features
item_mean = monthly_sales.groupby("item_id")["item_cnt_month"].mean().reset_index()
item_mean.rename(columns={"item_cnt_month": "item_mean_cnt"}, inplace=True)
shop_mean = monthly_sales.groupby("shop_id")["item_cnt_month"].mean().reset_index()
shop_mean.rename(columns={"item_cnt_month": "shop_mean_cnt"}, inplace=True)
monthly_sales = pd.merge(monthly_sales, item_mean, on="item_id", how="left")
monthly_sales = pd.merge(monthly_sales, shop_mean, on="shop_id", how="left")
# Prepare training data
X = monthly_sales.drop(["item_cnt_month", "year", "month"], axis=1)
y = monthly_sales["item_cnt_month"].clip(0, 20)
# Train/test split
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)
# Model training
model = LGBMRegressor()
model.fit(X_train, y_train)
# Predictions
y_pred = model.predict(X_val).clip(0, 20)
rmse = np.sqrt(mean_squared_error(y_val, y_pred))
print(f"Validation RMSE: {rmse}")
# Prepare test set
test = pd.merge(
test,
monthly_sales.drop(["item_cnt_month"], axis=1),
on=["shop_id", "item_id"],
how="left",
).fillna(0)
# Drop 'year' and 'month' columns to match training data
test.drop(["year", "month"], axis=1, inplace=True)
# Make predictions on test set
test["item_cnt_month"] = model.predict(test.drop(["ID"], axis=1)).clip(0, 20)
# Save submission
test[["ID", "item_cnt_month"]].to_csv("./working/submission.csv", index=False)
|