Proučite dataset Walmart.csv i definirajte što je target varijabla. Iz dataseta izbacite sve značajke koje nisu numeričke, a ako negdje nedostaje vrijednost umetnite medijan. Model trenirajte sa linearnom regresijom. Dataset pripremite sa train_test_split funkcijom i pri tom koristite random_state=21, a 20% neka vam ostane za testiranje. Nakon toga sa preostalih 80% koristite metodu cross_val_score, ali za cv koristite LeaveOneOut. Za izračun greške koristite scoring='neg_mean_squared_error'. Na kraju izračunajte grešku modela (RMSE) i sa testnim podacima. Ispišite srednju vrijednost niza s greškama (RMSE), ali i grešku (RMSE) sa testnim podacima.
import numpy as np
import pandas as pd
from sklearn.impute import SimpleImputer
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from sklearn import model_selection
from sklearn import linear_modeldf = pd.read_csv("Walmart.csv", low_memory=False)
df.head()Loading...
target = df["Weekly_Sales"]
features_num = df.select_dtypes(include=[np.number])
imputer = SimpleImputer(strategy="median")
features_num_imputed = pd.DataFrame(imputer.fit_transform(features_num), columns=features_num.columns)
features = features_num_imputed.drop(columns=["Weekly_Sales"]).select_dtypes(include=[np.number])train_features, test_features, train_target, test_target = train_test_split(features, target, test_size=0.2, random_state=21)
loo = model_selection.LeaveOneOut()
algorithm = linear_model.LinearRegression()
scores = model_selection.cross_val_score(algorithm, train_features, train_target, cv=loo, scoring="neg_mean_squared_error")
model = algorithm.fit(train_features, train_target)
predictions = algorithm.predict(test_features)
rmse = np.sqrt(mean_squared_error(test_target, predictions))print("Scores", scores)
print("Scores mean", scores.mean())
print("RMSE", rmse)Scores [-5.14172037e+11 -9.88224063e+08 -3.14030541e+10 ... -6.61381457e+10
-8.96998884e+10 -1.72327466e+11]
Scores mean -271714625710.2181
RMSE 532298.7675798994