Supermarket Regression 2 Notebook

dataset = ""

import json
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split

dataset = pd.DataFrame.from_dict(json.loads(dataset))
y_target = dataset['Product_Supermarket_Sales']
dataset.drop(['Product_Supermarket_Sales'], axis=1, inplace=True)

X_train, X_test, y_train, y_test = train_test_split(dataset, y_target, test_size = 0.3)

print("Training data is", X_train.shape)
print("Training target is", y_train.shape)
print("test data is", X_test.shape)
print("test target is", y_test.shape)

from sklearn.preprocessing import RobustScaler, StandardScaler
scaler = RobustScaler()

scaler.fit(X_train)

X_train = scaler.transform(X_train) 
X_test = scaler.transform(X_test)

X_train[:5, :5]

from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import KFold, cross_val_score


def cross_validate(model, nfolds, feats, targets):
    score = -1 * (cross_val_score(model, feats, targets, cv=nfolds, scoring='neg_mean_absolute_error'))
    return np.mean(score)

n_estimators=150
max_depth=3
max_features='sqrt'
min_samples_split=4
random_state=2

from sklearn.ensemble import GradientBoostingRegressor

gb_model = GradientBoostingRegressor(n_estimators=n_estimators, max_depth=max_depth, max_features=max_features, min_samples_split=min_samples_split, random_state=random_state)

mae_score = cross_validate(gb_model, 10, X_train, y_train)
print("MAE Score: ", mae_score)

from flytekitplugins.papermill import record_outputs
record_outputs(mae_score=float(mae_score))