# What I am trying to achieve.

I want to forecast Natural Gas prices under the column "NG Open" based on other parameters in the data set below for all Contract Months ,which is scraped from a public website. I have copied only few rows as sample as total rows are 100 that are scraped.I am using XG Boosting algorithm .I am using KFold Validation to be on safe side instead of train_test_split

 Contracts     NG Open  NGHigh  NGLow   NGLast    NGVolumes

2018-12-01    3.907   4.384   3.907   4.272       0
2019-01-01    3.917   4.408   3.917   4.291       264295
2019-02-01    3.800   4.267   3.785   4.148       155303
2019-03-01    3.515   4.007   3.496   3.865       51299
2019-04-01    2.735   2.829   2.704   2.793       73226
2019-05-01    2.632   2.691   2.602   2.667       54540
2019-06-01    2.638   2.719   2.634   2.692       34269


# Code

 from matplotlib import pyplot
import numpy as np
import pandas as pd
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.metrics import mean_squared_error

dataset['CO Last'] = dataset['CO Last'].str.rstrip('s')
dataset['Contracts'] = dataset['Contracts'].str.rstrip('(E)')
dataset['Contracts'] = pd.to_datetime(dataset['Contracts'])
dataset  = dataset.set_index('Contracts')

X = dataset[['NG High', 'NG Low', 'NG Last', 'NG Volumes']]
y = dataset['NG Open']

kfold = KFold(n_splits=10, random_state=7, shuffle = True)
results = cross_val_score(gbrt, X, y, cv=kfold)
print("Accuracy: %.2f%% (%.2f%%)" % (results.mean()*100, results.std()*100))

gbrt.fit(X_train, y_train)
y_pred = gbrt.predict(X_test)

lin_mse = mean_squared_error(y_pred, y_test)
lin_rmse = np.sqrt(lin_mse)
print('Liner Regression RMSE: %.4f' % lin_rmse)


# Performance on model.

Accuracy: 99.04% (1.06%) Liner Regression RMSE: 0.0482

Now that the model is showing an accuracy of 99% how can I generate the predicted "NG Open" prices collated to contract months for all the rows and new set of data based on other columns?

I assume its is y_newdata = gbrt.predict(X1?) X1 being a new table?

## closed as off-topic by Sycorax, kjetil b halvorsen, Peter Flom♦Nov 19 '18 at 11:27

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If this question can be reworded to fit the rules in the help center, please edit the question.

• Are you asking how to call gbrt.predict on all of your data or something else? – Sycorax Nov 19 '18 at 4:02
• Yes. I want to predict all the values based on contra t months as index. – Siddharth Kulkarni Nov 19 '18 at 9:24
• Can someone reopen the question? I just looking for pointers with respect to last code. – Siddharth Kulkarni Nov 19 '18 at 12:44