Please check the Jupyter notebook first as it has explanation of what I have done and why I have done it

Python version: 3.6

Packges: Tensorflow, Pandas and NumPy

USIO Energy Data Science Exercise

You will use historic energy usage data from a number of households to forecast their future usage.

Data

usage_train.csv contains historic data in a single file. The columns are:

id: a unique household ID
datetime: datetime of the half-hour interval for which usage is recorded
usage: usage for the given household and half-hour (in kWh)

usage_test.csv is similar to usage_train.csv, but is missing the usage column. We would like you to predict usage for each row (household/half-hour) in usage_test.csv.

Submission

We are interested in how you approach an open-ended machine learning problem. We encourage plotting and data exploration, as well as readable and re-usable code.

use python 3
fork this repository
commit all your code (including Jupyter Notebooks, exploratory work etc.)
submit a pull request when you are finished
please make sure the code includes two files:
- train.py should read the training data, perform any steps needed, and save a single file containing the trained forecasting model (e.g. model.ext)
- predict.py should read the test data (usage_test.csv) and the model file (model.ext), and save predictions in usage_test_predictions.csv.
feel free to organise additional files as you see fit
please update QUESTIONS.md and try to answer questions therein.
please include a description of external libraries needed to run your code