  Techniques for making future predictions based upon the present and past
data, has always been an area with direct application to various real life
problems. We are discussing a similar problem in this paper. The problem
statement is provided by Kaggle, which also serves as an ongoing competition on
the Kaggle platform. In this project, we worked with a challenging time-series
dataset consisting of daily sales data, kindly provided by one of the largest
Russian software firms - 1C Company. The objective is to predict the total
sales for every product and store in the next month given the past data.
  In order to perform forecasting for next month, we have deployed eXtreme
Gradient Boosting (XGBoost) and Long Short Term Memory (LSTM) based network
architecture to perform learning task. Root mean squared error (RMSE) between
the actual and predicted target values is used to evaluate the performance, and
make comparisons between the deployed algorithms. It has been found that
XGBoost fared better than LSTM over this dataset which can be attributed to its
relatively higher sparsity.
