About

Time series analysis deals with time series based data to extract patterns for predictions and other characteristics of the data. It uses a model for forecasting future values in a small time frame based on previous observations. It is widely used for non-stationary data, such as economic data, weather data, stock prices, and retail sales forecasting.

The Challenge

The challenge is divided into two parts:

First Task is to provide Exploratory Data Analysis (EDA) for the given data in (.ipynb and its corresponding .pdf)

A Sample Jupyter Notebook ( Chicago Crime Dataset Sample EDA.ipynb and its '.pdf' form ) has been given to provide a basic understanding of EDA, Chicago Crime Dataset is used for the example. It has nothing to do with the actual training data, and only serves as an example to help the participants develop a good understanding of the task. The actual training data is given in a separate csv file "train.csv".

Second task is to design a model to predict sales for the next week based on previous data observations.

The next 7 dates consisting of the week will be chosen based on the latest 'Order Date'.
Example: If latest "Order Date" is 2018-06-20, the prediction date starts from 2018-06-21.
sample-output.csv has been given as an example of final output file.

Dataset

Superstore Sales Dataset is used for the challenge.

It consists of 18 attributes with "Sales" being the target attribute for prediction.

NOTE: Some values of attribute "Ship Date" can be NaN as those products had not been shipped by the provided dates

The training data is given in the file "train.csv"

Citation

Data Source

LICENSE : Not Specified

This dataset will not be used for any commercial product development. It is provided for Research Analysis only.

Samruddhi Mhatre's Certificate

Share with your friends

Download

About Us

Quick Links

Subscribe to our mailing list