MACHINE LEARNING INTERN HIRING CHALLENGE by DOCKSHIP.IO

ALERT:  Sales Forecasting and EDA Challenge by Dockship.io

This challenge involves the task of Weekly Sales Forecasting and Exploratory Data Analytics (EDA).

About the Challenge 

1. First Task is to provide Exploratory Data Analysis (EDA) for the given data in (.ipynb and it's 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".

2. The 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 the 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 the final output file.

ALERT: DATASET DETAILS 

• Superstore Sales Dataset is used for the challenge.
• It consists of 18 attributes with "Sales" being the target attribute for prediction.

ALERT: JUDGEMENT RULES 

1. Accuracy using RMSE error
2. Exploratory Data Analysis (EDA) Report.

RULES:

1. Submission must not include copyrighted code. If a violation is found, the submission will be rejected.
2. The submission should be in a proper format as described by "Submission Guidelines".
3. Late submission will not be accepted beyond the provided deadline (Indian Standard Time).

Submission Guidelines

The final submission should include:

1. Exploratory Data Analysis (EDA) notebook (.ipynb and corresponding .pdf) providing detailed analysis of the "train.csv" (Note: EDA is only required for training files and not the test files)
2. Model Files (Python-based)
3.  requirements.txt (providing details of modules required to run your submission)
4. Forecasting Model Training Files (.ipynb)
5.  Code Execution script (for prediction of weekly sales) (only .py.) (run.py)

ALERT: PRIZES 

• The candidates will be invited for an Internship Interview based on their performance.
• Certificates will be provided after successful submission of a solution to this challenge from dockship.io

For FAQs and any other queries go to https://dockship.io/challenges

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