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The problem set was to predict that which customer will visit the upcoming new stores . The prediction was entirely based on the demographic and transnational history of customer. Also the stores data set was given for the analysis.

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title

LMG Analytics & Data Science Hiring Challenge in November, 2018 on HackerEarth

Introduction:

Landmark Group and hackerearth presents an amazing opportunity to showcase one's analytical abilities and talent.

Problem Statement

Max is a fashion brand within Landmark Group and has launched certain new stores in the year 2018. The objective is to determine whether an existing customer of Max UAE will shop in each of the new stores.

Data Details

  • Customer_Demographics: Customer demographics details for 100k customers

  • Customer_Transaction: Customer-Store-Week level transaction details for the last two years

  • Store_Master: Store attribute details

  • Test_Set: Submission file at Customer- New Store prediction level. Use this to create an additional column of Prediction tagging each

  • customer-store: into a 1 or a 0 for the submission file.

Evaluation Method

F1 – Score at Customer Store level in the Test_Set for 1’s (Customers who buy in new stores) .

Approach

My approach towards the problem was simple rule based segregation of the customers base and mapping thier corresponding stores on the basis of distance travelled. I had calculated the distance from the new coming stores to all the rest of stores, thereby giving me a proximate periphery of what stores are likely to be visited, on the basis of distance.

Leaderboard

Public LB : 10th Rank
Private LB : 10th Rank

Link to hackathon

https://www.hackerearth.com/challenge/hiring/LMG-analytics-data-science-hiring-challenge/problems/

About

The problem set was to predict that which customer will visit the upcoming new stores . The prediction was entirely based on the demographic and transnational history of customer. Also the stores data set was given for the analysis.

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