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HackerEarth's Reduce Marketing Waste

To all the money we've spent before: HackerEarth Machine Learning Challange (parv619/hackerearths-reduce-marketing-waste)   []

Problem statement Most SaaS organizations spend a chunk of their revenue for various marketing initiatives - digital marketing, media outreach, search engine optimization, and more. However, if there’s a way to target a highly qualified set of customers to buy your product, the organization reaps multiple benefits, such as enhanced revenue generation, higher deal closure rates, and increase in profit margins. Task An organization that offers a hiring assessment platform is looking at reducing its yearly marketing spends and you have been appointed as the Machine Learning engineer for this project. Your task is to build a sophisticated Machine Learning model that predicts the probability percentage of marketing leads purchasing their product, based on information provided in the given dataset. Dataset The dataset consists of parameters such as the deal value and pitch, the lead’s source, its revenue and funding information, assigned points of contact for the lead (internal and external), and the like. The benefits of practicing this problem by using Machine Learning techniques are as follows: This challenge encourages you to apply your Machine Learning skills to build a model that predicts the probability percentage of a marketing lead to convert into a client and purchase the product. This challenge will help you enhance your knowledge of regression. Regression is one of the basic building blocks of Machine Learning. We challenge you to build a model that successfully predicts the probability percentage of a marketing lead to convert into a client and purchase the product. Prizes Considering these unprecedented times that the world is facing due to the Coronavirus pandemic, we wish to do our bit and contribute the prize money for the welfare of society. Overview Machine Learning is an application of Artificial Intelligence (AI) that provides systems with the ability to automatically learn and improve from experiences without being explicitly programmed. Machine Learning is a Science that determines patterns in data. These patterns provide a deeper meaning to problems. First, it helps you understand the problems better and then solve the same with elegance. Here’s presenting HackerEarth Machine Learning challenge: Reducing marketing spends This challenge is designed to help you improve your Machine Learning skills by competing and learning from fellow participants. Why should you participate? To analyze and implement multiple algorithms and determine which is more appropriate for a problem To get hands-on experience in Machine Learning problems Who should participate? Working professionals Data Science or Machine Learning enthusiasts College students (if you understand the basics of predictive modeling)

Data summary

  • File 'sample_submission.csv'

    • Table ‘sample submission’ consists of five data rows along two dimensions: ‘Deal_title’ and ‘Success_probability’
  • File 'test.csv'

    • Table ‘test’ consists of 2093 data rows along 22 dimensions: ‘Deal_title’, ‘Lead_name’, ‘Industry’, ‘Deal_value’, ‘Weighted_amount’, ‘Date_of_creation’, ‘Pitch’, ‘Contact_no’, ‘Lead_revenue’, ‘Fund_category’ and 12 other dimensions
  • File 'train.csv'

    • Table ‘train’ consists of 7007 data rows along 23 dimensions: ‘Deal_title’, ‘Lead_name’, ‘Industry’, ‘Deal_value’, ‘Weighted_amount’, ‘Date_of_creation’, ‘Pitch’, ‘Contact_no’, ‘Lead_revenue’, ‘Fund_category’ and 13 other dimensions

Size: 882.0 KBSource: KaggleLast updated: 2021-09-30 23:33

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Creative Commons License

These analysis results by Inspirient GmbH are licensed under a Creative Commons Attribution 4.0 International License in conjunction with the licence of the source dataset.