<<< Customer Analytics: Preparing Data for Modeling

Abstract:

Large-scale datasets are a valuable asset for generating business insights, yet their size often poses computational challenges, with predictive models requiring days to produce results. Efficient data storage and preprocessing strategies are therefore essential to enable scalable machine learning applications without sacrificing dataset richness.

This project, conducted for Training Data Ltd., addresses the optimization of a large student dataset that will ultimately be used to predict job-seeking behavior. Using a representative subset (customer_train.csv), which contains anonymized information on student demographics, education, professional experience, and training history, the study explores data cleaning and efficient storage techniques as a proof-of-concept. The dataset includes features such as city development index, education level, major discipline, company size, and training hours, with the target variable (job_change) indicating whether a student is actively seeking new employment opportunities.

By streamlining dataset storage and preparing the data for predictive modeling, this work lays the foundation for building scalable machine learning solutions that can accurately forecast job change tendencies. The outcomes are expected to help connect students with recruiters more effectively while significantly reducing computational overhead, enabling models to deliver business value within practical timeframes.

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