Retail Customer Insights Ease for Business

The aim of this project is to build a machine learning model that predicts whether a customer will make a purchase (Purchase_History) based on various customer-related features. The project involves exploring the dataset, preprocessing the data, and applying classification algorithms to achieve this goal. understanding customer purchase behavior is crucial for businesses aiming to enhance sales and customer satisfaction. This project focuses on predicting whether a customer will make a purchase based on various demographic and behavioral features. Utilizing a comprehensive dataset that includes attributes such as age, income, gender, and customer satisfaction, the analysis aims to identify key factors influencing purchasing decisions. The primary objectives include data preprocessing, exploratory data analysis (EDA), and the application of various classification algorithms, including Logistic Regression, Stochastic Gradient Descent (SGD), and AdaBoost. By evaluating model performance through metrics such as accuracy, precision, and recall, the project seeks to provide actionable insights for improving marketing strategies and customer engagement. Ultimately, the findings will inform future work on enhancing predictive accuracy and understanding customer behavior in the retail sector. Keywords: Data-Driven, Evaluation Metrics, Accuracy.