This project focuses on building a predictive model to determine customers' purchase intent using classification and boosting machine learning techniques. The dataset includes essential variables like age, gender, account balance, occupation, and credit-related information. The goal is to predict whether a customer is "Willing to Buy" (1) or "Not Willing to Buy" (0). Model evaluation is performed using ROC curves and confusion metrics to assess the model's predictive performance.
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"Project: Predicting Customer Purchase Intent with ML. Dataset: Customer data, target variable: 'Willing to Buy' (1) or 'Not Willing to Buy' (0). Used classification and boosting techniques, evaluated with ROC curves and confusion metrics."
AnalystExplorer/Machine-learning-Algorithm
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"Project: Predicting Customer Purchase Intent with ML. Dataset: Customer data, target variable: 'Willing to Buy' (1) or 'Not Willing to Buy' (0). Used classification and boosting techniques, evaluated with ROC curves and confusion metrics."
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