CUSTOMER PURCHASE DECISION PREDICTION FOR PRE- OWNED CARS USING LOGISTIC REGRESSION

This study presented a machine learning-based approach for predicting customer purchase decisions in the pre-owned car market using Logistic Regression. The proposed model effectively analyzes customer attributes and identifies patterns that influence purchasing behavior. Experimental results demonstrate that the model provides reliable predictions and achieves satisfactory performance in classifying potential buyers and non-buyers. By utilizing data-driven techniques, the system helps dealers improve customer targeting, optimize marketing strategies, and enhance decision-making processes. The study highlights the effectiveness of Logistic Regression as a simple and efficient classification technique for customer purchase prediction.

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