Customer Deposit Forecasting - Optimizing Deposit Predictions through CRM and Machine Learning Integration

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This machine learning project revolves around predicting customer deposits within a banking context. The banking sector heavily relies on revenue generated from long-term deposits by customers. Understanding customer characteristics is crucial for banks to enhance product sales, leading to the employment of marketing strategies aimed at target customers. The advent of data- driven decisions has prompted the utilization of data analysis, feature selection, and machine learning techniques to analyze customer characteristics and predict their decisions accurately. Leveraging a comprehensive dataset encompassing customer demographics, financial histories, and interaction records, the project employs thorough exploratory data analysis (EDA) and preprocessing techniques to unveil patterns and relationships. Categorical features like job, marital status, and education are scrutinized, providing valuable insights. Preprocessing steps include encoding categorical variables, addressing outliers, and handling imbalances in the dataset. The dataset is partitioned into training and testing sets, and machine learning models such as Random Forest Classifier and XG Boost Classifier are employed for prediction, optimized through GridSearchCV. Cross-validation scores reveal the models' efficacy, with Random Forest and XGBoost demonstrating promising performance. This project underscores the significance of meticulous data analysis and preprocessing in constructing accurate predictive models within the banking domain. The identified strengths of our model suggest its potential application in optimizing customer deposit forecasts. This contribution advances the field of financial predictive modeling by introducing a robust solution tailored for accurate customer deposit prediction, thereby facilitating informed decision-making in the banking industry. Keywords:-Random Forest Classifier, XGB Classifier, GridSearch CV, XGBoost