The primary objective of this churn modeling report is to analyze and understand the factors that influence customer churn in the bank. The dataset contains details of the bank's customers, and the target variable is a binary indicator representing whether the customer closed their account (churned) or continues to be a customer.
To achieve the business objective, I conducted exploratory data analysis and applied various machine learning algorithms to build churn prediction models. The steps followed in the analysis are as follows:
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Data Exploration: I performed an initial exploration of the dataset to understand its structure, identify missing values, and gain insights into the distributions and relationships between variables.
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Feature Engineering: I preprocessed the data, handled missing values, and transformed categorical variables as needed to prepare it for model training.
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Model Selection: I considered three different machine learning algorithms: Logistic Regression, Support Vector Machine (SVM), Decision Tree, and Random Forest.
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Model Training: I trained each selected model using the processed dataset and split it into training and testing sets.
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Model Evaluation: I evaluated the performance of each model using appropriate metrics such as accuracy, precision, recall, and F1-score.
Through our analysis, I have identified significant factors that contribute to customer churn. I have gained valuable insights into customer behaviors and characteristics that are associated with higher churn rates. This information will help the bank in making data-driven decisions and implementing targeted strategies to reduce customer churn.
Based on our findings, I recommend the following strategies to the bank:
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Improving Customer Experience: Enhance the overall customer experience by addressing pain points and improving service quality.
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Targeted Marketing Campaigns: Develop personalized marketing campaigns to engage and retain high-risk customers.
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Customer Retention Programs: Implement loyalty programs and incentives to reward and retain loyal customers.
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Proactive Customer Support: Provide proactive customer support to identify and address issues before they lead to churn.
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Continuous Monitoring: Continuously monitor and analyze customer behavior to adapt strategies as needed.
In conclusion, this churn modeling report provides actionable insights into the factors driving customer churn. By understanding customer behaviors and implementing the recommended strategies, the bank can significantly improve customer retention, leading to better business performance and long-term profitability.