This project aims to develop a robust machine learning system that leverages call center data to improve the success rate of calls made to customers for various products offered by our clients in the European banking market. The focus is on providing high success outcomes while ensuring interpretability for clients to make informed decisions. The solution will be designed to handle problems such as fraud detection, sentiment classification, and customer intention prediction and classification.

The data used in this project comes from the direct marketing efforts of a European banking institution. The marketing campaign involves making phone calls to customers, particularly for subscribing to a term deposit. The attributes in the dataset include:

• Age: Age of the customer (numeric)
• Job: Type of job (categorical)
• Marital: Marital status (categorical)
• Education: Education level (categorical)
• Default: Whether the customer has credit in default (binary)
• Balance: Average yearly balance in euros (numeric)
• Housing: Whether the customer has a housing loan (binary)
• Loan: Whether the customer has a personal loan (binary)
• Contact: Contact communication type (categorical)
• Day: Last contact day of the month (numeric)
• Month: Last contact month of the year (categorical)
• Duration: Last contact duration in seconds (numeric)
• Campaign: Number of contacts performed during this campaign and for this client (numeric, including last contact)

The desired target variable (output) is whether the client has subscribed to a term deposit (binary).

The objective of this project is to develop a machine learning solution that can accurately predict the success of calls made to customers for various products. By leveraging the available call center data, we aim to improve the success rate of these calls and provide interpretable insights for clients to make informed decisions.

The machine learning solution will involve the following steps:

1. Data Preprocessing: Cleaning the data, handling missing values, and encoding categorical variables.
2. Exploratory Data Analysis: Analyzing the relationships between variables, identifying patterns, and gaining insights from the data.
3. Feature Engineering: Creating new features and transforming existing ones to enhance the predictive power of the model.
4. Model Selection: Evaluating and selecting appropriate machine learning algorithms based on the problem requirements and dataset characteristics.
5. Model Training and Evaluation: Training the selected models on the data, tuning hyperparameters, and evaluating their performance using appropriate evaluation metrics.
6. Interpretability: Explaining the model's predictions and providing insights to clients for informed decision-making.
7. Deployment: Integrating the trained model into a production environment for real-time predictions.

• Python 3.x
• scikit-learn
• pandas
• numpy
• matplotlib
• seaborn

The results of the machine learning solution will be presented in the form of evaluation metrics, interpretability insights, and visualizations. The success rate of calls made to customers will be measured based on appropriate metrics such as accuracy, precision, recall, and F1-score. The interpretability component will provide insights into the factors influencing the success of calls and their impact on the target variable.