Banking Marketing Campaign - Logistic Regression Analysis

This project focuses on predicting customer engagement for a Portuguese bank's long-term deposit marketing campaigns, using data from phone calls made to customers. The analysis employs logistic regression to create a ranking algorithm, predicting the likelihood of a customer signing up for a long-term deposit, thus allowing the bank to focus its marketing efforts more efficiently.

Dataset Overview

The dataset used is the "Bank Marketing Campaign Data" which includes the following variables:

• age: Age of customer (numeric)
• job: Type of job (categorical)
• marital: Marital status (categorical)
• education: Level of education (categorical)
• default: Credit in default? (categorical)
• housing: Has housing loan? (categorical)
• loan: Has personal loan? (categorical)
• contact: Contact communication type (categorical)
• month, day_of_week: Last contact month and day (categorical)
• duration: Duration of the last contact (numeric)
• campaign: Number of contacts during this campaign (numeric)
• pdays: Number of days since last contact (numeric)
• previous: Number of contacts before this campaign (numeric)
• poutcome: Outcome of the previous marketing campaign (categorical)
• Economic indicators like emp.var.rate, cons.price.idx, cons.conf.idx, euribor3m, and nr.employed

The dataset was preprocessed to handle categorical variables and scaled using MinMaxScaler.

Model Training and Evaluation

Logistic Regression Model

• A logistic regression model was initially trained with default parameters.
• The model was optimized through GridSearchCV to refine hyperparameters.

Results Summary

• The initial logistic regression model achieved an accuracy of approximately 89.2%.
• After optimization, the improved model achieved an accuracy of approximately 89.4%.

Usage

To replicate the analysis:

• Load the dataset.
• Perform exploratory data analysis and preprocessing.
• Train the logistic regression model.
• Evaluate the model and optimize it using hyperparameter tuning.

For more detailed steps and code execution, see the provided Jupyter notebooks.

Contact

For inquiries or contributions, please contact spomar36@gmail.com.

Production Services

To ensure our banking marketing model operates seamlessly in a production environment, we leverage the following cloud services and ML operations practices:

AWS

• Amazon S3: Store and retrieve any amount of data at any time.
• AWS Lambda: Run code in response to data changes in S3 buckets.
• Amazon SageMaker: Train, tune, and deploy machine learning models.

Azure

• Azure Blob Storage: For large-scale data storage.
• Azure Functions: Event-driven compute for data processing.
• Azure Machine Address Learning Service: Build, train, and deploy models.

GCP

• Google Cloud Storage: Durable and scalable object storage.
• Cloud Functions: Serverless execution of functions in response to cloud events.
• AI Platform: End-to-end machine learning model lifecycle management.

ML Operations

• Continuous Training: Implement pipelines to retrain models with new data.
• Data Validation: Use services like AWS Data Pipeline or Azure Data Factory for data integrity checks.
• Model Monitoring: Track model performance and data drift to maintain accuracy over time.

These services ensure our model stays updated, scalable, and maintainable, with robust ML operations that support continuous improvement.

Quickstart

git clone https://github.com/GOSS-hash/logistic-regression-banking-omar