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Bank Marketing: Term Deposits Classification 📊

Overview

Under Development

This project aims to classify whether a client will subscribe to a term deposit based on historical marketing campaign data from a Portuguese banking institution. The data consists of phone calls made to clients and various client attributes.

Table of Contents

1. Frame the Problem and Look at the Big Picture
2. Get the Data
3. Explore the Data
4. Prepare the Data
5. Short-List Promising Models
6. Fine-Tune the System
7. Present Your Solution
8. Launch
9. How to Run the Project

Frame the Problem and Look at the Big Picture 🔍

1. Defining the Objective:
   • The goal is to predict whether a client will subscribe to a term deposit based on features from previous marketing campaigns. 🎯
2. Solution Usage:
   • The solution will enable the bank to target potential customers more effectively, improving marketing campaign efficiency. 📈
3. Current Solutions:
   • The bank might use generic marketing strategies without targeted client predictions. 💬
4. Problem Framing:
   • This is framed as a supervised classification problem, where the aim is to predict a categorical outcome (subscription) using input features. 🔮
5. Performance Measurement:
   • Performance is measured using metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. 📊
6. Alignment with Business Objectives:
   • These metrics align with the business objective of accurately predicting term deposit subscriptions. 🏆
7. Minimum Performance Requirements:
   • The minimum acceptable performance is determined by business needs, focusing on high precision and recall for the positive class. 🚀
8. Comparable Problems:
   • Similar problems include customer churn prediction and lead scoring, which may offer reusable tools and techniques. 🔄
9. Availability of Expertise:
   • Expertise in data science and marketing analytics is available to guide the development and interpretation of models. 👩‍💻
10. Manual Solution Approach:
    • Manually, analyzing customer data and marketing effectiveness helps target clients based on their likelihood to subscribe. 📝
11. Assumptions:
    • Assumptions include the relevance of provided features and the representativeness of historical data. 📜
12. Verification of Assumptions:
    • Verifying assumptions involves checking data distribution and feature relevance through exploratory data analysis and model validation. ✔️

Get the Data 📥

1. Data Requirements:
   • The project requires historical marketing data, including client attributes and subscription outcomes. The dataset includes over 45,000 records with 34 columns. 📋
2. Data Sources:
   • Data is accessible on Kaggle: Bank Marketing Term Deposits Classification. 🌐
3. Data Size and Storage:
   • The dataset size is approximately 4.13 MB. 💾
4. Legal Considerations:
   • The dataset is licensed under Apache 2.0, and no additional authorization is required. 🏛️
5. Access Authorizations:
   • Ensure Kaggle account access for downloading the dataset. 🔑
6. Workspace Setup:
   • Create a local workspace and a virtual workspace on GitHub to manage the project. 🛠️
7. Data Acquisition:
   • Download the data files (train.csv and test.csv). 📥
8. Data Format Conversion:
   • Convert the data into a DataFrame format for analysis. 🔄
9. Sensitive Information Handling:
   • The dataset does not contain sensitive information. 🔒
10. Data Type and Size Analysis:
    • The data includes tabular data with client attributes and subscription outcomes and is a sample dataset. 📊

Explore the Data 🔍

1. Data Exploration Copy:
   • Create a copy of the data for exploration, potentially sampling it down to a manageable size if necessary. 💾
2. Exploration Documentation:
   • Use a Jupyter notebook to document the data exploration process. 📓
3. Attribute Study:
   • Examine each attribute’s characteristics, including name, type (categorical, int/float, etc.), percentage of missing values, and type of noise. 🔬
4. Target Attribute Identification:
   • Identify the target attribute(s) for supervised learning tasks, specifically y (subscription outcome). 🎯
5. Data Visualization:
   • Visualize the data to understand distributions and relationships. 📈
6. Correlation Analysis:
   • Study correlations between attributes to identify potential relationships. 🔗
7. Manual Solution Approach Analysis:
   • Analyze how the problem would be approached manually. 🧐
8. Promising Transformations:
   • Identify and plan promising transformations for the features. 🛠️
9. Additional Data Needs:
   • Determine if additional data could enhance the analysis (refer to “Get the Data” if needed). 📥
10. Exploration Documentation:
    • Document key findings and insights from the data exploration phase. 📝

Prepare the Data ⚙️

1. Data Cleaning:
   • Address outliers and missing values by fixing or removing them, or by filling them with appropriate values. 🧹
2. Feature Selection:
   • Select relevant features by dropping those that do not contribute useful information for the task. 🔍
3. Feature Engineering:
   • Apply feature engineering techniques such as discretizing continuous features, decomposing features, adding transformations, and aggregating features. 🛠️
4. Feature Scaling:
   • Standardize or normalize features to ensure consistent scaling. 📏

Short-List Promising Models 🏆

1. Model Training:
   • Train initial models using various classification algorithms, including logistic regression, decision trees, random forests, support vector machines (SVM), and gradient boosting machines. 🤖
2. Performance Evaluation:
   • Evaluate the performance of each model using cross-validation and metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. 📊
3. Variable Analysis:
   • Analyze significant variables to understand which features contribute most to the predictions. 🔍
4. Error Analysis:
   • Investigate types of errors made by each model and identify patterns in misclassifications. 🕵️‍♂️
5. Feature Engineering and Selection:
   • Refine feature selection and engineering based on model performance. 🔧
6. Model Comparison:
   • Compare different models and select the top performers based on their classification metrics and generalization ability. 🏅

Fine-Tune the System 🔧

1. Hyperparameter Tuning:
   • Fine-tune hyperparameters using cross-validation and consider random search or Bayesian optimization for exploring hyperparameter space. 🧩
2. Ensemble Methods:
   • Combine multiple models to improve performance. 🧠
3. Final Performance Measurement:
   • Assess the final model's performance on a test set to estimate generalization error without further tweaking. 📈

Present Your Solution 🎤

1. Documentation:
   • Document the solution, including methods and findings. 📝
2. Presentation Creation:
   • Create a presentation highlighting the key aspects of the solution and its alignment with business objectives. 📊
3. Explanation of Achievements:
   • Explain how the solution meets the business objective and discuss any interesting findings. 🏆
4. Visualization of Findings:
   • Use visualizations to communicate key points and results effectively. 📉

Launch 🚀

1. Production Readiness:
   • Prepare the solution for production, including integrating data inputs and writing unit tests. 🛠️
2. Monitoring Setup:
   • Implement monitoring code to track live performance and trigger alerts for performance drops or issues. 📡
3. Model Retraining:
   • Regularly update models with fresh data and automate the retraining process where possible. 🔄

How to Run the Project 🏃‍♂️

1. Clone the Repository:

   git clone https://github.com/victorlcastro-dsa/Bank-Marketing-Term-Deposit-Classifier
   cd Bank-Marketing-Term-Deposit-Classifier

2. Install Dependencies:

   • Ensure you have Python 3.11.9+ installed.
   • Create a virtual environment and install the required packages.

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`
   pip install -r requirements.txt

Credits and Acknowledgements 🙏

• Kaggle for providing the dataset.
• Various open-source libraries and tools used throughout the project.

Contact 📬

For any questions or feedback, please reach out to my email.

Happy analyzing! 🎉

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