Predicting Term Deposit Subscriptions Using Machine Learning

Retail Banking, Financial Services, Marketing Analytics

Problem Statement:

The client is a retail banking institution. Term deposits are a major source of income for the bank. A term deposit is a cash investment held at a financial institution for an agreed rate of interest over a fixed term. The bank employs various outreach plans to sell term deposits to their customers, including email marketing, advertisements, telephonic marketing, and digital marketing. Telephonic marketing campaigns remain one of the most effective ways to reach out to people but require significant investment due to the large call centers needed to execute these campaigns. Therefore, it is crucial to identify customers most likely to convert beforehand so they can be specifically targeted via call. Given client data such as age, job type, marital status, and information about the call such as duration, day, and month, your task is to predict if the client will subscribe to a term deposit.

Business Use Cases:

• Targeted Marketing: Optimize telephonic marketing campaigns by identifying clients most likely to subscribe to term deposits.
• Cost Efficiency: Reduce marketing costs by focusing resources on high-probability customers.
• Increased Revenue: Boost term deposit subscriptions through more effective customer targeting.
• Customer Relationship Management: Improve customer relationships by offering relevant financial products.

🛠 Skills

Classification, Machine Learning and Neural Networks

Approach

• Data Collection: Use the provided datasets (train.csv and test.csv) containing client and call details.

• Data Preprocessing: Clean and preprocess the data, handling missing values, encoding categorical variables, and normalizing numerical features.

• Model Building: Train various classification models such as Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, and Neural Networks.

• Model Evaluation: Evaluate models using accuracy as the primary metric. Use cross-validation to ensure model robustness.

• Hyperparameter Tuning: Optimize model performance through techniques like Grid Search and Random Search.

• Prediction: Use the best-performing model to predict the likelihood of new clients subscribing to term deposits.

• submission: Save predictions in the Submission.csv format and validate accuracy

Run Locally

Clone the project

  git clone https://github.com/Vijay6383/Predicting-Term-Deposit-Subscriptions-Using-Machine-Learning-and-NN-models.git

Go to the project directory

  cd Predicting-Term-Deposit-Subscriptions-Using-Machine-Learning-and-NN-models
  cd 'Predicting Term deposit Subscription'

Install dependencies

  pip install scikit-learn, scipy, seaborn, tensorflow 

Run File

  jupyter notebook "Predicting Term Deposit Subscriptions Using Machine Learning.ipynb"

Tags

• Classification
• Machine Learning
• Data Preprocessing
• Feature Engineering
• Model Evaluation
• Hyperparameter Tuning

🔗 Links