Predictive Marketing Campaign Analysis Analysis for Bank Term Deposit Subscription Using Decision Trees
This project focuses on analyzing a bank's marketing campaign dataset to gain insights and make data-driven recommendations for improving the effectiveness of the campaign. The dataset contains information about clients and their interactions with the bank's marketing campaigns. The goal is to identify patterns, trends, and factors that influence clients' decision to subscribe to a term deposit, and provide actionable recommendations to enhance the campaign strategy.
The project is structured as follows:
- Data Understanding and Exploration: Initial exploration of the dataset to understand its structure, data types, missing values, and summary statistics.
2.Data Preprocessing: Data preprocessing steps include handling missing values, encoding categorical variables, and feature engineering.
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Exploratory Data Analysis (EDA): In-depth analysis of different features, their distributions, correlations, and their relationship with the target variable (subscription).
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Modeling: Building and evaluating a vanilla Decision Tree Classifier model, as well as a tuned version, to predict clients' subscription decisions.
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Feature Importance Analysis: Analyzing the importance of different features in influencing clients' subscription decisions.
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Results and Recommendations: Summarizing the analysis findings and providing actionable recommendations for the bank's marketing strategy.
- Clone the repository to your local machine.
- Install the required libraries by running pip install -r requirements.txt.
- Open the Jupyter Notebook Bank_Marketing_Analysis.ipynb to run the analysis step-by-step.
The project uses the following libraries:
- pandas
- numpy
- matplotlib
- seaborn
- sklearn
- prettytable These dependencies are listed in the requirements.txt file for easy installation.
The analysis provides insights into clients' demographics, communication preferences, and financial behavior. Key findings include:
- Demographic insights, such as age groups and common job roles.
- The impact of campaign details, including contact frequency and duration, on subscription success.
- Influence of financial features like balance and previous campaign outcomes on subscriptions.
- Feature importance ranking, indicating which features have the greatest impact on subscription decisions.
##Recommendations Based on the analysis, the following recommendations are made:
- Targeted Campaigns: Focus marketing efforts on demographic segments more likely to subscribe.
- Optimal Contact Strategy: Determine the right balance of contact frequency to engage clients effectively.
- Engaging Communication: Ensure communication channels are engaging and relevant to clients.
- Customized Offerings: Tailor offerings based on clients' financial situations.
- Follow-up Strategy: Prioritize clients who responded positively to previous campaigns.
This project showcases how data analysis can provide actionable insights to optimize marketing campaigns. By implementing the recommendations and continuously evaluating campaign performance, the bank can improve its subscription rate, enhance client engagement, and achieve greater success in its marketing efforts.
Isaac Muturi
Follow me on Twitter 🐦, connect with me on LinkedIn 🔗, and check out my GitHub 🐙. You won't be disappointed!
👉 Twitter: https://twitter.com/NdiranguMuturi1?t=xXF2OKsqOUeb5J_4yysFKg&s=09
👉 LinkedIn: https://www.linkedin.com/in/isaac-muturi-3b6b2b237
👉 GitHub: https://github.com/Isaac-Ndirangu-Muturi-749
