Loan Prediction

Description:

This project, Loan Prediction, leverages Business Intelligence and advanced visualization techniques using Python, Seaborn, Sklearn, and StreamLit. I have undertaken a comprehensive analysis of historical bank campaign data to forecast future loan customers effectively.

Through rigorous data cleaning and employing multivariate analysis techniques, meticulously prepared the dataset. This involved fine-tuning one hot encoding to optimize model's predictive capabilities. This approach focuses on developing an advanced predictive model that can accurately forecast potential loan customers based on historical patterns and campaign outcomes.

Using Python libraries such as Seaborn for data visualization and Sklearn for machine learning algorithms, I have built a robust framework. StreamLit serves as our platform for interactive visualization, allowing stakeholders to explore insights derived from our predictive model.

Overall, this project aims to provide actionable insights to stakeholders in the banking sector, facilitating informed decision-making regarding loan approvals and customer engagement strategies.

TechStack:
- Python
- Scikit-learn
- Google Colab
- Streamlit
- Seaborn

Link : Streamlit (bhargavm123-loan-predictor-app-knk01w.streamlit.app)