bhaveshk22 / predicting-personalloan-uptake

The classification goal is to predict the likelihood of a liability customer buying personal loans

Predicting Personal Loan Uptake with Machine Learning

This repository contains a Machine Learning model that predicts whether a person will take a personal loan or not. The model uses various features such as income, family size, education level, and mortgage to make accurate predictions. The goal of this project is to help financial institutions in their decision-making process for approving personal loans.

Roadmap

• Data Preprocessing: The first step was to clean and preprocess the data, which included handling missing values, removing outliers, and encoding categorical variables.

• Model Training: I experimented with different machine learning algorithms including Logistic Regression, Decision Trees, and Random Forests. Each model was trained and validated using cross-validation to ensure robust performance.

• Model Evaluation: The performance of each model was evaluated using metrics such as accuracy, precision, recall, and F1 score. This helped in identifying the most accurate model for prediction.

Lessons Learned

This project was a great opportunity to apply machine learning concepts to a real-world problem in the banking sector. It was particularly interesting to see how different algorithms performed on the same task and how model performance can be optimized.

I’m looking forward to exploring more ways to leverage machine learning in the future.

Authors

• @bhavesh

Hi, I'm Bhavesh! 👋

🚀 About Me

I'm a Full Stack Data Scientist

🔗 Links

🛠 Skills

1. C, C++, Python
2. SQL
3. Machine Learning
4. Deep Learning
5. Data Science

Summary of My Journey

👩‍💻 I'm currently a student

🧠 Btech Computer Science

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