- This project focuses on predicting the loan approval status for applicants.
- The code is implemented in a Jupyter Notebook.
- The notebook contains the necessary steps to load and preprocess the loan dataset.
- Exploratory Data Analysis (EDA) techniques are applied to gain insights into the dataset.
- Various machine learning algorithms such as Logistic Regression, Random Forest, and XGBoost are implemented for loan approval status prediction.
- The notebook includes data preprocessing steps like handling missing values, encoding categorical features, and feature scaling.
- Model training and evaluation processes are carried out to assess the performance of different algorithms.
- Performance metrics such as accuracy, precision, recall, and F1-score are computed to evaluate the models.
- The notebook also showcases how to tune hyperparameters using techniques like Grid Search and Randomized Search.
- The best-performing model is selected based on evaluation results and used to make loan approval status predictions.
- The results and insights obtained from the analysis are presented in a clear and concise manner.
Feel free to visit the code for more details and to explore the implementation.
Hi there! I am Sai Vamsi, a final-year computer science undergraduate specializing in Artificial Intelligence at Vel Tech University, Chennai. I have a strong passion for software development and Artificial Intelligence.
I'm proficient in C, C++, Java, and Python programming languages. I also have expertise in Data Structures and Algorithms. Besides, I am a Full-stack Web Developer familiar with front-end development technologies like HTML, CSS, JavaScript, and React, and back-end development with Node.js, Express.js, and MongoDB.
On my GitHub profile, you can find a collection of projects that demonstrate my programming and development skills.
Thank you for taking the time to learn about me, and feel free to check out my projects on GitHub!