Loan approval prediction is a popular machine learning project, especially in the banking and finance industry. The goal of this project is to build a predictive model that can determine whether a loan application will be approved or not based on the applicant's information such as income, credit history, and loan amount.

This repository contains results of the completed tasks for the Quantium Data Analytics Virtual Experience Program by Forage, designed to replicate life in the Retail Analytics and Strategy team at Quantium, using Python.

This project uses data analytics and machine learning to assess and predict credit risk. It includes data preprocessing, exploratory analysis, feature engineering, and model building with Python, Pandas, and Scikit-learn to help financial institutions make informed decisions.

modeling of pricing and analysis of stock and options

NLP-driven sentiment analysis on financial news headlines to reveal patterns and correlations with stock price fluctuations, powering data-driven investment predictions.

This project involves a comprehensive data analysis of stock market trends and performance. The analysis aims to uncover patterns, trends, and insights that can aid in making informed investment decisions. By leveraging various data analytics techniques, this project provides valuable visualizations and interpretations of stock market data.

Welcome to my WorldQuant Applied Data Science Lab repository! This repo showcases my completed projects from the WorldQuant Data Science Program, covering real-world applications in machine learning, time-series analysis, predictive modeling, and data engineering.