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.

I will be uploading the lab work in this repo

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.

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.

modeling of # and analysis of stock and options

🚀 AlphaCrew: Production-grade multi-agent hedge fund platform powered by CrewAI Enterprise. Features live trading via Alpaca, real-time performance monitoring with Grafana, and human oversight through Slack. Built for sophisticated algorithmic trading and portfolio management.

Predict loan approval status using machine learning techniques. This project demonstrates data preprocessing, feature engineering, model training, and evaluation, along with an interactive Streamlit app for real-time predictions. Ideal for financial decision-making.