Machine Learning for Job Market Analytics -- 2

Machine Learning Pipeline – Job Market Analytics & Salary Prediction Created an end-to-end machine learning pipeline for looking at job market data and guessing what salaries might be. Key Features: Worked on. Fixed 10,000 or more job-market records using Python, Pandas and NumPy. Made an automated ETL/data preprocessing pipeline to take care of data cleaning, changing data making it all the same and checking if it is all the same. Added a PostgreSQL (Neon) database to keep data in a good way and help with machine learning work. Did analysis of the data. Looked at the features to find what helps predict salary. Trained a Random Forest regression model to guess salaries. Found that employee_residence is one of the things to use for guessing salaries. Built a FastAPI backend with places to get analytics and predictions. Linked the machine learning backend to a React frontend to show information and results in a way. Technologies: Python, Pandas, NumPy, Scikit-learn, Random Forest, PostgreSQL, Neon, FastAPI, React, ETL. This project shows experience, in data preprocessing, machine learning, database integration, API development and full machine learning deployment.