This section contains projects that focus on analyzing, preparing and utilization of data with various machine learning algorithms to build regression and classification models.
Projects
- DineRate
- HeartGuard
- HomeValueQuest
DineRate
DineRate is a machine learning application that predicts the ratings of restaurants in Bangalore. By leveraging a dataset obtained from Zomato, the project trains a predictive model that takes into account factors such as location, cuisine, restaurant type, average cost, and user reviews. The goal is to provide users with accurate rating predictions, helping them make informed decisions when selecting a restaurant. The project is deployed and accessible through a user-friendly interface on the deployment site, allowing users to input restaurant details and receive predicted ratings remotely.
HeartGuard
The HeartGuard project aims to develop a machine learning model for accurately predicting heart failure in patients. By analyzing a dataset of 299 patients' medical records, including 13 clinical features, the project explores various ML algorithms and techniques. The best-performing model is selected based on evaluation metrics like recall and F-score. The project's repository contains a Jupyter Notebook (`Heart_Failure.ipynb`) that provides code, documentation, and step-by-step instructions. The project strives to provide healthcare professionals with a reliable tool to improve patient outcomes and intervention decisions.
HomeValueQuest
This project is a Kaggle competition focused on predicting the sales prices of residential homes in Ames, Iowa. The dataset consists of 79 variables describing various aspects of the houses. The goal is to develop regression models using advanced techniques and creative feature engineering to accurately estimate the sale prices. The competition evaluates submissions based on the Root-Mean-Squared-Error (RMSE) metric. The provided Jupyter Notebook and dataset enable participants to analyze and reproduce the results. This project is an opportunity to enhance regression skills and gain experience in real-world predictive modeling.
About Me
Hello, I'm Emuejevoke Eshemitan, a passionate data professional specializing in Machine
Learning Engineering and Data Science. My portfolio showcases projects reflecting my
dedication to leveraging data for insightful and innovative solutions.
As a Machine Learning Engineer, I excel in designing cutting-edge models using TensorFlow and
scikit-learn, building scalable data pipelines, and deploying them in production
environments.
As a Data Scientist, I analyze valuable patterns, develop predictive
models, and communicate insights through impactful visualizations.
Let's collaborate on a data-driven journey! Explore my portfolio for more.