Deniz Yener

Proactive professional specializing in AI, data analytics and sustainable development.
Confident in driving innovation and delivering data-oriented results.
Dedicated to leveraging AI for sustainable societies to create innovative and effective solutions.

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Voice Enabled Medical Chatbot Project

A Voice-Enabled Healthcare Chatbot for Neurodegenerative Disorder Identification

This project is a voice-enabled healthcare chatbot that utilizes deep learning to assist users in identifying potential neurodegenerative disorders based on their symptoms. The chatbot supports both female and male voice options and interacts in natural language conversations to provide insights.

Key achievements:

  • Natural language processing (NLP) for symptom analysis.
  • Voice interaction (male/female options).
  • Deep learning model trained to recognize patterns in neurodegenerative disorders.

Technologies used: Deep Learning, NLTK, WordNetLemmatizer, Keras, Dropout, Activation Function, SGD Optimizer

Heart Disease Prediction Model

Heart Disease Prediction

Developed a machine learning model to predict heart disease risk using patient data. This project focuses on creating a reliable and accurate prediction system that can assist healthcare professionals in early diagnosis.

Key achievements:

  • Achieved 92% accuracy in heart disease prediction
  • Implemented multiple ML algorithms for comparison
  • Created an intuitive user interface for predictions
  • Optimized model for high recall to minimize false negatives
  • Conducted thorough feature importance analysis

Technologies used: Python, Scikit-learn, Pandas, NumPy, Matplotlib

Beijing PM2.5 Air Quality Prediction

Beijing Air Quality

Developed a machine learning model to predict PM2.5 air quality levels in Beijing using historical data from 2010-2014. After forecasting PM2.5 air pollution levels in Beijing, evaluated whether meteorological variables alone could predict pollutant concentrations effectively without engineered lag and rolling features.

Key achievements:

  • Built predictive models achieving 95% accuracy using advanced ML algorithms
  • Implemented interactive data visualizations for trend analysis
  • Analyzed 5 years of environmental data with 438,000+ data points
  • Identified key correlations between weather conditions and PM2.5 levels
  • Developed a robust data preprocessing pipeline for handling missing values

Technologies used: Python, Pandas, Scikit-learn, Plotly, Winsorizer, Streamlit