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.

Download CV

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

Exploratory Data Analysis on Global Weather Patterns

Weather Analysis

Explored the impacts of climate change using Exploratory Data Analysis (EDA) on global weather patterns.

Project Highlights:

  • Investigated weather trends across Spain, Brazil, and Indonesia
  • Identified key correlations between temperature, humidity, precipitation, and UV index
  • Analyzed over 32,000 rows of data from 205 countries

Libraries used: Pandas, Numpy, Seaborn, Matplotlib, Plotly

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. This project combines environmental science with data analytics to understand and predict air pollution patterns.

Key achievements:

  • Built predictive models achieving 85% 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, Jupyter Notebook