Chetan Jonnalagadda

Data Science Apprentice skilled in transforming complex datasets into actionable insights using Python, R, SQL, and Power BI. Skilled in predictive modeling, unsupervised techniques, and data/ concept drift mitigation to optimize performance in collaborative environments. Passionate about leveraging data-driven strategies to optimize performance and enhance decision-making.

• Microsoft Certified: Azure Data Scientist Associate [Sep 2024 - Sep 2025]

• IBM DATA SCIENCE CERTIFICATE: https://coursera.org/share/309b552dd1c29c49901385fe235872c8
• IBM PYTHON FOR AI CERTIFICATE: https://coursera.org/share/8a5012979660e971238f6b45af31494a

RETINA IMAGE SEGMENTATION (ML/DL-CV) – Major Project – Reva University, Bangalore, KA                    May 2023  
● Developed CNN model with U-Net and U-Net++ architectures for Retina Image Segmentation to compare the accuracy and loss.  
● Implemented data augmentation techniques and dropout regularization to enhance model performance and minimize variance.  
● Conducted the comparison on 40*40pixel train and validation datasets to evaluate segmentation accuracy.  

DATA EXPLORATION USING PYTHON – Personal Project – Bangalore, KA                    July 2023  
● Analyzed a crime dataset, demonstrating proficiency in data exploration and manipulation.  
● Employed the popular pandas library for data cleaning, including duplicate removal and data type conversion.
Utilized Seaborn for data visualization, creating insightful charts and heatmaps.  
● Applied NumPy for numerical operations, enhancing data analysis capabilities.  
● Highlighted key findings, such as common offense groups and trends in crime occurrence by day, hour, and
month. Conducted district-specific and yearly crime analysis.  
● Improved data presentation by styling values below average in blue and highlighting maximum values in dark green.  

DATA ANALYSIS (PANDAS & TABLEAU) – Personal Project – Bangalore, KA                    August 2023  
● Proficiency demonstrated in data preprocessing, focusing on a London bike-sharing dataset.  
● Data extraction from a zip file, followed by data cleaning using pandas.  
● Cleaning tasks included column renaming, data type conversion, and categorical value mapping.  
● Meticulous preparation ensured dataset readiness for analysis.  
● Utilized Tableau for advanced data analytics and visualization.  
● Unveiled valuable insights into bike-sharing patterns through visualization.  
● Stored the cleaned data as 'london_bikes_final.xlsx' for integration into further analysis tools.