Ganta Praneeth Kumar

Data Analyst | Data Scientist

I am a highly motivated Data Analyst and Data Scientist with a strong passion for leveraging data to drive business operations and decision-making. With a background in IT business knowledge and a recent retraining in data analysis, I am eager to combine my newfound skills with my existing expertise to bring unique insights to any role I undertake. I am currently seeking a challenging opportunity in an organization that recognizes and nurtures my true potential while allowing me to further develop my analytical and technical skills.

Education:

Post Graduate Program in Artificial Intelligence and Machine Learning, University of Texas at Austin - Great Learning, 08/2022 - Present
Electronics and Communications Engineering, Guru Nanak Institute of Technology, 06/2017 - 06/2021

Work Experience:
Project Engineer - Data Scientist, Wipro Technologies, 12/2021 - 02/2023

During my tenure at Wipro Technologies, I have successfully delivered impactful projects by applying my data science expertise and driving innovation. Developed an ML-based demand forecasting solution for  Demand Forecasting Application, aiming to streamline and improve the accuracy of monthly forecasts. By utilizing XGBoost and Random Forest models.

Collected POD data and created an ML-based model for Compute Prediction, which predicts the number of computes needed for new pod creation. Random Forest and XGBoost algorithms were leveraged for modeling, and the evaluation metric used was the F1-score.

Skills:

Amazon Web Services (AWS): EC2, S3, IAM, Load Balancer, SageMaker, Lambda, Glue, Kinesis, etc.
Google Cloud Platform: [Provide Link]
Python: Numpy, Pandas, Scikit-learn, Keras, 
SQL, Jenkins, 
Data Visualization: Seaborn,  Tableau, Excel, Power BI
Internet of Things
Machine Learning: Supervised Learning, Unsupervised Learning, Feature Engineering, Bagging, Boosting
Deep Learning: Artificial Neural Networks, Computer Vision, Image Preprocessing

Academic Projects - Machine Learning:

Capstone Project - AIML-Pneumonia Detection from X-ray: Developed an intelligent system utilizing the UNet model for regression, taking X-ray images of the lungs as input and outputting the likelihood of pneumonia. Achieved a test accuracy of 71.1% after running the model for 30 epochs.

Ensemble Technique: Built a simple ML workflow that accepts a single '.csv' file as input and returns a trained base model for predictions. Logistic Regression was identified as the best model, with minimal difference between train and test accuracy.

Collected Data from different source like S3 Buckets, EC2 Instances, Confluence pages using python

Developed an ML-based demand forecasting solution for the Demand Forecasting Application, which improved the accuracy of monthly forecasts by leveraging XGBoost and Random Forest models.

Collected POD data and created an ML-based model for Compute Prediction, which predicts the number of computes needed for new pod creation. Random Forest and XGBoost algorithms were leveraged for modeling, and the F1-score was used as the evaluation metric.

I'm Studying PG Program in artificial intelligence and Machine Learning, done some interesting project like pneumonia detection  using U -Net , Yolo and MobileNet, Pose Detection of Hand and Body ,Created a GL Chatbot using NLP . Currently studying NLP and Generative AI. And working on a Generative AI project for Video Generation using cricket pitch data.