Omharika Peddapudi

Software Engineer

Hyderabad, Telangana, India3 yrs 5 mos experience

Most Likely To SwitchHighly Stable

Key Highlights

Proficient in Machine Learning and Deep Learning techniques.
Hands-on experience in developing predictive models for healthcare.
Strong ability to analyze customer data for strategic insights.

Stackforce AI infers this person is a Machine Learning Engineer with a focus on Automotive and Healthcare applications.

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Skills

Core Skills

Machine LearningDeep Learning

Other Skills

K-Nearest Neighbors (KNN)Helping Clients SucceedTransportation SafetyConvolutional Neural Networks (CNN)Logistic RegressionGoogle AdsDatabase Management System (DBMS)Microsoft Visual Studio CodeNonparametric StatisticsModel TrainingCustomer Segmentation StrategySecurity TrainingJavaScriptArtificial Intelligence (AI)Market Segmentation

About

I am enthusiastic about trends and innovations and I thrive in dynamic and challenging environments. I can contribute my ideas and skills by adapting advanced technologies which helps in my career growth.

Experience

3 yrs 5 mos

Total Experience

2 yrs 10 mos

Average Tenure

3 yrs 5 mos

Current Experience

Python, jupyter, github, machine learning

Customer Segmentation

Dec 2023 – Present · 2 yrs 5 mos

Customer Segmentation using machine learning is known as unlocking customer insights for enhanced business strategy. In today’s highly competitive business landscape, understanding and effectively engaging with customers is paramount for success. This application explores by using K-Nearest Neighbors (KNN), this holds significance in the realm of customer segmentation within machine learning due to its non-parametric nature, simplicity, and adaptability. This process enables businesses to better understand their customers and tailor marketing strategies, product offerings, and customer experiences accordingly.

K-Nearest Neighbors (KNN)Helping Clients SucceedMachine Learning

Python, github, vscode, deep learning

Traffic Sign Classification

Aug 2023 – Present · 2 yrs 9 mos

Traffic sign classification is a critical task in the development of self-driving cars, as it enables the cars to recognize and respond to different types of traffic signs accurately. The task of traffic sign classification has been approached using various techniques, but deep learning has proven to be a highly effective approach. Deep learning techniques such as convolutional neural networks (CNNs) and keras, the model is trained on a large dataset of labeled images of traffic signs. The trained model can then classify traffic signs in real-time, allowing self-driving cars to operate efficiently and it improves safety, traffic flow, reduce accidents.

Transportation SafetyDeep Learning

Python, vscode, github, machine learning

Heart Disease Prediction

Dec 2022 – Present · 3 yrs 5 mos

In collaboration with a team of four members, we successfully developed a web application focused on predicting Heart Disease. The major challenge in heart disease is its detection. There are instruments available which can predict heart disease but either it are expensive or are not efficient to calculate chance of heart disease in human. Our proposed model was quiet satisfying and was able to predict evidence of having a heart disease in a particular individual by using KNN and Logistic Regression which showed a good accuracy in comparison to the previously used classifiers.

Helping Clients SucceedLogistic RegressionMachine Learning