venkatanarayana143 Goto Github PK

followers: 0.0 following: 16.0 repos: 41.0 gists: 0.0

Name: VenkataNarayana Bommanaboina

Type: User

Bio: Passionate about learning new things !

Twitter: Venkat515739

Location: Pune

VenkataNarayana Bommanaboina 😃

About 😎

I am a Software Engineer having 1.5 years of experience at Cognizant Technology Solutions Pvt Ltd. I have completed my Bachelor's degree in Computer Engineering specialization. Passionate about Data Science, Machine Learning and open source. Also, have some experience in Full-stack Development and Data Analysis.

Communities :

Contributer at Omdena ( An Open Source AI & Data Science organization.)
Member of Computer Society of India

Technologies 🔥

Machine Learning
Python
C++
Java
JavaScript
SpringBoot
HTML & CSS
Flask
Scikit-Learn
SQL & MySQL
Heroku
Google Cloud Platform
Competitive Programming

Languages and Tools:

Contact Me 💬

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VenkataNarayana Bommanaboina's Projects

awesome-profile-readme-templates

A collection of awesome readme templates to display on your profile

bank-note-authentication-ml-app

cc-spring

chatbot-

covid-19-data-visualization

credit-card-fraud-detection

darknet

YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )

data-science-ipython-notebooks

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

datavisualization

webscrapping and visualizing data using matplotlib

dbms-project-studentprojectmanagementsystem

demo-self-driving

Streamlit app demonstrating an image browser for the Udacity self-driving-car dataset with realtime object detection using YOLO.

diabetes-prediction-webapp-using-flask

dockers

face-detection-

Detecting face using OpenCV in python

fake-news-detection

This advanced python project of detecting fake news deals with the fake and real news. Using sklearn, we build a TfidfVectorizer on our dataset. Then we initialize a PassiveAggressiveClassifier and fit the model. In the end, the accuracy score and the confusion matrix tell us how well our model fares.