Shivam Bhirud (@ShivamBhirud)
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ShivamBhirud

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Top repositories

1

Capital-Bike-Share-Data-Streamlit-Web-Application

It is a web app tutorial project made with streamlit, a ML web app tool. It has some super cool features that can eliminate the need of any web framework. So, a data scientist can focus entirely on the analytics part rather than worrying about managing frontend and backend with any sort of framework. Do check out this project!
Python
22
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2

Indian-Crime-Prediction-and-Visualization-Online-Platform

Predictive Analytics and Visualisations based on Crime Patterns in India. It a python based web application which runs Machine Learning algorithm in the back-end for prediction purposes.
Jupyter Notebook
16
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3

Diabetes-Prediction-using-ML

Diabetes Prediction is performed based on patient's reports using supervised machine learning Naive Baye's Classification algorithm using python Scikit-Learn
Jupyter Notebook
2
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4

Team-up

A Team-up online platform where you can create and join various teams that serve various purposes. For example, someone might have published a team-up advertisement having a vacancy for 5 people, so that they can form a team and participate in a hackathon next month. People will be able to see the advertisements in the respective sections on the portal and can request to join that team. Similarly, everyone can join or create their own advertisements. You can judge someone whether they are good for your team based on their detailed profile on this portal.
JavaScript
2
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5

Vision-Impaired-People--Text-Reader

Random text from document or image reader to help listen to the Vision Impaired people.
Python
1
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6

IMDBmoviesDB

histogram showing total count of movies released in slot of certain years.
Jupyter Notebook
1
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7

Capital-Bike-Share-Trip-History-Dataset-Classification

Bike Trip History dataset is used to classify whether the rider is a permanent member or a casual rider. It is performed using Decision tree classifier ML algorithm.
Jupyter Notebook
1
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8

Twitter-Sentiment-Analysis

Twitter Sentiment analysis is performed on a captured dataset of tweets. Regex is used to filter the data and predict how many positive, negative , and neutral tweets were there in the dataset. The model used to perform sentiment analysis is textblob.
Jupyter Notebook
1
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