Surya Pradeep Kumar (@SuryaPradeepM)
github

SuryaPradeepM

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  • Stars
    star
    23
  • Global Rank 584,012 (Top 21 %)
  • Followers 82
  • Following 219
  • Registered over 9 years ago
  • Most used languages
    Jupyter Notebook
         33.3 %
    R
         22.2 %
    HTML
         22.2 %
    Python
         11.1 %
    LabVIEW     11.1 %
  • Location ๐Ÿ‡ฎ๐Ÿ‡ณ India
  • Country Total Rank 25,882
  • Country Ranking
    LabVIEW     2
    R
         291
    Jupyter Notebook
         5,616
    HTML
         7,612

Top repositories

1

Labview-Projects

Projects on the Labview Platform. Simulation, Control and Design
LabVIEW
4
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2

Comprehensive-Sentiment-Analysis-of-Movie-Reviews-IMDB-dataset

Comprehensive Sentiment Analysis of Movie Reviews using models from LogisticRegression to Bert
Jupyter Notebook
3
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3

UTAustin-IITB-PG-AIML

Course projects for the Post Graduate Program in Artificial Intelligence and Machine Learning designed by leading academic and industry experts with IIT-Bombay faculty recognised by The University of Texas at Austin and Great Lakes. Facilitated by GreatLearning.
HTML
3
star
4

Advanced-ML-Specialization-HSE-Coursera

Advanced Machine Learning specialisation by HSE University
Jupyter Notebook
2
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5

R-Projects-Harvard-DataScience-Professional-Program

Repository for the Data Science Professional Program from Prof. Rafael Irizarry, Harvard University on edX
R
2
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6

Hurricane-Maria-Puerto-Rico-Mortality-Analysis-R

We extract actual daily mortality data from Puerto Rico and investigate whether the Hurricane Marรญa had an immediate effect on daily mortality compared to unaffected days in September 2015-2017.
R
2
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7

Certificates

Repo for my Certificates
1
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8

Visiting-cards-Information-Extraction

Extracting phone numbers, names, addresses, etc from visiting cards
Python
1
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9

Masters_Deakin_AI

Jupyter Notebook
1
star
10

ML-Heart-disease-prediction

We Explore the UCI-Heart Dataset to identify correlations among the variables, Perform Preprocessing, and apply various Machine Learning algorithms along with some parameter tuning and later, comparing the Performance of Machine Learning Algorithms in Cardiovascular Disease prediction as part of our Final Year Project
HTML
1
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