ShivSmit's repositories
Machine-Learning-II
Neural Net exercises
WSDSHackathon
Laurels for Learning
2018-n5--Pre-RFinance-meetup
In the spirit of the conference, this meetup will feature a series of short talks on practical applications with R!
HappinessQuotient
Shiny App
Leetcode-DataStructures-and-Algorithms
Leetcode and hackerrank
TwitterSentimentAnalysis
Checking the sentiments of @CVSHealth account's tweets and #CVSAetna. Segregating the sentiments into positive, negative and neutral buckets. Creating a small visualization and wordcloud out of the results.
ANLY560smitha
Repository for Functional Programming
ccxt
A JavaScript / Python / PHP cryptocurrency trading library with support for more than 100 bitcoin/altcoin exchanges
dash-sample-apps
Apps hosted in the Dash Gallery
emperors
Roman Emperors from 26 BC to 395 AD
hello-world
My new repository
Java
101
Machine-Learning-with-Python
Python code for common Machine Learning Algorithms
MachineLearning
Machine learning for beginner(Data Science enthusiast)
MedicalData
This is a comprehensive Exploratory Data Analysis for the [Personalized Medicine: Redefining Cancer Treatment](https://www.kaggle.com/c/msk-redefining-cancer-treatment) challenge. Using *ggplot2* and the *tidyverse* tools to study and visualise the structures in the data. Challenge to automatically classify genetic mutations that contribute to cancer tumor growth (so-called "drivers") in the presence of mutations that don't affect the tumors ("passengers"). The [data](https://www.kaggle.com/c/msk-redefining-cancer-treatment/data) comes in 4 different files. Two csv files and two text files: - *training/test variants:* These are csv catalogues of the gene mutations together with the target value *Class*, which is the (manually) classified assessment of the mutation. The feature variables are *Gene*, the specific gene where the mutation took place, and *Variation*, the nature of the mutation. The test data of course doesn't have the *Class* values. This is what we have to predict. These two files each are linked through an *ID* variable to another file each, namely: - *training/test text:* Those contain an extensive description of the evidence that was used (by experts) to manually label the mutation classes. The text information holds the key to the classification problem and will have to be understood/modelled well to achieve a useful accuracy.
pandas
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Projects
ProjectReports
Python_Tutorials
Python tutorials in both Jupyter Notebook and youtube format.
scala-at-light-speed
The repository for the free Scala at Light Speed mini-course
Spoon-Knife
This repo is for demonstration purposes only.
SQL
Leetcode and geeksforgeeks SQL
Stadium-Explorer
Stadium Recommender
Unsupervised-Learning-with-R
Market segmentation, strategic analysis, and a variety of methods to help you understand your data better