Kshitij Bhatnagar's repositories
vaex
Out-of-Core DataFrames for Python, visualize and explore big tabular data at a billion rows per second.
vasculitis
Complete package which prompts the user to add the address of the file and directory and returns the smart binder file, a merged in-line file and a merged stacked file.
Tool-for-data-restructuring
Data restructuring, smart bind, and spell check package with additional tool with Rscripting at backend and java front-end
imbalanced-learn
Python module to perform under sampling and over sampling with various techniques.
Data-Visualization
Visualisation of game of thrones character in d3
Game-of-thrones-visualisation
Game of thrones Visualisation in d3
Yelp-data-set
analysis of yelp dataset to predict best restaurant in Toronto. The analysis focusses on driving features for a good rating.
European-Commission-fleet
Analysis of European Commission fleet data using R
Data-analysis-on-Employee-attrition-data
single decision tree, Extreme gradient boosting and bagging model used
red-wine-data-set
Machine letting to predict the quality of wine with upscaling-python
Cyclometric-complexity
To find the cyclometric complexity of GitHub commits in python
Scaleable-Computing-ChatBot
Python code implementing chatbot
K-means-clustering
Machine learning won python using K-means clustering and gradient descent algorithm to minimise the cost.
Support-vector-machine
Machine learning with SVM and gradient descent algorithm, with test cases
machine-learning-on-R-using-Caret
Random forest in R on various data sets with k-fold cross validation
machine-learning_knn_reg
KNN on various data set which are divided into various chunks
machine-learning_svm_scikit
SVM on scikit learn on python on various data set which are divided into chunks which are used to train the model
machine-learning_logreg
Logical regression using scikit learn on various data chunks which are divided into chunks and used for model learning
Machine-learning-Linear_reg
Linear regression using scikit learn on python. The data sets are divided into chunks of various size and Model is trained on the chunks.