Surrogate Modeling Toolbox
Data Science Live Book
An open source book to learn data science, data analysis and machine learning, suitable for all ages!
📍 Interactive Studio for Explanatory Model Analysis
ANNdotNET - deep learning tool on .NET Platform.
A micro neural network multilayer perceptron for MicroPython (used on ESP32 and Pycom modules)
A Julia machine learning framework
Python Library for Model Interpretation/Explanations
Pyncov-19: Learn and predict the spread of COVID-19
moDel Agnostic Language for Exploration and eXplanation
OpenChem: Deep Learning toolkit for Computational Chemistry and Drug Design Research
Retentioneering: product analytics, data-driven customer journey map optimization, marketing analytics, web analytics, transaction analytics, graph visualization, and behavioral segmentation with customer segments in Python. Opensource analytics, predictive analytics over clickstream, sentiment analysis, AB tests, machine learning, and Monte Carlo Markov Chain simulations, extending Pandas, Networkx and sklearn.
An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code
A Data science challenge - "Mekktronix Sales Forecasting" organised by ZS through Hackerearth platform. Rank: 223 out of 4743.
A python multi-variate time series prediction library working with sklearn
Market Mix Modelling for an eCommerce firm to estimate the impact of various marketing levers on sales
In this code we implement and compared Collaborative Filtering algorithm, prediction algorithms such as neighborhood methods, matrix factorization-based ( SVD, PMF, SVD++, NMF), and many others.
Developer Version of the R package CAST: Caret Applications for Spatio-Temporal models
Exploratory, Inferential and Predictive data analysis. Feel free to show your ❤️ by giving a star ⭐
BAS R package https://merliseclyde.github.io/BAS/
Kaggle Kernels (Python, R, Jupyter Notebooks)
Drug-Drug Interaction Predicting by Neural Network Using Integrated Similarity
This is a Statistical Learning application which will consist of various Machine Learning algorithms and their implementation in R done by me and their in depth interpretation.Documents and reports related to the below mentioned techniques can be found on my Rpubs profile.
Machine Learning Finite State Machine Models from Data with Genetic Algorithms