Imagine you are the front runner for democratic party primaries in 2008 - 1 week into elections you have won a few states(Obama) and your opponent (Hillary) is catching up. How you can use analytics to predict which of the remaining seats will you win using demographic data from states you won and lost. Can we accurately classify win or lose for…

Six machine learning models for predicting bladder cancer progression using Caret package

Time Series Forecasting using K-Nearest Neighbors Algorithm (Parallel approach)

This repo stores the script, the data, and its description for the kernels published on kaggle.

Objective of the repository is to learn and build machine learning models using Python for data Personal Loan Acceptance( UniversalBank )

Using several machine learning algorithms and models primarily in R, upon several data sets. Using EDA on these datasets and visualization with tidyr, dplyr and ggplot2

The repository contains several Jupyter notebooks, each of which covers a different machine learning project, such as sentiment analysis, image classification, and customer segmentation. You also have code examples and datasets included to help users understand and implement the projects themselves.

Using the K-Nearest Neighbor (KNN) model to interpolate non-overlapping datasets in time and space