sohilsshah91/nyhais-tcga-series-kmeans

Spatio-Temporal-Crime-Analysis-Time-Series-NYC

This project gives an overview of crime time analysis in New York City . We have created Python Jupyter notebooks for spatial analysis of different crime types in the city using Pandas, Numpy, Plotly and Leaflet packages. As a second part to this analysis, we worked on ARIMA model on R for predicting the crime counts across various localities in the city based on correlations of various demographics correlation in each locality.

Airline-Stock-Prediction-Using-Google-Trends-Oil-Prices

This project highlights a Spark application built on Scala. It utilizes Spark Core, Spark SQL and Spark ML (Machine Learning libraries) for predicting stock prices of specific airline companies. We have used the Google trending words (searched on internet and relevant to financial domain) and also macro-economic oil prices as alternate data to predict stock prices.

Lymphedema-Patient-Clustering-Research

This project is a research focused project highlighting application of unsupervised Machine learning techniques in predicting Lymphedema disease without having knowledge about the symptoms or fields. Libraries Used: sklearn, pandas, numpy, matplotlib, sklearn. cluster KMeans and sklearn decomposition PCA.

Employee-Attrition-Prediction-Case-Study

This project is based on a case study that focuses on Employee Attrition. The data is taken from IBM Watson's sample case study data. I have utilized data mining and basic machine learning algorithms to predict the Employee Attrition of a pharmaceutical company. One of the most important resource for successful functioning of any organization or company is the People resource. Hence, losing the right people from the company can be a huge setback. Thus, understanding the factors or reasons for attrition makes it, all the more, necessary for a company or organization.