Ricky Bilakhia's repositories
Sales-Force-Alignment-Clustering-
Define and align the most effective and balanced sales territories based on geography and physician prescribing habits, in order to maximize promotion of new medications. Pharmaceutical companies spend millions of dollars annually on their sales force in order to promote recently discovered medications, thereby increasing their company’s revenue. The sales force is aligned with geographical territories that are defined to maximize representative effectiveness and reach, and minimize travel time, while providing “fair” and approximately equal sales potential for each representative.
Hub_miner
This is a set of functions and scripts for a) extracting all human-written text from pull requests, b) cleaning and structuring the data in a well-organized way, and c) performing text mining (topic modeling) on the dataset so as to classify distinct text comments into conceptual categories. Use the package 'rgithub' to extract the data Extract all the text comments for each pull request for a single project Retain timestamps and pull request ids for each comment
Review_Analysis-Python
•Collected TV set reviews from different websites using Python script •Performed a text mining process on the reviews
Sentimental-Analysis-Python
Build a classifier that predicts whether a restaurant review is positive or negative, based on the review text only. Used classification algorithms like KNN, Logistic Regression and Naive Bayes to classify the reviews either positive or negative. 85% accuracy was achieved using various combination of above algorithm.
Web-Analytic-Python
Collect the full HTML text of every resume listed in this link: http://www.indeed.com/resumes?q=%22data+scientist%22&co=US We need the FULL HTML of the page that you get after after you click on a resume link.
Stock-Price-Prediction
Applied back propagation Neural Network models to predict the daily S&P500 Exchange Composite Index. Constructed learning algorithm and gradient search technique in the models. Evaluated the prediction models. Used past data form stock exchange to predict stocks and the potential increase or decrease of a company’s stock prices. Technology used: Language: R Software: R Studio, Shiny R Packages used: tseries, quantmod, neuralnet.