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DataMiningUsingTwitter
February 2, 2016 – February 2, 2016
DataMiningUsingTwitter — GitHub repository
View ProjectMining_TwitterData
January 28, 2016 – February 2, 2016
Mining_TwitterData — GitHub repository
View ProjectAnalyse-Enron-Email-Dataset-using-Hadoop
January 18, 2016 – January 18, 2016
Analyse-Enron-Email-Dataset-using-Hadoop — GitHub repository
View ProjectMining-YouTube-using-Python
October 10, 2015 – October 14, 2015
The goal is to fetch the data from YouTube channel using API on particular search term and
View Projectk-Means-clustering-using-MATLAB
September 16, 2015 – September 16, 2015
I have done coursework which involved writing a program in MATLAB that implements the K-means clustering algorithm and testing it on 2 different datasets of gene expression data and genetic information on hereditary diseases. The relationships between the points in the datasets examined using different distance metrics and correlation.
View ProjectEnsemble-Methods-using-R
September 3, 2015 – October 14, 2015
I have done my individual project (dissertation) on ensemble methods. In which I first did the background study on different ensemble methods and then implemented Boosting, AdaBoost, Bagging and random forest techniques on underlying machine learning algorithms. I used boosting method to boost the performance of weak learner like decision stumps. Implemented bagging for decision trees (both regression and classification problems) and for KNN classifier. Used random forest for classification trees. I have implemented a special algorithm of boosting called “AdaBoost” on logistic regression algorithm using different threshold values. Then plotted the different graphs like an error rate as a function of boosting, bagging and random forest iterations. Compared results of bagging with boosting. Analysed the performance of classifier before applying ensemble methods and after applying ensemble methods. Used different model evaluation techniques like cross-validation, MSE, PRSS, ROC curves, co
View ProjectCultural Fit Analysis
The candidate's projects show a strong individual initiative and a focus on data science and machine learning topics, which aligns with a Data Scientist role. However, the projects are predominantly personal and academic, lacking evidence of collaborative work or real-world business impact. The diversity of tools (R, Python, MATLAB, Hadoop) indicates a willingness to learn and adapt, but the depth of experience with each is not fully clear from the descriptions.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. No psychometric test results or interview feedback provided.