I am Moazum Munawer
Data Scientist | Tennis Player | Entrepreneur
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I currently work as a Quantitative Analyst in the Management Analytics team where I provide analytical models and reports for sales transformation and sales acceleration activities, for First Republic's entire leadership and sales force.
Pursued a Master of Applied Statistics. I wanted to increase my knowledge in statistics and data science. In addition, I was interested to learn more about this exciting and important field.
In undergrad, I majored in Applied Mathematics with a concentration in Economics. I was given a full-ride scholarship and graduated at the age of 20.
Using classification techniques including K-nearest neighbors, decision trees, support vector machines, boosting, and neural networks to predict which country a user will choose for their destination.
Modeling relationship of the effects of various aerobic fitness attributes on the oxygen consumption of runners.
A multi-part simulation study to evaluate the performance of a Monte Carlo cross-validation method for model selection.
Predict which brand of OJ customers will purchase based on various characteristics using full and pruned decision trees.
Applying Linear Discriminant Analysis, Quadratic Discriminant Analysis and K-nearest neighbors to classify weekly S&P 500 stock index returns.
Predict use behavior of Wikipedia by teachers based on their attributes and responses to survey items. Used PCA to identify any relationships between survey items and if they cluster along with any attributes. Classified use behavior by performing Logistic Regression, LDA, QDA, and KNN.
Determine the relationship between maximal expiratory pressure and several other variables related largely to body size and lung function in patients. Fitted PCR, OLS based on Sliced Inverse Regression directions, PLS, Sparse PLS, and compared their predictive performance against Best Subset Selection, Lasso, and Ridge Regression.
Perform cluster analysis (e.g., k-means and hierarchical clustering) on a data set which contains transactions for a UK-based online retail store.
Apply Principal Components Analysis to data from the United States Department of Agriculture Food Composition Database.
Predict the number of applicants for a large number of US Colleges from the 1995 issue of US News and World Report - using Best Subsets, Ridge Regression, Lasso, Principal Components Regression, and Partial Least Squares.
Exploratory data analysis of data for a large number of US Colleges from the 1995 issue of US News and World Report.