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Projects

The MSBA program at Foster is instrumental in giving me the data skills that are needed to support business decisions.In the last 1 year I have used Supervised Machine Learning: Linear Regression, Logistic Regression, Decision Tree, K-Nearest Neighbors (KNN), Support Vector Machine (SVM) - Unsupervised Machine Learning: K-Means Clustering, Dimensionality Reduction: Principal Components Analysis (PCA) - Ensemble Learning: Random Forest, Gradient Boosting - Resampling Method: K-fold Cross-Validation - Shrinkage Methods: Regularization (Ridge/Lasso) - Model Performance Assessment: Confusion Matrix, Precision/Recall, Accuracy, Area under the ROC Curve (AUC)

Hands-on experience in - Python Programming (NumPy, Pandas, Scikit-Learn, Matplotlib, Seaborn) - R Programming (tidyverse, ggplot2, dplyr, plotly, shiny, ggvis, RColorBrewer, rmarkdown, forecast) - Database Management: MySQL, SQL Server,  Data Visualization: Tableau, R • Familiar with StatTools,QlikView,Oracle Crystal ball and Google Analytics.

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Below are a few individual projects that broadened my data skills. click on them to read more

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Coursework and Group Projects

Finding signals in data to distinguish leisure, business and bleisure travel for Hilton Hotels using Adobe Analytics

Adobe Customer Journey Analytics

Objective: To find signals in dataset provided by Hilton and distinguish whether a trip represents business, leisure, or “bleisure” travel, and make recommendations on digital properties

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Dataset:  Hilton dataset from January 2021 to Oct 2022

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Approach: I analysed the data based on number of sessions for a booking,time spent/session, membership tiers, devices used, number of children booked, number of nights booked. Further dived deep into number of children=0 to understand business or bleisure travellers

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Findings: Indicator1: Number of children travelling equals 0 means business or bleisure, to find purely leisure Indicator 2: Time spent/session less for Business destinations, events/reservation more for leisure travellers

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Tariff Barrier Analysis of Automobile Export from India

Excel, World Trade Organisation Tariff Analysis, TradeMap, WITS database

Objective: To find the tariff and non-tariff barriers that hinder trade of automobiles from India

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Dataset:  Monthly, quarterly and yearly trade flows of 10000 products across 220 countries

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Challenges: Combining metrics such as volume of exports, world rankings, average tariffs,Trade Balance,etc. across 3 different databases and weighing tradeoffs to finalize top 3 potential destinations of export.

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Findings: Argentina, Phillipines, Mexico and Australia as top export or joint venture destination and recommended four changes in govt policy to enjoy unutilised tariff concessions 

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Visualising Covid-19 using R

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