PROJECTS

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ADVANCED DATA ANALYSIS

HR Analytics - Advanced Data Analysis with R

The goal of this project was to predict employee attrition rate and monthly income. Logistic Regression was used to predict the probability of attrition, while Linear Regression was used to predict monthly income. Other statistical techniques were also applied with R to explore hidden relationships among variables. In addition to group efforts, my individual report (responsible for both coding and the corresponding portions of technical report) is attached on page 27. The complete R code (page 40) and supplementary graphs are also attached as appendices.

My Contributions:

1. Exploratory Data Analysis
2. Ordinary Least Square
3. Correspondence Analysis
4. Lasso Regression
5. Partial Least Square

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TIME SERIES ANALYSIS & FORECASTING

Walmart Sales Prediction - Time Series Analysis and Forecasting with R

Utilized Time Series Analysis with R to find optimal ways with which a major retail company can most accurately forecast future sales. This project includes various forecasting models, including Simple Exponential Smoothing, Holt’s Method, ARIMA, Time Series Regression, and more. Both log transformation and differencing were applied to stabilize the model performance. Lastly, the accuracy of the models was evaluated based on RMSE and MAE. The complete R code is attached on page 15 as an appendix.

Contributions:

1. Group efforts throughout coding, presentation, and final report
2. Presented the best-performed forecasting model at the final presentation

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DATA ANALYSIS & REGRESSION

Airbnb Rentals in Melbourne, Australia - Data Analysis and Regression with SAS

Utilized SAS for thorough data exploration, variable transformation, outliers detection, and data validation. The objectives of this project were to reveal insights and conducted prediction on the price point of Airbnb in Melbourne, Australia. The methodology of my individual analysis is included on page 4 to 6. The detailed report of my analysis and findings are included on page 17 to 25.

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Airbnb Rentals in Melbourne, Australia - SAS Code

The complete SAS code for my individual analysis.

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ADVANCED MARKET RESEARCH

Factor Analysis & Cluster Analysis

Applied Principal Component Analysis with SPSS to analyze survey results with 300 respondents. As for clustering the respondents based on message rating (cluster analysis, page 23), both hierarchical clustering (Ward’s method) and non-hierarchical clustering (K-means) were applied, with a Two-step cluster analysis at the end to evaluate the results.

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Discriminant Analysis

Used Discriminant Analysis to identify the attribute which has the strongest discriminatory power and the strongest correlation with the discriminant function. As for classification, the discriminant function correctly classified 61.5% of the total 299 responses.

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MARKETING STRATEGIES & PLANNING

Amazon Strategic Alternatives - Marketing Strategies

As part of the complete course project, the file featured is my individual contribution. 3 strategic alternatives were proposed for Amazon to compete with their direct and indirect competitors in the market. Each strategic alternative includes pros and cons, as well as the short-term and long-term impact on Amazon’s business. Associated with each strategic alternative, Chain Ration Method was used to conduct a 3-year revenue forecast, based on the predefined assumptions.

Marketing Strategies & Technique Covered:

1. Strategic Withdrawal Strategy
2. Flanker Strategy
3. Position Defense Strategy
4. Chain Ratio Method

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