Aspiring Data Scientist
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Breast-cancer-dataset-classification-
August 30, 2018 – December 21, 2019
The objective of the project was to build various models and compare their prediction performance based on accuracy.
View ProjectCustomer-Analytics
July 25, 2018 – August 23, 2020
The project concerns an international e-commerce company* based in the USA who want to discover key insights from their customer database. They want to use some of the most advanced machine learning techniques to study their customers.
View ProjectGlobal-Trade-Analytics
March 10, 2018 – March 11, 2018
The objective of the project was to create innovative and interactive Tableau dashboards that focus on potential commodities, countries, year, trade amount and quantity. The client wanted to launch a new business unit, focusing on global trade and logistics, majorly in the countries such as USA, Canada and Australia The dataset provided by the client contained 59090 observations of 10 variables. The client insisted the data to be cleaned using Excel or R. The Dataset contained missing values and was cleaned using the R programming language. Tableau dashboards were created from the cleaned dataset.
View Projectcapstoneproject-realestate
February 26, 2018 – February 26, 2018
Data: Boston Housing Dataset (HousingData.csv) Programming language(s): R Tool(s): RStudio Business problem: To understand the drivers behind the value of houses in Boston and provide data-driven recommendation to the client on how they can increase the value of housing.The Boston housing dataset consisted of 506 observations and 14 variables. Project challenge(s): MEDV (Median value of homes in Boston) was identified as the dependent variable. While the rest, were the independent variables. The goal was to find out which among the independent variables were statistically significant in driving the house prices (MEDV). The dataset consisted of missing values and outliers. Some of the variables had a skewed distribution. There was multicollinearity among few independent variables. Our Approach: Prior to model building, we tidied up our dataset by eliminating the rows that contained missing values. Replacing the missing values with median and mean of those variables were also done. Consi
View ProjectCultural Fit Analysis
The candidate's projects demonstrate an interest in applying data science techniques to diverse business problems (global trade, customer analytics, real estate). The focus on R and Jupyter Notebook aligns with common tools in data science roles. The personal nature of all projects suggests self-driven learning and initiative, which can be a positive cultural fit indicator. However, the lack of team-based projects or professional experience limits the assessment of collaboration and broader cultural alignment.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. The psychometric test score is 0, indicating no assessment was completed or results are unavailable.