Aneriben Savani

Studying at California State University, Ful
Living in United States
Website https://savanianeri.medium.com/

About

Master of Science in Information Systems student with proven abilities in data visualization, data cleaning and preparation, business data transformation, data exploration and analysis, and predictive modeling, currently seeking an opportunity that would enhance my organization’s operational and decision-making capabilities in the future.

Experience
Matrix (DatabaseAdministrator Intern) June 2015 - September 2015
• Analyzed business data using diverse data mining techniques, power query, lookup, and reference functions in Excel.
• Generated reports for the management team using MS Access and SQL, and performed database troubleshooting.
• Solved database anomalies and performed system issue resolution during ETL.
Projects
Exploratory Data Analysis of New York City Airbnb in Python
• Handled 20% of numeric continuous missing values, removed outliers using IQR from the Airbnb NYC dataset 2019.
• Explored relationship and distribution of variables using univariate/bivariate analysis, histograms, and scatterplots.
• Key finding of top hosts, neighborhood, traffic, room types, availability of hosts, reviews is helpful to define the price.
https://www.kaggle.com/anerisavani/eda-of-new-york-city-airbnb

E-commerce Data Analysis
•Analyzed traffic source and found marketing channels that are driving the highest quality traffic and their conversion rates, which is crucial in bid optimization using MYSQL workbench for e-commerce database named Maven Fuzzy Factory. Also, found that two paid campaigns were not performing well and device type (desktop) has positive impact on volume of sessions.
•Tested conversion funnels to understand each step of users’ experience and found that very few customers have made through thank-you page for specific time period for Mr_fuzzy product.
•Maximized the effectiveness of marketing budget by optimizing spend across multi-channel portfolio and concluded that organic/direct traffic is improving through campaigns.
•Anticipated future trends through seasonality, the effect of a new product launch on overall revenue, cross-sell analysis, product refund analysis, purchasing behavior of users, and repeat visits.
https://medium.com/@savanianeri/e-commerce-data-analysis-in-sql-5c68305a8937

Visualization of COVID-19 pandemic
•Analyzed timeline scenario of confirmed cases, deaths, recovered cases, and active cases worldwide from January 22 to July 8 2020 in Jupyter notebook and found tremendous 99.9% increase in the number of cases, months with highest recovery etc.
•Found countries and their provinces with highest cases, deaths and recovered cases from the dataset provided by Johns Hopkins University.
•Discovered recovery rate and death rate worldwide and country wise individually.
https://www.kaggle.com/anerisavani/visualization-of-covid-19-pandemic