Phi Beta Lambda
Analyzed relation between iris type and petal length using Python and
Panda
Used NumPy and Panda to do linear regression and estimate the price of a
house based on different attributes. In this graph, it is comparing price to the % lower status of
the population
Used NumPy, Panda and Seaborn to create multiple kinds of graphs that
classify phone prices. Here, we have a jointplot
Used NumPy, Panda and Seaborn to create multiple kinds of graphs that
classify phone prices. Here, we have a distribution plot
Used NumPy, Panda and Seaborn to create multiple kinds of graphs that
classify phone prices. Here, we have parallel categories
Used NumPy, Panda and Seaborn to create multiple kinds of graphs that
classify phone prices. Here, we have a 3D scatterplot
Used NetworkX to create networks based on cost, capacities, and demands
Project information
- Role: Tech Fall Analyst
- Dates: Fall 2019
- Skillset: Python, Pandas, Selenium
Berkeley Phi Beta Lambda (PBL) is a chapter of Future Business Leaders of America-Phi Beta Lambda, the largest student business organization in the nation. PBL helps members build the necessary technical and interpersonal skillset to succeed in wide range of business pursuits and intersections.
As a fall analyst, my role included:
- Using Python and Panda to analyze Facebook data and draw correlations between usage and user
- creating a machine learning algorithm that predicts real estate prices
- creating an algorithm that would generate an optimal schedule based on the preferences used by 65+ members
- using Selenium and web scraping to automate the process of booking library study rooms