Search Results for: Python

I imported a CSV file containing data from a bank and used Python in Jupiter Notebook to train a model that predicts customer outcomes. I produced a decision tree that evaluated the likelihood that a customer would leave the bank based on numerous demographics and metrics.

This project involved analyzing the 2025 IPL Auction dataset using Decision Tree modeling to predict whether a player is “Capped” or “Uncapped” based on their base price and winning bid. The analysis was conducted as part of an academic assignment for MIS2502, focused on applying data analysis techniques to a new dataset.

My role included data preprocessing, building the decision tree model, and extracting meaningful insights. I consolidated multiple team-specific datasets into a unified format, selected relevant features, and implemented the model using Python and Jupyter Notebook. Key deliverables included a detailed Word document summarizing the findings, a visual representation of the decision tree, predictions for test examples, and the Jupyter Notebook used for the analysis.

Through this activity, I developed a deeper understanding of Decision Tree algorithms, feature selection, and the trade-offs involved in tuning model parameters like minimum split. This hands-on experience reinforced my ability to handle real-world datasets, draw insights from data, and effectively communicate results through structured documentation and visualizations.

For this project, I chose a dataset from OpenDataPhilly focusing on the food landscape in Philadelphia neighborhoods. The dataset ‘Neighborhood Food Retail’ examines the availability of stores that offer healthy food options such as supermarkets and farmers’ markets, versus those offering fewer healthy options, like dollar stores and convenience stores. The outcome variable, “ACCESS,” aims to predict whether an area has access to healthy food options. Initially, the “ACCESS” column held three categories: being “Low Access,” “High or Moderate Access,” or “No Access” to healthy food options. To make it compatible with the decision tree model, I changed cells holding “Low” or “No access” to 0 and “High” or “Moderate” to 1. Utilizing Python and the CSV file, I was able to create a decision tree to determine the traits that determine the likelihood of a neighborhood having low quality options. Analyzing this dataset provides valuable insights into the food landscape and access to healthy food options in Philadelphia neighborhoods

 

The data suggests that supermarkets may not see value in opening locations in these neighborhoods, possibly due to the residents’ limited ability to afford fresh produce and higher quality products. This information can be useful for charities, funding organizations, and initiatives like the Cherry Pantry, as it highlights the specific areas that need help.

NeighborhoodFoodCSV

Decision Tree Results and Code

For my class project in MIS 2502 taught by Professor Bauman, I chose to make a decision tree in Python that predicts if a patient has diabetes based on certain health factors. I learned about cleaning data and preparing it for use in a decision tree, as well as how minimum-split and maximum-depth affect how a decision tree is formed.

 

Artificial Intelligence

Artificial intelligence, also known as AI, is said to be the future of technology. It is defined as a computer or a robot that is able to process and carry out the same functions as intelligent beings aka humans (Encyclopædia Britannica). There have been many mentions of AIs in fiction and a lot of risks that are brought up, but what exactly can it do with it?

For a long time, the topic of robots being able to “think” on their own has been a part of many fictional stories. When asked about artificial intelligence, most people would likely imagine a robot that is in the form of a human. However, this is not true, it is all around us. Since computers were first invented and more and more information could be stored in them, artificial intelligence was constantly improving. It started off small, but now we can see the growth. There were many generations of computers, soon we will have one that will be more powerful than we could ever know. Even now there are AIs that it is almost impossible to disguise from a human. We have gotten so far that an article from Forbes called How AI Will Impact the Future of Work and Life? by Ashley Stanl explains how when we search something up in a search engine, an AI is sorting through all the possible results to find sources connected to what you are asking. Other than that, Stanl speaks about Facebook’s infamous system. We are surrounded by it, in our personal lives and career. It has become such an important tool, but even so, it has not reached the level of having a mind of its own just yet. They are only able to do what they are programmed to do.

The topics covered in MIS2502 use AI technology. MySQL, MongoDB, Python, and basically all topics use AIs to process data and get the information needed. By entering a query into the database, an AI returns the results that correspond with the query.

 

In this project, our task was to scrape a website with notable celebrity deaths and create insights from it. To do this project, we utilized Python to scrape the Wikipedia Page, and Excel to clean and chart our data. Our project URL is http://project.mis.temple.edu/cleansingbigdata/ . I learned a lot from this project. The main thing I learned is Python. Going into the project, I had never worked with Python before, but this project gave me a great introduction to it and gave me the chance to establish a foundation in python if I ever use it in the future. I also learned more advanced excel formulas and functions. While I had strong Excel skills going into the project, I solidified and strengthened those skills through my work. Overall, this was an excellent project and a great way to wrap up my MIS coursework.

Our task was to transform raw data on thousands notable deaths scraped from Wikipedia into a
high-quality dataset that can be used for statistical analyses.

http://project.mis.temple.edu/cbd4596/

I learned hard skills like coding in Python, and leveraging advanced features in Excel and Google Spreadsheets. I also learned how to evaluate and compare proposed projects based on requirements and technology, as well as how IT organizations are structured. Finally, I learned soft skills like how to communicate efficiently with other teammates, manage deadlines, and mediate and resolve conflicts.