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This project utilized Decision Tree Analysis on Kaggle’s Titanic Survival Dataset to predict passenger survival probabilities. Using features such as gender, age, and ticket class, the model achieved a training accuracy of 83.52% and a validation accuracy of 76.09% with a minimum split of 20. The analysis highlighted significant survival patterns, specifically identifying that women aged 56.5 and younger had the highest survival probability (100%), while males aged 11.5 to 22 had the lowest (0%).”

For this project, I analyzed an employee dataset to predict attrition using decision tree models. The dataset included various features such as salary, workload, years of experience, commmute time, and overtime. With this data the decision tree had two outcome variables. Whether an employee would continue to work at the company, or would leave the company. I was responsible for exploring the data, selectiong decision tree parameters, and interpreting the model results for my audience. During the analysis, I identified key foactors that influence employee attrition and developed solutions to improve employee retention. This project enhanced my skills in data prprocessing, model tuning, and intepreting data in busincess context that is useful in everyday problems.

The project, which was sponsored by Professor Shuhua Wu, provided me with experience using Decision Tree analysis on a dataset. The main challenge was to use a Jupyter Notebook to create a classification model that predicted an outcome variable. I needed to choose an appropriate dataset, explain the features, and then modify the algorithm by determining the best value for the minimum split. This step was essential for avoiding overfitting. I learned how to thoroughly interpret the final tree structure, particularly by analyzing the nodes with the highest and lowest probability, in order to acquire actionable insights into the feature rules that drive the prediction. Successfully identifying sample data points tested my comprehension of how Decision Trees use data to provide specific business rules.

The project, which was sponsored by Professor Shuhua Wu, provided me with experience using Decision Tree analysis on a dataset. The main challenge was to use a Jupyter Notebook to create a classification model that predicted an outcome variable. I needed to choose an appropriate dataset, explain the features, and then modify the algorithm by determining the best value for the minimum split. This step was essential for avoiding overfitting. I learned how to thoroughly interpret the final tree structure, particularly by analyzing the nodes with the highest and lowest probability, in order to acquire actionable insights into the feature rules that drive the prediction. Successfully identifying sample data points tested my comprehension of how Decision Trees use data to provide specific business rules.

For my Web Application Development Project at Temple University, sponsored by Professor Laurel Miller, I developed two special purpose web calculators (Loan repayment, Water intake) using HTML, CSS, JavaScript, jQuery, and Bootstrap. The project focused on applying front end development concepts to build functional, user friendly tools that handle both valid and invalid user input without using browser alerts or prompts. I was responsible for designing the layout, implementing the calculation logic, and testing the applications in a live server environment. Through this project, I strengthened my understanding of JavaScript event handling, DOM manipulation, and building web applications based on real academic requirements.

This project was focused on building a Gas-Distance-Cost calculator that allows a user to estimate the trip cost based on distance, fuel economy in MPG, and gas price. The goal was to create a functional and simple tool to calculate real-world travel scenarios. The result was a working calculator that outputs estimated gas cost. In this project, I learned how to effectively debug code, identify logic and syntax errors, and get hands-on practice with troubleshooting during development.

URL: https://misdemo.temple.edu/tuo03246j/project2/

This project’s goal was to use Python and the pandas library to analyze a CSV dataset and to build a decision tree model for predicting a patient’s likelihood of having diabetes based on various inputs. I explored the data and applied a decision tree model to identify patterns within the data. The results showed that the model could make predictions based on the data, such as number of pregnancies, glucose levels, insulin levels, BMI, age, etc. Through this project, I have learned how to process real-world data, apply decision tree models, and interpret outputs.

Using AI assistance, I created two simple special-purpose calculators with html code. The first is a basic loan repayment calculator, and the second is a monthly commute calculator. Through this project, I learned to effectively combine AI tools along with my existing coding knowledge to generate functional web applications. This experience will be useful in the future for developing more complex web tools that can support real business needs. The final results can be seen below:

Loan Repayment Calculator: https://misdemo.temple.edu/tup02990n/project/

Monthly Commute Calculator: https://misdemo.temple.edu/tup02990n/project2/

For this project, I used an AI-assisted workflow to develop two interactive web applications. The first was a Loan Repayment Calculator, where I used AI to generate an initial structure. The tool allows users to enter a loan amount, term length, and APR, and it calculates the monthly payment, total amount paid, and total interest. For my original idea, I created a Restaurant Tip & Split Bill Calculator that lets users input a bill total, select a tip percentage, and specify how many people are splitting the check. The calculator returns the tip amount in dollars, the total bill with tip, and each person’s share. This project strengthened my front-end development skills and showed me how to use AI effectively as part of the coding process.

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