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MIS2502-Extra_Points_Assignment

This assignment was to find a data set and insert it into a decision tree to see the true or false values to help understand what made machines fail. This was impactful for my learning because it allowed me to find a data set from a field I am interested in and figure out how to create a viewable chart that would allow me to show employers what to look for in machines to prevent them from failing in the workplace.

Within this project, I created two web-based special purpose calculators using Google Gemini A.I. assistance. The first calculator is a loan repayment calculator that takes user input for a loan amount, loan term, and an APR. This application then calculates the monthly payment, total amount paid, and total interest paid. The second special purpose calculator focuses on water intake and will retrieve a user’s weight and calculate how much water the user should have daily.

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.