Bryce Bowles's Projects
A couple is deciding where to rent at an Airbnb in New York. Our team helped evaluate factors we thought would help them choose the best location using a Tableau dashboard story.
Performed segmentation analysis and predictive modeling on insurance broker performance to conclude a random forest model (highest AUC of 73%) predicted whether 2020 Gross Written Premium will increase or decrease from 2019 with a misclassification rate of 35%. Four classification models (classification trees, logistic regression, random forests, and support vector machines) were built, evaluated, and then tuned for prescriptive measures to analyze broker performance. Explored, visualized, and described five groups of brokers using principal component analysis.
ARIMA Model created with r.
A guide to writing an Awesome README. Read the full article in Towards Data Science.
The most basic free website that a total beginner can create!
README
Terms, classification models, test and training dataset splits, logistic regression models, classification tree models, ROC curves, AUC, confusion matrix, support vector machines, variance, bias, leakage, MAE and RMSE, R squared, LASSO approach (penalty on the coefficients) etc.
Used Excel Goal seek to negotiate a car purchased with variables such as Price, APR, Years, Payment/month.
US Census Bureau data K-Means cluster analysis and Logistic Regression conducted using KNIME and Tableau
Descriptive statistics on the race and ethnicity of children in foster care analyzing statistics on variables such as Child Maltreatment, Children Waiting for Adoption, children adopted etc.
SF Brigade's Data Science Working Group.
Probabilities, Decision Trees and Influence Diagram scenarios
Diet Problem and Manufacturing Problem: Decided how much of each of each dessert to consume per day so that taste index is maximized, and calories and grams of fat are minimized, subject to constraints (Algebraic Formulation).
Differencing using r.
In depth analyses on each: Industry Analysis, Environmental Analysis, Strategic Review, and Growth Through Acquisition.
Comprehensive review with questions and answers on all topics learned including a variety of forecasting methods and examples. Case scenarios to answer questions on topics such as confidence intervals, forecast adjustments, classical decomposition, exponential smoothing, Croston’s method, holt’s Exponential Smoothing, MSE, and , seasonal adjusted series, Damping Coefficient, difference Autocorrelation, MAPE, take-off points etc.
Forecasting described from the perspective of using R and R studio software.
Proposed optimization and simulation framework to benefit helpdesk request distribution and simulate future request volume.
K-Means cluster analysis conducted using KNIME and Tableau
KJ Manufacturing Company case scenario: Discussed the forecasting process at KJ Manufacturing, any relevant factors about the company and industry that are pertinent to the new forecast and Ken’s forecast. Forecasted monthly revenues for KJ Manufacturing for the coming year. Used a variety of methods and graphically displayed them. Explained and supported the new forecasting approach as well as the choice of models and the rational for parameters selected. Prepared a report to owner explaining and supporting the forecast.
Built a logistic regression model and a classification tree model for predicting the final status of a loan based on various variables available. Confusion matrix and misclassification rate for each model for a test dataset. Variables that appear to be important for predicting outcome. Plotted and described the ROC curves and AUC for the four models.
We are a group of investors, looking for the target group of people to give out a personal loan with expectations that it will be fully paid off. Used KNIME logistic regression and MS Excel data table to conclude our target group and focus factors.
Performed a Kmeans cluster analysis to identify 7 groups or clusters of the borrowers by income, loan amount, employment length, home ownership status, and debt-to-income ratio. Included Data Preprocessing and Removing Outliers.
Lowe’s industry analysis for the market space, brand positioning, environmental assessment, and strategic opportunities/dilemmas.
MDA Course Info
Mobile Munchies is deciding how much of each type of juice to prepare for the week. Given the ingredients and cost, a python model using Pyomo and GLPK determined the optimal amount of each type of lemonade to produce so the profits maximized subject to the constraints.
Music sales displayed in a Tableau dashboard with a variety of graphs
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent