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Lift off is a reddit-based stock trend analysis platform using sentiment analysis machine learning models.
We use an iterative agglomerative clustering algorithm to group similar agencies and match to potential vendors.
Using Facebook's prophet to carry out time series forecasting and predict future hotdog sales for 4 stores.
Recommending Vendors to Agencies based on MCC Descriptions and vendor state proximity
We use TF-IDF to find similarities in government agencies' spendings to provide them alternate vendors to consider.
A study on the relationship between poverty level and recreational land use in Houston.
Understanding business networks via financial transactions
Glizzy predicting machine!
Cessession uses machine learning to find correlations between smoke shops and poverty to propose actionable policy decisions to better our community.
Modeled data using Prophet in R to forecast hotdog demand.
Hot Dog Prediction: A model with time series and random forest.
Agencies in DC contract with thousands of vendors each year. With so many options, how should these agencies optimally choose which vendors to work with? The answer lies in collaborative filtering.
In this project, we applied several statistical machine learning algorithms to forecast the agency spending and vendor receivables.
Using Various ML Classifiers to predict Chevron Hotdog Sales
Figuring out what are the factors that positively and negatively impact college matriculation for Texas high school graduates.
We tried visualising some pokemon data from kaggle
Prevent hot-dogs to be thrown away by forecasting the sales in order to have a robust cook plan for every day.
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