Based on our EDA in the previous post, we found that there is a two-month lag between temperature increase and the increase in our case_counts […]
Machine Learning: EDA
Now that we’ve merged our datasets and cleaned the data, we now move onto getting a feel for the data tells us through Exploratory Data […]
Machine Learning: Data Cleaning
In this project, we will walk through the steps of building a predictive machine learning model. We will first begin with Data Cleaning, then move […]
Data Cleaning: Organizing Messy Data
In a recent project, the client asked Maderas Partners to run a series of calculations related to their employees’ work history. The calculations themselves were rather basic. However, the real challenge was organizing the dataset in such a way that allowed for the calculations to be run for all employees over the entire time period.
The Difference-in-Difference (DiD) analysis is a powerful econometric tool with very practical uses. The classical use of DID is to analyze the difference between two groups after some sort of treatment.
Machine Learning: K-Nearest Neighbors, Statistical EDA
Maderas Partners sought to build a model to predict whether a person would be arrested or frisked during a stop based on the other information provided by the city. To accomplish this goal, we employed a commonly used machine learning technique called K-Nearest Neighbors (KNN)
Machine Learning: K-Nearest Neighbors, Model Building
Relying on the same dataset used in the linear regression from Part 1, we built a K-Nearest Neighbors predictive model in Python.