We will use the heirarchical clustering to find the most distinctive clusters of existing vehicles, which will help manufacturers make decisions about the supply of new models.
Feb 19, 2020
We will be looking at Agglomerative Hierarchical Clustering.
Feb 15, 2020
We will create a model for a telecommunications company using Logistic Regrssion to predict when its customers will leave for a competitor, so that they can take some action to retain the customers.
Feb 6, 2020
We will be looking at the crime data in the city of San Francisco.
Jan 30, 2020
We will explore neighborhoods in New York City and group them into clusters.
Jan 26, 2020
We will use k-means clustering for customer segmentation.
Jan 19, 2020
We will get an introduction to k-means clustering
Dec 26, 2019
We will use the Decision Tree classification algorithm to build a model from the historical data of patients, and their response to different medications. Then we'll use the trained decision tree to predict the class of an unknown patient or find a proper drug for a new patient.
Dec 10, 2019
We will build a classifier to predict the class of new or unknown customers for a telecommunications provider. We will use a specific type of classification called K-Nearest Neighbors.
Dec 2, 2019
We will use scikit-learn to implement different types of linear regression on our dataset. Then, we will split our data into training and testing sets, create a model using the training set, evaluate the model using a test set, and finally use the model to predict an unknown value.
Nov 5, 2019
