📄️ Analyze Customer Churn with Machine Learning
Being able to predict the future can give your business the edge it needs or make it easier to make decisions. In DataChat, you can quickly and easily create machine learning models with the Analyze skill. In this example, we'll investigate some customer data to find why customers churn and create a model that can help us predict whether a customer is likely to churn in the future.
📄️ Clean and Illustrate Time Data
You often need to manipulate your data a bit before you can create meaningful charts or models. In this example, we'll use some data from a bike sharing company to illustrate a bit of data wrangling goes a long way toward discovering deeper insights from your data.
📄️ Create, Explore, and Predict Moving Averages with Time Data
In this example, we'll explore trends in COVID cases over the summer and fall of 2021 to learn how to create moving averages and predict how the average might change in the future.
📄️ Define and Use Complex Expressions
The Define skill is a useful tool to define complex expressions. In this example, we'll explore how to use defined expressions to simplify using other skills, such as Keep and Visualize.
📄️ Explore Data with the Chart Builder
The Chart Builder is a great way to start exploring your data and identifying trends. In this example, we'll explore the data about the passengers aboard the Titanic.
📄️ Handle Different Types of Outliers
Outliers can represent measurement errors, entry errors, poor sampling, and more. They can have a large impact on your analysis and can skew your findings. In DataChat, there are several ways to find and handle various types of outliers.
📄️ Summarize Large Datasets with Pivot and Reshape
Summarizing large datasets can help you to better understand, identify, and compare specific aspects of your data. In this example, we'll demonstrate how to create pivot tables and reshape large datasets to compare New York's and California's Covid-19 cases.
📄️ Use Rename, Keep, and Extend to Analyze Datasets
In this example, we'll explore two datasets, learning how to keep or drop specific columns and extend datasets together to test our hypothesis that a country's overall happiness is affected more by alcohol consumption than by GPD per capita.