For all the self-taught geeks out there, here is our content library with most of the learning materials we have produced throughout the years.
It makes sense to start learning by reading and watching videos about fundamentals and how things work.
Data Science and Machine Learning - 16 wks
Full-Stack Software Developer - 16w
Search from all Lessons
Curated list of small interactive and incremental exercises you can take to get better at any coding skill.
Curated section of projects to build while learning with simple instructions, videos, solutions, and more.
Guides on different topics related to the technologies that we teach in our courses
Social & live learning
The most efficient way to learn: Join a cohort with classmates just like you, live streams, impromptu coding sessions, live tutorials with real experts, and stay motivated.
You will not be forking this time, please take some time to read these instructions:
Once you have finished solving the exercises, be sure to commit your changes, push to your repository and go to 4Geeks.com to upload the repository link.
Sociodemographic and health resource data have been collected by county in the United States and we want to find out if there is any relationship between health resources and sociodemographic data.
To do this, you need to set a target variable (health-related) to conduct the analysis.
The dataset can be found in this project folder under the name
demographic_health_data.csv. You can load it into the code directly from the link (
https://raw.githubusercontent.com/4GeeksAcademy/regularized-linear-regression-project-tutorial/main/demographic_health_data.csv) or download it and add it by hand in your repository. In this dataset you will find a large number of variables, which you will find defined here.
This second step is vital to ensure that we keep the variables that are strictly necessary and eliminate those that are not relevant or do not provide information. Use the example Notebook we worked on and adapt it to this use case.
Be sure to conveniently divide the data set into
test as we have seen in previous lessons.
Start solving the problem by implementing a linear regression model and analyze the results. Then, using the same data and default attributes, build a Lasso model and compare the results with the baseline linear regression.
Analyze how evolves when the hyperparameter of the Lasso model changes (you can for example start testing from a value of 0.0 and work your way up to a value of 20). Draw these values in a line diagram.
After training the Lasso model, if the results are not satisfactory, optimize it using one of the techniques seen above.