[Advanced] Solve a machine learning problem using a UCI data set.

TensorFlow

The University of California at Irvine (UCI) has released a large number of open-source data sets that are particularly useful for machine learning projects. For this task, you will be expected to formulate a machine learning problem based on one of these data sets, and use TensorFlow to solve it.

Feel free to use either high-level TF APIs (tf.keras, tf.estimator) or low-level operations (tf.math, etc.) to complete this assignment. Traditional machine learning methods, like logistic regression or linear regression, are also accepted -- not just neural nets.

It is promoted to learn through tutorials but please do not present others' code as your own. Go through multiple tutorials, study how they go through the problem and present your own code with your way. Do not plagiarise.

To receive credit for this assignment, submit a link to a functioning, self-contained Colab notebook for review. You will need to include your Python code, relevant graphs/plots, as well as an explanation of your logic in markdown format.

Task tags

python
tensorflow
documentation
tutorial
machine learning

Students who completed this task

Sahithi, Irina, jimmy_page, rpal, Joseph, Tom, Rachin, arpand, jedlimlx, William6495, Abu Syed, malder8, ShridharS, As1234, msteknoadam, Hitsuji, Devaa, Qwerty71, MaanavS, anigasan, Radhika Pal, Rick Wierenga, An Onimous Boy, TheTrailblazer, Ryan, boron, prudentWish, abhaykashyap03, WZHANG, surya_nayar, abhisood, Rishit Dagli, Sambhav Gupta, JuanC, Ja-sniff/Javismb, RichieX, Cking100, chuanhao01, Dennis Yang

Task type

Code
Documentation / Training