diff --git a/readme.md b/readme.md
index b9cb0ef47c2004f12897d9e7201840b28c4bf4fc..67bc4c65bdd5cd1596e39b6653de3ddadfa662c1 100644
--- a/readme.md
+++ b/readme.md
@@ -1,11 +1,41 @@
+# Python - Trump Tweet Predictions
 
-# CS5300 a3
 
+## Description
+Recurrent Neural Networks using the Keras library.
 
-## Author
-Brandon Rodriguez
+This project reads in archives of trump tweets (specifically, the dataset was his 2017 tweets).
 
+If I recall correctly, the network would handle character by character, starting with the first one of the sentence.
+With each progressive character (hence the "recurrent" part), it would read all predicted characters of the tweet so
+far, up until the current character. It would then use this data to predict the next character.
 
-## Description
-Neural Networks using Keras (High level Tensorflow library).
+All tweets would have a "starting delimiter" character as the first character by default. And then either end at once
+the network output an "ending delimiter" character, or if it hit the maximum possible length of tweets.
+
+### Notes on Future Iterations
+While it's been years, so I would need to do research again to examine current trends, I remember specifically wanting
+to try a new iteration of this network, back when I took this class.
+
+Essentially, I wanted to try teaching the network to output an entire word at a time, instead of a single character at a
+time.
+
+Obviously, this would be more complicated and likely take longer to train, but my thought was that it would result in
+more coherent sentence structure, and better understanding of grammar.
+
+
+## Keras
+Keras is a high-level library built ontop of TensorFlow. The intention is to make it even easier and more user-friendly
+than ever to build a Neural Network.
+
+
+## Why Twitter and Trump?
+There were two main reasons for the class-wide choice:
+1) Twitter was chosen due to generally limiting tweet size, which inherently limits complexity of the network.
+2) Trump was specifically chosen due to being a very active account that happens to also be fairly well known and
+archived.
+
+Thus, working with data from this account was simpler, and very little formatting or normalization was needed. Twitter +
+known archives took care of most of that for us.
 
+Of course, at the time, we had no idea that Trump would raise to the level of controversy and infamy that he now has.