Update readme

README.md

@@ -31,6 +31,21 @@ directly on a D-Wave quantum computer's quantum processing unit (QPU).
 
 ---
 
+## Installation
+
+You can run this example without installation in cloud-based IDEs that support 
+the [Development Containers specification](https://containers.dev/supporting)
+(aka "devcontainers").
+
+For development environments that do not support ``devcontainers``, install 
+requirements:
+
+    pip install -r requirements.txt
+
+If you are cloning the repo to your local system, working in a 
+[virtual environment](https://docs.python.org/3/library/venv.html) is 
+recommended.
+
 ## Usage
 
 Run `python app.py` and open http://127.0.0.1:8050/ in your browser.  A
@@ -47,6 +62,63 @@ features and redundancy penalty).  Solutions typically take 1-3 seconds.  Once
 complete, the bar chart will update to reflect the selected features, and the
 bar graph for accuracy scores will also be updated.
 
+## Problem Description
+
+The goal for this feature selection application is to choose features that will help 
+the machine-learning model learn by promoting diversity between features and strong
+relationships to the target variable. The model sets the following objectives and constraints 
+to achieve this goal:
+
+**Objectives:**  minimize the redundancy metric (correlation between features) between each 
+pair of features to promote diversity and maximize correlation between features and the target
+to promote a strong relationship. 
+
+**Constraints:** choose the requested number of features.
+
+## Model Overview
+
+In this example we use the Titanic and Scene datasets to generate a constrained quadratic model. 
+The datasets are assumed to be clean, meaning there are no missing entries or repeated features. 
+The features of the dataset are used to build a correlation matrix which compares the features to 
+each other as well as a correlation matrix that compares the features to the target variable. Those 
+correlation matrices are used to build the objective function. 
+
+---
+**Note:** Although a model overview is provided here, all of the code to build
+the feature selection model is contained within 
+[D-Wave's scikit-learn plug-in](https://github.com/dwavesystems/dwave-scikit-learn-plugin).
+
+---
+
+### Parameters
+
+These are the parameters of the problem:
+
+- `num_features`: the number of features to select 
+- `redund_val`: used to determine factor applied to redundancy terms
+  - 0: features will be selected as to minimize the redundancy without any consideration to quality
+  - 1: places the maximum weight on the quality of the features
+
+### Variables
+- `x_i`: binary variable that shows if feature `i` is selected
+
+
+### Objective
+The objective function has two terms. The first term minimizes the correlation between 
+chosen features in the dataset (this term is weighted by the redundancy parameter, `redund_val`). The 
+second term maximizes the correlation between the features and the target variable. 
+### Constraints
+A single constraint is used to require that the model select a number of features equal to the `num_features`
+parameter.
+## Code Overview
+
+Given a selected value for the `num_features` and `redund_val` sliders, the code proceeds as follows:
+
+* The selected dataset and parameters are passed to D-Wave's feature selection scikit-learn plugin
+* The resulting selected features are returned from the plugin
+* A random forest classifier model is trained on the selected features and accuracy score is calculated
+* The display image is updated to reflect the selected features and the classifier accuracy 
+
 ## References
 
 Milne, Andrew, Maxwell Rounds, and Phil Goddard. 2017. "Optimal Feature