Add Table of Contents and Instructions

Author: Elijas

README.md

@@ -11,5 +11,12 @@ Both algorithms have achieved very similar cross-validation scores, so we can co
 #### 4. How would you compare selected classification methods if the dataset was imbalanced?
 If the frequency of label samples in the dataset were imbalaced, then I would have to use a performance metric that is capable of handling such situation. A basic accepted approach is to take [Precision and Recall](https://en.wikipedia.org/wiki/Precision_and_recall) metrics (two ratios of True Positive predictions for each label). If it were to be appropriate to give equal importance to the two, then they would be combined into a one score by using a harmonic mean (i.e. the [F1-score](https://en.wikipedia.org/wiki/F1_score)). This would constitute a proper handling of an imbalanced dataset.
 
+# Project Structure and Instructions
+Runnables are available in these folders:
+- `notebook` - Detailed exploration steps and performance evaluation.
+- `src/modeling` - Run the public scripts in this folder to train the models.
+
+Install the required dependencies by running `pip install -r requirements.txt` in the shell.
+
 # Dataset
 [sentence polarity dataset v1.0](https://www.cs.cornell.edu/people/pabo/movie-review-data/) (includes sentence polarity dataset README v1.0): 5331 positive and 5331 negative processed sentences / snippets. Introduced in Pang/Lee ACL 2005. Released July 2005.