Updated Readme

Author: kmock930

AUC_diff_choc.png

@@ -4,6 +4,15 @@
 2. Predict whether a person is a consumer of chocolate and magic mushroom.
 3. Choose the best and worst classifiers for each dataset. 
 4. Explain AI models in a scientific manner which should be convincable to non-technical people.
+5. Implement models with Semi-Supervised Learning.
+## Preview
+Comparing a Pipeline of 6 classifiers on 2 datasets
+![alt text](AUC_diff_choc.png)
+![alt text](AUC_diff_mush.png)
+Explainable AI
+![alt text](XAI.png)
+Semi-Supervised Learning
+![alt text](semi-supervised-learning-comparison.png)
 # Dataset: Drug Consumption Analysis Dataset
 The dataset can be found at this link: https://archive.ics.uci.edu/dataset/373/drug+consumption+quantified.
 ## Description of the Dataset
@@ -16,6 +25,7 @@ The dataset can be found at this link: https://archive.ics.uci.edu/dataset/373/d
 - Provide results from Evaluation with some useful plots and metrics.
 - Summarize the analysis in a report.
 - Explain whether certain classifiers make trustable predictions, with the calculation of SHAP values and some visualization plots.
+- Prepared labelled and unlabelled data, Implemented and Compared different semi-supervised learning algorithms based on the gradient boosting classifier from assignment 1.
 # Project Structure
 - You should expect a report in `.pdf` format at the root level.
 - Please expand the folder at the root level to view codes.
@@ -24,6 +34,11 @@ The dataset can be found at this link: https://archive.ics.uci.edu/dataset/373/d
   2. Evaluation - please check the file `CSI5155 Assignment 1 Evaluation Part - Kelvin Mock 300453668.ipynb`
   3. Calculation of SHAP Values - please check the file `CSI5155 Assignment 2 - Kelvin Mock 300453668.ipynb`
   4. Visualizing the SHAP Values - please check the file `CSI5155 Assignment 2 Plots - Kelvin Mock 300453668.ipynb`
+  5. Baseline Model (Gradient Boosting classifier) - `CSI5155 Project - baseline.ipynb`
+  6. Self Training method applied on baseline model - `CSI5155 Project - Self Training.ipynb`
+  7. Co-Training method applied on baseline model - `CSI5155 Project - Co Training.ipynb`
+  8. Semi-Boost method applied on baseline model - `CSI5155 Project - Semi Boost.ipynb`
+  9. Label Spreading method applied on baseline model - `CSI5155 Project - Label Spreading.ipynb`
 - Models are data dumped into several `.pkl` files from time-to-time in different phases to maintain the code's maintainability.
 - The training sets and test sets are also data dumped into several `.pkl` files.
 - `choc` directory shows data dumped files related to the Chocolate dataset (which is split from the original dataset).