Updated Training Notebook and Readme

README.md

-# Diamond Price Prediction Project
-| Name          | Student ID |
-|---------------|------------|
-| Mutote, Michael | 22202956   |
-| Gattousi, Fadi  | 22211572   |
 
+Mutote, Michael, 22202956  
+Gattousi, Fadi,  22211572
+
+# Diamond Price Prediction Project
 Recommendation Systems Project 2
 https://mygit.th-deg.de/mm13956/ws-23-sas-02
 
 -------------------
+## Task Distribution
+| Task                         | Assigned to |
+|-------------------------------|----------|
+| GUI                       | Michael Mutote |
+| Plot Functionality             | Michael Mutote |           
+| Data Preprocessing, Analysis and Model Training | Fadi Gattousi |
+|General Python Programming | Both |
+| Documentation and Readme.md | Fadi Gattousi |
 
 ## Project Overview
 This project aims to predict diamond prices using four different regression models available in scikit-learn. It includes a Python script (`Main.py`) with a graphical user interface (GUI) for visualizing and interacting with the data and predictions. The dataset (`diamonds.csv`) consists of various attributes of diamonds. A Jupyter notebook (`training.ipynb`) is provided for data preprocessing, model training, and evaluation.
@@ -35,6 +42,13 @@ The following models are available for prediction:
 - **Multi-layer Perceptron Regressor**: This model is a feedforward neural network with multiple hidden layers. We used the `MLPRegressor` class from scikit-learn to train this model. The model achieved an R2 score of **0.91** and a mean squared error of **31,366,191.38** and takes the longest time to train.
 - **Random Forest Regressor**: This model is an ensemble of decision trees. We used the `RandomForestRegressor` class from scikit-learn to train this model. The model achieved an R2 score of **0.98** and a mean squared error of **292,726.76** <u> **Making it the best model for this dataset.**</u>
 
+| Model                         | R2 Score | Mean Squared Error |
+|-------------------------------|----------|--------------------|
+| Linear Regression             | 0.88     | 1,896,296.20      |
+| XGBoost Regressor             | 0.98     | 303,969.39        |
+| Multi-layer Perceptron Regressor | 0.91     | 31,366,191.38     |
+| Random Forest Regressor       | 0.98     | 292,726.76        |
+
 
 ## Project Structure
 - `diamonds.csv`: Dataset containing diamond attributes.

Main.py → main.py

@@ -313,4 +313,4 @@ app = QApplication(sys.argv)
 
 window = MyTabs()
 window.show()
-app.exec()
+app.exec()
\ No newline at end of file