diff --git a/README.md b/README.md
index 50e73109a34f188df4fbc85796c181622413d341..ae001fafffeb819bd2bc61c2464fd9eec3aa4e58 100644
--- a/README.md
+++ b/README.md
@@ -1 +1,97 @@
-womens-diabetics-prediction-system
\ No newline at end of file
+
+**Members:**
+
+Member 1: Haque, Syed Wassi Ul 22108223
+<br/><br/>
+Member 2: Mahir, Wasik , 00817406
+
+<br/><br/>
+
+
+**Title:** Women Diabetes Prediction System
+
+<br/><br/>
+
+**MyGit Repository Link: https://mygit.th-deg.de/sh13223/womens-diabetics-prediction-system**
+
+**MyGit Wiki Repository Link:__**
+
+<br/><br/>
+
+
+**Project description:**
+
+A machine learning model designed to predict diabetes in female patients.
+This project uses SVM(Support Vector Machine) Algorithm to train on a given dataset.
+Interactive GUI is developed using PyQt6 and css.
+
+
+**Prerequisites:**
+
+1. Pycharm or Vscode or any types of IDE.
+
+2. Creating a virtual environment.
+
+3. Libraries to be installed in virtual environment:
+ - PyQt6 = 6.6.1
+ - Pyqtgraph = 0.13.3
+ - Python = 3.11.7
+ - Numpy = 1.26.3
+ - Matplotlib = 3.8.2
+ - Pandas = 2.1.4
+ - Scikit-learn = 1.3.2
+
+4. Diabetes Dataset(Source: Kaggle):
+Link to download the data: (https://www.kaggle.com/code/sandragracenelson/diabetes-prediction/input)
+
+**Installation:**
+
+ - Create and run a virtual environment
+
+ - Clone the git repository with the dataset file.
+
+ - Install necessary packages from requirement.txt file.
+
+ - Run main_diabetics.py.
+
+**Basic Usage:**
+
+1. Run the main_diabetics.py in any IDE.
+2. Upon execution, a window will prompt the user to input their Glucose level, Blood Pressure, Age, Insulin, BMI, and Pregnancy status using sliding buttons. For Pregnancy status, use a tick button to indicate if applicable.
+3. After inputting all details, click the "Predict" button to determine diabetic status."
+4. To exit the widget, simply press the cross button in the top right corner of the widget.
+
+
+
+**Implementation of the Requests:**
+1. A Desktop App with PyQT6 has been developed.
+2. A requirements.txt file is added with the necessary python libraries.
+3. A README.md file is created with the structure description.
+4. Virtual environment (venv) is created and used.
+5. A free data source has been from Kaggle(Link has given above).
+6. Data has been imported using csv format. (File name: diabetes_clean_03042021.csv) and can be seen from the terminal after running the app.
+7. The data must be analyzed with Pandas methods, so that a user gets an overview.
+8. The following functions have been used to get the overview of the data: dataframe.info(), dataframe.describe(),dataframe.corr(), dataframe.head(), dataframe.tail().
+9. Three input widgets have been created.
+10. Model has been trained using Scikit training model algorithm.( for our case we used SVM model algorithm)
+11. Two output canvas have been for data visualization.
+12. The app is reacting interactively according to the change of input parameter with a new prediction.
+
+<br/><br/>
+
+**Contribution List:**
+
+
+1. Syed Wassi Ul Haque:
+ - Worked on Collection and Preprocessing of Dataset using Pandas.
+ - Modifying and Preparing Data with use of numpy arrays.
+ - Bug solving and creating Main window Graph to visualize the data on app using matplotlib.pyplot.
+ - Creating Histogram window Interface using PyQt6.
+ - Designing the app using css stylesheet.
+
+2. Wasik Mahir:
+ - Creating a GUI Interface using PyQt6( Main window, Plot graph window)
+ - Getting User-Inputs using different GUI elements or creating widgets.
+ - Training and working Scikit-learn SVM Model.
+ - Integrating or connecting Scikit learn model with pyqt6 in order to get predictions in the app.
+ - Designing the app using css stylesheet.
diff --git a/chart-medium.png b/chart-medium.png
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diff --git a/diabetes_clean_03042021.csv b/diabetes_clean_03042021.csv
new file mode 100644
index 0000000000000000000000000000000000000000..46fe6af0da2d8b94c287546d264b747fc3d0aaeb
--- /dev/null
+++ b/diabetes_clean_03042021.csv
@@ -0,0 +1,769 @@
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\ No newline at end of file
diff --git a/health.png b/health.png
new file mode 100644
index 0000000000000000000000000000000000000000..5eef03248e50ff5ce2228b5340925aeaae233df0
Binary files /dev/null and b/health.png differ
diff --git a/image.jpg b/image.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b286e764d17e8cf09196e8e53da01ea51d4e1e81
Binary files /dev/null and b/image.jpg differ
diff --git a/main_diabetics.py b/main_diabetics.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3dd32ea464c3bbe6854a8599c8e24f49e571c81
--- /dev/null
+++ b/main_diabetics.py
@@ -0,0 +1,451 @@
+import sys
+from PyQt6 import QtWidgets,QtGui
+from pyqtgraph import PlotWidget, plot
+import pyqtgraph as pg
+
+from PyQt6.QtWidgets import QWidget,QHBoxLayout, QApplication,QSlider, QMainWindow,QLabel, QPushButton,QHBoxLayout, QVBoxLayout, QGridLayout, QCheckBox
+from PyQt6.QtCore import Qt
+from PyQt6.QtGui import QAction, QIcon,QPalette, QColor
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+
+import matplotlib
+import matplotlib.pyplot as plt
+from matplotlib.backends.backend_qtagg import FigureCanvasQTAgg as FigureCanvas
+from matplotlib.figure import Figure
+matplotlib.use('Qt5Agg')
+
+from sklearn.preprocessing import StandardScaler
+from sklearn.model_selection import train_test_split
+from sklearn import svm
+from sklearn.metrics import accuracy_score
+
+from PyQt6 import QtGui
+
+
+diabetes = pd.read_csv('diabetes_clean_03042021.csv')
+
+
+# highest correlation ---- x=age y=blood pressure
+
+
+class MplCanvas(FigureCanvas):
+
+ def __init__(self, parent=None, width=5, height=2, dpi=100):
+ self.figure = Figure(figsize=(width, height), dpi=dpi)
+ self.axes = self.figure.add_subplot(111)
+ super(MplCanvas, self).__init__(self.figure)
+
+class secondWindow(QMainWindow):
+ def __init__(self):
+ super().__init__()
+ # use global data
+
+ global diabetes
+
+ widget_histogram = QWidget()
+
+ # Vertical Layout
+ histogram_layout = QVBoxLayout()
+
+ # Create a new Canvas
+ self.histogram_screen = MplCanvas(self, width=5, height=2, dpi=100)
+
+ diabetes.hist(ax=self.histogram_screen.axes)
+
+ histogram_layout.addWidget(self.histogram_screen)
+ self.setCentralWidget(widget_histogram)
+ widget_histogram.setLayout(histogram_layout)
+ self.setMinimumSize(1500, 1000)
+
+
+class thirdWindow(QMainWindow):
+
+ def __init__(self):
+ super().__init__()
+
+ global diabetes
+
+ self.graphWidget = pg.PlotWidget()
+ self.setCentralWidget(self.graphWidget)
+
+ self.x = diabetes["BMI"]
+ self.y = diabetes["Outcome"]
+
+
+ self.graphWidget.setBackground('w')
+ self.graphWidget.setLabel("left", "Diabetic Or Not Diabetic")
+ self.graphWidget.setLabel("bottom", "BMI")
+
+ pen = pg.mkPen(color=(255, 0, 0))
+ self.data_line = self.graphWidget.plot(self.x, self.y, pen=pen)
+ self.setMinimumSize(800, 400)
+
+
+class MainWindow(QMainWindow):
+
+ def design(self):
+
+ layout3 = QHBoxLayout()
+ MainLayout = QHBoxLayout()
+ leftVerticalLayout = QVBoxLayout()
+ RightVerticalLayout = QVBoxLayout()
+
+
+ # setting window icon
+ self.setWindowIcon(QtGui.QIcon('health.png'))
+ #setting screen min size
+ self.setMinimumSize(1200, 800)
+
+ mainLabel = QLabel(" Women Diabetics Prediction System")
+ leftVerticalLayout.addWidget(mainLabel)
+
+
+
+ # checkbox button pregnancies
+ pregnanciesLabel = QLabel("Pregnancies", alignment=Qt.AlignmentFlag.AlignCenter)
+ pregnant_instruction = QLabel("Please click the box if Pregnant.", alignment=Qt.AlignmentFlag.AlignCenter)
+ leftVerticalLayout.addWidget(pregnanciesLabel)
+ leftVerticalLayout.addWidget(pregnant_instruction)
+ self.pregnancies_checkbox = QCheckBox(text="Not Pregnant")
+ leftVerticalLayout.addWidget(self.pregnancies_checkbox)
+ self.pregnancies_checkbox.stateChanged.connect(self.Pregnancies_onStateChanged)
+
+ # Silder button glucose
+ self.Glucose = QSlider(Qt.Orientation.Horizontal)
+ self.Glucose.setMinimum(30)
+ self.Glucose.setMaximum(200)
+ self.Glucose.valueChanged.connect(self.updateSlider_Glucose)
+ self.Glucose.setGeometry(5, 3, 20, 20)
+ self.selectedValue_glucose = QLabel(self)
+ self.selectedValue_glucose.setText(str(30))
+ glocoseLabel = QLabel("Glucose [30 - 200]", alignment=Qt.AlignmentFlag.AlignCenter)
+ leftVerticalLayout.addWidget(glocoseLabel)
+ leftVerticalLayout.addWidget(self.Glucose)
+ leftVerticalLayout.addWidget(self.selectedValue_glucose)
+
+
+ # Silder button blood pressure
+ self.BloodPressure = QSlider(Qt.Orientation.Horizontal)
+ self.BloodPressure.setMinimum(30)
+ self.BloodPressure.setMaximum(120)
+ self.BloodPressure.valueChanged.connect(self.updateSlider_BloodPressure)
+ self.BloodPressure.setGeometry(5, 3, 20, 20)
+ self.selectedValue_blood = QLabel(self)
+ self.selectedValue_blood.setText(str(30))
+ bloodpressureLabel = QLabel("Blood Pressure [30 - 120]", alignment=Qt.AlignmentFlag.AlignCenter)
+ leftVerticalLayout.addWidget(bloodpressureLabel)
+ leftVerticalLayout.addWidget(self.BloodPressure)
+ leftVerticalLayout.addWidget(self.selectedValue_blood)
+
+
+ # Silder button skin thickness
+ self.SkinThickness = QSlider(Qt.Orientation.Horizontal)
+ self.SkinThickness.setMinimum(0)
+ self.SkinThickness.setMaximum(100)
+ self.SkinThickness.valueChanged.connect(self.updateSlider_skinThickness)
+ self.SkinThickness.setGeometry(5, 3, 20, 20)
+ self.selectedValue_skin = QLabel(self)
+ self.selectedValue_skin.setText(str(0))
+ skinThicknessLabel = QLabel("Skin Thickness[0 - 100]", alignment=Qt.AlignmentFlag.AlignCenter)
+ leftVerticalLayout.addWidget(skinThicknessLabel)
+ leftVerticalLayout.addWidget(self.SkinThickness)
+ leftVerticalLayout.addWidget(self.selectedValue_skin)
+
+
+ # Silder button Insulin
+ self.Insulin = QSlider(Qt.Orientation.Horizontal)
+ self.Insulin.setMinimum(5)
+ self.Insulin.setMaximum(700)
+ self.Insulin.valueChanged.connect(self.updateSlider_insulin)
+ self.Insulin.setGeometry(5, 3, 20, 20)
+ self.selectedValue_insulin = QLabel(self)
+ self.selectedValue_insulin.setText(str(5))
+ insulinLabel = QLabel("Insulin [5 - 700]", alignment=Qt.AlignmentFlag.AlignCenter)
+ leftVerticalLayout.addWidget(insulinLabel)
+ leftVerticalLayout.addWidget(self.Insulin)
+ leftVerticalLayout.addWidget(self.selectedValue_insulin)
+
+
+
+
+ # Silder button BMI
+ self.BMI = QSlider(Qt.Orientation.Horizontal)
+ self.BMI.setMinimum(10)
+ self.BMI.setMaximum(80)
+ self.BMI.valueChanged.connect(self.updateSlider_BMI)
+ self.BMI.setGeometry(5, 3, 20, 20)
+ self.selectedValue_BMI = QLabel(self)
+ self.selectedValue_BMI.setText(str(10))
+ bmiLabel = QLabel("BMI [10 - 80]", alignment=Qt.AlignmentFlag.AlignCenter)
+ leftVerticalLayout.addWidget(bmiLabel)
+ leftVerticalLayout.addWidget(self.BMI)
+ leftVerticalLayout.addWidget(self.selectedValue_BMI)
+
+
+ # Silder button DiabetesPredegreeFunction
+ self.DiabetesPredegreeFunction = QSlider(Qt.Orientation.Horizontal)
+ self.DiabetesPredegreeFunction.setMinimum(0)
+ self.DiabetesPredegreeFunction.setMaximum(4)
+ self.DiabetesPredegreeFunction.setTickInterval(4)
+ self.DiabetesPredegreeFunction.valueChanged.connect(self.updateSlider_dibeticsPredegree)
+ self.DiabetesPredegreeFunction.setGeometry(5, 3, 20, 20)
+ self.selectedValue_predegree = QLabel(self)
+ self.selectedValue_predegree.setText(str(0))
+ label7 = QLabel("Diabetes Predegree Func [0 - 4]", alignment=Qt.AlignmentFlag.AlignCenter)
+ leftVerticalLayout.addWidget(label7)
+ leftVerticalLayout.addWidget(self.DiabetesPredegreeFunction)
+ leftVerticalLayout.addWidget(self.selectedValue_predegree)
+
+
+
+ # Silder button Age
+ self.age = QSlider(Qt.Orientation.Horizontal)
+ self.age.setMinimum(18)
+ self.age.setMaximum(150)
+ self.age.valueChanged.connect(self.updateSlider_Age)
+ self.age.setGeometry(5, 3, 20, 20)
+ self.selectedValue_age = QLabel(self)
+ self.selectedValue_age.setText(str(18))
+ label8 = QLabel("Age [18 - 150]", alignment=Qt.AlignmentFlag.AlignCenter)
+ leftVerticalLayout.addWidget(label8)
+ leftVerticalLayout.addWidget(self.age)
+ leftVerticalLayout.addWidget(self.selectedValue_age)
+
+
+ # Push button Predict
+ self.predict = QPushButton("Predict")
+ leftVerticalLayout.addWidget(self.predict)
+ self.predict.clicked.connect(self.predictiveSystem)
+ self.predictedValue = QLabel(" Prediction: None", self)
+ leftVerticalLayout.addWidget(self.predictedValue)
+
+
+ ####----------------Main Window Graph ------------
+
+ self.setWindowTitle('Women Diabetics Prediction')
+
+ x = diabetes["Age"]
+ y = diabetes["Glucose"]
+
+
+ fig = Figure()
+ ax =fig.add_subplot(111)
+
+ ax.set_title('Glucose Levels Across Age:Predicting Diabetes Risk.')
+ x_label= ax.set_xlabel('Age')
+ y_label = ax.set_ylabel('Glucose')
+
+ #ax.bar(x,y)
+ ax.bar(x,y)
+ graph =FigureCanvas(fig)
+
+
+ ###--------Connecting (all the buttons to left vertical layout) & (graph to the right vertical layout)
+ RightVerticalLayout.addWidget(graph)
+ MainLayout.addLayout(leftVerticalLayout)
+ MainLayout.addLayout(RightVerticalLayout)
+
+
+ mainWidget = QWidget()
+ mainWidget.setLayout(MainLayout)
+ self.setCentralWidget(mainWidget)
+
+
+
+
+
+
+ # menu
+
+ # action 1 from menu
+ self.window2 = None
+ histogram_action = QAction(QIcon("chart-medium.png"), "Histogram", self)
+ histogram_action.setStatusTip("View Histogram.")
+ histogram_action.triggered.connect(self.show_hist_window)
+
+ # action 2 from menu
+ self.window3 = None
+ plot_action = QAction(QIcon("plot.png"), "Plot Graph", self)
+ plot_action.setStatusTip("Real Time Graph")
+ plot_action.triggered.connect(self.show_realTime_window)
+
+ menu = self.menuBar()
+ file_menu = menu.addMenu("&Menu")
+ file_menu.addAction(histogram_action)
+ file_menu.addAction(plot_action)
+
+
+
+
+ def Pregnancies_onStateChanged(self):
+ if self.pregnancies_checkbox.isChecked():
+ self.pregnancies_checkbox.setText("Pregnant")
+ else:
+ self.pregnancies_checkbox.setText("Not Pregnant")
+
+ def updateSlider_pregnancies(self):
+ val = self.pregnancies.value()
+ self.selectedValue_preg.setText(str(val))
+ def updateSlider_Glucose(self):
+ val = self.Glucose.value()
+ self.selectedValue_glucose.setText(str(val))
+ def updateSlider_BloodPressure(self):
+ val = self.BloodPressure.value()
+ self.selectedValue_blood.setText(str(val))
+ def updateSlider_skinThickness(self):
+ val = self.SkinThickness.value()
+ self.selectedValue_skin.setText(str(val))
+ def updateSlider_insulin(self):
+ val = self.Insulin.value()
+ self.selectedValue_insulin.setText(str(val))
+ def updateSlider_BMI(self):
+ val = self.BMI.value()
+ self.selectedValue_BMI.setText(str(val))
+ def updateSlider_dibeticsPredegree(self):
+ val = self.DiabetesPredegreeFunction.value()
+ self.selectedValue_predegree.setText(str(val))
+ def updateSlider_Age(self):
+ val = self.age.value()
+ self.selectedValue_age.setText(str(val))
+
+ def show_hist_window(self):
+ if self.window2 is None:
+ self.window2 = secondWindow()
+ self.window2.show()
+
+ def show_realTime_window(self):
+ if self.window3 is None:
+ self.window3 = thirdWindow()
+ self.window3.show()
+
+ def generateData(self):
+ global diabetes
+ self.diabetes = pd.read_csv('diabetes_clean_03042021.csv')
+ print("self.diabetes.head(8):")
+ print(self.diabetes.head(10))
+ print("self.diabetes.tail(5): ")
+ print(self.diabetes.tail(5))
+ print("self.diabetes.info():")
+ print(self.diabetes.info())
+
+ # number of rows and columns
+ print("Rows: ", self.diabetes.shape[0])
+ print("Columns: ", self.diabetes.shape[1])
+ print("self.diabetes.describe(): ")
+ print(self.diabetes.describe())
+ print("Total Diabetic(1) and Non-diabetic(0) patient:", self.diabetes['Outcome'].value_counts())
+ print("[Mean] grouped by diabetic(1) and Non-diabetic(0) patient: ")
+ print(self.diabetes.groupby('Outcome').mean())
+
+ # ----------- Machine learning ------
+ print("-----Training data-----")
+ x = self.diabetes.drop(columns='Outcome', axis=1) # data
+ y = self.diabetes['Outcome'] # model
+
+ # Data Standardization
+ self.scaler = StandardScaler()
+ self.scaler.fit(x)
+ standardized_data = self.scaler.transform(x)
+ # print(standardized_data)
+
+ x = standardized_data # data
+ y = self.diabetes['Outcome'] # model
+
+ # Train Test Split
+ x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.20, random_state=42)
+ # test_size = 0.20 means 20% test data and 80% train data
+
+ # print(x.shape, x_train.shape, x_test.shape)
+
+ # Training The Model with SVM Algorithm
+ self. svm_classifier = svm.SVC(kernel='linear')
+ # training the support vector machine classifier
+ self.svm_classifier.fit(x_train, y_train)
+
+ # Accuracy Check
+ # Train Data
+ x_train_prediction = self.svm_classifier.predict(x_train)
+ training_data_accuracy = accuracy_score(x_train_prediction, y_train)
+ print("Accuracy Score of the training data: ", training_data_accuracy)
+
+ # Test data
+ x_test_prediction = self.svm_classifier.predict(x_test)
+ test_data_accuracy = accuracy_score(x_test_prediction, y_test)
+ print("Accuracy Score of the testing data: ", test_data_accuracy)
+
+
+ # correlation ---- highest x=age y=blood pressure
+ corr_matrix = self.diabetes.corr()
+ print("self.diabetes.corr(): ", corr_matrix)
+
+ if (x_test_prediction[0] == 0):
+ result = print("The person is not diabetic. ")
+ else:
+ result = print("The person is diabetic. ")
+
+ # calculating the values for the predictive System
+ def predictiveSystem(self):
+ global result
+ from random import randint
+ count = randint(0, 767)
+
+ # converting checkbox value into 0 or 1
+ if self.pregnancies_checkbox.isChecked() == True:
+ pregnancies_checkbox = 1
+ else:
+ pregnancies_checkbox = 0
+
+ Glucose = self.Glucose.value()
+ BloodPressure =self.BloodPressure.value()
+ SkinThickness = self.SkinThickness.value()
+ Insulin = self.Insulin.value()
+ bmi = self.BMI.value()
+ DiabetesPedigreeFunction = self.DiabetesPredegreeFunction.value()
+ Age = self.age.value()
+
+ # inputData = [value,1, 110, 92, 0, 0, 37.6,0.191, 30]
+ sampleData = [count, pregnancies_checkbox, Glucose, BloodPressure, SkinThickness, Insulin, bmi, DiabetesPedigreeFunction,
+ Age]
+
+ # change the input data to numpy array
+ sampleData_numpy = np.asarray(sampleData)
+
+ # reshaping array as we are predicting for one instance
+ reshaping = sampleData_numpy.reshape(1, -1)
+
+ # standardize the input data
+ standardize_sampleData = self.scaler.transform(reshaping)
+ # print(std_data)
+
+ prediction = self.svm_classifier.predict(standardize_sampleData)
+ print(prediction)
+ if (prediction[0] == 0):
+
+ self.predictedValue.setText("Prediction: The person is not diabetic. ")
+ else:
+
+ self.predictedValue.setText("Prediction: The person is diabetic. ")
+
+
+
+
+if __name__ == '__main__':
+ app = QApplication(sys.argv)
+
+ with open("style.css", "r") as file:
+ app.setStyleSheet(file.read())
+
+ window = MainWindow()
+ #window.setFixedSize(1200,800)
+
+
+ window.design()
+ window.generateData()
+
+ window.show()
+
+ sys.exit(app.exec())
diff --git a/plot.png b/plot.png
new file mode 100644
index 0000000000000000000000000000000000000000..24827761ba223993fa33b01bfbc26077d77e1cd0
Binary files /dev/null and b/plot.png differ
diff --git a/style.css b/style.css
new file mode 100644
index 0000000000000000000000000000000000000000..617ad35488e5fc2be677f2408c4f73fd252f25dd
--- /dev/null
+++ b/style.css
@@ -0,0 +1,42 @@
+QMainWindow {
+ background-color: #1e1f22;
+ border-radius: 5px;
+ background-image: url('image.jpg'); /* set the path to your image */
+ background-repeat: no-repeat; /* prevent the image from repeating */
+ background-position: center; /* position the image in the center */
+
+}
+
+QSlider::handle:horizontal {
+ background: #13fcec;
+ width: 10px;
+ margin: -5px -1px;
+ border-radius: 5px;
+ border: 1px solid #2a2a2a;
+}
+QSlider::groove:horizontal {
+ background: #ffffff6a;
+ height:2px;
+ border: 1px solid #2a2a2a;
+}
+
+QPushButton {
+ background-color: #1ffcd3; /* set the background color */
+ color: #0f3f33; /* set the text color */
+ border: 1px solid #0fe6b490; /* add a border */
+ padding: 5px 10px; /* add padding */
+ border-radius: 5px;
+}
+QLabel {
+ color: #ffffff; /* set the text color for labels */
+ font-size: 14px; /* set the font size for labels */
+ font-family: 'Courier New', Monospace; /* set the font family for labels */
+ font-weight: bold;
+}
+
+QCheckBox {
+ color: #ffffff; /* set the text color for labels */
+ font-size: 18px; /* set the font size for labels */
+ font-family: 'Courier New', Monospace; /* set the font family for labels */
+ font-weight: bold;
+}