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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+src/Diabetes-Detection/diabetes.sav filter=lfs diff=lfs merge=lfs -text

.github/workflows/main.yaml

@@ -0,0 +1,34 @@
+name: Deploy Streamlit App to Hugging Face Spaces
+
+on:
+  push:
+    branches:
+      - main
+
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v2
+
+      - name: Setup Python
+        uses: actions/setup-python@v2
+        with:
+          python-version: '3.10'
+
+      - name: Install dependencies
+        run: |
+          pip install streamlit
+          pip install -r requirements.txt
+          pip install tensorflow 
+          pip install huggingface_hub
+          
+
+      - name: Run Streamlit app
+        run: streamlit run app.py
+
+      - name: Publish to Hugging Face Spaces
+        uses: hf spaces push -o praj2408/Onsite-Health-Diagnostic app.py
+        env:
+          HUGGINGFACE_API_KEY: ${{ secrets.HUGGINGFACE_API_KEY }}

.gitignore

@@ -0,0 +1,161 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+OHD/
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 Prajwal Krishna
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

Onsite_Health_Diagnostic.egg-info/PKG-INFO

@@ -0,0 +1,6 @@
+Metadata-Version: 2.1
+Name: Onsite-Health-Diagnostic
+Version: 0.0.1
+Author: Prajwal Krishna
+Author-email: [email protected]
+License-File: LICENSE

Onsite_Health_Diagnostic.egg-info/SOURCES.txt

@@ -0,0 +1,7 @@
+LICENSE
+README.md
+setup.py
+Onsite_Health_Diagnostic.egg-info/PKG-INFO
+Onsite_Health_Diagnostic.egg-info/SOURCES.txt
+Onsite_Health_Diagnostic.egg-info/dependency_links.txt
+Onsite_Health_Diagnostic.egg-info/top_level.txt

Onsite_Health_Diagnostic.egg-info/dependency_links.txt

@@ -0,0 +1 @@
+

Onsite_Health_Diagnostic.egg-info/top_level.txt

@@ -0,0 +1 @@
+

README.md

@@ -1,12 +1,40 @@
----
-title: Dummy
-emoji: 📚
-colorFrom: purple
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.32.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Onsite-Health-Diagnostic (OHD)
+Onsite Health Diagnostic (OHD) is the web application that allows users to predict whether the user has been infected with a menacing disease or not. These diseases can be very dangerous to health if they are not treated properly. The main objective of OHD is to help people predict the disease in case of absence of medical professionals, strikes or any related uncertainties.
+
+## Website
+link:
+
+## Table Of Contents
+
+-   [Datasets](#Datasets)
+-   [Clone](#Clone)
+-   [Licence](#Licence)
+
+
+## Datasets
+ - Pneumonia            :              [Dataset](https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia)
+ - Brain tumour         :              [Dataset](https://www.kaggle.com/ahmedhamada0/brain-tumor-detection)
+ - Diabetes             :              [Dataset](https://github.com/praj2408/Onsite-Health-Diagnostics/blob/main/src/Diabetes-Detection/diabetes.csv)
+ - Heart disease        :              [Dataset](https://github.com/praj2408/Onsite-Health-Diagnostics/blob/main/src/Heart-Disease/heart.csv)
+ - Breast Cancer        :              [Dataset](https://github.com/praj2408/Onsite-Health-Diagnostics/blob/main/src/Breast%20Cancer/data.csv)
+ - Malaria-Detection    :              - [Dataset](https://lhncbc.nlm.nih.gov/LHC-publications/pubs/MalariaDatasets.html#:~:text=Abstract%3A,the%20Malaria%20Screener%20research%20activity.&text=The%20dataset%20contains%20a%20total,of%20parasitized%20and%20uninfected%20cells.)
+
+
+## Clone
+
+1. Clone the repository:
+```
+git clone https://github.com/praj2408/Onsite-Health-Diagnostics
+```
+2. Install dependencies
+```
+pip install -r requirements.txt
+```
+3. Run the application
+```
+streamlit run app.py
+```
+
+
+## License
+This project is licensed under the MIT License - see the LICENSE file for details.

app.py

@@ -0,0 +1,249 @@
+
+import streamlit as st
+from streamlit_extras import add_vertical_space
+import streamlit.components.v1 as components
+from annotated_text import annotated_text
+
+import tensorflow as tf
+from tensorflow import keras
+
+from keras.models import load_model
+from PIL import Image
+import numpy as np
+
+
+from prediction_pipeline import diabetes_prediction, breast_cancer_prediction, heart_disease_prediction
+
+
+
+#st.set_page_config(layout='wide')
+
+import pandas as pd
+
+import json
+
+
+
+with st.sidebar:
+    st.title("Onsite Health Diagnostics-OHD")
+    
+
+    diseases = ["Diabetes Prediction","Breast Cancer","Heart Disease Prediction","Malaria Detection", "Pneumonia Detection", "Brain Tumour Detection"]
+
+    
+
+
+    selected_diseases = st.selectbox("Select Diseases to Predict", diseases)
+
+
+    
+if selected_diseases == "Diabetes Prediction":
+    
+    st.title("DIABETES PREDICTION")
+    
+    # Input fields for user to input data
+    pregnancies = st.number_input("Number of Pregnancies", 0, 17, 1)
+    glucose = st.number_input("Plasma Glucose Concentration (mg/dL)", 0, 200, 100)
+    blood_pressure = st.number_input("Diastolic Blood Pressure (mm Hg)", 0, 122, 70)
+    skin_thickness = st.number_input("Skin Thickness (mm)", 0, 99, 20)
+    insulin = st.number_input("Insulin Level (mu U/mL)", 0, 846, 79)
+    bmi = st.number_input("Body Mass Index (BMI)", 0.0, 67.1, 30.0)
+    dpf = st.number_input("Diabetes Pedigree Function", 0.078, 2.42, 0.3725)
+    age = st.number_input("Age (years)", 21, 81, 25)
+    
+    if st.button("Predict"):
+        prediction = diabetes_prediction(data=[pregnancies,glucose,blood_pressure,skin_thickness,insulin,bmi,dpf,age])
+        
+        if prediction==1:
+            st.error("The patient has diabetes")
+        else:
+            st.success("The patient does not have diabetes")
+
+
+if selected_diseases == "Breast Cancer":
+    
+    st.title("BREAST CANCER PREDICTION")
+    
+    # Input fields for user to input data
+    radius_mean = st.number_input("Radius Mean", 6.981, 28.11, 14.127)
+    area_mean = st.number_input("Area Mean", 143.5, 2501.0, 654.889)
+    compactness_mean = st.number_input("Compactness Mean", 0.019, 0.345, 0.104)
+    concavity_mean = st.number_input("Concavity Mean", 0.0, 0.427, 0.089)
+    concave_points_mean = st.number_input("Concave Points Mean", 0.0, 0.201, 0.049)
+    area_worst = st.number_input("Area Worst", 185.200000, value=686.500000)
+    compactness_worst = st.number_input("Compactness Worst",0.027290, value=0.211900)
+    concavity_worst = st.number_input("Concavity Worst",0.000000, value=0.226700)
+    area_se = st.number_input("Area Se", 6.802000, value=24.530000)
+    fractal_dimension_se = st.number_input("Fractal Dimension Mean", 0.05, 0.097, 0.062)
+    symmetry_worst = st.number_input("Symmetry Worst", 0.106, 0.304, 0.181)
+    fractal_dimension_worst = st.number_input("Fractal_Dimension_Worst", 0.055040, value=0.080040)
+
+    if st.button("Predict"):
+        prediction = breast_cancer_prediction(data=[radius_mean,area_mean,compactness_mean,concavity_mean,concave_points_mean,area_worst,compactness_worst,concavity_worst,area_se,fractal_dimension_se,symmetry_worst,fractal_dimension_worst])
+        
+        if prediction==1:
+            st.error("The patient has Breast Cancer")
+        else:
+            st.success("The patient does not have Breast Cancer")
+
+
+
+if selected_diseases == "Heart Disease Prediction":
+    
+    st.title("HEART DISEASE PREDICTION")
+    
+    # Input fields for user to input data
+    age = st.number_input("Age", 29, 77, 50)
+    sex = st.selectbox("Sex", ["Male", "Female"])
+    ChestPainType = st.selectbox("Chest Pain Type", ["Typical Angina", "Atypical Angina", "Non-anginal Pain", "Asymptomatic"])
+    RestingBP = st.number_input("Resting Blood Pressure (mm Hg)", 94, 200, 120)
+    Cholesterol = st.number_input("Serum Cholesterol (mg/dl)", 126, 564, 240)
+    FastingBS = st.selectbox("Fasting Blood Sugar > 120 mg/dl", ["True", "False"])
+    RestingECG = st.selectbox("Resting Electrocardiographic Results", ["Normal", "ST-T wave abnormality", "Probable or Definite Left Ventricular Hypertrophy"])
+    MaxHR = st.number_input("Maximum Heart Rate Achieved", 71, 202, 150)
+    ExerciseAngina = st.selectbox("Exercise Induced Angina", ["Yes", "No"])
+    Oldpeak = st.number_input("ST Depression Induced by Exercise Relative to Rest", 0.0, 6.2, 2.0)
+    ST_Slope = st.selectbox("Slope of the Peak Exercise ST Segment", ["Upsloping", "Flat", "Downsloping"])
+
+    #converting categorical into numerical
+    sex = 1 if sex == "Male" else 0
+    
+    if ChestPainType == "Typical Angina":
+        ChestPainType = 0
+    elif ChestPainType == "Atypical Angina":
+        ChestPainType = 1
+    elif ChestPainType == "Non-anginal Pain":
+        ChestPainType = 2
+    else:
+        ChestPainType = 3
+        
+        
+    if FastingBS == "True":
+        FastingBS = 1
+    else:
+        FastingBS = 0
+        
+    if RestingECG == "Normal":
+        RestingECG = 0
+    elif RestingECG == "ST-T wave abnormality":
+        RestingECG = 1
+    else:
+        RestingECG = 2
+        
+    if ExerciseAngina == "Yes":
+        ExerciseAngina = 1
+    else:
+        ExerciseAngina = 0
+        
+    if ST_Slope == "Upsloping":
+        ST_Slope = 0
+    elif ST_Slope == "Flat":
+        ST_Slope = 1
+    else:
+        ST_Slope = 2
+        
+        
+    if st.button("Predict"):
+        prediction = heart_disease_prediction(data=[age,sex,ChestPainType,RestingBP,Cholesterol,FastingBS,RestingECG,MaxHR,ExerciseAngina,Oldpeak,ST_Slope])
+        
+        if prediction==1:
+            st.error("The patient has Heart Disease")
+        else:
+            st.success("The patient does not have Heart Disease")
+    
+    
+
+if selected_diseases == "Malaria Detection":
+    
+    st.title("MALARIA DISEASE DETECTION")
+    
+    
+    uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+
+    if uploaded_file is not None:
+        # Display the uploaded image
+        image = Image.open(uploaded_file)
+        st.image(image, caption='Uploaded Image', use_column_width=True)
+        
+    model = load_model('src/Malaria-Detection/malaria.h5')
+
+    
+    def preprocess_image(image_file):
+        img = Image.open(image_file)
+        img = img.resize((128, 128))  # Resize the image to match the input size of the model
+        img_array = np.array(img) / 255.0  # Normalize pixel values
+        img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+        return img_array
+    
+    
+    def predict_malaria(image_file):
+        img_array = preprocess_image(image_file)
+        prediction = model.predict(img_array)
+        return prediction
+        
+
+        # When the user clicks the predict button
+    if st.button("Predict"):
+        # Make prediction
+        prediction = predict_malaria(uploaded_file)
+        # Display prediction
+        if prediction[0][0] > 0.5:
+            st.success("The image does not contain malaria parasites.")
+        else:
+            st.error("The image contains malaria parasites.")
+                
+                
+                
+ 
+                
+                
+if selected_diseases == "Pneumonia Detection":
+    
+    st.title("PNEUMONIA DISEASE DETECTION")
+    
+
+    # Load the pre-trained model
+    model = load_model('src/Pneumonia-Detection/pneumonia_detection.h5')
+
+    # Function to preprocess the image
+    def preprocess_image(image_file):
+        img = Image.open(image_file)
+        img = img.resize((150, 150))  # Resize the image to match the input size of the model
+        img_array = np.array(img) / 255.0  # Normalize pixel values
+        img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+        return img_array
+
+    # Function to make prediction
+    def predict_pneumonia(image_file):
+        img_array = preprocess_image(image_file)
+        prediction = model.predict(img_array)
+        return prediction
+
+   
+
+    # File uploader for user to upload an image
+    uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+
+    # When the user uploads an image and clicks the predict button
+    if uploaded_file is not None:
+        # Display the uploaded image
+        img = Image.open(uploaded_file)
+        st.image(img, caption='Uploaded Image', use_column_width=True)
+
+        # When the user clicks the predict button
+        if st.button("Predict"):
+            # Make prediction
+            prediction = predict_pneumonia(uploaded_file)
+            # Display prediction
+            if prediction[0][0] > 0.5:
+                st.error("The image indicates pneumonia.")
+            else:
+                st.success("The image is normal.")
+
+
+
+if selected_diseases == "Brain Tumour Detection":
+    
+    st.title("BRAIN TUMOUR DETECTION")
+    
+    st.write("Working on it, coming soon!")

prediction_pipeline.py

@@ -0,0 +1,82 @@
+
+import pickle
+import joblib
+import numpy as np
+from sklearn.preprocessing import StandardScaler
+
+
+# Diabetes prediction
+
+def diabetes_prediction(data):
+    
+    with open("src/Diabetes-Detection/scaler.pkl", "rb") as f:
+        scaler = pickle.load(f)
+
+    data = np.array(data).reshape(1,-1)
+    scaled_data = scaler.transform(data)
+    
+    with open("src/Diabetes-Detection/model.pkl", "rb") as f:
+        model = pickle.load(f)
+        
+    pred = model.predict(scaled_data)
+    
+    
+    return pred
+
+# new_data = np.array([6,148,72,35,0,33.6,0.627,50]).reshape(1, -1)
+
+# pred = diabetes_prediction(new_data)
+
+# if pred==1:
+#     print("The patient has diabetes")
+
+
+
+#breast cancer prediction
+def breast_cancer_prediction(data):
+    
+    with open("src/Breast-Cancer/scaler.pkl", "rb") as f:
+        scaler = pickle.load(f)
+
+    data = np.array(data).reshape(1,-1)
+    scaled_data = scaler.transform(data)
+    
+    with open("src/Breast-Cancer/model.pkl", "rb") as f:
+        model = pickle.load(f)
+        
+    pred = model.predict(scaled_data)
+    
+    
+    return pred
+
+
+# new_data = np.array([17.99,	1001.0,	0.262779,	0.3001,	0.14710,	2019.0,	0.665600,	0.7119,	153.40,	0.006193,	0.460100,	0.11890]).reshape(1, -1)
+
+# pred = breast_cancer_prediction(new_data)
+
+# if pred==1:
+#     print("The patient has Breast cancer")
+# else:
+#     print("The patient does not have Breast cancer")
+
+def heart_disease_prediction(data):
+    
+    with open("src/Heart-Disease/scaler.pkl", "rb") as f:
+        scaler = pickle.load(f)
+
+    data = np.array(data).reshape(1,-1)
+    scaled_data = scaler.transform(data)
+    
+    with open("src/Heart-Disease/heart_model.pkl", "rb") as f:
+        model = pickle.load(f)
+        
+    pred = model.predict(scaled_data)
+    
+    return pred
+
+
+
+
+
+def malaria_detection():
+    pass

requirements.txt

@@ -0,0 +1,14 @@
+numpy==1.26.4
+opencv-python==4.9.0.80
+scikit-learn==1.2.2
+pandas==2.2.1
+missingno==0.5.2
+matplotlib==3.8.3
+seaborn==0.13.2
+streamlit==1.32.2
+tensorflow==2.16.1
+streamlit-extras==0.4.0
+st-annotated-text==4.0.1
+
+
+-e .

setup.py

@@ -0,0 +1,24 @@
+from setuptools import find_packages,setup
+from typing import List
+
+"""HYPEN_E_DOT='-e .'
+
+def get_requirements(file_path:str)->List[str]:
+    requirements=[]
+    with open(file_path) as file_obj:
+        requirements=file_obj.readlines()
+        requirements=[req.replace("\n","") for req in requirements]
+
+        if HYPEN_E_DOT in requirements:
+            requirements.remove(HYPEN_E_DOT)
+
+    return requirements"""
+
+setup(
+    name='Onsite-Health-Diagnostic',
+    version='0.0.1',
+    author='Prajwal Krishna',
+    author_email='[email protected]',
+    #install_requires=["scikit-learn","pandas","numpy"],
+    packages=find_packages()
+)

src/Brain-Tumour-classification/.ipynb_checkpoints/Untitled-checkpoint.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

src/Brain-Tumour-classification/Untitled.ipynb

@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "55922f23-dcb3-40a9-a29e-754862cd5c39",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

src/Breast-Cancer/model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:77884907c6a2b33998380f9fe09938246d5e3a7d876660692b01935f04948eb1
+size 762

src/Breast-Cancer/model.py

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+
+#import libraries
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn import preprocessing
+from sklearn import metrics
+from sklearn.metrics import mean_squared_error
+from sklearn.preprocessing import StandardScaler
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import classification_report
+from sklearn.linear_model import LogisticRegression
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.metrics import plot_confusion_matrix, accuracy_score, classification_report
+import joblib
+
+# load the dataset
+data = pd.read_csv("data.csv")
+
+### let's remove the column id and Unnamed:32
+data = data.drop(['id','Unnamed: 32'],axis=1)
+# selecting only the skewed columns to transform it in gaussian distribution
+data_temp = data[['radius_mean', 'area_mean',
+       'compactness_mean', 'concavity_mean', 'concave points_mean',
+       'area_worst', 'compactness_worst',
+       'concavity_worst', 'area_se','fractal_dimension_se',
+         'symmetry_worst', 'fractal_dimension_worst']].copy()
+
+#### Label encoding
+label_encoder = preprocessing.LabelEncoder()
+data['diagnosis'] = label_encoder.fit_transform(data['diagnosis'])
+# label
+y = data['diagnosis'].copy()
+
+# standard scaler
+scaler = StandardScaler()
+data_temp = scaler.fit_transform(data_temp)
+
+
+'''Train test split'''
+X_train, X_test, y_train, y_test= train_test_split(data_temp, y, test_size = 0.2, random_state=42)
+
+
+'''Logistic Regression'''
+
+log = LogisticRegression()
+log.fit(X_train, y_train)
+# save the model
+filename = 'breast_model.sav'
+joblib.dump(log, filename)
+
+#load the model 
+loaded_model = joblib.load(filename)
+
+X_test = scaler.fit_transform(X_test)
+pred = loaded_model.predict(X_test)

src/Breast-Cancer/scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8139fd73b7e3c19eb417c16dba115f4ce0cfcdad9202d64f686ae1f178e018b5
+size 1035

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src/Diabetes-Detection/diabetes.py

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+
+# import the libraries
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+import missingno as msno
+from sklearn.preprocessing import StandardScaler
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LogisticRegression
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import mean_squared_error
+from sklearn import metrics
+from sklearn.metrics import accuracy_score, classification_report
+import joblib
+
+# read the dataset
+data = pd.read_csv("diabetes.csv")
+
+# Remove outlier
+Q1 = data.quantile(0.25)
+Q3 = data.quantile(0.75)
+IQR = Q3 - Q1
+
+
+# remove the outlier
+
+data = data[~((data < (Q1 - 1.5 * IQR)) | (data > (Q3 + 1.5 * IQR))).any(axis = 1)]
+
+# prepare the data
+y = data["Outcome"].copy()
+X = data.drop("Outcome",axis=1)
+
+'''Train test split'''
+
+X_train, X_test , y_train, y_test = train_test_split(X , y, test_size = 0.2, random_state = 42)
+
+rf = RandomForestClassifier().fit(X_train,y_train)
+
+# save and load model
+
+filename = 'diabetes.sav'
+
+joblib.dump(rf, filename)

src/Diabetes-Detection/diabetes.sav

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+size 1317225

src/Diabetes-Detection/model.pkl

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src/Diabetes-Detection/scaler.joblib

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src/Diabetes-Detection/scaler.pkl

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src/Heart-Disease/heart.csv

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+64,M,ASY,144,0,0,ST,122,Y,1,Flat,1
+60,M,ASY,140,281,0,ST,118,Y,1.5,Flat,1
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+40,M,ASY,125,0,1,Normal,165,N,0,Flat,1
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+41,M,ASY,104,0,0,ST,111,N,0,Up,0
+57,M,ASY,139,277,1,ST,118,Y,1.9,Flat,1
+63,M,ASY,136,0,0,Normal,84,Y,0,Flat,1
+59,M,ASY,122,233,0,Normal,117,Y,1.3,Down,1
+51,M,ASY,128,0,0,Normal,107,N,0,Up,0
+59,M,NAP,131,0,0,Normal,128,Y,2,Down,1
+42,M,NAP,134,240,0,Normal,160,N,0,Up,0
+55,M,NAP,120,0,0,ST,125,Y,2.5,Flat,1
+63,F,ATA,132,0,0,Normal,130,N,0.1,Up,0
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+56,M,ATA,124,224,1,Normal,161,N,2,Flat,0
+53,M,ASY,126,0,0,Normal,106,N,0,Flat,1
+68,M,ASY,138,0,0,Normal,130,Y,3,Flat,1
+53,M,ASY,154,0,1,ST,140,Y,1.5,Flat,1
+60,M,NAP,141,316,1,ST,122,Y,1.7,Flat,1
+62,M,ATA,131,0,0,Normal,130,N,0.1,Up,0
+59,M,ASY,178,0,1,LVH,120,Y,0,Flat,1
+51,M,ASY,132,218,1,LVH,139,N,0.1,Up,0
+61,M,ASY,110,0,1,Normal,108,Y,2,Down,1
+57,M,ASY,130,311,1,ST,148,Y,2,Flat,1
+56,M,NAP,170,0,0,LVH,123,Y,2.5,Flat,1
+58,M,ATA,126,0,1,Normal,110,Y,2,Flat,1
+69,M,NAP,140,0,1,ST,118,N,2.5,Down,1
+67,M,TA,142,270,1,Normal,125,N,2.5,Up,1
+58,M,ASY,120,0,0,LVH,106,Y,1.5,Down,1
+65,M,ASY,134,0,0,Normal,112,Y,1.1,Flat,1
+63,M,ATA,139,217,1,ST,128,Y,1.2,Flat,1
+55,M,ATA,110,214,1,ST,180,N,0.4,Up,0
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+65,M,TA,140,252,0,Normal,135,N,0.3,Up,0
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+72,M,NAP,120,214,0,Normal,102,Y,1,Flat,1
+75,M,ASY,170,203,1,ST,108,N,0,Flat,1
+49,M,TA,130,0,0,ST,145,N,3,Flat,1
+51,M,NAP,137,339,0,Normal,127,Y,1.7,Flat,1
+60,M,ASY,142,216,0,Normal,110,Y,2.5,Flat,1
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+58,M,ASY,132,458,1,Normal,69,N,1,Down,0
+61,M,ASY,146,241,0,Normal,148,Y,3,Down,1
+67,M,ASY,160,384,1,ST,130,Y,0,Flat,1
+62,M,ASY,135,297,0,Normal,130,Y,1,Flat,1
+65,M,ASY,136,248,0,Normal,140,Y,4,Down,1
+63,M,ASY,130,308,0,Normal,138,Y,2,Flat,1
+69,M,ASY,140,208,0,ST,140,Y,2,Flat,1
+51,M,ASY,132,227,1,ST,138,N,0.2,Up,0
+62,M,ASY,158,210,1,Normal,112,Y,3,Down,1
+55,M,NAP,136,245,1,ST,131,Y,1.2,Flat,1
+75,M,ASY,136,225,0,Normal,112,Y,3,Flat,1
+40,M,NAP,106,240,0,Normal,80,Y,0,Up,0
+67,M,ASY,120,0,1,Normal,150,N,1.5,Down,1
+58,M,ASY,110,198,0,Normal,110,N,0,Flat,1
+60,M,ASY,136,195,0,Normal,126,N,0.3,Up,0
+63,M,ASY,160,267,1,ST,88,Y,2,Flat,1
+35,M,NAP,123,161,0,ST,153,N,-0.1,Up,0
+62,M,TA,112,258,0,ST,150,Y,1.3,Flat,1
+43,M,ASY,122,0,0,Normal,120,N,0.5,Up,1
+63,M,NAP,130,0,1,ST,160,N,3,Flat,0
+68,M,NAP,150,195,1,Normal,132,N,0,Flat,1
+65,M,ASY,150,235,0,Normal,120,Y,1.5,Flat,1
+48,M,NAP,102,0,1,ST,110,Y,1,Down,1
+63,M,ASY,96,305,0,ST,121,Y,1,Up,1
+64,M,ASY,130,223,0,ST,128,N,0.5,Flat,0
+61,M,ASY,120,282,0,ST,135,Y,4,Down,1
+50,M,ASY,144,349,0,LVH,120,Y,1,Up,1
+59,M,ASY,124,160,0,Normal,117,Y,1,Flat,1
+55,M,ASY,150,160,0,ST,150,N,0,Up,0
+45,M,NAP,130,236,0,Normal,144,N,0.1,Up,0
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+72,M,ASY,143,211,0,Normal,109,Y,1.4,Flat,1
+50,M,ASY,133,218,0,Normal,128,Y,1.1,Flat,1
+64,M,ASY,143,306,1,ST,115,Y,1.8,Flat,1
+55,M,ASY,116,186,1,ST,102,N,0,Flat,1
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+59,M,ASY,125,222,0,Normal,135,Y,2.5,Down,1
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+62,M,NAP,133,0,1,ST,119,Y,1.2,Flat,1
+74,M,ASY,150,258,1,ST,130,Y,4,Down,1
+54,M,ASY,130,202,1,Normal,112,Y,2,Flat,1
+57,M,ASY,110,197,0,LVH,100,N,0,Up,0
+62,M,NAP,138,204,0,ST,122,Y,1.2,Flat,1
+76,M,NAP,104,113,0,LVH,120,N,3.5,Down,1
+54,F,ASY,138,274,0,Normal,105,Y,1.5,Flat,1
+70,M,ASY,170,192,0,ST,129,Y,3,Down,1
+61,F,ATA,140,298,1,Normal,120,Y,0,Up,0
+48,M,ASY,132,272,0,ST,139,N,0.2,Up,0
+48,M,NAP,132,220,1,ST,162,N,0,Flat,1
+61,M,TA,142,200,1,ST,100,N,1.5,Down,1
+66,M,ASY,112,261,0,Normal,140,N,1.5,Up,1
+68,M,TA,139,181,1,ST,135,N,0.2,Up,0
+55,M,ASY,172,260,0,Normal,73,N,2,Flat,1
+62,M,NAP,120,220,0,LVH,86,N,0,Up,0
+71,M,NAP,144,221,0,Normal,108,Y,1.8,Flat,1
+74,M,TA,145,216,1,Normal,116,Y,1.8,Flat,1
+53,M,NAP,155,175,1,ST,160,N,0.3,Up,0
+58,M,NAP,150,219,0,ST,118,Y,0,Flat,1
+75,M,ASY,160,310,1,Normal,112,Y,2,Down,0
+56,M,NAP,137,208,1,ST,122,Y,1.8,Flat,1
+58,M,NAP,137,232,0,ST,124,Y,1.4,Flat,1
+64,M,ASY,134,273,0,Normal,102,Y,4,Down,1
+54,M,NAP,133,203,0,ST,137,N,0.2,Up,0
+54,M,ATA,132,182,0,ST,141,N,0.1,Up,0
+59,M,ASY,140,274,0,Normal,154,Y,2,Flat,0
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+61,M,ASY,141,292,0,ST,115,Y,1.7,Flat,1
+41,M,ASY,150,171,0,Normal,128,Y,1.5,Flat,0
+71,M,ASY,130,221,0,ST,115,Y,0,Flat,1
+38,M,ASY,110,289,0,Normal,105,Y,1.5,Down,1
+55,M,ASY,158,217,0,Normal,110,Y,2.5,Flat,1
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+69,M,ASY,140,110,1,Normal,109,Y,1.5,Flat,1
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+67,M,ASY,146,369,0,Normal,110,Y,1.9,Flat,1
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+51,M,ASY,131,152,1,LVH,130,Y,1,Flat,1
+48,M,ASY,140,208,0,Normal,159,Y,1.5,Up,1
+69,M,ASY,122,216,1,LVH,84,Y,0,Flat,1
+69,M,NAP,142,271,0,LVH,126,N,0.3,Up,0
+64,M,ASY,141,244,1,ST,116,Y,1.5,Flat,1
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+53,M,ASY,124,243,0,Normal,122,Y,2,Flat,1
+37,M,NAP,118,240,0,LVH,165,N,1,Flat,0
+67,M,ASY,140,219,0,ST,122,Y,2,Flat,1
+74,M,NAP,140,237,1,Normal,94,N,0,Flat,1
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+58,M,ASY,100,213,0,ST,110,N,0,Up,0
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+64,M,ASY,130,258,1,LVH,130,N,0,Flat,1
+58,M,ASY,160,256,1,LVH,113,Y,1,Up,1
+60,M,ASY,130,186,1,LVH,140,Y,0.5,Flat,1
+57,M,ASY,122,264,0,LVH,100,N,0,Flat,1
+55,M,NAP,133,185,0,ST,136,N,0.2,Up,0
+55,M,ASY,120,226,0,LVH,127,Y,1.7,Down,1
+56,M,ASY,130,203,1,Normal,98,N,1.5,Flat,1
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+61,M,NAP,140,284,0,Normal,123,Y,1.3,Flat,1
+61,M,NAP,120,337,0,Normal,98,Y,0,Flat,1
+74,M,ASY,155,310,0,Normal,112,Y,1.5,Down,1
+68,M,NAP,134,254,1,Normal,151,Y,0,Up,0
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+62,M,ASY,160,254,1,ST,108,Y,3,Flat,1
+53,M,ASY,144,300,1,ST,128,Y,1.5,Flat,1
+62,M,ASY,158,170,0,ST,138,Y,0,Flat,1
+46,M,ASY,134,310,0,Normal,126,N,0,Flat,1
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+55,M,ASY,122,223,1,ST,100,N,0,Flat,1
+58,M,ASY,140,385,1,LVH,135,N,0.3,Up,0
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+70,M,ASY,130,322,0,LVH,109,N,2.4,Flat,1
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+64,M,ASY,128,263,0,Normal,105,Y,0.2,Flat,0
+74,F,ATA,120,269,0,LVH,121,Y,0.2,Up,0
+65,M,ASY,120,177,0,Normal,140,N,0.4,Up,0
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+71,F,ASY,112,149,0,Normal,125,N,1.6,Flat,0
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+64,M,TA,110,211,0,LVH,144,Y,1.8,Flat,0
+40,M,TA,140,199,0,Normal,178,Y,1.4,Up,0
+67,M,ASY,120,229,0,LVH,129,Y,2.6,Flat,1
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+43,M,ASY,115,303,0,Normal,181,N,1.2,Flat,0
+47,M,ASY,112,204,0,Normal,143,N,0.1,Up,0
+54,F,ATA,132,288,1,LVH,159,Y,0,Up,0
+48,F,NAP,130,275,0,Normal,139,N,0.2,Up,0
+46,F,ASY,138,243,0,LVH,152,Y,0,Flat,0
+51,F,NAP,120,295,0,LVH,157,N,0.6,Up,0
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+71,F,NAP,110,265,1,LVH,130,N,0,Up,0
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+66,M,ASY,160,228,0,LVH,138,N,2.3,Up,0
+37,F,NAP,120,215,0,Normal,170,N,0,Up,0
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+50,M,ASY,144,200,0,LVH,126,Y,0.9,Flat,1
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+61,M,ASY,140,207,0,LVH,138,Y,1.9,Up,1
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+42,M,NAP,130,180,0,Normal,150,N,0,Up,0
+48,M,ASY,122,222,0,LVH,186,N,0,Up,0
+40,M,ASY,152,223,0,Normal,181,N,0,Up,1
+62,F,ASY,124,209,0,Normal,163,N,0,Up,0
+44,M,NAP,130,233,0,Normal,179,Y,0.4,Up,0
+46,M,ATA,101,197,1,Normal,156,N,0,Up,0
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+44,M,ASY,110,197,0,LVH,177,N,0,Up,1
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+42,M,ASY,136,315,0,Normal,125,Y,1.8,Flat,1
+52,M,ATA,128,205,1,Normal,184,N,0,Up,0
+65,F,NAP,140,417,1,LVH,157,N,0.8,Up,0
+63,F,ATA,140,195,0,Normal,179,N,0,Up,0
+45,F,ATA,130,234,0,LVH,175,N,0.6,Flat,0
+41,F,ATA,105,198,0,Normal,168,N,0,Up,0
+61,M,ASY,138,166,0,LVH,125,Y,3.6,Flat,1
+60,F,NAP,120,178,1,Normal,96,N,0,Up,0
+59,F,ASY,174,249,0,Normal,143,Y,0,Flat,1
+62,M,ATA,120,281,0,LVH,103,N,1.4,Flat,1
+57,M,NAP,150,126,1,Normal,173,N,0.2,Up,0
+51,F,ASY,130,305,0,Normal,142,Y,1.2,Flat,1
+44,M,NAP,120,226,0,Normal,169,N,0,Up,0
+60,F,TA,150,240,0,Normal,171,N,0.9,Up,0
+63,M,TA,145,233,1,LVH,150,N,2.3,Down,0
+57,M,ASY,150,276,0,LVH,112,Y,0.6,Flat,1
+51,M,ASY,140,261,0,LVH,186,Y,0,Up,0
+58,F,ATA,136,319,1,LVH,152,N,0,Up,1
+44,F,NAP,118,242,0,Normal,149,N,0.3,Flat,0
+47,M,NAP,108,243,0,Normal,152,N,0,Up,1
+61,M,ASY,120,260,0,Normal,140,Y,3.6,Flat,1
+57,F,ASY,120,354,0,Normal,163,Y,0.6,Up,0
+70,M,ATA,156,245,0,LVH,143,N,0,Up,0
+76,F,NAP,140,197,0,ST,116,N,1.1,Flat,0
+67,F,ASY,106,223,0,Normal,142,N,0.3,Up,0
+45,M,ASY,142,309,0,LVH,147,Y,0,Flat,1
+45,M,ASY,104,208,0,LVH,148,Y,3,Flat,0
+39,F,NAP,94,199,0,Normal,179,N,0,Up,0
+42,F,NAP,120,209,0,Normal,173,N,0,Flat,0
+56,M,ATA,120,236,0,Normal,178,N,0.8,Up,0
+58,M,ASY,146,218,0,Normal,105,N,2,Flat,1
+35,M,ASY,120,198,0,Normal,130,Y,1.6,Flat,1
+58,M,ASY,150,270,0,LVH,111,Y,0.8,Up,1
+41,M,NAP,130,214,0,LVH,168,N,2,Flat,0
+57,M,ASY,110,201,0,Normal,126,Y,1.5,Flat,0
+42,M,TA,148,244,0,LVH,178,N,0.8,Up,0
+62,M,ATA,128,208,1,LVH,140,N,0,Up,0
+59,M,TA,178,270,0,LVH,145,N,4.2,Down,0
+41,F,ATA,126,306,0,Normal,163,N,0,Up,0
+50,M,ASY,150,243,0,LVH,128,N,2.6,Flat,1
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+61,F,ASY,130,330,0,LVH,169,N,0,Up,1
+54,M,ASY,124,266,0,LVH,109,Y,2.2,Flat,1
+54,M,ASY,110,206,0,LVH,108,Y,0,Flat,1
+52,M,ASY,125,212,0,Normal,168,N,1,Up,1
+47,M,ASY,110,275,0,LVH,118,Y,1,Flat,1
+66,M,ASY,120,302,0,LVH,151,N,0.4,Flat,0
+58,M,ASY,100,234,0,Normal,156,N,0.1,Up,1
+64,F,NAP,140,313,0,Normal,133,N,0.2,Up,0
+50,F,ATA,120,244,0,Normal,162,N,1.1,Up,0
+44,F,NAP,108,141,0,Normal,175,N,0.6,Flat,0
+67,M,ASY,120,237,0,Normal,71,N,1,Flat,1
+49,F,ASY,130,269,0,Normal,163,N,0,Up,0
+57,M,ASY,165,289,1,LVH,124,N,1,Flat,1
+63,M,ASY,130,254,0,LVH,147,N,1.4,Flat,1
+48,M,ASY,124,274,0,LVH,166,N,0.5,Flat,1
+51,M,NAP,100,222,0,Normal,143,Y,1.2,Flat,0
+60,F,ASY,150,258,0,LVH,157,N,2.6,Flat,1
+59,M,ASY,140,177,0,Normal,162,Y,0,Up,1
+45,F,ATA,112,160,0,Normal,138,N,0,Flat,0
+55,F,ASY,180,327,0,ST,117,Y,3.4,Flat,1
+41,M,ATA,110,235,0,Normal,153,N,0,Up,0
+60,F,ASY,158,305,0,LVH,161,N,0,Up,1
+54,F,NAP,135,304,1,Normal,170,N,0,Up,0
+42,M,ATA,120,295,0,Normal,162,N,0,Up,0
+49,F,ATA,134,271,0,Normal,162,N,0,Flat,0
+46,M,ASY,120,249,0,LVH,144,N,0.8,Up,1
+56,F,ASY,200,288,1,LVH,133,Y,4,Down,1
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src/Heart-Disease/heart_failure.py

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+
+# import the libraries
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import classification_report
+from sklearn.preprocessing import LabelEncoder
+from sklearn.linear_model import LogisticRegression
+from sklearn.preprocessing import StandardScaler
+from sklearn.metrics import plot_confusion_matrix, accuracy_score, classification_report
+from sklearn import metrics
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import RandomForestClassifier
+import joblib
+
+# load the data
+data = pd.read_csv("heart.csv")
+
+'''LabelEncoder'''
+labelencoder = LabelEncoder()
+data["Sex"] = labelencoder.fit_transform(data["Sex"])
+
+data["ChestPainType"] = labelencoder.fit_transform(data["ChestPainType"])
+data["RestingECG"] = labelencoder.fit_transform(data["RestingECG"])
+data["ExerciseAngina"] = labelencoder.fit_transform(data["ExerciseAngina"])
+data["ST_Slope"] = labelencoder.fit_transform(data["ST_Slope"])
+
+#### Remove the outlier
+Q1 = data.quantile(0.25)
+Q3 = data.quantile(0.75)
+IQR = Q3-Q1
+
+data = data[~((data < (Q1 - 1.5 * IQR)) | (data > (Q3 + 1.5 * IQR))).any(axis = 1)]
+
+'''Prepare the dataset'''
+
+label = data["HeartDisease"].copy()
+data = data.drop("HeartDisease",axis=1)
+
+'''Train test split'''
+X_train, X_test, y_train, y_test = train_test_split(data, label, test_size = 0.2, random_state = 42)
+
+scale = StandardScaler()
+X_train = scale.fit_transform(X_train)
+X_test = scale.fit_transform(X_test)
+
+'''Decision Tree Model'''
+
+mdl = DecisionTreeClassifier(criterion="entropy", max_depth=6)
+mdl.fit(X_train,y_train)
+
+# save the model
+heart_file = 'heart_model.sav'
+joblib.dump(mdl, heart_file)
+
+load_model = joblib.load(heart_file)
+y_p = load_model.predict(X_test)
+
+print(y_p)
+print()
+print(y_test)

src/Heart-Disease/heart_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1eea631c94b402ca2c376d1eb9a2c88c40b13a028efd541949eeece9c33e6b8f
+size 4128

src/Heart-Disease/scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0427bda7da16b7b20d10929e2f8857c5cc9ac23c4d0d61895e3e06ff9d0d520d
+size 713

src/Malaria-Detection/.virtual_documents/Untitled.ipynb

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+
+
+import tensorflow
+
+
+# import the necessary libraries
+import os
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from tensor
+import keras
+from keras.models import Sequential
+from keras.layers import Dense, Conv2D , MaxPool2D , Flatten , Dropout , BatchNormalization
+from keras.preprocessing.image import ImageDataGenerator
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import classification_report,confusion_matrix
+from keras.callbacks import ReduceLROnPlateau
+import cv2
+from keras.models import load_model
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