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Cardiovascular_Disease_Dataset.csv

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app.py

@@ -0,0 +1,105 @@
+import streamlit as st 
+import joblib
+import numpy as np
+## Heading 
+html_temp = """
+    <div style="background-color:black;padding:10px">
+    <h2 style="color:red;text-align:center;">Cardiovascular Health Prediction App </h2>
+    </div>
+    """
+st.markdown(html_temp, unsafe_allow_html=True)
+
+st.write("Enter the Patient  details:")
+
+# model
+
+health_model=joblib.load("health_model.joblib")
+
+scaler_model=joblib.load('scaler_the data_model.joblib')
+
+# Input variables
+
+col1, col2 = st.columns(2)
+with col1:
+    
+    ## Age
+    age=st.slider("Slide the AGE of the Patient:",max_value=80,min_value=20)
+    
+with col2:
+    
+    ## gender
+    option_gen=["Female "," Male"]
+    gen=st.selectbox("Select the Gender",option_gen) 
+    gender_value=option_gen.index(gen)
+    
+col1, col2 = st.columns(2)
+with col1:
+    ## chestpain
+    chestpain=st.slider("Slide the chestpain Value:",min_value=0,max_value=3)
+    
+with col2:
+    ## restingBP
+    restingBP=st.slider("Slide the Resting  BP of the Patient:",min_value=90,max_value=200)
+
+col1,col2=st.columns(2)
+with col1:
+    ## serumcholestrol
+    serumcholestrol=st.slider("Slide The Serum cholestrol Of a Ptient :",min_value=0,max_value=600)
+    
+with col2:
+    ## fastingbloodsugar
+    fastingbloodsugar= st.radio("Is Patient blood is Fasting:", ["YES", "NO"])
+    if fastingbloodsugar=="YES":
+        fastingbloodsugar_value=1
+    else:
+       fastingbloodsugar_value=0
+    
+col1,col2=st.columns(2)
+
+with col1:
+   ## restingrelectro
+    restingrelectro=st.slider("Slide The restingrelectro of a Patient :",min_value=0,max_value=2)
+        
+with col2:
+    ## maxheartrate
+    maxheartrate=st.slider("Slide The max heart rate  of the Patient :",min_value=70,max_value=200)
+
+col1,col2=st.columns(2)
+with col1:
+    ## exerciseangia
+    exerciseangia=st.slider("Slide the exerciseangia Value:",min_value=0,max_value=1)
+    
+
+with col2:
+    ##oldpeak'
+    oldpeak=st.slider("Silde the oldpeak :",max_value=6.2,min_value=0.0,value=0.1)
+    
+    
+col1,col2=st.columns(2)
+with col1:
+    ## slope
+    slope=st.slider("Silde the slope of the Patient :",max_value=3,min_value=0,value=1)
+    
+with col2:
+    ## noofmajorvessels
+    noofmajorvessels=st.slider("Silde the No of Major Vessels to  a Patient :",max_value=3,min_value=0,value=1)
+
+
+if st.button("Submit"):
+    
+    # st.write("before scaleing:",[age,gender_value,chestpain,restingBP,serumcholestrol,fastingbloodsugar_value,restingrelectro,
+    #                              maxheartrate,exerciseangia,oldpeak,slope,noofmajorvessels])
+    # Scaler the values
+    scaler_val = scaler_model.transform([[age,gender_value,chestpain,restingBP,serumcholestrol,fastingbloodsugar_value,restingrelectro,
+                                 maxheartrate,exerciseangia,oldpeak,slope,noofmajorvessels]])
+    
+    # st.write("after scaleing:",scaler_val)
+    
+    output = health_model.predict(scaler_val)
+
+    
+    if output ==0:
+        st.write("The patient is Healthy")
+    else:
+        st.write("The Patient has Heart Disease")
+    

health_model.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8e27fde24bad6f4c0faf99d405106bd6c760adf85623f74204dfa45153377aff
+size 1590569

requirements.txt

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+joblib==1.2.0
+matplotlib==3.7.1
+matplotlib-inline==0.1.6
+numpy==1.26.4
+pandas==1.5.3
+scikit-learn==1.6.0
+streamlit==1.41.1
+tabulate==0.8.10
+imblearn==0.13.0

scaler_the data_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d656dfe054c1e7f608f33f4d3c7f8b15bfdeb9e359798d1e7fbc4cca33a99679
+size 1655