first commit

Heart Attack.csv

@@ -0,0 +1,1320 @@
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app.py

@@ -0,0 +1,9 @@
+import streamlit as st 
+import eda
+import prediction
+
+page = st.sidebar.selectbox('Pilih Halaman :', ('EDA', 'Prediction'))
+if page == 'EDA':
+    eda.run()
+else:
+    prediction.run()

eda.py

@@ -0,0 +1,56 @@
+# import libraries 
+import streamlit as st 
+import pandas as pd 
+import seaborn as sns 
+import matplotlib.pyplot as plt 
+import plotly.express as px 
+from PIL import Image
+
+def run():
+
+    # membuat title
+    st.title('Heart Disease Prediction')
+
+    # membuat subheader
+    st.subheader('EDA untuk analisa dataset heart disease classification')
+
+    # menambahkan gambar 
+    image = Image.open('heart_disease_1.jpeg')
+    st.image(image, caption = 'Heart Disease')
+
+    # menambahkan deskripsi
+    st.write('Web App ini berfungsi untuk memprediksi penyakit jantung menggunakan machine learning (by: Hammam Mahdy)')
+
+    # membuat markdown
+    st.markdown('---------------')
+
+    # dataframe
+    df = pd.read_csv('Heart Attack.csv')
+
+    # membuat bar plot
+    st.write('#### Plot Gender')
+    fig = plt.figure(figsize=(15,5))
+    sns.countplot(x=df['gender'], data=df)
+
+    # membuat histogram plot
+    st.write('#### Histogram Age')
+    fig = plt.figure(figsize=(15,5))
+    sns.histplot(df['age'], bins=30, kde=True)
+    st.pyplot(fig)
+
+    # membuat bar plot berdasarkan inputan user
+    st.write('#### Bar Plot berdasarkan pilihan user')
+    option = st.selectbox('pilih column :', ('gender', 'class'))
+    fig = plt.figure(figsize=(15,5))
+    sns.countplot(x=option, data=df)
+    st.pyplot(fig)
+
+    # membuat histogram berdasarkan inputan user
+    st.write('#### Histogram Plot berdasarkan pilihan user')
+    option = st.selectbox('pilih column :', ('age', 'impluse', 'pressurehight', 'pressurelow', 'glucose', 'kcm', 'troponin'))
+    fig = plt.figure(figsize=(15,5))
+    sns.histplot(df[option], bins=30, kde=True)
+    st.pyplot(fig)
+    
+if __name__ == '__main__':
+    run()

model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4948ad0b5ceb739f2be98bbf20a61893c40210a0ef6f701c89bab3997b552d28
+size 179463

prediction.py

@@ -0,0 +1,87 @@
+import streamlit as st 
+import pandas as pd 
+import numpy as np 
+import pickle
+from PIL import Image
+
+def run():
+
+    # membuat title
+    st.title('Heart Disease Prediction')
+
+    # membuat subheader
+    st.subheader('Data Form Input Heart Disease Classification')
+
+    # menambahkan gambar 
+    image = Image.open('heart_disease_1.jpeg')
+    st.image(image, caption = 'Heart Disease')
+
+    with st.form('Form Heart Disease Prediction'):
+    
+        # field age
+        age = st.number_input('Age', min_value=18, max_value=90)
+
+        # field gender
+        gender = st.number_input('Gender', min_value=0, max_value=1)
+
+        # field impluse
+        impluse = st.number_input('Impluse', min_value=50 , max_value=200)
+
+        # field pressurehight
+        pressurehight = st.number_input('pressurehight', min_value=60 , max_value=250)
+
+        # field pressurelow
+        pressurelow = st.number_input('pressurelow', min_value=60, max_value=250)
+
+        # field glucose
+        glucose = st.number_input('glucose', min_value=50, max_value=370)
+
+        # field kcm
+        kcm = st.number_input('kcm', min_value=24, max_value=500)
+
+        # field troponin
+        troponin = st.number_input('troponin', min_value=1, max_value=500)
+
+        # submit button
+        submitted = st.form_submit_button('Predict')
+
+
+    # inference
+    # load all files
+    
+    with open('scaler.pkl', 'rb') as file_1:
+        scaler = pickle.load(file_1)
+    with open('model.pkl', 'rb') as file_2:
+        model = pickle.load(file_2)
+
+    
+    data_inf = {
+        'age' : age,
+        'gender' : gender,
+        'impluse' : impluse,
+        'pressurehight' : pressurehight,
+        'pressurelow' : pressurelow,
+        'glucose' : glucose,
+        'kcm' : kcm,
+        'troponin' : troponin
+    }
+
+    # memasukkan data inference ke dataframe
+
+    data_inf = pd.DataFrame([data_inf])
+    st.dataframe(data_inf)
+
+    # logic ketika predict button ditekan
+    if submitted:
+        data_inf_drop = data_inf.drop(['gender', 'impluse', 'pressurehight', 'pressurelow', 'glucose'], axis=1)
+
+        data_inf_scaled = scaler.transform(data_inf_drop)
+
+    # predict
+        y_pred_inf = model.predict(data_inf_scaled)
+        st.write('## Heart Disease :', str(int(y_pred_inf)))
+        st.write('### Positive : 1, Negative : 2')
+
+if __name__ == '__main__':
+    run()
+

requirements.txt

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+streamlit==0.59.0
+pandas
+numpy
+matplotlib
+seaborn
+plotly
+Pillow
+scikit-learn==1.2.2

scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:dba6c87c114f100d28d45eabb0c3436ff7e314af0873512481285f5089847a63
+size 711