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app.py

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+import streamlit as st 
+import eda 
+import predict 
+
+navigation = st.slidebar.selectbox('Pilih Halaman:', {'EDA':'Predict'})
+
+if navigation == 'EDA':
+    eda.run()
+else:
+    predict.run()

eda.py

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+import streamlit as st 
+import pandas as pd 
+import seaborn as sns 
+import matplotlib.pyplot as plt 
+import plotly.express as px
+
+st.set_page_config(
+    page_title = 'FIFA 2022 - EDA',
+    layout = 'wide',
+    initial_sidebar_state = 'expanded'
+)
+
+def run():
+    st.title("FIFA 2022 Player Rating Prediction")
+
+    st.subheader('EDA untuk Analisis Dataset FIFA 2022')
+
+    st.image('https://e2e85xpajrr.exactdn.com/wp-content/uploads/2022/09/21190008/shutterstock_2190840355-scaled.jpg?strip=all&lossy=1&ssl=1', 
+             caption='World Cup Champion')
+
+    st.write('Page ini dibuat oleh Vincent')
+    st.write('# Head')
+    st.write('## SubHeader')
+    st.write('### SubsubHeader')
+
+    st.markdown('---')
+
+    '''
+    Pada page ini, penulis akan melakukan eksplorasi sederhana, Dataset yang digunakan adalah dataset FIFA 2022. 
+    Dataset ini berasal dari web [sofia.com](www.google.com)
+    '''
+
+    # Show dataframe 
+    df = pd.read_csv('https://raw.githubusercontent.com/FTDS-learning-materials/phase-1/master/w1/P1W1D1PM%20-%20Machine%20Learning%20Problem%20Framing.csv')
+    st.dataframe(df)
+
+    st.write('### Plot AttackingWorkRate')
+    fig = plt.figure(figsize=(15,5))
+    sns.countplot(x= 'AttackingWorkRate', data=df)
+    st.pyplot(fig)
+
+    # Membuat histogram berdasarkan input user 
+    st.write(' ### Histogram berdasarkan pilihan-mu')
+    pilihan = st.selectbox('Pilih feature: ',('Age','Height','Weight'))
+    fig = plt.figure(figsize= (15,5))
+    sns.histplot(df[pilihan], bins = 30, kde = True)
+    st.pyplot(fig)
+
+    # Membuat plotlt plot 
+    st.write('### Plot antara ValueEur dengan Price')
+    fig = px.scatter(df,x='ValueEUR',y='Overall', hover_data=['Name','Age'])
+    st.plotly_chart(fig)
+
+if __name__ == '__main__':
+    run()
+

model_svr.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2b675bcdf0b49515c8d6d6b211db0f270dd85bcbf079ea8e6c1da1ef28a7cd5f
+size 1579177

predict.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import pickle
+with open('model_svr.pkl', 'rb') as file_6:
+  model = pickle.load(file_6)
+
+def run():
+    with st.form("my_form"):
+        
+        nama = st.text_input('masukan nama player', value='nama')
+        age = st.number_input('masukan usia player', min_value=15, max_value= 40, value=20)
+        height = st.slider('Height',50, 250, 170)
+        weight = st.number_input('Weight',50,100,70)
+        price = st.number_input('Price',0,1000000,10000, help="Harga Pemain dalam euro " )
+        # Every form must have a submit button.
+        st.write('-'*50)
+        attack = st.selectbox('Attacking Work Rate', {'Low','Medium','High'},index=1)
+        defense = st.radio('Defensive Work Rate', {'Low','Medium','High'},index=1)
+        st.markdown('---')
+
+        pace  = st.number_input('Pace',0,100,50)
+        shoot  = st.number_input('Shoot',0,100,50)
+        passing = st.number_input('Passing',0,100,50)
+        dribble = st.number_input('Dribble',0,100,50)
+        defend = st.number_input('Defend',0,100,50)
+        physicality = st.number_input('Physicality',0,100,50)
+        submitted = st.form_submit_button("Submit")
+
+    st.write("Outside the form")
+
+    data_inf = {
+        'Name': nama,
+        'Age' : age,
+        'Height' : height,
+        'Weight' : weight,
+        'Price' : price,
+        'AttackingWorkRate': attack,
+        'DefensiveWorkRate': defense,
+        'PaceTotal': pace,
+        'ShootingTotal': shoot,
+        'PassingTotal': passing,
+        'DribblingTotal': dribble,
+        'DefendingTotal': defend,
+        'PhysicalityTotal': physicality
+    }
+
+    data_inf = pd.DataFrame([data_inf])
+
+    if submitted:
+        result= model.predict(data_inf)
+        st.write(f'## Player Rating: {round(result[0])}')
+        st.balloons()
+        st.snow()
+
+if __name__ == '__main__':
+    run()

requirement.txt

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+streamlit
+pandas 
+seaborn 
+matplotlib 
+numpy 
+scikit-learn==1.3.0
+plotyly