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app.py

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+import streamlit as st
+import eda
+import predict
+
+navigation = st.sidebar.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():
+    # Membuat title
+    st.title('Fifa 2022 Player Rating Prediction')
+
+    # Membuat Subheader
+    st.subheader('EDA untuk Analisis Dataset FIFA 2022')
+
+    # Menambah Gambar
+    st.image('https://digitalhub.fifa.com/transform/34dd7fb5-4887-4015-b61d-bbbf6bdfa34a/Argentina-v-France-Final-FIFA-World-Cup-Qatar-2022?&io=transform:fill,aspectratio:16x9&quality=75',
+             caption= 'World Cup Champion')
+    
+    # Menambah Deskripsi
+    st.write('Page ini dibuat oleh gigis')
+    st.write('#Head')
+    st.write('##SubHeader')
+    st.write('###SubSubHeader')
+
+    # membuat garis lurus
+    st.markdown('---')
+
+    # Magic syntax
+    '''
+    Pada page ini, penulis akan melakukan explorasi sederhana
+    dataset yang digunakan adalah dataset fifa
+    dataset ini diambil dari sofia.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)
+
+    # Membuat Barplot
+    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 pilihanmu')
+    pilihan= st.selectbox('pilih features:',('Age','Height','Weight'))
+    fig = plt.figure(figsize= (15,5))
+    sns.histplot(df[pilihan], bins=30, kde=True)
+    st.pyplot(fig)
+
+    # membuat 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 sha256:6c9b58639df04b184e8d9acc8a1dcd31d6f6e307e6358589293cc38facaf2dc7
+size 1579208

predict.py

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+import pandas as pd
+import numpy as np
+import pickle
+import json
+import streamlit as st
+with open('model_svr.pkl', 'rb') as file_6:
+  model = pickle.load(file_6)
+
+with st.form("my_form"):
+    st.write("Inside the form")
+    nama = st.text_input('masukan nama player',value='nama', help= 'disini masukan nama player')
+    age = st.number_input('masukan usia player', min_value=15, max_value=100)
+    height = st.slider('Height', 50,250,170)
+    weight = st.slider('Weight', 50,100,170)
+    price = st.number_input('Price', 0,1000000,10000)
+    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('PaceTotal', 0,100,100)
+    shooting = st.number_input('ShootingTotal', 0,100,100)
+    passing = st.number_input('PassingTotal', 0,100,100)
+    dribbling = st.number_input('DribblingTotal', 0,100,100)
+    defending = st.number_input('DefendingTotal', 0,100,100)
+    physicality = st.number_input('PhysicalityTotal', 0,100,100)
+
+   # Every form must have a submit button.
+    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': shooting,
+    'PassingTotal': passing,
+    'DribblingTotal': dribbling,
+    'DefendingTotal': defending,
+    'PhysicalityTotal': physicality
+}
+
+data_inf = pd.DataFrame([data_inf])
+data_inf
+
+if submitted:
+    result= model.predict(data_inf)
+    st.write(f'## Player Rating: {round(result[0])}')
+    st.balloons()
+    st.snow()
+
+if __name__ =='__main__':
+   data_inf()

requirements.txt

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