mt

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
+from PIL import Image
+
+st.set_page_config(
+    page_title='FIFA 2022',
+    layout='wide',
+    initial_sidebar_state='expanded'
+)
+def run():
+    st.title('fifa 2022 player rating data analysis')
+    st.subheader('eda untuk analisa dataset')
+
+    image= Image.open('soccer.jpg')
+    st.image(image, caption='fifa 2022')
+
+if __name__== '__main__':
+    run()

import.py

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+import streamlit as st
+import subprocess
+
+def run_prediction():
+    result = subprocess.run(['python', 'prediction.py'], stdout=subprocess.PIPE)
+    return result.stdout.decode('utf-8')
+
+def run_eda():
+    result = subprocess.run(['python', 'eda.py'], stdout=subprocess.PIPE)
+    return result.stdout.decode('utf-8')
+
+pages = {
+    'Prediction': run_prediction,
+    'EDA': run_eda,
+}
+
+selected_page = st.sidebar.selectbox('Select a page', list(pages.keys()))
+
+page_content = pages[selected_page]()
+st.write(page_content)

list_cat_cols.txt

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+["AttackingWorkRate", "DefensiveWorkRate"]

list_num_cols.txt

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+["Age", "Height", "Weight", "Price", "PaceTotal", "ShootingTotal", "PassingTotal", "DribblingTotal", "DefendingTotal", "PhysicalityTotal"]

main.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
+from PIL import Image
+import eda
+import prediciton
+
+navigation=st.sidebar.selectbox('Pilih Halaman:', ('EDA','Predict A player'))
+
+if navigation== 'EDA':
+    eda.run()
+else:
+    prediciton.run()

model_encoder.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2240ea3c82dad58d6247c4654a4c46ab2c0f0ca495f01515a364924fbe2c9c3e
+size 539

model_lin_reg.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:70087a6eef90d9e5071121722f1e174ce2138cd18db8ba5330a757e5150844e5
+size 651

model_scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:38558e0c33e6ab5e79b980f8c436f2bf761625562a30f7635281ee4aaa15fd11
+size 1096

prediciton.py

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+import pickle
+import streamlit as st
+import json
+import pandas as pd
+import numpy as np
+import sklearn
+# Load All Files
+def run():
+  with st.form(key='formfifa2022'):
+      name =st.text_input('Name', value='')
+      age =st.number_input('Age', min_value=16, max_value=60,help='usia pemain')
+      weight=st.number_input('Weight', min_value=50, max_value=150, value=70)
+      height=st.slider('Height', 50, 250, 170)
+      price=st.number_input('Price', min_value=0, max_value=1000000000, value=0)
+      st.markdown('---')
+      attacking_work_rate = st.selectbox('AttackingWorkRate',('Low','Medium','High'), index=1)
+      defensive_work_rate = st.selectbox('DefensiveWorkRate',('Low','Medium','High'), index=1)
+      st.markdown('---')
+      pace =st.number_input('Pace', min_value=0, max_value=100, value=50)
+      defending =st.number_input('Defending', min_value=0, max_value=100, value=50)
+      physicality=st.number_input('Physicality', min_value=0, max_value=100, value=50)
+      Passing =st.number_input('Passing', min_value=0, max_value=100, value=50)
+      Shooting =st.number_input(' Shooting ', min_value=0, max_value=100, value=50)
+      Dribbling =st.number_input('Dribbling', min_value=0, max_value=100, value=50)
+      submitted =st.form_submit_button('Predict')
+
+  with open('model_lin_reg.pkl', 'rb') as file_1:
+    model_lin_reg = pickle.load(file_1)
+
+  with open('model_scaler.pkl', 'rb') as file_2:
+    model_scaler = pickle.load(file_2)
+
+  with open('model_encoder.pkl','rb') as file_3:
+    model_encoder = pickle.load(file_3)
+
+  with open('list_num_cols.txt', 'r') as file_4:
+    list_num_cols = json.load(file_4)
+
+  with open('list_cat_cols.txt', 'r') as file_5:
+    list_cat_cols = json.load(file_5)
+
+  data_inf = {
+      'Name': name,
+      'Age':age,
+      'Height': height,
+      'Weight': weight,
+      'Price':price,
+      'AttackingWorkRate': attacking_work_rate,
+      'DefensiveWorkRate': defensive_work_rate,
+      'PaceTotal': pace,
+      'ShootingTotal': Shooting,
+      'PassingTotal': Passing,
+      'DribblingTotal': Dribbling,
+      'DefendingTotal': defending,
+      'PhysicalityTotal': physicality
+  }
+
+  data_inf = pd.DataFrame([data_inf])
+  data_inf
+
+  data_inf_num = data_inf[list_num_cols]
+  data_inf_cat = data_inf[list_cat_cols]
+  data_inf_num
+
+  # Feature Scaling and Feature Encoding
+
+  data_inf_num_scaled = model_scaler.transform(data_inf_num)
+  data_inf_cat_encoded = model_encoder.transform(data_inf_cat)
+  data_inf_final = np.concatenate([data_inf_num_scaled, data_inf_cat_encoded], axis=1)
+
+  y_pred_inf = model_lin_reg.predict(data_inf_final)
+  print('y_pred_inf')
+
+if __name__== '__main__':
+    run()

requirement.txt

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+matplotlib
+json
+pickle
+pandas
+numpy
+plotly
+seaborn
+scikit-learn==1.2.1
+PIL