fifa

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():
+    # membuat title
+    st.title('FIFA 2022 Player Rating Prediction')
+
+    # membuat sub header
+    st.subheader ('EDA untuk Analisa Dataset FIFA 2022')
+
+    # Menambahkan Gambar
+    image = Image.open('soccer.jpg')
+    st.image(image,caption = 'FIFA 2022')
+
+    # Menambahkan Deskripsi
+    st.write('Page ini dibuat oleh')
+    st.write('# Halo')
+
+
+    # show dataframe
+    data = pd.read_csv('https://raw.githubusercontent.com/ardhiraka/FSDS_Guidelines/master/p1/v3/w1/P1W1D1PM%20-%20Machine%20Learning%20Problem%20Framing.csv')
+    st.dataframe(data)
+
+    # membuat barplot
+    st.write('#### Plot AttackingWorkRate')
+    fig =plt.figure(figsize=(15,5))
+    sns.countplot(x='AttackingWorkRate',data=data)
+    st.pyplot(fig)
+
+    # Membuat histogram
+    st.write('### Histogram of Rating')
+    fig = plt.figure(figsize=(15,5))
+    sns.histplot(data['Overall'],bins=30,kde=True)
+    st.pyplot(fig)
+
+    # Membuat Plotly Plot
+    st.write('#### PlotlyPlots - ValueEUR dengan Overall')
+    fig = px.scatter(data, x='ValueEUR', y='Overall',hover_data=['Name','Age'])
+    st.plotly_chart(fig)
+
+    # Membuat histogram berdasarkan input user
+    st.write('### Histogram berdasarkan input user')
+    pilihan = st.selectbox('Pilih column :',('Age','Weight','Height','ShootingTotal'))
+    fig = plt.figure(figsize=(15,5))
+    sns.histplot(data[pilihan],bins=30,kde=True)
+    st.pyplot(fig)
+
+if __name__ == '__main__':
+    run()

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 eda # python file
+import prediction # python file
+
+navigation = st.sidebar.selectbox('Pilih Halaman: ',('EDA','Predict a Player'))
+
+if navigation == 'EDA':
+    eda.run()
+else:
+    prediction.run()
+

model_encoder.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d679ce958dcc5fde5d08221484e8d07eeb6a0acb2298a30087aa791acc886bf7
+size 572

model_lin_reg.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ce6fbcfd793f11352525ffe462688212f5d081f879efd4dd1ccbf8f990336cf9
+size 595

model_scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8e1f92e11e77b25cb4695aa80cadd3867d32115656e74a2c1b4417080e138841
+size 1096

prediction.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import pickle
+import json
+# Load All Files
+
+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)
+
+def run():
+  with st.form(key='form_fifa_2022'):
+      name = st.text_input('Name',value='')
+      age = st.number_input('Age',min_value=16,max_value=60,value=25,step=1,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.radio('DefensiveWorkRate',('Low','Medium','High'),index=1)
+
+      st.markdown ('---')
+
+      pace = st.number_input('Pace',min_value=0,max_value=100,value=50)
+      shooting = st.number_input('Shooting',min_value=0,max_value=100,value=50)
+      passing = st.number_input('Passing',min_value=0,max_value=100,value=50)
+      dribbling = st.number_input('Dribbling',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)
+
+      submitted = st.form_submit_button('predict')
+
+  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])
+  st.dataframe(data_inf)
+
+  if submitted:
+      # Split between Numerical Columns and Categorical Columns
+
+      data_inf_num = data_inf[list_num_cols]
+      data_inf_cat = data_inf[list_cat_cols]
+      
+      # 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)
+
+      # Predict using Linear Regression
+
+      y_pred_inf = model_lin_reg.predict(data_inf_final)
+      st.write('# Rating: ',str(int(y_pred_inf)))
+
+if __name__ == '__main__':
+    run()

requirements.txt

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+streamlit
+pandas
+seaborn
+matplotlib
+numpy
+scikit-learn==1.2.1