test

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('gambar.jpg')
+    st.image(image, caption='FIFA 2022')
+
+    # Menambahkan Deskripsi
+    st.write('Page ini dibuat oleh Theo Nugraha')
+    st.write('# Teks 1')
+    st.write('## Teks 2')
+    st.write('### Teks 3')
+
+    # Membuat Garis Lurus
+    st.markdown('---')
+
+    # Show Data Frame
+    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 Bar Plot
+    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 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)
+
+    # Membuat Plotly Plot
+    st.write('#### Plotly Plot - ValueEUR dengan Overall')
+    fig = px.scatter(data, x='ValueEUR', y='Overall', hover_data=['Name', 'Age'])
+    st.plotly_chart(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
+import prediction
+
+
+page = st.sidebar.selectbox ('Pilih Halaman : ', ('eda', 'predict A player'))
+
+if page == 'eda':
+    eda.run ()
+else:
+    prediction.run()

model_encoder.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0e95575e4f4325a8b2cc3751e09de7f29dec00be64588df3e060c44b17ef7e3d
+size 572

model_lin_reg.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:61b5177c7282ce0ad6f60601b1b9c4b0e3b25ea7fb558db8f240077726a5b47a
+size 595

model_scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:886a94e22bb659265592ec555c491e70ab234b9e3aa33b0f2546b5d69ea2f0e6
+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('list_cat_cols.txt', 'r') as file_1:
+  list_cat_cols = json.load(file_1)
+
+with open('list_num_cols.txt', 'r') as file_2:
+  list_num_cols = json.load(file_2)
+
+with open('model_scaler.pkl', 'rb') as file_3:
+  scaler = pickle.load(file_3)
+
+with open('model_encoder.pkl', 'rb') as file_4:
+  encoder = pickle.load(file_4)
+
+with open('model_lin_reg.pkl', 'rb') as file_5:
+  model_lin_reg = pickle.load(file_5)
+  
+def run():  
+    # Membuat Form
+    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, 180)
+        price = st.number_input('Price', min_value=0, max_value=10000000, 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)
+        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)
+        dribling = st.number_input('Dribling', 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)
+        
+        submited = 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': dribling,
+        'DefendingTotal': defending,
+        'PhysicalityTotal' : physicality
+    }
+
+    data_inf = pd.DataFrame([data_inf])
+    st.dataframe(data_inf)
+
+    if submited:  
+        #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 = scaler.transform(data_inf_num)
+        data_inf_cat_encoded = encoder.transform(data_inf_cat)
+        data_inf_final = np.concatenate([data_inf_num_scaled, data_inf_cat_encoded], axis = 1)
+        #Predict usiing 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
+pillow
+numpy
+scikit-learn==1.2.2