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model-deployment-038/app.py

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+import streamlit as st
+import eda
+import prediction
+
+halaman = st.sidebar.selectbox('Pilih Halaman : ', ('EDA', 'Predict A Player'))
+
+if halaman == 'EDA':
+    eda.run()
+
+else:
+    prediction.run()

model-deployment-038/eda.py

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+import streamlit as st
+import pandas as pd
+import seaborn as sns
+from PIL import Image
+import plotly.express as px
+import matplotlib.pyplot as plt
+
+
+
+
+def run():
+
+    # Membuat Title
+    st.title('Aplikasi Prediksi Rating Pemain FIFA 2022')
+
+    # Membuat Sub Header
+    st.subheader('Page mengenai Exploratory Data Analysis dari dataset FIFA 2022')
+
+    # Menambahkan Gambar
+    image = Image.open('soccer.jpg')
+    st.image(image, caption='FIFA 2022')
+
+    # Menambahkan Teks
+    st.write('Page ini dibuat oleh ***Danu Purnomo***')
+    st.write('# Halo')
+    st.write('## Halo')
+    st.write('### Halo')
+
+    # Show DataFrame
+    data = pd.read_csv('https://raw.githubusercontent.com/FTDS-learning-materials/phase-1/master/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()

model-deployment-038/encoder.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:983eb55948c85a6ed10ef56a1b9da005da09261c6fd6ac8f6d6dc38ea343e2ee
+size 636

model-deployment-038/list_cat_cols.txt

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+["AttackingWorkRate", "DefensiveWorkRate"]

model-deployment-038/list_num_cols.txt

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

model-deployment-038/model_lin_reg.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9da3168e15beff16b96ae2a8d8f53f3f18ab3d9a660b803f5aec1811f1460249
+size 595

model-deployment-038/prediction.py

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+import streamlit as st
+import pickle
+import json
+import pandas as pd
+import numpy as np
+
+st.set_page_config(
+   page_title='FIFA 2022',
+   layout='wide',
+   initial_sidebar_state='expanded'
+)
+
+#Load 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('encoder.pkl', 'rb') as file_3:
+  encoder = pickle.load(file_3)
+
+with open('scaler.pkl', 'rb') as file_4:
+  scaler = 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, 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=0)
+        defensive_work_rate = st.radio('DefensiveWorkRate', ('Low', 'Medium', 'High'), index=1)
+        st.markdown('---')
+
+        pace = st.number_input('Kecepatan Lari', 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 categorical and numerical
+        data_inf_num = data_inf[list_num_cols]
+        data_inf_cat = data_inf[list_cat_cols]
+        
+        #Scaling and Encoding
+        data_inf_scaled = scaler.transform(data_inf_num)
+        data_inf_encoded = encoder.transform(data_inf_cat)
+        data_inf_final = np.concatenate([data_inf_scaled, data_inf_encoded], axis = 1)
+
+        #predict using linreg
+        y_pred_inf = model_lin_reg.predict(data_inf_final)
+
+        st.write('# Rating : ', str(int(y_pred_inf)))
+
+
+
+
+
+if __name__ == '__main__':
+  run()

model-deployment-038/requirements.txt

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+streamlit
+pandas
+numpy
+seaborn
+matplotlib
+plotly
+pillow
+scikit-learn==1.5.2

model-deployment-038/scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9ecd617314342916b5738cf62db3df623b8a987d995cf442719cd4d5d4d55c7c
+size 1096