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app.py

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+import streamlit as st
+import eda
+import prediction
+
+navigation = st.sidebar.selectbox('Pilih Halaman: ', ('EDA', 'Predict a Player'))
+
+if navigation == 'EDA':
+    eda.run()
+else:
+    prediction.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
+from PIL import Image
+
+def run():
+    # membuat Tittle
+    st.title('FIFA 2022 Player Rating Prediction')
+
+    # membuat sub header
+    st.subheader('Eda untuk Analisis Dataset FIFA 2022')
+
+
+    # menambahkan gambar
+    st.image('https://cdn.pixabay.com/photo/2016/06/22/08/40/cow-1472655_1280.png', caption='SAPI')
+
+    st.image('gambar1.png')
+
+    # menambahkan deskripsi
+    st.write('Page ini dibuat oleh Kumala Chann') #anggap seperti markdown
+    st.write('# Halo')
+    st.write('## Halo')
+    st.write('### Halo')
+
+    # membuat garis lurus 
+    st.markdown('---')
+
+    # MAGIC syntax
+    '''
+    Pada page kali ini penulis akan melakukan eksplorasi sederhana,
+    dataset yang digunakan adalah dataset FIFA 2022.
+    Dataset ini berasal dari web sofifa.com
+    '''
+
+    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)
+    st.write('Ini analisisnya')
+
+    # membuat histogram
+    st.write('### Histogram Berdasarkan Overall')
+    fig = plt.figure(figsize=(15,5))
+    sns.histplot(df['Overall'], bins=30, kde=True)
+    st.pyplot(fig)
+
+    # membuat histogram berdasrakan input user
+    st.write('### Histogram Berdasarkan Input User')
+    pilihan = st.selectbox('Pilih kolom: ', ('Age','Weight','Height','ShootingTotal'))
+    fig = plt.figure(figsize=(15,5))
+    sns.histplot(df[pilihan], bins=30, kde=True)
+    st.pyplot(fig)
+
+    if pilihan == 'Height':
+        st.write('Kolom height sangat skew')
+    elif pilihan == 'Weight':
+        st.write('Kolom weight sangat skew')
+    elif pilihan == 'Age':
+        st.write('Kolom age sangat skew')
+    else:
+        st.write('Kolom shooting total sangat skew')
+
+    # interactif chart
+    st.write('### Plotly Plot - ValueEUR dengan Overall')
+    fig = px.scatter(df, x='ValueEUR', y='Overall',hover_data=['Name','Age'])
+    st.plotly_chart(fig)
+
+
+if __name__ == '__main__':
+    run()

list_cat_columns.txt

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+["AttackRate", "DefenseRate"]

list_num_columns.txt

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

model_encoder.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1ddd248a5fdd882efccbbc8915d77c9e9812927e46bc602d2d31f34a0394384d
+size 629

model_lin_reg.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a500c8ffbd62182601c3cfd2cab7a037a15498e6ee017ce469537536e4db3bf7
+size 601

model_scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d53ae4a0f5d781eeda4c537f90ef117899a38cc7856ed24cd2946b6fc243f976
+size 1102

prediction.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import pickle
+import json
+
+# Load datanya
+with open('list_num_columns.txt', 'r') as file_1:
+    list_num_cols = json.load(file_1)
+
+with open('list_cat_columns.txt', 'r') as file_2:
+    list_cat_cols = json.load(file_2)
+
+with open('model_scaler.pkl', 'rb') as file_3:
+    model_scaler = pickle.load(file_3)
+
+with open('model_encoder.pkl', 'rb') as file_4:
+    model_encoder = pickle.load(file_4)
+
+with open('model_lin_reg.pkl', 'rb') as file_5:
+    model_lin_reg = pickle.load(file_5)
+
+def run():
+    with st.form(key='Form Parameter'):
+        name = st.text_input('Name', value='')
+        age = st.number_input('Age', min_value=0, max_value=70, step=1)
+        weight = st.number_input('Weight', min_value=0, max_value=150, step=1)
+        height = st.slider('Height', 150, 225, 165)
+        price = st.number_input('Price', min_value=0, max_value=1000000000, step=1000)
+        st.markdown('---')
+        AttackingWorkRate = st.selectbox('Attacking Work Rate', ('Low','Medium','High'),index=0)
+        DefensiveWorkRate = st.selectbox('Defensive Work Rate', ('Low','Medium','High'),index=1)
+        st.markdown('---')
+        pace = st.number_input('Pace', min_value=0, max_value=100, step=1)
+        shooting = st.number_input('Shooting Total', min_value=0, max_value=100, step=1)
+        passing = st.number_input('Passing', min_value=0, max_value=100, step=1)
+        dribbling = st.number_input('Dribbling', min_value=0, max_value=100, step=1)
+        defending = st.number_input('Defending', min_value=0, max_value=100, step=1)
+        physicality = st.number_input('Physicality', min_value=0, max_value=100, step=1)
+
+        submitted = st.form_submit_button('Predict')
+
+        data_inf = {'Name': name,
+        'Age': age,
+        'Height': height,
+        'Weight': weight,
+        'Price': price,
+        'AttackRate':AttackingWorkRate,
+        'DefenseRate': DefensiveWorkRate,
+        'PaceTotal': pace,
+        'ShootingTotal':shooting,
+        'PassingTotal':passing ,
+        'DribblingTotal':dribbling,
+        'DefendingTotal': defending,
+        'PhysicalityTotal': physicality}
+
+        df = pd.DataFrame([data_inf])
+        st.dataframe(df)
+
+        
+        if submitted:
+            # split antara num and cat column
+            data_inf_num = df[list_num_cols]
+            data_inf_cat = df[list_cat_cols]
+
+            # scaling and encoding
+            data_inf_num_scaled = model_scaler.transform(data_inf_num)
+            data_inf_cat_encoded = model_encoder.transform(data_inf_cat)
+
+            # merge
+            data_inf_final = np.concatenate([data_inf_num_scaled, data_inf_cat_encoded], axis=1)
+
+            # predict
+            y_pred_inf = model_lin_reg.predict(data_inf_final)
+            st.write('# Rating: ',str(np.round(y_pred_inf))[1:3])
+
+if __name__ == '__main__':
+    run()

requirements.txt

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+streamlit == 1.31.1
+pandas == 2.2.1
+numpy == 1.26.4
+seaborn == 0.13.2
+matplotlib == 3.8.3
+scikit-learn == 1.4.1.post1
+plotly == 5.19.0
+Pillow ==