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app.py

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+import streamlit as st
+import eda
+import prediction
+
+page = st.sidebar.selectbox('Pilih Halaman: ', ('EDA', 'Prediction'))
+
+if page == '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 judul
+    st.title('FIFA 2022 Player Rating Prediction')
+
+    #membuat sub header
+    st.subheader('EDA untuk Analisa Dataset FIFA 2022')
+
+    #tambahkan gambar
+    image = Image.open('dada.jpg')
+    st.image(image, caption = 'FIFA 2022')
+
+    #menambahkan deskripsi
+    st.write('Page ini dibuat oleh bagus')
+
+    #font size
+    #font terbesar
+    st.write('# Halo')
+    st.write('## Halo')
+    #bold
+    st.write('**Tes**')
+    #italic
+    st.write('*Tes*')
+
+    #mmebuat batas dengan garis lurus
+    st.markdown('---')
+
+    #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')
+    option = st.selectbox('Pilih column : ', ('Age', 'Weight', 'Height', 'ShootingTotal'))
+    fig = plt.figure(figsize=(15,5))
+    sns.histplot(data[option], bins = 30, kde = True)
+    st.pyplot(fig)
+
+    #membuat plotly plot
+    #membandingkan ratingpemain bola dengan proce nya
+    st.write('#### Plotly plot - ValueEUR vs 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"]

model_encoder.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4329a128d619c1c5effa1b49e354bfb8f76f5f39ccfb9672fa1a687156d30f5c
+size 578

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
+#Load model
+
+with open('list_cat_cols.txt', 'r') as file_1:
+  list_cat_col = json.load(file_1)
+
+with open('list_num_cols.txt', 'r') as file_2:
+  list_num_col = json.load(file_2)
+
+with open('model_encoder.pkl', 'rb') as file_3:
+  model_encoder = pickle.load(file_3)
+
+with open('model_scaler.pkl', 'rb') as file_4:
+  model_scaler = 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('form_fifa_2022'):
+        #nama, value untuk default value
+        name = st.text_input('Name', value = ' ')
+
+        #age, min_value untuk minimum nilai yang bisa diisi, max_value maksimum nilai yang bisa diisi
+        age = st.number_input('Age', value = 25, min_value = 15, max_value = 60, help = 'isi dengan usia pemain')
+
+        #height 
+        height = st.number_input('Height', value = 170, min_value = 100, help = 'in cm')
+
+        weight = st.slider('Weight', value = 70, min_value = 50, max_value = 150)
+
+        price = st.number_input('Price', value = 0)
+
+        st.markdown('---')
+        #index untuk default value di selctbox/radio button
+        attacking_work_rate = st.selectbox('Attacking Work Rate', ('Low', 'Medium', 'High'), index = 1)
+
+        defensive_work_rate = st.radio('Defensive Work Rate', ('Low', 'Medium', 'High'), index = 1)
+
+        pace = st.number_input('Pace', min_value = 0, max_value = 100, value = 50)
+        shooting = st.number_input('Shooting Score', min_value = 0, max_value = 100, value = 50)
+        passing = st.number_input('Passing Score', min_value = 0, max_value = 100, value = 50)
+        dribbling = st.number_input('Dribbling Score', min_value = 0, max_value = 100, value = 50)
+        defending = st.number_input('Defending Score', min_value = 0, max_value = 100, value = 50)
+        physicality = st.number_input('Pysicality Score', min_value = 0, max_value = 100, value = 50)
+
+        #bikin submit button form
+        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 and categorical columns
+        data_inf_num = data_inf[list_num_col]
+        data_inf_cat = data_inf[list_cat_col]
+
+        #feature scaling and 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 reg model
+
+        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==1.2.2
+Pillow
+plotly