commit

eda.py

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+import streamlit as st
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+import numpy as np
+
+st.set_page_config(
+    page_title='FIFA 2022 - EDA',
+    layout='wide',
+    initial_sidebar_state='expanded'
+)
+
+def run():
+    # Title
+    st.title('House Price Prediction by Area Characteristics')
+
+    # Membuat Sub Header
+    st.subheader('EDA untuk Analisa Dataset FIFA 2022')
+
+    # Membuat Deskripsi
+    st.write('Page ini dibuat oleh *Danu Purnomo*')
+
+    # Menambahkan Gambar
+    #image = Image.open('soccer.jpg')
+    #st.image(image, caption='FIFA 2022')
+
+    # Membuat Garis Lurus
+    st.markdown('---')
+
+    # Magic Syntax
+    '''
+    Pada page kali ini, penulis akan melakukan eskplorasi sederhana.
+    Dataset yang digunakan adalah dataset FIFA 2022.
+    Dataset ini berasal dari web sofifa.com.
+    '''
+
+    # 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 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()

main.py

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+import streamlit as st
+import eda
+import prediction
+
+navigation = st.sidebar.selectbox('Pages : ', ('Model EDA', 'Predict House Price'))
+
+if navigation == 'Model EDA':
+    eda.run()
+else:
+    prediction.run()

model_lin_reg.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:06f4c14e2ea824ccf574f7ee53e52f77dd218e83caa2b1d350f21d402526593f
+size 632

pipeline.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:656494e15a705d39ee63dac0a8b2b97a4486efaa9b8d2359181297e4538b8a91
+size 2101

prediction.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
+import numpy as np
+import joblib
+import json
+
+# Load All Files
+with open('model_lin_reg.pkl', 'rb') as file_1:
+  model_lin_reg = joblib.load(file_1)
+
+with open('pipeline.pkl', 'rb') as file_2:
+  preprocessor = joblib.load(file_2)
+
+def run():
+    # Membuat Form
+    with st.form(key='form_parameters'):
+        income = st.number_input('Average Income', min_value=0, max_value=60, value=25, step=1)
+        age = st.number_input('House Age', min_value=0, max_value=60, value=25, step=1)
+        rooms = st.number_input('Number of Rooms', min_value=50, max_value=150, value=70)
+        bedrooms = st.number_input('Number of Bedrooms', min_value=50, max_value=150, value=70)
+        population = st.number_input('Area Population', min_value=50, max_value=150, value=70)
+        # price = st.slider('Price', 0, 100000000, 0)
+        #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.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)
+        #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)
+        st.markdown('---')
+
+        submitted = st.form_submit_button('Predict')
+
+    data_inf = {
+        'Income': income, 
+        'Age': age,
+        'Rooms': rooms,
+        'Bedrooms': bedrooms,
+        'Population': population
+    }
+
+    data_inf = pd.DataFrame([data_inf])
+    st.dataframe(data_inf)
+
+    if submitted:
+        # Feature Preprocessing
+        X_inf = preprocessor.transform(data_inf)
+
+        # Predict using Linear regression
+        y_pred_inf = model_lin_reg.predict(X_inf)
+
+        st.write('# House Price : ', str(int(y_pred_inf)))
+
+if __name__ == '__main__':
+    run()