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import streamlit as st |
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import pandas as pd |
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import seaborn as sns |
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import matplotlib.pyplot as plt |
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import plotly.express as px |
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from PIL import Image |
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def run(): |
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st.title('FIFA 2022 Player Rating Prediction') |
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st.subheader('EDA untuk Analisa Dataset FIFA 2022') |
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image = Image.open('bola.jpg') |
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st.image(image, caption = 'FIFA 2022') |
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st.write('Page ini dibuat oleh Bani') |
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st.write('**Mardhya Malik**') |
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st.write('*Mardhya Malik*') |
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st.write('# Mardhya Malik') |
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st.write('## Mardhya Malik') |
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st.write('### Mardhya Malik') |
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st.markdown('----') |
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df = pd.read_csv('https://raw.githubusercontent.com/FTDS-learning-materials/phase-1/master/w1/P1W1D1PM%20-%20Machine%20Learning%20Problem%20Framing.csv') |
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st.dataframe(df) |
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st.write('#### Plot AttackingWorkRate') |
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fig = plt.figure(figsize=(15,5)) |
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sns.countplot(x='AttackingWorkRate', data = df) |
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st.pyplot(fig) |
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st.write('#### Histogram of Rating') |
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fig = plt.figure(figsize=(15,5)) |
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sns.histplot(df['Overall'], bins = 30, kde = True) |
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st.pyplot(fig) |
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st.write('#### Histogram berdasarkan input user') |
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option = st.selectbox('Pilih Column : ', ('Age', 'Weight', 'Height', 'ShootingTotal')) |
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fig = plt.figure(figsize= (15,5)) |
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sns.histplot(df[option], bins = 30, kde = True) |
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st.pyplot(fig) |
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st.write('#### Plotly Plot - ValueEUR vs Overall') |
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fig = px.scatter(df, x = 'ValueEUR', y = 'Overall', hover_data = ['Name', 'Age']) |
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st.plotly_chart(fig) |
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if __name__ == '__main__': |
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run() |
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