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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('ball.jpg')
st.image(image, caption = 'FIFA 2022')
#menambahkan deskripsi
st.write('Page ini dibuat oleh Hana')
#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()
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