Spaces:
Runtime error
Runtime error
First Commit
Browse files- eda.py +71 -0
- list_cat_cols.txt +1 -0
- list_num_cols.txt +1 -0
- main.py +10 -0
- model_encoder.pkl +3 -0
- model_lin_reg.pkl +3 -0
- model_scaler.pkl +3 -0
- prediction.py +82 -0
- requirements.txt +8 -0
eda.py
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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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# Melebarkan visualisasi untuk memaksmalkan browser
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st.set_page_config(
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page_title='FIFA 2022',
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layout='wide',
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initial_sidebar_state='expanded'
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)
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def run():
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# Membuat title
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st.title('Fifa 2022 Player Rating Prediction')
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# Membuat Sub Headrer
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st.subheader('EDA untuk Analisa Dataset FIFA 2022')
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# Menambahkan Gambar
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image = Image.open('soccer.jpg')
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st.image(image, caption='FIFA 2022')
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# Menambahkan Deskripsi
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st.write('Page ini dibuat oleh ***Fadya***')
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st.write('# Halo')
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st.write('## Halo')
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st.write('### Halo')
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# Membuat Garis Lurus
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st.markdown('---')
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# Magic Syntax
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'''
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Pada page kali ini, penulis akan melakukan eksplorasi sederhana.
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Dataset yang digunakan adalah dataset FIFA 2022.
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Dataset ini berasal dari web sofifa.com.
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'''
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# Show DataFrame
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data = pd.read_csv('https://raw.githubusercontent.com/ardhiraka/FSDS_Guidelines/master/p1/v3/w1/P1W1D1PM%20-%20Machine%20Learning%20Problem%20Framing.csv')
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st.dataframe(data)
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# Membuat Barplot
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st.write('#### Plot AttackingMorkRate')
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fig = plt.figure(figsize=(15,5))
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sns.countplot(x='AttackingWorkRate', data=data)
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st.pyplot(fig)
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# Membuat Histogram
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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(data['Overall'], bins=30, kde=True)
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st.pyplot(fig)
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# Membuat Plotly Plot
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st.write('#### Plotly Plot - ValueEUR dengan Overall')
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fig = px.scatter(data, x='ValueEUR', y='Overall', hover_data=['Name', 'Age'])
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st.plotly_chart(fig)
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# Membuat Histogram Berdasarkan Input User
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st.write('#### Histogram berdasarkan Input User')
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pilihan = st.selectbox('Pilih Column : ', ('Age', 'Weight', 'Height', 'ShootingTotal'))
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fig = plt.figure(figsize=(15,5))
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sns.histplot(data[pilihan], bins=30, kde=True)
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st.pyplot(fig)
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if __name__ == '__main__':
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run()
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list_cat_cols.txt
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["AttackingWorkRate", "DefensiveWorkRate"]
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list_num_cols.txt
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["Age", "Height", "Weight", "Price", "PaceTotal", "ShootingTotal", "PassingTotal", "DribblingTotal", "DefendingTotal", "PhysicalityTotal"]
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main.py
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import streamlit as st
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import eda
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import prediction
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navigation = st.sidebar.selectbox('Pilih Halaman : ', ('EDA', 'Project A Player'))
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if navigation == 'EDA':
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eda.run()
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else:
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prediction.run()
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model_encoder.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:d679ce958dcc5fde5d08221484e8d07eeb6a0acb2298a30087aa791acc886bf7
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size 572
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model_lin_reg.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:ce6fbcfd793f11352525ffe462688212f5d081f879efd4dd1ccbf8f990336cf9
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size 595
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model_scaler.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:8e1f92e11e77b25cb4695aa80cadd3867d32115656e74a2c1b4417080e138841
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size 1096
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prediction.py
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import streamlit as st
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import pandas as pd
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import numpy as np
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import pickle
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import json
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# Load All Files
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with open('model_lin_reg.pkl', 'rb') as file_1:
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model_lin_reg = pickle.load(file_1)
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with open('model_scaler.pkl', 'rb') as file_2:
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model_scaler = pickle.load(file_2)
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with open('model_encoder.pkl','rb') as file_3:
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model_encoder = pickle.load(file_3)
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with open('list_num_cols.txt', 'r') as file_4:
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list_num_cols = json.load(file_4)
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with open('list_cat_cols.txt', 'r') as file_5:
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list_cat_cols = json.load(file_5)
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def run():
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with st.form(key='form_fifa_2022'):
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name = st.text_input('Name', value='')
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age = st.number_input('Age', min_value=16, max_value=60, value=25, step=1, help='Usia Pemain')
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weight = st.number_input('Weight', min_value=50, max_value=150, value=70)
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height = st.slider('Height', 50, 250, 170)
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price = st.number_input('Price', min_value=0, max_value=1000000000, value=0)
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st.markdown('---')
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attacking_work_rate = st.selectbox('AttackingWorkRate', ('Low', 'Medium', 'High'), index=1)
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defensive_work_rate = st.radio('DefensiveWorkRate', ('Low', 'Medium', 'High'), index=1)
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st.markdown('---')
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pace = st.number_input('Pace', min_value=0, max_value=100, value=50)
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shooting = st.number_input('Shooting', min_value=0, max_value=100, value=50)
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passing = st.number_input('Passing', min_value=0, max_value=100, value=50)
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dribbling = st.number_input('Dribbling', min_value=0, max_value=100, value=50)
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defending = st.number_input('Defending', min_value=0, max_value=100, value=50)
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physicality = st.number_input('Physicality', min_value=0, max_value=100, value=50)
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submitted = st.form_submit_button('Predict')
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data_inf = {
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'Name': name,
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'Age': age,
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'Height': height,
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'Weight': weight,
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'Price': price,
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'AttackingWorkRate': attacking_work_rate,
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'DefensiveWorkRate': defensive_work_rate,
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'PaceTotal': pace,
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'ShootingTotal': shooting,
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'PassingTotal': passing,
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'DribblingTotal': dribbling,
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'DefendingTotal': defending,
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'PhysicalityTotal': physicality
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}
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data_inf = pd.DataFrame([data_inf])
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st.dataframe(data_inf)
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if submitted:
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# Split between Numerical Columns and Categorical Columns
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data_inf_num = data_inf[list_num_cols]
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data_inf_cat = data_inf[list_cat_cols]
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data_inf_num
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# Feature Scaling and Feature Encoding
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data_inf_num_scaled = model_scaler.transform(data_inf_num)
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data_inf_cat_encoded = model_encoder.transform(data_inf_cat)
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data_inf_final = np.concatenate([data_inf_num_scaled, data_inf_cat_encoded], axis=1)
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# Predict using Linear Regression
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y_pred_inf = model_lin_reg.predict(data_inf_final)
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st.write('# Rating : ', str(int(y_pred_inf)))
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if __name__ == '__main__':
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run()
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requirements.txt
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# Berisi daftar library yang kita butuhkan
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streamlit
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pandas
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seaborn
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matplotlib
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numpy
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scikit-learn==1.2.1
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