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app.py

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+import streamlit as st
+import eda
+import prediction
+from PIL import Image
+
+img1 = Image.open('balls.jpg')
+
+page = st.sidebar.selectbox('Choose Page: ', 
+                            ('Landing Page','Data Exploration','Data Prediction'))
+
+if page == 'Landing Page':
+    st.title('FIFA 2022 Player Rating Prediction')
+    st.write('')
+    st.write('Name      : Yosef')
+    st.write('Batch     : HCK-007')
+    st.write('Objective : Ovuvwevewe Onyetevwevwe Ugwemugwem Ossass')
+    st.write('')
+    st.write('Please select menu on the left bar')
+    st.write('')
+    # st.image(img1, caption="Balls")
+    with st.expander('Background Dataset'):
+        st.caption('Dataset background explaination')
+    with st.expander('Problem Statement'):
+        st.caption('Saya mau uang')
+    with st.expander('SPONSOR'):
+        st.caption('FIFA')
+        # st.image(img1)
+
+elif page == 'Data Exploration':
+    eda.run()
+else:
+    prediction.run()

eda.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+import plotly.express as px
+from PIL import Image
+
+st.set_page_config(
+    page_title='FIFA 2022 Prediction',
+    layout='wide',
+    initial_sidebar_state='expanded')
+
+def run():
+    st.title("FIFA 2022 Exploratory Data Analysis")
+
+    st.subheader("Analysis Data for 2022 Football Player by FIFA")
+
+    # # Add image
+    # image = Image.open('socerfield.jpg')
+    # st.image(image, caption='FIFA 2022')
+
+    # Create Outline
+    st.write("# Outline A")
+    st.write("## Outline A.1")
+    st.write("### Outline A.1.a")
+    st.write("# Outline B")
+
+    # Create markdown line
+    st.markdown("---")
+
+    # Show datafreame
+    st.write("# Dataframe FIFA 2022")
+    df = pd.read_csv('https://raw.githubusercontent.com/FTDS-learning-materials/phase-1/master/w1/P1W1D1PM%20-%20Machine%20Learning%20Problem%20Framing.csv')
+    st.dataframe(df)
+    st.markdown("---")
+
+    # Visualization
+    st.write('# Visualization')
+
+    # Create Barplot
+    st.write('## Attacking Work Rate Plot')
+    fig = plt.figure(figsize=(15,5))
+    sns.countplot(x='AttackingWorkRate', data=df)
+    st.pyplot(fig)
+    st.write('Explanation')
+
+    # Create Histplot
+    st.write('## Histogram of Rating')
+    fig = plt.figure(figsize=(15,5))
+    sns.histplot(x='Overall',
+                data=df,
+                bins=50,
+                kde=True)  # Add line
+    st.pyplot(fig)
+    st.write("Explanation")
+
+    # Create scatterplot
+    st.write('## Scatterplot of Weight and Height')
+    fig = plt.figure(figsize=(15,5))
+    sns.scatterplot(x='Weight',
+                    y='Height',
+                    data=df)
+    st.pyplot(fig)
+    st.write("Explanation")
+
+    # Create histogram by user input
+    st.write('# Visualization by User Input')
+    choice = st.radio('Select Feature: ',
+                        ('Age','Weight','Height','ShootingTotal'),
+                        help='Select the feature you want!')
+    fig = plt.figure(figsize=(15,5))
+    sns.histplot(df[choice],
+                bins=35,
+                kde=True)  # Add line
+    st.pyplot(fig)
+    st.write('Explaination')
+
+    # Create plotly plot
+    st.write('# Plotly Plot - Player Price againts Rating')
+    fig = px.scatter(df,
+                    x='ValueEUR',
+                    y='Overall',
+                    hover_data=['Name','Age'])
+    st.plotly_chart(fig)
+    st.write('Explain!')
+
+if __name__ == '__main__':
+    run()

list_num_columns.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 sha256:4eaee6b8047336daf8785247ccea985d34ddc47bdad5747cf963acdd7eefa1e7
+size 617

model_lin_reg.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ecadb5a4600e367fba8e82068775352bcaa3cb260acd2f04c4b7949f0887047c
+size 595

model_scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f07647394f461fe77584b199f6a6f0cc70f06cd5274f04f3830873e66e8bce24
+size 1096

prediction.py

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+import streamlit as st
+import pickle
+import json
+import pandas as pd
+import numpy as np
+
+# Load model
+with open('list_num_columns.txt', 'r') as file_1:
+    list_num_cols = json.load(file_1)
+
+with open('list_cat_columns.txt', 'r') as file_2:
+    list_cat_cols = json.load(file_2)
+
+with open('model_scaler.pkl', 'rb') as file_3:
+    model_scaler = pickle.load(file_3)
+
+with open('model_encoder.pkl', 'rb') as file_4:
+    model_encoder = 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'):
+        name = st.text_input('Name', value='', help='Player name')
+        age = st.number_input('Age', 
+                            min_value=12, 
+                            max_value=48, 
+                            value=28, 
+                            step=1,
+                            help='Player age')
+        weight = st.number_input('Weight',
+                                min_value=30,
+                                max_value=100,
+                                value=80,
+                                help='Player weight')
+        height = st.number_input('Height',
+                                min_value=140,
+                                max_value=210,
+                                value=180,
+                                help='Player height')
+        price = st.slider('Price', 0, 200000000, 0)
+        st.markdown('---')
+        defense = st.radio('Defending Work Rate',
+                        ('Low','Medium','High'),
+                        index=1)
+        attack = st.radio('Attacking Work Rate',
+                        ('Low','Medium','High'),
+                        index=1)
+        st.markdown('---')
+        pace = st.number_input('Pace', 0, 100, 50)
+        shoot = st.number_input('Shooting', 0, 100, 50)
+        passing = st.number_input('Passing', 0, 100, 50)
+        dribble = st.number_input('Dribbling', 0, 100, 50)
+        defend = st.number_input('Defending', 0, 100, 50)
+        physical = st.number_input('Phisicality', 0, 100, 50)
+        st.markdown('---')
+        submitted = st.form_submit_button('PREDICT')
+
+    data_inf = {
+        'Name': name,
+        'Age': age,
+        'Weight': weight,
+        'Height': height,
+        'Price': price,
+        'AttackRate': attack,
+        'DefenseRate': defense,
+        'PaceTotal': pace,
+        'ShootingTotal': shoot,
+        'PassingTotal': passing,
+        'DribblingTotal': dribble,
+        'DefendingTotal': defend,
+        'PhysicalityTotal': physical
+    }
+    data_inf = pd.DataFrame([data_inf])
+    st.dataframe(data_inf)
+
+    if submitted:
+        # Numeric-Categoric split
+        data_inf_num = data_inf[list_num_cols]
+        data_inf_cat = data_inf[list_cat_cols]
+        # Numeric
+        data_inf_num_scaled = model_scaler.transform(data_inf_num)
+        # Categoric
+        data_inf_cat_encoded = model_encoder.transform(data_inf_cat)
+        # Concatenate
+        data_inf_final = np.concatenate([data_inf_num_scaled,data_inf_cat_encoded], axis=1)
+        # Predict
+        y_inf_pred = model_lin_reg.predict(data_inf_final)
+        # Show prediction
+        st.write('# Rating: ', str(int(y_inf_pred)))
+
+if __name__ == '__main__':
+    run()

requirements.txt

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+streamlit
+pandas
+numpy
+matplotlib
+seaborn
+plotly.express
+scikit-learn