comit pertama

eda.py

@@ -0,0 +1,70 @@
+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
+
+st.set_page_config(
+    page_title = 'FIFA 2022 - EDA',
+    layout = 'wide',
+    initial_sidebar_state='expanded'
+)
+#function Run eda
+def run():  
+    # Membuat Title
+    st.title('FIFA 2022 Player Rating Prediction')
+
+    # Membuat Sub Header
+    st.subheader('EDA untuk Analisa Dataset FIFA 2022')
+
+    # Menambahkan Gambar
+    image = Image.open('soccer.jpg')
+    st.image(image, caption='FIFA 2022')
+
+    # Menambahkan Deskripsi
+    st.write('Page ini dibuat oleh *Danu Purnomo*')
+    st.write('# Halo')
+    st.write('## Halo')
+    st.write('### Halo')
+
+    # Membuat Garis Lurus
+    st.markdown('---')
+
+    # Magic Syntax
+    '''
+    Pada page kali ini, penulis akan melakukan eksplorasi 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()

list_cat_cols.txt

@@ -0,0 +1 @@
+["AttackingWorkRate", "DefensiveWorkRate"]

list_num_cols.txt

@@ -0,0 +1 @@
+["Age", "Height", "Weight", "Price", "PaceTotal", "ShootingTotal", "PassingTotal", "DribblingTotal", "DefendingTotal", "PhysicalityTotal"]

main.py

@@ -0,0 +1,9 @@
+import streamlit as st
+import eda
+import prediction
+
+navigation =st.sidebar.radio('Pilih halaman : ', ('EDA','Prediction'),index=0)
+if navigation == 'EDA':
+    eda.run()
+else:
+    prediction.run()

model_encoder.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0e95575e4f4325a8b2cc3751e09de7f29dec00be64588df3e060c44b17ef7e3d
+size 572

model_lin_reg.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3438706bd0366bca697182bbd80794617319e929556af5c80f2f69a5554b15a7
+size 595

model_scaler.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:034959baab04bdd88c67ab44db7038187d1b4b7a68b23701a2414a6d58f081d9
+size 1096

prediction.py

@@ -0,0 +1,83 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import pickle
+import json
+
+# Load All Files
+
+with open('model_lin_reg.pkl', 'rb') as file_1:
+  model_lin_reg = pickle.load(file_1)
+
+with open('model_scaler.pkl', 'rb') as file_2:
+  model_scaler = pickle.load(file_2)
+
+with open('model_encoder.pkl','rb') as file_3:
+  model_encoder = pickle.load(file_3)
+
+with open('list_num_cols.txt', 'r') as file_4:
+  list_num_cols = json.load(file_4)
+
+with open('list_cat_cols.txt', 'r') as file_5:
+  list_cat_cols = json.load(file_5)
+  
+def run():
+
+  with st.form('key=form_fifa_2022'):
+      name = st.text_input('Full Name', value='')
+      age = st.number_input('Age', min_value=16, max_value=60, value=25, step=1, help='Usia Pemain')
+      weight = st.number_input('Weight', min_value=50, max_value=150, value=70)
+      height = st.slider('Height', 150, 250, 170)
+      price = st.number_input('Price', min_value=0, max_value=100000000, value=0)
+      st.markdown('---')
+
+      attacking_work_rate = st.radio('Attacking Work Rate', ('Low', 'Medium', 'High'), index=2)
+      defensive_work_rate = st.selectbox('Defensive Work Rate', ('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)
+
+      submitted = st.form_submit_button('Predict')
+
+  data_inf = {
+      'Name': name,
+      'Age': age,
+      'Height': height,
+      'Weight': weight,
+      'Price': price,
+      'AttackingWorkRate': attacking_work_rate,
+      'DefensiveWorkRate': defensive_work_rate,
+      'PaceTotal': pace,
+      'ShootingTotal': shooting,
+      'PassingTotal': passing,
+      'DribblingTotal': dribbling,
+      'DefendingTotal': defending,
+      'PhysicalityTotal': physicality
+  }
+
+  data_inf = pd.DataFrame([data_inf])
+  st.dataframe(data_inf)
+
+  if submitted: 
+      # Split between Numerical Columns and Categorical Columns
+      data_inf_num = data_inf[list_num_cols]
+      data_inf_cat = data_inf[list_cat_cols]
+
+      # Feature Scaling and Feature Encoding
+      data_inf_num_scaled = model_scaler.transform(data_inf_num)
+      data_inf_cat_encoded = model_encoder.transform(data_inf_cat)
+      data_inf_final = np.concatenate([data_inf_num_scaled, data_inf_cat_encoded], axis=1)
+
+      # Predict using Linear Regression
+      y_pred_inf = model_lin_reg.predict(data_inf_final)
+
+      st.write('# Rating : ', str(int(y_pred_inf)))
+
+
+
+