commit

.vscode/launch.json

@@ -0,0 +1,16 @@
+{
+    // Use IntelliSense to learn about possible attributes.
+    // Hover to view descriptions of existing attributes.
+    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Python: Current File",
+            "type": "python",
+            "request": "launch",
+            "program": "${file}",
+            "console": "integratedTerminal",
+            "justMyCode": true
+        }
+    ]
+}

eda.py

@@ -1,41 +1,33 @@
 import streamlit as st
+from PIL import Image
 import pandas as pd
 import seaborn as sns
 import matplotlib.pyplot as plt
 import numpy as np
 
+
+
+import plotly.express as px
+
 st.set_page_config(
-    page_title='FIFA 2022 - EDA',
+    page_title='US House Price - EDA',
     layout='wide',
     initial_sidebar_state='expanded'
 )
 
 def run():
     # Title
-    st.title('House Price Prediction by Area Characteristics')
-
-    # Membuat Sub Header
-    st.subheader('EDA untuk Analisa Dataset FIFA 2022')
-
-    # Membuat Deskripsi
-    st.write('Page ini dibuat oleh *Danu Purnomo*')
+    st.title('House Price Prediction EDA')
 
     # Menambahkan Gambar
-    #image = Image.open('soccer.jpg')
-    #st.image(image, caption='FIFA 2022')
+    image = Image.open('image.jpeg')
+    st.image(image, caption='US Suburbs')
 
     # Membuat Garis Lurus
     st.markdown('---')
 
-    # Magic Syntax
-    '''
-    Pada page kali ini, penulis akan melakukan eskplorasi 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')
+    data = pd.read_csv('USA_Housing.csv')
     st.dataframe(data)
 
     # Membuat BarPlot