-
Notifications
You must be signed in to change notification settings - Fork 12
/
1_💻_Enter_Page.py
40 lines (27 loc) · 1.34 KB
/
1_💻_Enter_Page.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
import streamlit as st
from streamlit.logger import get_logger
import streamlit.components.v1 as components
import datetime
LOGGER = get_logger(__name__)
thedate = datetime.date.today()
def run():
st.image(r'./resources/enter_page_image.jpg', use_column_width=True)
# st.set_page_config(page_title="Enter Page", page_icon="💻")
st.write("""
# Welcome to House Price Prediction!
""")
st.markdown(
"""
This repo has been developed for the Istanbul Data Science Bootcamp, organized in cooperation with IBB & Kodluyoruz.
Prediction for house prices was developed using the Kaggle House Prices - Advanced Regression Techniques competition dataset.
### Goal
The goal of this project is to predict the price of a house in Ames using the features provided by the dataset.
------
###### Group 2 | Machine Learning Model Deployment
###### Version: 1.0
"""
)
st.write("###### Date: ", thedate)
st.markdown('![Visitor count](https://shields-io-visitor-counter.herokuapp.com/badge?page=https://share.streamlit.io/your_deployed_app_link&label=VisitorsCount&labelColor=000000&logo=GitHub&logoColor=FFFFFF&color=1D70B8&style=for-the-badge)')
if __name__ == "__main__":
run()