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HousePrice.py
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HousePrice.py
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import pandas as pd
import numpy as np
from PIL import Image,ImageDraw,ImageFont
from pandas.core.frame import DataFrame
import time
from requests.api import options
import streamlit as st
import requests
import base64
import xgboost as xgb
from sklearn.base import TransformerMixin
from scipy.sparse import csr_matrix
from scipy.sparse import hstack
import joblib
from sklearn.pipeline import Pipeline
from streamlit import caching
from streamlit.scriptrunner import RerunException
from PIL import Image,ImageDraw,ImageFont
st.set_page_config(page_title='House Price Project', page_icon=':house_with_garden')
#Menü gizleme
st.markdown(""" <style>
#MainMenu {visibility: hidden;}
footer {visibility: hidden;}
</style> """, unsafe_allow_html=True)
#Tek sayfaya sığdırma
padding = 0
st.markdown(f""" <style>
.reportview-container .main .block-container{{
padding-top: {padding}rem;
padding-right: {padding}rem;
padding-left: {padding}rem;
padding-bottom: {padding}rem;
}} </style> """, unsafe_allow_html=True)
dil = ["TR", "EN"]
col1, col2, col3, col4, col5,col6,col7,col8,col9,col10,col11,col12 = st.columns([1,1,1,1,1,1,1,1,1,1,1,5])
detail = False
with col12:
dil_secenek = st.radio("Language",dil)
st.write('<style>div.row-widget.stRadio > div{flex-direction:row;}</style>', unsafe_allow_html=True)
class SparseMatrix(TransformerMixin):
def __init__(self):
None
def fit(self, X, y=None):
return self
def transform(self, X, y=None):
categorical_columns = ['MSSubClass','MSZoning', 'Street', 'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'Heating', 'Electrical', 'Functional', 'FireplaceQu', 'GarageType', 'GarageFinish','GarageQual','GarageCond', 'PavedDrive', 'Fence', 'SaleType', 'SaleCondition']
X[categorical_columns] = X[categorical_columns].astype(str)
ohe = joblib.load('ohe.joblib')
hot = ohe.transform(X[categorical_columns].astype(str))
cold_df = X.select_dtypes(exclude=["object"])
cold = csr_matrix(cold_df.values)
final_sparse_matrix = hstack((hot, cold))
final_csr_matrix = final_sparse_matrix.tocsr()
return final_csr_matrix
data_pipeline = Pipeline([('sparse', SparseMatrix())])
bst = xgb.Booster()
bst.load_model('./model/housepricexgb.model')
single_row = pd.read_csv('./data/single_row.csv',index_col =0)
if "load_state" not in st.session_state:
st.session_state.load_state = False
if (dil_secenek == "TR")&(detail == False) :
if "load_state" not in st.session_state:
st.session_state.load_state = False
if (st.sidebar.button("Haydi başlayalım! 🔍") or st.session_state.load_state) != True :
col1, col2, col3, col4, col5,col6,col7,col8,col9,col10,col11,col12 = st.columns([1,1,1,1,1,1,1,1,1,1,1,5])
with col12:
if dil_secenek == "TR":
button = st.button("Beğen 👍")
if button:
st.write("Teşekkür ederiz 💗")
file1 = open("counter.txt","r")
count = file1.read()
count_int = count.replace("'","")
count_int = int(count_int) + 1
with open('counter.txt', 'w') as f:
f.write(str(count_int))
st.title("H O Ş G E L D İ N İ Z !")
st.title("Lütfen istediğiniz evin özelliklerini seçiniz!")
st.title("Proje hakkında")
#Buradaki bilgiler bizim projemiz için değiştirilerek bir giriş yazısı yazılacak
st.markdown('<span style="font-family:Papyrus; font-size:1.5em;"><b><i>Ev fiyatı tahminleme aracı:</i></b> Ev satın almak isteyen tüketicilerin kendileri için uygun bulduğu özelliklerdeki evin yaklaşık fiyatını gösteren bir Python projesidir.</span>', unsafe_allow_html=True)
st.markdown('<span style="font-family:Papyrus; font-size:1.5em;"> Ev satın almak isteyen tüketicilerin kendileri için uygun bulduğu özelliklerdeki evin yaklaşık fiyatını gösteren bir Python projesidir.</span>', unsafe_allow_html=True)
st.markdown('<span style="font-family:Papyrus; font-size:1.5em;">Siz de hayallerinizde evin ederini görmek için sol taraftaki açılır pencereden özellikleri seçin!</span>', unsafe_allow_html=True)
st.markdown(" ")
#Proje Geliştiricileri olarak kendimizi ekleyeceğiz
st.title("Proje Geliştiricileri")
st.markdown(" ")
col1, col2, col3, col4, col5, = st.columns([1,1,1,1,1])
with col1:
st.markdown("<b><i>Ömer Topcu</i></b>", unsafe_allow_html=True)
st.markdown("[](https://www.linkedin.com/in/drot/)")
st.markdown("[](https://github.com/dromertopcu)")
with col2:
st.markdown("<b><i>Enes Bol</i></b>", unsafe_allow_html=True)
st.markdown("[](https://www.linkedin.com/in/enesbol/)")
st.markdown("[](https://github.com/enesbol)")
with col3:
st.markdown("<b><i>Yasin Tarakcı</i></b>", unsafe_allow_html=True)
st.markdown("[](https://www.linkedin.com/in/yasintarakci)")
st.markdown("[](https://github.com/ysntrkc)")
with col2:
st.markdown("<b><i>Eren Güneştaş</i></b>", unsafe_allow_html=True)
st.markdown("[](https://www.linkedin.com/in/erengunestas/)")
st.markdown("[](https://github.com/shuharii)")
with col1:
st.markdown("<b><i>Tunahan Demirkol</i></b>", unsafe_allow_html=True)
st.markdown("[](https://www.linkedin.com/in/tunahandemirkol/)")
st.markdown("[](https://github.com/TunahanDemirkol)")
with col3:
st.markdown("<b><i>Furkan Güneştaş</i></b>", unsafe_allow_html=True)
st.markdown("[](https://www.linkedin.com/in/fgunestas/)")
st.markdown("[](https://github.com/fgunestas)")
with col4:
st.markdown("<b><i>Kumru Orkun</i></b>", unsafe_allow_html=True)
st.markdown("[](https://www.linkedin.com/in/kumru-orkun-30848b198/)")
st.markdown("[](https://github.com/kumruo)")
with col4:
st.markdown("<b><i>Baranalp Özkan</i></b>", unsafe_allow_html=True)
st.markdown("[](https://www.linkedin.com/in/baranalpozkan/)")
st.markdown("[](https://github.com/baranalpozkan)")
with col5:
st.markdown("<b><i>M.Yavuz Gökmen</i></b>", unsafe_allow_html=True)
st.markdown("[](https://www.linkedin.com/in/myavuzgokmen/)")
st.markdown("[](https://github.com/AbyssWatcher-17)")
else:
st.session_state.load_state = True
st.title("Lütfen istediğiniz evin özelliklerini seçiniz!")
with st.sidebar:
#Buradaki optionların isimleri Türkçeye çevrilecek
#CAT_OPTIONS
LandContour_options =['', 'Daireye Yakın/Seviye', 'Yatırılmış - Hızlı ve önemli artış', 'Yamaç', 'Depresyon']
LandContour_dict ={'':'' ,'Daireye Yakın/Seviye':'Lvl', 'Yatırılmış - Hızlı ve önemli artış':'Bnk', 'Yamaç':'HLS', 'Depresyon':'Low'}
LandContour = st.sidebar.selectbox('Mülkün düzlüğü',options=LandContour_options)
PavedDrive_options=['', 'Döşeli', 'Kısmi Kaldırım', 'Kir/Çakıl']
PavedDrive_dict={'':'','Döşeli':'Y', 'Kısmi Kaldırım':'P', 'Kir/Çakıl':'N'}
PavedDrive= st.sidebar.selectbox('Döşeme yolu',options=PavedDrive_options)
BsmtQual_options = ['','Mükemmel', 'İyi', 'Ortalama', 'İdare Eder']
BsmtQual_dict = {'':'','Mükemmel':'5','İyi':'4', 'Ortalama':'3', 'İdare Eder':'2'}
BsmtQual = st.sidebar.selectbox('Bodrum Kalitesi Türü',options=BsmtQual_options)
Condition1_options =['','Ana caddeye bitişik','Besleyici caddeye bitişik', 'Normal', "Kuzey-Güney Demiryolunun 200'ü İçinde", 'Kuzey-Güney Demiryoluna Bitişik', 'Pozitif saha dışı özelliği-- park, yeşil kuşak, vb.','Pozitif tesis dışı özelliğe bitişik', "Doğu-Batı Demiryolunun 200'ü içinde",'Doğu-Batı Demiryoluna Bitişik']
Condition1_dict ={'':'','Ana caddeye bitişik':'Artery','Besleyici caddeye bitişik':'Feedr', 'Normal':'Norm', "Kuzey-Güney Demiryolunun 200'ü İçinde":'RRNn', 'Kuzey-Güney Demiryoluna Bitişik':'RRAn','Pozitif saha dışı özelliği-- park, yeşil kuşak, vb.':'PosN','Pozitif tesis dışı özelliğe bitişik':'PosA',"Doğu-Batı Demiryolunun 200'ü içinde":'RRNe','Doğu-Batı Demiryoluna Bitişik':'RRAe'}
Condition1 = st.sidebar.selectbox('Çeşitli koşullara yakınlık',options=Condition1_options)
FireplaceQu_options = ['','Olağanüstü Duvar Şömine','Ana seviyede duvar şöminesi','Ana yaşam alanında Prefabrik Şömine veya bodrum katında Yığma Şömine','Bodrumda Prefabrik Şömine','Ben Franklin Soba'] #TA ?
FireplaceQu_dict = {'':'','Olağanüstü Duvar Şömine':'5', 'Ana seviyede duvar şöminesi':'4','Ana yaşam alanında Prefabrik Şömine veya bodrum katında Yığma Şömine':'3','Bodrumda Prefabrik Şömine':'2','Ben Franklin Soba':'1'}
FireplaceQu = st.sidebar.selectbox('Şömine Kalitesi',options=FireplaceQu_options)
Alley_options = ['','Çakıl', 'Döşeli']
Alley_dict = {'':"",'Çakıl':'Grvl','Döşeli':'Pave'}
Alley = st.sidebar.selectbox('Mülke geçit erişimi türü', options=Alley_options)
Neighborhood_options = ['',"Bloomington Heights","Bluestem",'Briardale','Brookside','Clear Creek','College Creek','Crawford','Edwards','Gilbert','Iowa DOT and Rail Road', 'Meadow Village', 'Mitchell', 'North Ames', 'Northridge', 'Northpark Villa', 'Northridge Heights', 'Northwest Ames', 'Old Town', 'South & West of Iowa State University', 'Sawyer', 'Sawyer West', 'Somerset', 'Stone Brook', 'Timberland', 'Veenker']
Neighborhood_dict = {'':'',
'Bloomington Heights':'Blmngtn',
'Bluestem':'Blueste',
'Briardale':'BrDale',
'Brookside':'BrkSide',
'Clear Creek':'ClearCr',
'College Creek':'CollgCr',
'Crawford':'Crawfor',
'Edwards':'Edwards',
'Gilbert':'Gilbert',
'Iowa DOT and Rail Road':'IDOTRR',
'Meadow Village':'MeadowV',
'Mitchell':'Mitchel',
'North Ames':'Names',
'Northridge':'NoRidge',
'Northpark Villa':'NPkVill',
'Northridge Heights':'NridgHt',
'Northwest Ames':'NWAmes',
'Old Town':'OldTown',
'South & West of Iowa State University':'SWISU',
'Sawyer':'Sawyer',
'Sawyer West':'SawyerW',
'Somerset':'Somerst',
'Stone Brook':'StoneBr',
'Timberland':'Timber',
'Veenker':'Veenker'}
Neighborhood = st.sidebar.selectbox('Ames şehir sınırları içindeki fiziksel konumlar',options=Neighborhood_options)
GarageQual_options = ['','Excellent', 'Good', 'Typical', 'Fair', 'Poor']
GarageQual_dict = {'':'','Excellent':'5','Good':'4', 'Typical':'3', 'Fair':'2','Poor':'1'}
GarageQual = st.sidebar.selectbox('Garaj Kalitesi',options=GarageQual_options)
###
#NUM_OPTIONS
BsmtFinSF1 = st.sidebar.select_slider(
'Tamamlanmış bodrum alanı',
options=[*range(0, 5645)])
FullBath = st.sidebar.select_slider(
'Üst sınıf banyolar',
options=[*range(0,4)])
KitchenQual= st.sidebar.select_slider(
'Mutfak kalitesi',
options=[*range(2,6)])
TotalBsmtSF= st.sidebar.select_slider(
'Bodrum alanının toplam metrekaresi',
options=[*range(0,6111)])
ndFlrSF = st.sidebar.select_slider(
'İkinci kat metrekare',
options=[*range(1,2066)])
GarageArea = st.sidebar.select_slider(
'Garajın metrekare cinsinden büyüklüğü',
options=[*range(0,1419)])
KitchenAbvGr = st.sidebar.select_slider(
'Sınıf Üstü Mutfaklar',
options = [*range(0,4)])
OverallQual = st.sidebar.select_slider(
'Evin genel malzemesi ve bitişinin değerlendirmesi',
options=[*range(1,11)])
GrLivArea = st.sidebar.select_slider(
'Zeminden yüksekteki yaşam alanı',
options=[*range(334,5643)])
GarageCars = st.sidebar.select_slider(
'Garajın araç kapasitesi',
options=[*range(0,5)])
ExterQual = st.sidebar.select_slider(
'Dış cephe malzeme kalitesi',
options=[*range(2,6)])
soru_list1 = [LandContour_dict,PavedDrive_dict,BsmtQual_dict,Condition1_dict,FireplaceQu_dict,Alley_dict,Neighborhood_dict,GarageQual_dict] #Categoric olanların dict'leri bu listeye eklenecek
soru_list2 = [LandContour,PavedDrive,BsmtQual,Condition1,FireplaceQu,Alley,Neighborhood,GarageQual] #categoric olanların seçimleri bu listeye eklenecek
soru_list3 = []
for i,j in zip(soru_list2,soru_list1):
soru_list3.append(j[i])
soru_list3 += [BsmtFinSF1,FullBath,KitchenQual,TotalBsmtSF,ndFlrSF,GarageArea,KitchenAbvGr,OverallQual,GrLivArea,GarageCars,ExterQual] #numeric olanların seçimleri bu listeye eklenecek
soru_list4 = ['LandContour','PavedDrive','BsmtQual','Condition1','FireplaceQu','Alley','Neighborhood','GarageQual','BsmtFinSF1','FullBath','KitchenQual','TotalBsmtSF','2ndFlrSF','GarageArea','KitchenAbvGr','OverallQual','GrLivArea','GarageCars','ExterQual'] #categoric ve numeric olanların isimleri bu listeye eklenecek
if st.sidebar.button('Evin Tahmini Fiyatını Göster'):
for i in range(len(soru_list3)):
if soru_list3[i] != '' :
single_row.loc[0,soru_list4[i]] = soru_list3[i]
single_row_transformed = data_pipeline.fit_transform(single_row)
xgmat = xgb.DMatrix(single_row_transformed,missing = -999.0)
ypred = bst.predict(xgmat)
st.title('Seçtiğiniz evin tahmini fiyatı : ')
st.title(np.round(ypred[0]))
if st.button('Baştan Başlayalım!') :
st.legacy_caching.clear_cache()
del st.session_state.load_state
st.experimental_rerun()
if (dil_secenek == "EN")&(detail ==0):
if "load_state" not in st.session_state:
st.session_state.load_state = False
if (st.sidebar.button("Let's start! 🔍") or st.session_state.load_state) != True :
col1, col2, col3, col4, col5,col6,col7,col8,col9,col10,col11,col12 = st.columns([1,1,1,1,1,1,1,1,1,1,1,5])
with col12:
if dil_secenek == "EN":
button = st.button("Like 👍")
if button:
st.write("Appreciated 💗")
file1 = open("counter.txt","r")
count = file1.read()
count_int = count.replace("'","")
count_int = int(count_int) + 1
with open('counter.txt', 'w') as f:
f.write(str(count_int))
st.title("W E L C O M E !")
st.title("Please select the properties of the house you want!")
st.title("About")
#Buraya projemiz hakkında ingilizce giriş yazısı eklenecek
st.markdown('<span style="font-family:Papyrus; font-size:1.5em;"><b><i>House price estimation tool: </i></b> It is a Python project that shows the approximate price of a house with features that consumers who want to buy a house find suitable for them.</span>', unsafe_allow_html=True)
st.markdown('<span style="font-family:Papyrus; font-size:1.5em;">This tool allows consumers to make a prediction about the price in terms of the features they want. At the same time, it is helpful to see giving up which features will get a more suitable price. The selected features are the features that affect the house price the most, respectively.</span>', unsafe_allow_html=True)
st.markdown('<span style="font-family:Papyrus; font-size:1.5em;">Select the features from the left-hand pop-up window to see the price of the dream house !</span>', unsafe_allow_html=True)
#Buraya yine developerlar olarak kendimizi ekleyeceğiz
st.markdown(" ")
st.title("Project Developers")
st.markdown(" ")
col1, col2, col3, col4, col5, = st.columns([1,1,1,1,1])
with col1:
st.markdown("<b><i>Ömer Topcu</i></b>", unsafe_allow_html=True)
st.markdown("[](https://www.linkedin.com/in/drot/)")
st.markdown("[](https://github.com/dromertopcu)")
with col2:
st.markdown("<b><i>Enes Bol</i></b>", unsafe_allow_html=True)
st.markdown("[](https://www.linkedin.com/in/enesbol/)")
st.markdown("[](https://github.com/enesbol)")
with col3:
st.markdown("<b><i>Yasin Tarakcı</i></b>", unsafe_allow_html=True)
st.markdown("[](https://www.linkedin.com/in/yasintarakci)")
st.markdown("[](https://github.com/ysntrkc)")
with col2:
st.markdown("<b><i>Eren Güneştaş</i></b>", unsafe_allow_html=True)
st.markdown("[](https://www.linkedin.com/in/erengunestas/)")
st.markdown("[](https://github.com/shuharii)")
with col1:
st.markdown("<b><i>Tunahan Demirkol</i></b>", unsafe_allow_html=True)
st.markdown("[](https://www.linkedin.com/in/tunahandemirkol/)")
st.markdown("[](https://github.com/TunahanDemirkol)")
with col3:
st.markdown("<b><i>Furkan Güneştaş</i></b>", unsafe_allow_html=True)
st.markdown("[](https://www.linkedin.com/in/fgunestas/)")
st.markdown("[](https://github.com/fgunestas)")
with col4:
st.markdown("<b><i>Kumru Orkun</i></b>", unsafe_allow_html=True)
st.markdown("[](https://www.linkedin.com/in/kumru-orkun-30848b198/)")
st.markdown("[](https://github.com/kumruo)")
with col4:
st.markdown("<b><i>Baranalp Özkan</i></b>", unsafe_allow_html=True)
st.markdown("[](https://www.linkedin.com/in/baranalpozkan/)")
st.markdown("[](https://github.com/baranalpozkan)")
with col5:
st.markdown("<b><i>M.Yavuz Gökmen</i></b>", unsafe_allow_html=True)
st.markdown("[](https://www.linkedin.com/in/myavuzgokmen/)")
st.markdown("[](https://github.com/AbyssWatcher-17)")
else:
st.session_state.load_state = True
st.title("Please select the properties of the house you want!")
with st.sidebar:
#CAT_OPTIONS
###
LandContour_options = ['','Near Flat/Level', 'Banked - Quick and significant rise','Hillside','Depression']
LandContour_dict = {'':'','Near Flat/Level':'Lvl', 'Banked - Quick and significant rise':'Bnk','Hillside':'HLS','Depression':'Low'}
LandContour = st.sidebar.selectbox('Flatness of the property',options=LandContour_options)
PavedDrive_options=['', 'Paved','Partial Pavement','Dirt/Gravel']
PavedDrive_dict={'':'', 'Paved':'Y','Partial Pavement':'P','Dirt/Gravel':'N'}
PavedDrive= st.sidebar.selectbox('Paved driveway',options=PavedDrive_options)
BsmtQual_options = ['','Excellent', 'Good', 'Typical', 'Fair']
BsmtQual_dict = {'':'','Excellent':'5','Good':'4', 'Typical':'3', 'Fair':'2'}
BsmtQual = st.sidebar.selectbox('Type of Basement Quality',options=BsmtQual_options)
Condition1_options = ['','Adjacent to arterial street','Adjacent to feeder street', 'Normal', "Within 200' of North-South Railroad", 'Adjacent to North-South Railroad','Near positive off-site feature--park, greenbelt, etc.','Adjacent to postive off-site feature',"Within 200' of East-West Railroad",'Adjacent to East-West Railroad']
Condition1_dict = {'':'','Adjacent to arterial street':'Artery','Adjacent to feeder street':'Feedr', 'Normal':'Norm', "Within 200' of North-South Railroad":'RRNn', 'Adjacent to North-South Railroad':'RRAn','Near positive off-site feature--park, greenbelt, etc.':'PosN','Adjacent to postive off-site feature':'PosA',"Within 200' of East-West Railroad":'RRNe','Adjacent to East-West Railroad':'RRAe'}
Condition1 = st.sidebar.selectbox('Proximity to various conditions',options=Condition1_options)
FireplaceQu_options = ['','Exceptional Masonry Fireplace','Masonry Fireplace in main level','Prefabricated Fireplace in main living area or Masonry Fireplace in basement','Prefabricated Fireplace in basement','Ben Franklin Stove'] #TA ?
FireplaceQu_dict = {'':'','Exceptional Masonry Fireplace':'5', 'Masonry Fireplace in main level':'4','Prefabricated Fireplace in main living area or Masonry Fireplace in basement':'3','Prefabricated Fireplace in basement':'2','Ben Franklin Stove':'1'}
FireplaceQu = st.sidebar.selectbox('Fireplace Quality',options=FireplaceQu_options)
Alley_options = ['','Gravel','Paved']
Alley_dict = {'':"",'Gravel':'Grvl','Paved':'Pave'}
Alley = st.sidebar.selectbox('Type of alley access to property', options=Alley_options)
Neighborhood_options = ['',"Bloomington Heights","Bluestem",'Briardale','Brookside','Clear Creek','College Creek','Crawford','Edwards','Gilbert','Iowa DOT and Rail Road', 'Meadow Village', 'Mitchell', 'North Ames', 'Northridge', 'Northpark Villa', 'Northridge Heights', 'Northwest Ames', 'Old Town', 'South & West of Iowa State University', 'Sawyer', 'Sawyer West', 'Somerset', 'Stone Brook', 'Timberland', 'Veenker']
Neighborhood_dict = {'':'',
'Bloomington Heights':'Blmngtn',
'Bluestem':'Blueste',
'Briardale':'BrDale',
'Brookside':'BrkSide',
'Clear Creek':'ClearCr',
'College Creek':'CollgCr',
'Crawford':'Crawfor',
'Edwards':'Edwards',
'Gilbert':'Gilbert',
'Iowa DOT and Rail Road':'IDOTRR',
'Meadow Village':'MeadowV',
'Mitchell':'Mitchel',
'North Ames':'Names',
'Northridge':'NoRidge',
'Northpark Villa':'NPkVill',
'Northridge Heights':'NridgHt',
'Northwest Ames':'NWAmes',
'Old Town':'OldTown',
'South & West of Iowa State University':'SWISU',
'Sawyer':'Sawyer',
'Sawyer West':'SawyerW',
'Somerset':'Somerst',
'Stone Brook':'StoneBr',
'Timberland':'Timber',
'Veenker':'Veenker'}
Neighborhood = st.sidebar.selectbox('Physical locations within Ames city limits',options=Neighborhood_options)
GarageQual_options = ['','Excellent', 'Good', 'Typical', 'Fair', 'Poor']
GarageQual_dict = {'':'','Excellent':'5','Good':'4', 'Typical':'3', 'Fair':'2','Poor':'1'}
GarageQual = st.sidebar.selectbox('Garage Quality',options=GarageQual_options)
#categoric olanlar buraya
###
#NUM_OPTIONS
BsmtFinSF1 = st.sidebar.select_slider(
'Finished Basement Area',
options=[*range(0, 5645)])
FullBath = st.sidebar.select_slider(
'Full bathrooms above grade',
options=[*range(0,4)])
KitchenQual= st.sidebar.select_slider(
'Kitchen quality',
options=[*range(2,6)])
TotalBsmtSF = st.sidebar.select_slider(
'Total square feet of basement area',
options=[*range(0,6111)])
ndFlrSF = st.sidebar.select_slider(
'Second floor square feet',
options=[*range(1,2066)])
GarageArea = st.sidebar.select_slider(
'Size of garage in square feet',
options=[*range(0,1419)])
KitchenAbvGr = st.sidebar.select_slider(
'Kitchens above grade' ,
options = [*range(0,4)])
OverallQual = st.sidebar.select_slider(
'Rates the overall material and finish of the house',
options=[*range(1,11)])
GrLivArea = st.sidebar.select_slider(
'Above grade (ground) living area square feet',
options=[*range(334,5643)])
GarageCars = st.sidebar.select_slider(
'The size of garage according to car capacity',
options=[*range(0,5)])
ExterQual = st.sidebar.select_slider(
'Evaluates the quality of the material on the exterior',
options=[*range(2,6)])
# numeric olanlar buraya
soru_list1 = [LandContour_dict,PavedDrive_dict,BsmtQual_dict,Condition1_dict,FireplaceQu_dict,Alley_dict,Neighborhood_dict,GarageQual_dict] #Categoric olanların dict'leri bu listeye eklenecek
soru_list2 = [LandContour,PavedDrive,BsmtQual,Condition1,FireplaceQu,Alley,Neighborhood,GarageQual] #categoric olanların seçimleri bu listeye eklenecek
soru_list3 = []
for i,j in zip(soru_list2,soru_list1):
soru_list3.append(j[i])
soru_list3 += [BsmtFinSF1,FullBath,KitchenQual,TotalBsmtSF,ndFlrSF,GarageArea,KitchenAbvGr,OverallQual,GrLivArea,GarageCars,ExterQual] #numeric olanların seçimleri bu listeye eklenecek
soru_list4 = ['LandContour','PavedDrive','BsmtQual','Condition1','FireplaceQu','Alley','Neighborhood','GarageQual','BsmtFinSF1','FullBath','KitchenQual','TotalBsmtSF','2ndFlrSF','GarageArea','KitchenAbvGr','OverallQual','GrLivArea','GarageCars','ExterQual'] #categoric ve numeric olanların isimleri bu listeye eklenecek
if st.sidebar.button('Show House Price'):
for i in range(len(soru_list3)):
if soru_list3[i] != '' :
single_row.loc[0,soru_list4[i]] = soru_list3[i]
single_row_transformed = data_pipeline.fit_transform(single_row)
xgmat = xgb.DMatrix(single_row_transformed,missing = -999.0)
ypred = bst.predict(xgmat)
st.title('With selected properties estimated Price of the House: ')
st.title(np.round(ypred[0]))
if st.button('Start over') :
st.legacy_caching.clear_cache()
del st.session_state.load_state
st.experimental_rerun()
detail = st.sidebar.checkbox('Details')
if detail:
# Create a page dropdown
st.write(" ")
st.write(" ")
st.title('Model Değerlendirmesi ve Başarı Metrikleri')
st.write('Model hakkında genel bilgiler.')
col1, col3, col5, = st.columns([1,3,3])
im1 = Image.open('./images/MSE-İterations.png').resize((500,300))
im2 = Image.open('./images/totalbms_saleprice.png').resize((450,305))
im3 = Image.open('./images/oiriginal_predicted.png')
with col1:
b1 = st.image(im1, width=400)
with col5:
b3 = st.image(im3, width=400)
with col1:
b2 = st.image(im2, width=400)
with col5:
st.write("MSE: 635030379.79")
st.write(" RMSE: 25199.81")
st.write(" R2_score: 0.904")