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app.py
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app.py
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#--------------------------------Prediction Model------------------------------------
import pgeocode# for getting lat & long from zipcode
nomi = pgeocode.Nominatim('us')
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
# import joblib
# model = joblib.load("GBR.gz")
file="kc_house_data.csv" # data file
data = pd.read_csv(file)
data = data.drop(['id','date'],axis =1)
Y = data['price']
del data['price']
X = data.values
X_train, X_test , Y_train , Y_test =train_test_split(X, Y , test_size = 0.10,random_state =2)
from sklearn.ensemble import GradientBoostingRegressor
model = GradientBoostingRegressor(n_estimators=100, max_depth=4)
model.fit(X_train, Y_train)
#--------------------------------Line-Graph-data-----------------------------------------------------------------------
df=pd.read_csv('compare.csv')
dx=df.iloc[:,1:]
#--------------------------------Correlation-bar-graph-data--------------------------------------------------------------
# dc = pd.read_csv('Features_corr.csv')
#--------------------------------disturbution-graph------------------------------------------------------------------------
kc = pd.read_csv(file)
features = ['bedrooms','bathrooms','floors','view','condition','grade'] # option for Distrubution graph
#--------------------------------Data-for-scatter---------------------------------------------------------------------------
kx=pd.read_csv(file)
kx['prices'] = kx.apply(lambda x: "{:,}".format(x['price']), axis=1)
kx['txt'] = 'Price:'+kx['prices'].astype(str)+' | '+'Zipcode:'+kx['zipcode'].astype(str)
feature = ['bedrooms','bathrooms','sqft_living','floors','grade','sqft_living15']# options for Scatter plot|kc.columns
#--------------------------------Dashboard-main-------------------------------------------------------=====================
import dash
import dash_table
import dash_daq as daq
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output, State
import plotly.graph_objs as go
import flask
app = dash.Dash(
__name__,
external_stylesheets=['https://codepen.io/chriddyp/pen/bWLwgP.css']
)
url_bar_and_content_div = html.Div([
dcc.Location(id='url', refresh=False),
html.Div(id='page-content')
])
#-----------------------------------------page-0-Layout home page----------------------------------------------------------------
layout_index = html.Div([
html.Table([
html.Tr([
html.Th(html.Img(src=app.get_asset_url('infogen.png')), style={'textAlign': 'left'}),
html.Th(html.H2("King's County Housing Data Analysis")),
html.Th(dcc.Link('Home', href='/page-0'),style={'text-align': 'center'}),
html.Th(dcc.Link('Data', href='/page-1'),style={'text-align': 'center'}),
html.Th(dcc.Link('Distribution', href='/page-2'),style={'text-align': 'center'}),
html.Th(dcc.Link('Scatter', href='/page-3'),style={'text-align': 'center'}),
html.Th(dcc.Link('Zipcode', href='/page-4'),style={'text-align': 'center'}),
html.Th(dcc.Link('Price Predictor', href='/page-5'),style={'text-align': 'center'}),
html.Th(dcc.Link('Results', href='/page-6'),style={'text-align': 'center'}),
])]),
html.Div(html.Img(src=app.get_asset_url('hpp1.png')), style={'textAlign': 'left'}),
])
#-----------------------------------------page1-Data----------------------------------------------------------------------
layout_page_1 = html.Div([
html.Table([
html.Tr([
html.Th(html.Img(src=app.get_asset_url('infogen.png')), style={'textAlign': 'left'}),
html.Th(html.H2("King's County Housing Data Analysis")),
html.Th(dcc.Link('Home', href='/page-0'),style={'text-align': 'center'}),
html.Th(dcc.Link('Data', href='/page-1'),style={'text-align': 'center'}),
html.Th(dcc.Link('Distribution', href='/page-2'),style={'text-align': 'center'}),
html.Th(dcc.Link('Scatter', href='/page-3'),style={'text-align': 'center'}),
html.Th(dcc.Link('Zipcode', href='/page-4'),style={'text-align': 'center'}),
html.Th(dcc.Link('Price Predictor', href='/page-5'),style={'text-align': 'center'}),
html.Th(dcc.Link('Results', href='/page-6'),style={'text-align': 'center'}),
])]),
html.Div([
dash_table.DataTable(
id='table-filtering-be',
columns=[
{"name": i, "id": i} for i in sorted(kc.columns)
],
# filtering='be', # for filtering
# filter=''
)])
# html.H2('Page 1'),
# dcc.Graph(id='bar',
# figure={'data':[
# go.Bar(
# x=dc['Features'], # assign x as the dataframe column 'x'
# y=dc['Cor_value'],
# marker=dict(
# cmax=39,
# cmin=0,
# color=[0, 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],
# # colorbar=dict(
# # title='Colorbar'
# # ),
# colorscale='Earth'),
# )],
# 'layout': go.Layout(
# title = 'Most important features relative to target(Price)',
# yaxis = {'title': 'Features'},
# hovermode='closest',
# autosize=False,
# width=1600,
# height=700,
# margin=go.layout.Margin(
# l=50,
# r=50,
# b=100,
# t=100,
# pad=4
# ),
# paper_bgcolor='#ffffff',
# plot_bgcolor='#ffffff'
# )}
# )
])
#-----------------------------------------page-2-bar graph-counts--------------------------------------------------------------
layout_page_2 = html.Div([
html.Table([
html.Tr([
html.Th(html.Img(src=app.get_asset_url('infogen.png')), style={'textAlign': 'left'}),
html.Th(html.H2("King's County Housing Data Analysis")),
html.Th(dcc.Link('Home', href='/page-0'),style={'text-align': 'center'}),
html.Th(dcc.Link('Data', href='/page-1'),style={'text-align': 'center'}),
html.Th(dcc.Link('Distribution', href='/page-2'),style={'text-align': 'center'}),
html.Th(dcc.Link('Scatter', href='/page-3'),style={'text-align': 'center'}),
html.Th(dcc.Link('Zipcode', href='/page-4'),style={'text-align': 'center'}),
html.Th(dcc.Link('Price Predictor', href='/page-5'),style={'text-align': 'center'}),
html.Th(dcc.Link('Results', href='/page-6'),style={'text-align': 'center'}),
])]),
# html.H2('Page 2'),
html.Div([
dcc.Dropdown(
id='feature',
options=[{'label': i.title(), 'value': i} for i in features],
value='bathrooms',
clearable=False,
searchable=True,
)
],style={'width': '30%', 'display': 'inline-block'}),
dcc.Graph(id='bar-graph')
])
#-----------------------------------------page3-scatter-plot---------------------------------------------------------------
layout_page_3 = html.Div([
html.Table([
html.Tr([
html.Th(html.Img(src=app.get_asset_url('infogen.png')), style={'textAlign': 'left'}),
html.Th(html.H2("King's County Housing Data Analysis")),
html.Th(dcc.Link('Home', href='/page-0'),style={'text-align': 'center'}),
html.Th(dcc.Link('Data', href='/page-1'),style={'text-align': 'center'}),
html.Th(dcc.Link('Distribution', href='/page-2'),style={'text-align': 'center'}),
html.Th(dcc.Link('Scatter', href='/page-3'),style={'text-align': 'center'}),
html.Th(dcc.Link('Zipcode', href='/page-4'),style={'text-align': 'center'}),
html.Th(dcc.Link('Price Predictor', href='/page-5'),style={'text-align': 'center'}),
html.Th(dcc.Link('Results', href='/page-6'),style={'text-align': 'center'}),
])]),
# html.H2('Page 3'),
html.Div([
dcc.Dropdown(
id='xaxis',
options=[{'label': i.title(), 'value': i} for i in feature],
value='sqft_living'
)],style={'width': '30%', 'display': 'inline-block'}),
html.Div(dcc.Dropdown(
id='yaxis',
options=[{'label': 'Price', 'value': 'price'}], #[{'label': i.title(), 'value': i} for i in feature],
clearable=False,
searchable=False,
value='price'),style={'width': '30%', 'display': 'inline-block'}),
dcc.Graph(id='feature-graphic'),
])
#-----------------------------------------page-4-zipcode-wise--------------------------------------------------------------
layout_page_4 = html.Div([
html.Table([
html.Tr([
html.Th(html.Img(src=app.get_asset_url('infogen.png')), style={'textAlign': 'left'}),
html.Th(html.H2("King's County Housing Data Analysis")),
html.Th(dcc.Link('Home', href='/page-0'),style={'text-align': 'center'}),
html.Th(dcc.Link('Data', href='/page-1'),style={'text-align': 'center'}),
html.Th(dcc.Link('Distribution', href='/page-2'),style={'text-align': 'center'}),
html.Th(dcc.Link('Scatter', href='/page-3'),style={'text-align': 'center'}),
html.Th(dcc.Link('Zipcode', href='/page-4'),style={'text-align': 'center'}),
html.Th(dcc.Link('Price Predictor', href='/page-5'),style={'text-align': 'center'}),
html.Th(dcc.Link('Results', href='/page-6'),style={'text-align': 'center'}),
])]),
# html.H2('Page 4'),
html.Div([
# html.Div('Zipcode range: 98001-98119',style={'width': '10%', 'float':'center'}),
dcc.Input(id='filt',type='number',min=98001, max=98119,value=98001,style={'width': '10%', 'float':'center'}),
dcc.Graph(id='line-graph')],style={'textAlign': 'center'})
])
#-----------------------------------------page-5-Price predictor---------------------------------------------------------------
layout_page_5 = html.Div([
html.Table([
html.Tr([
html.Th(html.Img(src=app.get_asset_url('infogen.png')), style={'textAlign': 'left'}),
html.Th(html.H2("King's County Housing Data Analysis")),
html.Th(dcc.Link('Home', href='/page-0'),style={'text-align': 'center'}),
html.Th(dcc.Link('Data', href='/page-1'),style={'text-align': 'center'}),
html.Th(dcc.Link('Distribution', href='/page-2'),style={'text-align': 'center'}),
html.Th(dcc.Link('Scatter', href='/page-3'),style={'text-align': 'center'}),
html.Th(dcc.Link('Zipcode', href='/page-4'),style={'text-align': 'center'}),
html.Th(dcc.Link('Price Predictor', href='/page-5'),style={'text-align': 'center'}),
html.Th(dcc.Link('Results', href='/page-6'),style={'text-align': 'center'}),
])]),
# html.H2('Page 5'),
html.Table([
html.Tr([
html.Th('No. of Bedrooms',style={'text-align': 'center'}),
html.Th('No. of Bathrooms',style={'text-align': 'center'}),
html.Th('Living Area',style={'text-align': 'center'}),
html.Th('Total Area',style={'text-align': 'center'}),
html.Th('No. of Floors',style={'text-align': 'center'}),
html.Th('Waterfront',style={'text-align': 'center'}),
html.Th('View',style={'text-align': 'center'}),
html.Th('Condition',style={'text-align': 'center'})
]),
html.Tr([
html.Th(html.Div(daq.NumericInput( id='my-dropdown',min=1,max=8,value=1,size=120),style={'text-align': 'center'})),
html.Th(html.Div(dcc.Dropdown(
id='my-dropdown1',
options=[
{'label': '0.5', 'value': 0.5},
{'label': '0.75', 'value': 0.75},
{'label': '1.0', 'value': 1.0},
{'label': '1.25', 'value': 1.25},
{'label': '1.5', 'value': 1.5},
{'label': '1.75', 'value': 1.75},
{'label': '2.0', 'value': 2.0},
{'label': '2.25', 'value': 2.25},
{'label': '2.5', 'value': 2.5},
{'label': '2.75', 'value': 2.75},
{'label': '3.0', 'value': 3.0},
{'label': '3.25', 'value': 3.25},
{'label': '3.5', 'value': 3.5},
{'label': '3.75', 'value': 3.75},
{'label': '4.0', 'value': 4.0},
{'label': '4.25', 'value': 4.25},
{'label': '4.5', 'value': 4.5},
{'label': '4.75', 'value': 4.75},
{'label': '5.0', 'value': 5.0},
{'label': '5.25', 'value': 5.25},
{'label': '5.5', 'value': 5.5},
{'label': '5.75', 'value': 5.75},
{'label': '6.0', 'value': 6.0},
{'label': '6.25', 'value': 6.25},
{'label': '6.5', 'value': 6.5},
{'label': '6.75', 'value': 6.75},
{'label': '7.0', 'value': 7.0},
{'label': '7.25', 'value': 7.25},
{'label': '7.5', 'value': 7.5},
{'label': '7.75', 'value': 7.75},
{'label': '8.0', 'value': 8.0}
],value='0.5',
clearable=False,
searchable=False,
# style={"height" : "25%", "width" : "40%"},
))),
html.Th(html.Div(daq.NumericInput( id='sqft_live',min=290, max=13540,value=290,size=120),style={'text-align': 'center'})),
html.Th(html.Div(daq.NumericInput( id='sqft_lot',min=520, max=1651359,value=290,size=120),style={'text-align': 'center'})),
html.Th(html.Div(dcc.Dropdown(
id='floor',
options=[
{'label': '1.0', 'value': 1.0},
{'label': '1.5', 'value': 1.5},
{'label': '2.0', 'value': 2.0},
{'label': '2.5', 'value': 2.5},
{'label': '3.0', 'value': 3.0},
{'label': '3.5', 'value': 3.5},
],value=1.0,#style={"height" : "55%", "width" : "70%"},
clearable=False,
searchable=False,
))),
html.Th(html.Div(dcc.Dropdown(
id='waterfront',
options=[
{'label': 'Yes', 'value': 1.0},
{'label': 'No', 'value': 0.0},
],value=0.0,#style={"height" : "25%", "width" : "70%"},
clearable=False,
searchable=False,
))),
html.Th(html.Div(daq.NumericInput( id='view',min=0, max=4,value=0,size=120),style={'text-align': 'center'})),
html.Th(html.Div(daq.NumericInput( id='condition',min=1, max=5,value=1,size=120),style={'text-align': 'center'}))
]),
html.Tr([
html.Td(id='output-container'),
html.Td(id='output-container1'),
html.Td(id='output-container2'),
html.Td(id='output-container3'),
html.Td(id='output-container4'),
html.Td(id='output-container5'),
html.Td(id='output-container6'),
html.Td(id='output-container7')
]),
html.Tr([
html.Th('Grade',style={'text-align': 'center'}),
html.Th('Area above ground',style={'text-align': 'center'}),
html.Th('Basement Area',style={'text-align': 'center'}),
html.Th('Year of built',style={'text-align': 'center'}),
html.Th('Year of Renovation',style={'text-align': 'center'}),
html.Th('Zipcode',style={'text-align': 'center'}),
html.Th('Living Area of nearby property',style={'text-align': 'center'}),
html.Th('Total Area of nearby property',style={'text-align': 'center'})
]),
html.Tr([
html.Th(html.Div(daq.NumericInput( id='grade',min=1, max=13,value=1,size=120),style={'text-align': 'center'})),
html.Th(html.Div(daq.NumericInput( id='sqft_above',min=1190, max=9410,value=1190,size=120),style={'text-align': 'center'})),
html.Th(html.Div(daq.NumericInput( id='sqft_basement',min=0, max=4820,value=0,size=120),style={'text-align': 'center'})),
html.Th(html.Div(daq.NumericInput( id='year_built',min=1900, max=2015,value=1900,size=120),style={'text-align': 'center'})),
html.Th(html.Div(daq.NumericInput( id='year_renew',min=0, max=2015,value=0,size=120),style={'text-align': 'center'})),
html.Th(html.Div(daq.NumericInput( id='zipcode',min=98001, max=98119,value=98001,size=120),style={'text-align': 'center'})),
html.Th(html.Div(daq.NumericInput( id='sqft_live_area_nearby',min=399, max=6210,value=399,size=120),style={'text-align': 'center'})),
html.Th(html.Div(daq.NumericInput( id='sqft_tot_area_nearby',min=651, max=871200,value=651,size=120),style={'text-align': 'center'}))
]),
html.Tr([
html.Td(id='output-container8'),
html.Td(id='output-container9'),
html.Td(id='output-container10'),
html.Td(id='output-container11'),
html.Td(id='output-container12'),
html.Td(id='output-container13'),
html.Td(id='output-container14'),
html.Td(id='output-container15')
]),
html.Tr([
html.Th(html.Button(id='submit', n_clicks=0, children='Estimate',style={'color': 'white','background-color': '#1fbfcf'}),style={'text-align': 'center'}),
html.Th('Estimated Cost: ',style={'text-align': 'center','color': '#1fbfcf','fontSize': 20}),
html.Th(id='output-container16',style={'text-align': 'left','color': 'red','fontSize': 20}),
html.Th('Accuracy of Model: ',style={'text-align': 'center'}),
html.Th(str(round(model.score(X_test, Y_test)*100,2))+'%',style={'text-align': 'left','color': 'green','fontSize': 20}),
]),
]),
html.Div(),
html.Br(),
dcc.Graph(
id='line',
figure={
'data': [
go.Scatter(
x = dx.index,
y = dx.Actual,
# z = random_z,
mode = 'lines+markers',
name = 'Actual'
),
go.Scatter(
x = dx.index,
y = dx.Predicted,
# z = random_z,
mode = 'lines',
name = 'Predicted')
],
'layout': go.Layout(
title = 'Line chart showing Actual Vs Predicted Price',
yaxis = {'title': 'Price (USD)'},
xaxis = {'title': 'Houses'},
# 'height': 1000,
# 'margin': {'l': 10, 'b': 20, 't': 0, 'r': 0}
# hovermode='closest',
width=1580,
height=700,
margin=go.layout.Margin(
l=50,
r=10,
b=100,
t=100,
pad=4
)
)
}
)
])
#---------------------------------------------Page-6-Result---------------------------------------------------
layout_page_6 = html.Div([
html.Table([
html.Tr([
html.Th(html.Img(src=app.get_asset_url('infogen.png')), style={'textAlign': 'left'}),
html.Th(html.H2("King's County Housing Data Analysis")),
html.Th(dcc.Link('Home', href='/page-0'),style={'text-align': 'center'}),
html.Th(dcc.Link('Data', href='/page-1'),style={'text-align': 'center'}),
html.Th(dcc.Link('Distribution', href='/page-2'),style={'text-align': 'center'}),
html.Th(dcc.Link('Scatter', href='/page-3'),style={'text-align': 'center'}),
html.Th(dcc.Link('Zipcode', href='/page-4'),style={'text-align': 'center'}),
html.Th(dcc.Link('Price Predictor', href='/page-5'),style={'text-align': 'center'}),
html.Th(dcc.Link('Results', href='/page-6'),style={'text-align': 'center'}),
])]),
html.Div([
dash_table.DataTable(
id='datatable-filtering-fex',
columns=[
{"name": i, "id": i, "deletable": True} for i in df.columns
],
data=df.to_dict('records'),
# filter_action="native",
),
html.Div(id='datatable-filter-container')
]),
])
def serve_layout():
if flask.has_request_context():
return url_bar_and_content_div
return html.Div([
url_bar_and_content_div,
layout_index,
layout_page_1,
layout_page_2,
layout_page_3,
layout_page_4,
layout_page_5,
layout_page_6
])
app.layout = serve_layout
# Index callbacks
@app.callback(Output('page-content', 'children'),
[Input('url', 'pathname')])
def display_page(pathname):
if pathname == "/page-1":
return layout_page_1
if pathname == "/page-2":
return layout_page_2
elif pathname == "/page-3":
return layout_page_3
elif pathname == "/page-4":
return layout_page_4
elif pathname == "/page-5":
return layout_page_5
elif pathname == "/page-6":
return layout_page_6
else:
return layout_index
#------------------------------Data-table---------------------------------------------
operators = [['ge ', '>='],
['le ', '<='],
['lt ', '<'],
['gt ', '>'],
['ne ', '!='],
['eq ', '='],
['contains '],
['datestartswith ']]
def split_filter_part(filter_part):
for operator_type in operators:
for operator in operator_type:
if operator in filter_part:
name_part, value_part = filter_part.split(operator, 1)
name = name_part[name_part.find('{') + 1: name_part.rfind('}')]
value_part = value_part.strip()
v0 = value_part[0]
if (v0 == value_part[-1] and v0 in ("'", '"', '`')):
value = value_part[1: -1].replace('\\' + v0, v0)
else:
try:
value = float(value_part)
except ValueError:
value = value_part
# word operators need spaces after them in the filter string,
# but we don't want these later
return name, operator_type[0].strip(), value
return [None] * 3
@app.callback(
Output('table-filtering-be', "data"),
[Input('table-filtering-be', "filter")])
def update_table(filter):
filtering_expressions = filter.split(' && ')
dff = kc
for filter_part in filtering_expressions:
col_name, operator, filter_value = split_filter_part(filter_part)
if operator in ('eq', 'ne', 'lt', 'le', 'gt', 'ge'):
# these operators match pandas series operator method names
dff = dff.loc[getattr(dff[col_name], operator)(filter_value)]
elif operator == 'contains':
dff = dff.loc[dff[col_name].str.contains(filter_value)]
elif operator == 'datestartswith':
# this is a simplification of the front-end filtering logic,
# only works with complete fields in standard format
dff = dff.loc[dff[col_name].str.startswith(filter_value)]
return dff.to_dict('records')
#--------------------------------------------dynamic-barplot-----------------------------------------
@app.callback(
Output('bar-graph', 'figure'),
[Input('feature', 'value')])
def update_graph(code):
dx = pd.Series.to_frame(kc[code].value_counts())
dx['count'] = list(dx.index)
return{'data':[
go.Bar(
x=dx['count'], # assign x as the dataframe column 'x'
y=dx[code],
# text=x,
# textposition = 'auto',
marker=dict(
cmax=39,
cmin=0,
color=[0, 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],
# colorbar=dict(
# title='Colorbar'
# ),
colorscale='Earth'),
)],
'layout': go.Layout(
title = 'Count of '+code.capitalize() ,
yaxis = {'title': 'Count'},
xaxis=dict(
title=code.capitalize(),
# tickmode='linear',
# ticks='outside',
# tick0=0,
# dtick=0.25,
# ticklen=8,
# tickwidth=1,
tickcolor='black'),
# hovermode='closest',
autosize=False,
width=1600,
height=600,
margin=go.layout.Margin(
l=100,
r=100,
b=100,
t=100,
pad=4
),
paper_bgcolor='#ffffff',
plot_bgcolor='#ffffff'
)}
#------------------------------------scatterplot------------------------------
@app.callback(
Output('feature-graphic', 'figure'),
[Input('xaxis', 'value'),
Input('yaxis', 'value')])
def update_graph(xaxis_name, yaxis_name):
return {
'data': [go.Scatter(
x=kx[xaxis_name],
y=kx[yaxis_name],
text=kx['txt'],
hoverinfo = 'text',
marker=dict(
size=8,
cmax=39,
cmin=0,
# color=[0, 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],
# colorscale='Earth',
opacity=0.5,
line={'width': 0.5, 'color': 'blue'}
),
# text=df['name'],
mode='markers',
)],
'layout': go.Layout(
xaxis={'title': xaxis_name.title()},
yaxis={'title': yaxis_name.title()},
margin={'l': 40, 'b': 40, 't': 10, 'r': 0},
hovermode='closest',autosize=False,
width=1500,
height=600,
)
}
#----------------------zipcode-wise-line-plot------------------------------------
@app.callback(
Output('line-graph', 'figure'),
[Input('filt', 'value')])
def update_graph(codes):
do=df.ix[df['Zipcode']==codes,1:3]
return {
'data': [
go.Scatter(
# x = dx.index,
y = do.Actual,
# z = random_z,
mode = 'lines+markers',
name = 'Actual'
),
go.Scatter(
# x = dx.index,
y = do.Predicted,
# z = random_z,
mode = 'lines',
name = 'Predicted')
],
'layout': go.Layout(
title = 'Line chart showing Actual Vs Predicted Price | Zipcode: '+str(codes),
yaxis = {'title': 'Price (USD)'},
xaxis = {'title': 'Houses'},
# hovermode='closest',
autosize=False,
width=1500,
height=600,
margin=go.layout.Margin(
l=50,
r=50,
b=100,
t=100,
pad=4
),
)
}
#--------------------------------------price-predictor--------------------------
@app.callback(
dash.dependencies.Output('output-container', 'children'),
[dash.dependencies.Input('my-dropdown', 'value')])
def update_output(value):
return 'Number of Bedrooms: {}'.format(value)
@app.callback(
dash.dependencies.Output('output-container1', 'children'),
[dash.dependencies.Input('my-dropdown1', 'value')])
def update_output(value):
return 'Number of Bathrooms: {}'.format(value)
@app.callback(
dash.dependencies.Output('output-container2', 'children'),
[dash.dependencies.Input('sqft_live', 'value')])
def update_output(value):
return 'Area of living: {} sqft'.format(value)
@app.callback(
dash.dependencies.Output('output-container3', 'children'),
[dash.dependencies.Input('sqft_lot', 'value')])
def update_output(value):
return 'Total area: {} sqft'.format(value)
@app.callback(
dash.dependencies.Output('output-container4', 'children'),
[dash.dependencies.Input('floor', 'value')])
def update_output(value):
return 'Number of floors: {}'.format(value)
@app.callback(
dash.dependencies.Output('output-container5', 'children'),
[dash.dependencies.Input('waterfront', 'value')])
def update_output(value):
if value==0:
return 'Waterfront present: No'
else:
return 'Waterfront present: Yes'
# return 'Water front: {}'.format(wf)
@app.callback(
dash.dependencies.Output('output-container6', 'children'),
[dash.dependencies.Input('view', 'value')])
def update_output(value):
return '(An index from 0 to 4 of how good the view of the property was).View of the property: {}'.format(value)
@app.callback(
dash.dependencies.Output('output-container7', 'children'),
[dash.dependencies.Input('condition', 'value')])
def update_output(value):
return '(Condition Rating 1 to 5)Condition of property: {}'.format(value)
@app.callback(
dash.dependencies.Output('output-container8', 'children'),
[dash.dependencies.Input('grade', 'value')])
def update_output(value):
return '(1-3:Low|7:Avg|11-13:High)Grade for quality level of construction and design: {}'.format(value)
@app.callback(
dash.dependencies.Output('output-container9', 'children'),
[dash.dependencies.Input('sqft_above', 'value')])
def update_output(value):
return 'Area of interior housing space above ground level: {} sqft'.format(value)
@app.callback(
dash.dependencies.Output('output-container10', 'children'),
[dash.dependencies.Input('sqft_basement', 'value')])
def update_output(value):
return 'Area of interior housing space below ground level: {} sqft'.format(value)
@app.callback(
dash.dependencies.Output('output-container11', 'children'),
[dash.dependencies.Input('year_built', 'value')])
def update_output(value):
return 'Year the house was built: {}'.format(value)
@app.callback(
dash.dependencies.Output('output-container12', 'children'),
[dash.dependencies.Input('year_renew', 'value')])
def update_output(value):
if value==0:
return 'Year the house was last renovated: Never'
else:
return 'Year the house was last renovated: {}'.format(value)
@app.callback(
Output(component_id='output-container13', component_property='children'),
[Input(component_id='zipcode', component_property='value')]
)
def update_output_div(input_value):
Latitude=nomi.query_postal_code(input_value)[9]
Longitude=nomi.query_postal_code(input_value)[10]
update_output_div.Latitude=nomi.query_postal_code(input_value)[9]
update_output_div.Longitude=nomi.query_postal_code(input_value)[10]
# othr=nomi.query_postal_code(input_value)
return 'Zipcode: {} | Latitude: {} | Longitude: {}'.format(input_value,Latitude,Longitude)
@app.callback(
dash.dependencies.Output('output-container14', 'children'),
[dash.dependencies.Input('sqft_live_area_nearby', 'value')])
def update_output(value):
return 'Area of living for the nearest 15 neighbors: {}'.format(value)
@app.callback(
dash.dependencies.Output('output-container15', 'children'),
[dash.dependencies.Input('sqft_tot_area_nearby', 'value')])
def update_output(value):
return 'Total area for the nearest 15 neighbors: {}'.format(value)
@app.callback(
Output('output-container16', 'children'),
[Input('submit', 'n_clicks')],
[State('my-dropdown', 'value'),
State('my-dropdown1', 'value'),
State('sqft_live','value'),
State('sqft_lot','value'),
State('floor','value'),
State('waterfront','value'),
State('view','value'),
State('condition','value'),
State('grade','value'),
State('sqft_above','value'),
State('sqft_basement','value'),
State('year_built','value'),
State('year_renew','value'),
State('zipcode','value'),
State('sqft_live_area_nearby','value'),
State('sqft_tot_area_nearby','value')])
def pred(n_clicks,No_of_Bedrooms,No_of_Bathrooms,Sqft_live_area,Sqft_tot_area,No_of_floors,
Water_front,View,Condition,Grade,Sqft_area_above,Sqft_area_basement,Year_built,
Year_renew,Zipcode,Sqft_live_area_nearby,Sqft_tot_area_nearby):
ip=[No_of_Bedrooms,No_of_Bathrooms,Sqft_live_area,Sqft_tot_area,No_of_floors,
Water_front,View,Condition,Grade,Sqft_area_above,Sqft_area_basement,Year_built,
Year_renew,Zipcode,update_output_div.Latitude,update_output_div.Longitude,Sqft_live_area_nearby,Sqft_tot_area_nearby]
print(ip)
user_input=np.asarray([No_of_Bedrooms,No_of_Bathrooms,Sqft_live_area,Sqft_tot_area,No_of_floors,
Water_front,View,Condition,Grade,Sqft_area_above,Sqft_area_basement,Year_built,
Year_renew,Zipcode,update_output_div.Latitude,update_output_div.Longitude,Sqft_live_area_nearby,Sqft_tot_area_nearby],dtype=np.float64)
print(user_input)
testme=user_input.reshape(1, -1)
# pred_price=model.predict(testme)
# acc=model.score(X_test, Y_test)*100
# pred_price=round(model.predict(testme)[0],2)
print (" ${:,.2f}".format(model.predict(testme)[0]))
# print(pred_price)
return " ${:,.2f}".format(model.predict(testme)[0])
# return ' ${}'.format(pred_price)
#---------------------------------------------Results-page-6-------------------------------------
@app.callback(
Output('datatable-filter-container', "children"),
[Input('datatable-filtering-fex', "data")])
def update_graph(rows):
if rows is None:
dff = df
else:
dff = pd.DataFrame(rows)
return html.Div()
if __name__ == '__main__':
app.run_server(debug=False)