kezeh32

## Final_Machine_Learning_Outcome.ipynb

      
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                kezeh32
                / Final_Machine_Learning_Outcome.ipynb
            
            
              Created
              February 17, 2022 04:16
            
              
                Machine prediction
              
          
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## gist:0843c4eb0af4a04ad81182afe38bc443
features =["floors", "waterfront","lat" ,"bedrooms" ,"sqft_basement" ,"view" ,"bathrooms","sqft_living15","sqft_above","grade","sqft_living"]
X = df[features]
Y = df['price']

x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.15, random_state=1)


print("number of test samples:", x_test.shape[0])
print("number of training samples:",x_train.shape[0])

## dash1.py
# This file makes use of the @appcallback function to connect inputs and outputs

# Import required libraries
import pandas as pd
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output, State
from jupyter_dash import JupyterDash
import plotly.graph_objects as go
	features =["floors", "waterfront","lat" ,"bedrooms" ,"sqft_basement" ,"view" ,"bathrooms","sqft_living15","sqft_above","grade","sqft_living"]
	X = df[features]
	Y = df['price']

	x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.15, random_state=1)


	print("number of test samples:", x_test.shape[0])
	print("number of training samples:",x_train.shape[0])
	# This file makes use of the @appcallback function to connect inputs and outputs

	# Import required libraries
	import pandas as pd
	import dash
	import dash_core_components as dcc
	import dash_html_components as html
	from dash.dependencies import Input, Output, State
	from jupyter_dash import JupyterDash
	import plotly.graph_objects as go