regression.h5.py

#!/usr/bin/env python
# coding: utf-8

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import numpy as np
import pandas as pd


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dataset=pd.read_csv("50_Startups AI.csv")


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dataset


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dataset.head(2)


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dataset.isnull().any()


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from sklearn.preprocessing import LabelEncoder
le=LabelEncoder()
dataset["State"]=le.fit_transform(dataset["State"])


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dataset.head(2)


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x=dataset.iloc[:,0:4].values
y=dataset.iloc[:,4].values


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from sklearn.preprocessing import OneHotEncoder
one=OneHotEncoder()
z=one.fit_transform(x[:,3:4]).toarray()
x=np.delete(x,3,axis=1)
x=np.concatenate((z,x),axis=1)


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from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=0)


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import keras
from keras.models import Sequential
from keras.layers import Dense


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model=Sequential()


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x_train.shape


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model.add(Dense(units=6,init = "random_uniform",activation="relu"))


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model.add(Dense(units=3,init = "random_uniform",activation="relu"))


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model.add(Dense(units=1,init = "random_uniform"))


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model.compile(optimizer="adam",loss="mse",accuracy=["mse"])


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model.fit(x_train,y_train,batch_size=10,epochs=10)


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y_pred=model.predict(x_test)


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y_pred


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y_test


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from sklearn.metrics import r2_score
accuracy=r2_score(y_test,y_pred)


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accuracy #0.752295052747021 *100=75% is accuracy


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model.save('regressor.h5')


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x_train.shape


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