Shubham Goyal shubham769

## torch_model_prediction.py
%%time
y_pred_torch=model_torch.predict(np.array(X_test))

## torch_model_prediction.py
%%time
y_pred_torch=model_torch.predict(np.array(X_test))

## model_convert.py
model_torch=convert(model,'pytorch')
model_torch.to('cuda')

## model_prediction.py
%%time

#prediction of labels for test data
y_pred=model.predict(np.array(X_test))

## model_prediction.py
%%time

#prediction of labels for test data
y_pred=model.predict(np.array(X_test))

## model_training.py
model=RandomForestClassifier(n_estimators=300)
model.fit(X_train,y_train)

## preprocessing.py
pima = pd.read_csv("diabetes.csv")
X = pima.iloc[:,:-1]
y = pima.iloc[:,-1]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1) # 70% training and 30% test

## install_library.py
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from hummingbird.ml import convert
from sklearn.model_selection import train_test_split

## hummingbird_install.py
!pip install hummingbird-ml

## Pytorch.py
# importing libraries
import numpy as np

import torch
	%%time

	#prediction of labels for test data
	y_pred=model.predict(np.array(X_test))
	model=RandomForestClassifier(n_estimators=300)
	model.fit(X_train,y_train)
	pima = pd.read_csv("diabetes.csv")
	X = pima.iloc[:,:-1]
	y = pima.iloc[:,-1]
	X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1) # 70% training and 30% test
	import numpy as np
	import pandas as pd
	import matplotlib.pyplot as plt
	from sklearn.ensemble import RandomForestClassifier
	from hummingbird.ml import convert
	from sklearn.model_selection import train_test_split