-
Notifications
You must be signed in to change notification settings - Fork 0
/
MushroomAnalysis.py
51 lines (35 loc) · 1.41 KB
/
MushroomAnalysis.py
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
41
42
43
44
45
46
47
48
# -*- coding: utf-8 -*-
"""
Created on Sat Dec 23 02:16:33 2017
@author: Sahil Manchanda
"""
import pandas as pd
import numpy as np
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.utils import to_categorical
mushroom_data = pd.read_csv('mushrooms.csv')
from sklearn.preprocessing import LabelEncoder
encoder = LabelEncoder()
for col in mushroom_data.columns:
mushroom_data[col] = encoder.fit_transform(mushroom_data[col])
#from sklearn.preprocessing import OneHotEncoder
#encoder = OneHotEncoder()
#for col in mushroom_data.columns:
# mushroom_data[col] = encoder.fit_transform(mushroom_data[col])
X = mushroom_data.iloc[:,1:22].values
Y = mushroom_data.iloc[:,0].values
from sklearn.cross_validation import train_test_split
x_train,x_test,y_train,y_test = train_test_split(X,Y,test_size = 0.25,random_state=42)
model = Sequential()
model.add(Dense(40,input_dim = 21,kernel_initializer="normal",activation = 'relu'))
model.add(Dense(10,kernel_initializer="normal",activation = 'relu'))
model.add(Dropout(0.1))
model.add(Dense(5,kernel_initializer="normal",activation = 'relu'))
model.add(Dense(1,kernel_initializer="normal",activation = 'sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.fit(x_train,y_train,epochs=200, verbose=2)
print(model.evaluate(x_test,y_test))