import pandas as pd
import numpy as np
df=pd.read_excel('survey_data3.xlsx')
df.head()
| Person | Age | Gender | State | Children | Salary | Opinion | Agree or Not | Strongly Agree or Not | Neutral or Not | Disagree or Not | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Middle-aged | 2 | Texas | 2 | 63017 | Strongly agree | 1 | 1 | 0 | 0 |
| 1 | 2 | Middle-aged | 2 | Virginia | 3 | 100302 | Strongly disagree | 0 | 0 | 0 | 1 |
| 2 | 3 | Middle-aged | 2 | California | 0 | 144043 | Strongly agree | 1 | 1 | 0 | 0 |
| 3 | 4 | Young | 2 | California | 0 | 36025 | Agree | 1 | 0 | 0 | 0 |
| 4 | 5 | Middle-aged | 1 | Texas | 0 | 97543 | Neutral | 0 | 0 | 1 | 0 |
df=df[['Age','Gender', 'State', 'Children','Salary', 'Neutral or Not']]
df.head()
| Age | Gender | State | Children | Salary | Neutral or Not | |
|---|---|---|---|---|---|---|
| 0 | Middle-aged | 2 | Texas | 2 | 63017 | 0 |
| 1 | Middle-aged | 2 | Virginia | 3 | 100302 | 0 |
| 2 | Middle-aged | 2 | California | 0 | 144043 | 0 |
| 3 | Young | 2 | California | 0 | 36025 | 0 |
| 4 | Middle-aged | 1 | Texas | 0 | 97543 | 1 |
df = pd.concat([df,pd.get_dummies(df['Age'], prefix='_', drop_first=True)],axis=1)
df.drop(['Age'],axis=1, inplace=True)
df = pd.concat([df,pd.get_dummies(df['State'], prefix='State', drop_first=True)],axis=1)
df.drop(['State'],axis=1, inplace=True)
df['Gender']=df['Gender']-1
df.head()
| Gender | Children | Salary | Neutral or Not | __Middle-aged | __Young | State_California | State_Florida | State_Illinois | State_Michigan | State_Minnesota | State_New York | State_Ohio | State_Texas | State_Virginia | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 2 | 63017 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 1 | 1 | 3 | 100302 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 2 | 1 | 0 | 144043 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 1 | 0 | 36025 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 0 | 0 | 97543 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
df1=df.copy()
df1['Gend_child']=df1['Gender']*df1['Children']
df1['log_Salary']=np.log(df1['Salary'])
df1['Salary_Sq'] = df1['Salary']**2
df1.head()
| Gender | Children | Salary | Neutral or Not | __Middle-aged | __Young | State_California | State_Florida | State_Illinois | State_Michigan | State_Minnesota | State_New York | State_Ohio | State_Texas | State_Virginia | Gend_child | log_Salary | Salary_Sq | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 2 | 63017 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 11.051160 | 3971142289 |
| 1 | 1 | 3 | 100302 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 11.515941 | 10060491204 |
| 2 | 1 | 0 | 144043 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11.877867 | 20748385849 |
| 3 | 1 | 0 | 36025 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10.491968 | 1297800625 |
| 4 | 0 | 0 | 97543 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 11.488049 | 9514636849 |
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix
df_copy = df.copy()
train_set = df_copy.sample(frac=0.80, random_state=0)
test_set = df_copy.drop(train_set.index)
train_set.head()
| Gender | Children | Salary | Neutral or Not | __Middle-aged | __Young | State_California | State_Florida | State_Illinois | State_Michigan | State_Minnesota | State_New York | State_Ohio | State_Texas | State_Virginia | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 132 | 0 | 0 | 97814 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 309 | 0 | 2 | 69817 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 334 | 0 | 1 | 48225 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| 196 | 0 | 0 | 37929 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 246 | 1 | 2 | 100062 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
test_set.head()
| Gender | Children | Salary | Neutral or Not | __Middle-aged | __Young | State_California | State_Florida | State_Illinois | State_Michigan | State_Minnesota | State_New York | State_Ohio | State_Texas | State_Virginia | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 9 | 0 | 3 | 87457 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 25 | 1 | 2 | 148075 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 28 | 1 | 2 | 37963 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 31 | 1 | 2 | 49505 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 32 | 0 | 3 | 49723 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
test_set_labels = test_set.pop('Neutral or Not')
train_set_labels = train_set.pop('Neutral or Not')
model = LogisticRegression(solver = 'liblinear', random_state=0).fit(train_set,train_set_labels)
model.predict_proba(train_set)
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model.predict(train_set)
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model.predict_proba(test_set)
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[0.88942101, 0.11057899],
[0.85001925, 0.14998075],
[0.87697546, 0.12302454],
[0.82928425, 0.17071575],
[0.81531726, 0.18468274],
[0.79097069, 0.20902931],
[0.70645534, 0.29354466],
[0.68635023, 0.31364977],
[0.81807032, 0.18192968],
[0.74584729, 0.25415271],
[0.77365551, 0.22634449],
[0.76189216, 0.23810784],
[0.83588659, 0.16411341],
[0.85755787, 0.14244213],
[0.69596147, 0.30403853],
[0.80923766, 0.19076234],
[0.70741852, 0.29258148],
[0.73042915, 0.26957085],
[0.88290782, 0.11709218],
[0.78646297, 0.21353703],
[0.66572453, 0.33427547],
[0.80727079, 0.19272921],
[0.81212912, 0.18787088],
[0.69930684, 0.30069316],
[0.90876304, 0.09123696],
[0.80671894, 0.19328106],
[0.82965814, 0.17034186],
[0.80201098, 0.19798902],
[0.74978909, 0.25021091],
[0.8321812 , 0.1678188 ],
[0.6956877 , 0.3043123 ],
[0.69436052, 0.30563948],
[0.81195745, 0.18804255],
[0.84675205, 0.15324795],
[0.79070401, 0.20929599],
[0.89807904, 0.10192096],
[0.85772495, 0.14227505],
[0.72610732, 0.27389268],
[0.85506449, 0.14493551],
[0.83341849, 0.16658151],
[0.8138364 , 0.1861636 ],
[0.92321128, 0.07678872],
[0.83238269, 0.16761731],
[0.78317051, 0.21682949],
[0.82884059, 0.17115941],
[0.8384776 , 0.1615224 ],
[0.74123962, 0.25876038],
[0.83694847, 0.16305153],
[0.81824603, 0.18175397],
[0.83078379, 0.16921621]])
model.predict(test_set)
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int64)
model.score(train_set,train_set_labels)
0.8244514106583072
model.score(test_set,test_set_labels)
0.85
confusion_matrix(train_set_labels,model.predict(train_set))
array([[263, 0],
[ 56, 0]], dtype=int64)
confusion_matrix(test_set_labels,model.predict(test_set))
array([[68, 0],
[12, 0]], dtype=int64)
import matplotlib.pyplot as plt
cm = confusion_matrix(test_set_labels, model.predict(test_set))
fig, ax = plt.subplots(figsize=(8, 8))
ax.imshow(cm)
ax.grid(False)
ax.xaxis.set(ticks=(0, 1), ticklabels=('Predicted 0s', 'Predicted 1s'))
ax.yaxis.set(ticks=(0, 1), ticklabels=('Actual 0s', 'Actual 1s'))
ax.set_ylim(1.5, -0.5)
for i in range(2):
for j in range(2):
ax.text(j, i, cm[i, j], ha='center', va='center', color='red')
plt.show()
model = LogisticRegression(C=10.0, solver = 'liblinear', random_state=0).fit(train_set,train_set_labels)
model.score(train_set,train_set_labels)
0.8244514106583072
prob1 = model.predict_proba(train_set)
predicted=[0 if i>0.84 else 1 for i in prob1[:,0]]
predicted
[0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1]
sum(abs(train_set_labels-predicted))
180
len(train_set_labels)
319
error_percent =sum(abs(train_set_labels-predicted))/319
1-error_percent
0.4357366771159875
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaled_features = scaler.fit_transform(train_set)
kmeans = KMeans(init="random", n_clusters=5, n_init=10, max_iter=300, random_state=42)
kmeans.fit(scaled_features)
KMeans(init='random', n_clusters=5, random_state=42)
kmeans.inertia_
2982.105883164649
kmeans.cluster_centers_
array([[-0.04971743, 0.01781439, 0.33015189, 0.3133736 , -0.52049624,
0.25232533, 0.26227165, -0.32218974, -0.37864122, -0.29160592,
0.34644297, 0.32731074, -0.38938997, 0.15103416],
[ 0.20089178, -0.12825991, 0.34720311, 0.20571739, -0.52049624,
-0.28522961, -0.33391355, -0.32218974, 1.02486446, -0.29160592,
-0.33968311, -0.35666298, 1.19350246, -0.31622777],
[-0.11496944, -0.07072974, 0.03824646, 0.02921187, -0.0321483 ,
-0.28522961, -0.33391355, -0.32218974, -0.37864122, 3.42928564,
-0.33968311, -0.35666298, -0.38938997, -0.31622777],
[-0.29038454, 0.15619483, 0.10758022, 0.24343225, -0.19493094,
-0.28522961, -0.33391355, 3.10376116, -0.37864122, -0.29160592,
-0.33968311, -0.35666298, -0.38938997, -0.31622777],
[ 0.06753546, 0.06443599, -1.23856512, -1.09544512, 1.92124347,
0.03605657, 0.1174357 , -0.32218974, -0.02037603, -0.29160592,
-0.06141164, 0.01830333, -0.18888085, 0.33230714]])
kmeans.n_iter_
19
kmeans.labels_[:]
array([0, 2, 4, 4, 0, 1, 4, 0, 2, 1, 0, 0, 1, 1, 2, 0, 0, 0, 4, 0, 0, 1,
1, 4, 2, 0, 0, 1, 1, 3, 1, 4, 0, 1, 4, 0, 1, 0, 4, 3, 4, 1, 1, 0,
0, 0, 4, 0, 4, 0, 3, 0, 4, 0, 1, 0, 1, 0, 0, 0, 4, 1, 1, 4, 0, 0,
0, 0, 2, 0, 1, 0, 0, 0, 0, 4, 1, 1, 2, 1, 3, 0, 2, 1, 0, 4, 1, 0,
0, 3, 1, 1, 1, 0, 2, 0, 2, 4, 2, 4, 0, 4, 3, 1, 0, 4, 0, 0, 3, 3,
1, 4, 3, 0, 1, 0, 0, 0, 4, 0, 1, 3, 1, 3, 1, 0, 1, 1, 0, 4, 0, 1,
3, 0, 0, 0, 4, 1, 3, 0, 1, 4, 1, 1, 2, 1, 0, 0, 0, 4, 1, 0, 1, 0,
4, 0, 0, 0, 0, 0, 3, 3, 0, 1, 4, 1, 3, 0, 1, 0, 0, 0, 0, 1, 0, 0,
0, 3, 2, 0, 0, 0, 4, 4, 3, 2, 0, 1, 4, 4, 0, 4, 4, 3, 0, 1, 0, 0,
4, 3, 1, 2, 1, 0, 0, 1, 1, 4, 1, 4, 0, 1, 0, 0, 4, 4, 3, 0, 0, 0,
4, 2, 0, 0, 0, 4, 2, 0, 0, 1, 3, 1, 0, 2, 0, 1, 0, 3, 0, 0, 0, 0,
1, 0, 3, 0, 0, 4, 3, 0, 1, 2, 4, 0, 0, 3, 0, 1, 2, 4, 4, 3, 3, 1,
4, 4, 4, 0, 0, 4, 0, 0, 0, 1, 4, 3, 1, 0, 1, 0, 0, 0, 2, 0, 0, 2,
4, 0, 2, 4, 4, 0, 4, 2, 4, 1, 1, 0, 0, 1, 0, 3, 2, 0, 0, 2, 1, 0,
4, 4, 1, 0, 0, 4, 0, 1, 1, 4, 0])
from sklearn.neighbors import KNeighborsClassifier
model = KNeighborsClassifier(n_neighbors=1)
model.fit(scaled_features,train_set_labels)
predicted = model.predict(scaled_features)
predicted
array([0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0,
0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,
0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,
0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0,
1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1,
0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0], dtype=int64)
sum(abs(predicted-train_set_labels))
0
from sklearn.datasets import make_classification
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier()
model.fit(train_set, train_set_labels)
RandomForestClassifier()
yhat = model.predict(train_set)
sum(abs(yhat-train_set_labels))
0
df1.describe().transpose()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| Gender | 399.0 | 5.864662e-01 | 4.930851e-01 | 0.000000e+00 | 0.000000e+00 | 1.000000e+00 | 1.000000e+00 | 1.000000e+00 |
| Children | 399.0 | 1.313283e+00 | 1.022166e+00 | 0.000000e+00 | 0.000000e+00 | 2.000000e+00 | 2.000000e+00 | 3.000000e+00 |
| Salary | 399.0 | 7.894247e+04 | 2.723687e+04 | 2.068700e+04 | 5.812850e+04 | 7.921200e+04 | 9.579650e+04 | 1.601340e+05 |
| Neutral or Not | 399.0 | 1.704261e-01 | 3.764788e-01 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 1.000000e+00 |
| __Middle-aged | 399.0 | 5.463659e-01 | 4.984706e-01 | 0.000000e+00 | 0.000000e+00 | 1.000000e+00 | 1.000000e+00 | 1.000000e+00 |
| __Young | 399.0 | 2.180451e-01 | 4.134366e-01 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 1.000000e+00 |
| State_California | 399.0 | 8.270677e-02 | 2.757843e-01 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 1.000000e+00 |
| State_Florida | 399.0 | 1.027569e-01 | 3.040223e-01 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 1.000000e+00 |
| State_Illinois | 399.0 | 9.523810e-02 | 2.939121e-01 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 1.000000e+00 |
| State_Michigan | 399.0 | 1.152882e-01 | 3.197704e-01 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 1.000000e+00 |
| State_Minnesota | 399.0 | 7.769424e-02 | 2.680259e-01 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 1.000000e+00 |
| State_New York | 399.0 | 1.152882e-01 | 3.197704e-01 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 1.000000e+00 |
| State_Ohio | 399.0 | 1.102757e-01 | 3.136263e-01 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 1.000000e+00 |
| State_Texas | 399.0 | 1.203008e-01 | 3.257213e-01 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 1.000000e+00 |
| State_Virginia | 399.0 | 9.774436e-02 | 2.973415e-01 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 1.000000e+00 |
| Gend_child | 399.0 | 8.020050e-01 | 1.009202e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 2.000000e+00 | 3.000000e+00 |
| log_Salary | 399.0 | 1.121107e+01 | 3.760868e-01 | 9.937261e+00 | 1.097039e+01 | 1.127988e+01 | 1.146998e+01 | 1.198377e+01 |
| Salary_Sq | 399.0 | 6.971901e+09 | 4.626720e+09 | 4.279520e+08 | 3.379095e+09 | 6.274541e+09 | 9.176971e+09 | 2.564290e+10 |
from sklearn.neural_network import MLPClassifier
mlp = MLPClassifier(hidden_layer_sizes=(13,3,2),max_iter=1000)
mlp.fit(scaled_features,train_set_labels)
C:\Users\Top\anaconda3\lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:614: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet. warnings.warn(
MLPClassifier(hidden_layer_sizes=(13, 3, 2), max_iter=1000)
MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(17,13,13,13), learning_rate='constant',
learning_rate_init=0.001, max_iter=1000, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
MLPClassifier(hidden_layer_sizes=(17, 13, 13, 13), max_iter=1000)
predictions = mlp.predict(train_set) #to predict with the test set you'd also have to scale it
predictions
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int64)
from sklearn.metrics import classification_report,confusion_matrix
print(confusion_matrix(train_set_labels,predictions)) #this is the same result we got with logistic regression
#experiment with settings to see if you can improve it
[[263 0] [ 56 0]]
print(classification_report(train_set_labels,predictions))
precision recall f1-score support
0 0.82 1.00 0.90 263
1 0.00 0.00 0.00 56
accuracy 0.82 319
macro avg 0.41 0.50 0.45 319
weighted avg 0.68 0.82 0.75 319
C:\Users\Top\anaconda3\lib\site-packages\sklearn\metrics\_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) C:\Users\Top\anaconda3\lib\site-packages\sklearn\metrics\_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) C:\Users\Top\anaconda3\lib\site-packages\sklearn\metrics\_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
import re
txt = "The rain in Spain is mainly on the plain."
x = re.findall("ai", txt)
print(x)
['ai', 'ai', 'ai', 'ai']
x = re.split("\s", txt)
print(x)
['The', 'rain', 'in', 'Spain', 'is', 'mainly', 'on', 'the', 'plain.']
x = re.split("\s", txt, 1) #splits at only the first space
print(x)
['The', 'rain in Spain is mainly on the plain.']
x = re.sub("\s", "-", txt)
print(x)
The-rain-in-Spain-is-mainly-on-the-plain.
x = re.search(r"\bS\w+", txt) #looks for a word with uppercase S at the start, gives start and end position of word
print(x.span())
(12, 17)
print(x.string) #prints whole string where the word appeared
The rain in Spain is mainly on the plain.
print(x.group()) #prints just the word
Spain
x = re.findall("[mat]", txt)
print(x)
['a', 'a', 'm', 'a', 't', 'a']
x = re.findall("ain+", txt)
print(x)
['ain', 'ain', 'ain', 'ain']
#https://www.w3schools.com/python/python_regex.asp more code keys here