In [2]:
import numpy as np
In [4]:
import pandas as pd
import seaborn as sns
In [6]:
from matplotlib import pyplot as plt
In [7]:
#today we will do support vector machine (svm)
In [8]:
df=pd.read_csv('Social_Network_Ads.csv')
df.head()
Out[8]:
| User ID | Gender | Age | EstimatedSalary | Purchased | |
|---|---|---|---|---|---|
| 0 | 15624510 | Male | 19 | 19000 | 0 |
| 1 | 15810944 | Male | 35 | 20000 | 0 |
| 2 | 15668575 | Female | 26 | 43000 | 0 |
| 3 | 15603246 | Female | 27 | 57000 | 0 |
| 4 | 15804002 | Male | 19 | 76000 | 0 |
In [9]:
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 400 entries, 0 to 399 Data columns (total 5 columns): User ID 400 non-null int64 Gender 400 non-null object Age 400 non-null int64 EstimatedSalary 400 non-null int64 Purchased 400 non-null int64 dtypes: int64(4), object(1) memory usage: 15.7+ KB
In [11]:
df.drop('User ID',axis=1,inplace=True)
In [12]:
df.head()
Out[12]:
| Gender | Age | EstimatedSalary | Purchased | |
|---|---|---|---|---|
| 0 | Male | 19 | 19000 | 0 |
| 1 | Male | 35 | 20000 | 0 |
| 2 | Female | 26 | 43000 | 0 |
| 3 | Female | 27 | 57000 | 0 |
| 4 | Male | 19 | 76000 | 0 |
In [13]:
sns.heatmap(df.corr())
Out[13]:
<matplotlib.axes._subplots.AxesSubplot at 0x26d3fb334a8>
In [14]:
gend={"Male":0,"Female":1}
In [16]:
df['Gender']=df['Gender'].map(gend)
In [17]:
df.head()
Out[17]:
| Gender | Age | EstimatedSalary | Purchased | |
|---|---|---|---|---|
| 0 | 0 | 19 | 19000 | 0 |
| 1 | 0 | 35 | 20000 | 0 |
| 2 | 1 | 26 | 43000 | 0 |
| 3 | 1 | 27 | 57000 | 0 |
| 4 | 0 | 19 | 76000 | 0 |
In [18]:
sns.heatmap(df.corr())
Out[18]:
<matplotlib.axes._subplots.AxesSubplot at 0x26d40e732b0>
In [19]:
df.drop('Gender',axis=1,inplace=True)
In [20]:
df.head()
Out[20]:
| Age | EstimatedSalary | Purchased | |
|---|---|---|---|
| 0 | 19 | 19000 | 0 |
| 1 | 35 | 20000 | 0 |
| 2 | 26 | 43000 | 0 |
| 3 | 27 | 57000 | 0 |
| 4 | 19 | 76000 | 0 |
In [21]:
from sklearn.preprocessing import StandardScaler
In [22]:
scaler=StandardScaler()
In [24]:
scaler.fit(df.drop('Purchased',axis=1))
Out[24]:
StandardScaler(copy=True, with_mean=True, with_std=True)
In [25]:
scale_arr=scaler.transform(df.drop('Purchased',axis=1))
In [26]:
scale_arr
Out[26]:
array([[-1.78179743, -1.49004624],
[-0.25358736, -1.46068138],
[-1.11320552, -0.78528968],
[-1.01769239, -0.37418169],
[-1.78179743, 0.18375059],
[-1.01769239, -0.34481683],
[-1.01769239, 0.41866944],
[-0.54012675, 2.35674998],
[-1.20871865, -1.07893824],
[-0.25358736, -0.13926283],
[-1.11320552, 0.30121002],
[-1.11320552, -0.52100597],
[-1.6862843 , 0.47739916],
[-0.54012675, -1.51941109],
[-1.87731056, 0.35993973],
[-0.82666613, 0.30121002],
[ 0.89257019, -1.3138571 ],
[ 0.70154394, -1.28449224],
[ 0.79705706, -1.22576253],
[ 0.98808332, -1.19639767],
[ 0.70154394, -1.40195167],
[ 0.89257019, -0.60910054],
[ 0.98808332, -0.84401939],
[ 0.70154394, -1.40195167],
[ 0.79705706, -1.37258681],
[ 0.89257019, -1.46068138],
[ 1.08359645, -1.22576253],
[ 0.89257019, -1.16703281],
[-0.82666613, -0.78528968],
[-0.63563988, -1.51941109],
[-0.63563988, 0.12502088],
[-1.01769239, 1.97500684],
[-1.59077117, -1.5781408 ],
[-0.92217926, -0.75592482],
[-1.01769239, 0.59485858],
[-0.25358736, -1.25512738],
[-0.44461362, -1.22576253],
[-0.73115301, -0.60910054],
[-1.11320552, 0.06629116],
[-1.01769239, -1.13766796],
[-1.01769239, -1.54877595],
[-0.44461362, -0.55037082],
[-0.25358736, 1.123426 ],
[-0.73115301, -1.60750566],
[-0.92217926, 0.41866944],
[-1.39974491, -1.46068138],
[-1.20871865, 0.27184516],
[-1.01769239, -0.46227625],
[-0.73115301, 1.91627713],
[-0.63563988, 0.56549373],
[-1.30423178, -1.1083031 ],
[-1.87731056, -0.75592482],
[-0.82666613, 0.38930459],
[-0.25358736, -1.37258681],
[-1.01769239, -0.34481683],
[-1.30423178, -0.4329114 ],
[-1.39974491, -0.63846539],
[-0.92217926, 0.27184516],
[-1.49525804, -1.51941109],
[-0.54012675, 1.38770971],
[-1.01769239, -1.46068138],
[-1.20871865, 0.50676401],
[-1.39974491, -0.10989798],
[-0.54012675, 1.47580428],
[ 2.03872775, 0.38930459],
[-1.30423178, -0.34481683],
[-1.30423178, -1.49004624],
[-1.39974491, 0.35993973],
[-1.49525804, -0.19799255],
[-0.63563988, -0.05116826],
[-1.20871865, 0.30121002],
[-1.30423178, -1.25512738],
[-1.6862843 , -1.37258681],
[-0.44461362, 1.27025028],
[-0.54012675, -1.51941109],
[-0.34910049, 1.24088543],
[-1.87731056, -0.52100597],
[-1.49525804, -1.25512738],
[-0.92217926, 0.50676401],
[-1.11320552, -1.54877595],
[-0.73115301, 0.30121002],
[ 0.12846516, -0.81465453],
[-1.6862843 , -0.60910054],
[-0.25358736, 0.53612887],
[-0.73115301, -0.2273574 ],
[-0.63563988, 1.41707457],
[-1.30423178, -0.4329114 ],
[-0.92217926, 0.4480343 ],
[-1.11320552, 0.33057487],
[-0.25358736, -0.57973568],
[-1.49525804, 0.33057487],
[-0.73115301, 1.35834485],
[-1.11320552, -1.60750566],
[-0.82666613, -1.22576253],
[-0.82666613, 0.38930459],
[-0.25358736, -0.75592482],
[-0.25358736, -1.3138571 ],
[-0.92217926, 1.56389885],
[-0.25358736, 0.09565602],
[-0.92217926, -0.96147882],
[-1.01769239, 0.53612887],
[-0.92217926, -0.31545197],
[-0.54012675, 0.47739916],
[-0.44461362, 2.32738512],
[-1.78179743, -1.43131652],
[-1.59077117, 0.06629116],
[-1.11320552, -1.02020853],
[-1.01769239, 0.56549373],
[-1.11320552, 0.47739916],
[ 0.03295203, 0.30121002],
[ 0.12846516, 0.03692631],
[-0.0625611 , 0.03692631],
[ 0.03295203, -0.25672226],
[-0.0625611 , -0.4329114 ],
[ 0.41500455, 0.30121002],
[ 0.22397829, -0.37418169],
[-0.25358736, 0.15438573],
[-0.15807423, -0.52100597],
[ 0.22397829, -0.31545197],
[ 0.31949142, -0.31545197],
[-0.15807423, 0.15438573],
[-0.0625611 , 0.06629116],
[ 0.22397829, 0.15438573],
[-0.25358736, -0.49164111],
[ 0.31949142, -0.55037082],
[ 0.12846516, -0.25672226],
[ 0.41500455, -0.13926283],
[-1.11320552, -1.1083031 ],
[-0.73115301, -1.54877595],
[-1.11320552, 0.41866944],
[-0.63563988, -0.34481683],
[-0.44461362, -1.13766796],
[-0.73115301, 0.50676401],
[-1.59077117, -0.05116826],
[-0.92217926, -0.4329114 ],
[-1.39974491, -0.19799255],
[-1.6862843 , 0.35993973],
[-0.73115301, 1.09406114],
[-0.92217926, -0.31545197],
[-1.78179743, -1.3138571 ],
[-1.78179743, 0.4480343 ],
[-1.87731056, -0.05116826],
[-0.25358736, -0.31545197],
[-0.73115301, 0.56549373],
[-0.34910049, -1.3138571 ],
[-1.30423178, 0.56549373],
[-1.01769239, 0.77104772],
[ 0.31949142, -1.16703281],
[-0.82666613, -0.25672226],
[-1.6862843 , 0.12502088],
[-1.11320552, -1.60750566],
[ 0.31949142, -0.72655996],
[-0.63563988, 0.18375059],
[-0.15807423, -0.57973568],
[ 0.22397829, -0.66783025],
[-0.63563988, -1.60750566],
[ 0.79705706, -0.31545197],
[-0.82666613, 0.15438573],
[-1.11320552, -1.16703281],
[-0.54012675, 1.91627713],
[-0.54012675, 0.88850715],
[-1.20871865, 0.59485858],
[-0.0625611 , -1.07893824],
[-0.25358736, -0.93211396],
[-0.44461362, -0.02180341],
[-1.87731056, 0.47739916],
[-1.49525804, -0.4329114 ],
[-0.25358736, 0.03692631],
[-0.82666613, 2.29802026],
[-0.82666613, -0.66783025],
[-1.59077117, 0.53612887],
[-0.34910049, 1.32898 ],
[-1.11320552, 1.41707457],
[-0.34910049, -0.78528968],
[-0.34910049, 0.06629116],
[-1.39974491, -1.22576253],
[-0.25358736, -0.66783025],
[-1.20871865, -1.40195167],
[-1.30423178, -1.37258681],
[-0.63563988, -1.04957339],
[-1.11320552, -1.5781408 ],
[-0.63563988, 0.03692631],
[-0.54012675, 1.38770971],
[-0.44461362, -0.78528968],
[-0.44461362, -0.28608712],
[-0.63563988, -0.10989798],
[-1.6862843 , 0.35993973],
[-0.44461362, -0.84401939],
[-0.25358736, 0.06629116],
[-0.92217926, -1.1083031 ],
[-1.30423178, 0.41866944],
[-1.78179743, -1.28449224],
[-0.82666613, -0.78528968],
[-1.78179743, 0.00756145],
[-0.92217926, 0.56549373],
[-0.34910049, -0.78528968],
[-0.73115301, 0.27184516],
[-1.6862843 , -0.99084367],
[-1.11320552, 0.30121002],
[-0.25358736, -1.40195167],
[-0.25358736, -0.9027491 ],
[ 1.08359645, 0.12502088],
[ 0.12846516, 1.88691227],
[ 0.31949142, 0.03692631],
[ 1.94321462, 0.917872 ],
[ 0.89257019, -0.66783025],
[ 1.65667523, 1.76945285],
[ 1.37013584, 1.29961514],
[ 0.22397829, 2.12183112],
[ 0.79705706, -1.40195167],
[ 0.98808332, 0.77104772],
[ 1.37013584, 2.35674998],
[ 2.03872775, -0.81465453],
[-0.25358736, -0.34481683],
[ 0.89257019, -0.78528968],
[ 2.13424088, 1.123426 ],
[ 1.08359645, -0.13926283],
[ 0.22397829, 0.2424803 ],
[ 0.79705706, 0.77104772],
[ 2.03872775, 2.15119598],
[ 0.31949142, 0.30121002],
[-0.25358736, 0.62422344],
[-0.0625611 , 2.18056084],
[ 2.13424088, 0.94723686],
[-0.25358736, -0.28608712],
[-0.0625611 , -0.49164111],
[-0.15807423, 1.65199342],
[ 1.75218836, 1.85754742],
[ 0.22397829, 0.06629116],
[ 0.41500455, 0.30121002],
[-0.25358736, 2.26865541],
[ 0.12846516, -0.81465453],
[ 0.22397829, 1.09406114],
[ 1.08359645, 0.47739916],
[ 0.03295203, 1.24088543],
[ 0.79705706, 0.27184516],
[ 0.22397829, -0.37418169],
[-0.0625611 , 0.30121002],
[ 0.79705706, 0.35993973],
[ 1.46564897, 2.15119598],
[ 0.41500455, 2.32738512],
[ 0.03295203, -0.31545197],
[ 1.17910958, 0.53612887],
[ 1.75218836, 1.00596657],
[ 0.31949142, 0.06629116],
[ 1.27462271, 2.23929055],
[-0.25358736, -0.57973568],
[ 1.84770149, 1.53453399],
[ 0.31949142, -0.52100597],
[-0.25358736, 0.80041258],
[ 0.60603081, -0.9027491 ],
[-0.0625611 , -0.52100597],
[ 0.98808332, 1.88691227],
[-0.0625611 , 2.23929055],
[ 1.17910958, -0.75592482],
[ 1.37013584, 0.59485858],
[ 0.31949142, 0.06629116],
[ 0.22397829, -0.37418169],
[ 1.94321462, 0.74168287],
[ 0.70154394, 1.7988177 ],
[-0.25358736, 0.21311545],
[-0.15807423, 2.18056084],
[ 1.65667523, 1.62262856],
[-0.25358736, 0.06629116],
[ 0.98808332, 0.59485858],
[ 0.41500455, 1.123426 ],
[ 0.22397829, 0.15438573],
[-0.0625611 , 0.12502088],
[ 0.89257019, 2.18056084],
[ 0.22397829, -0.25672226],
[ 0.51051768, 1.85754742],
[ 2.03872775, 0.18375059],
[ 2.13424088, -0.81465453],
[ 0.12846516, 1.06469629],
[ 1.84770149, -1.28449224],
[ 1.84770149, 0.12502088],
[ 0.03295203, 0.03692631],
[ 1.08359645, 0.53612887],
[ 1.37013584, -0.93211396],
[ 1.17910958, -0.99084367],
[ 2.03872775, 0.53612887],
[-0.25358736, -0.25672226],
[-0.0625611 , 0.00756145],
[ 1.37013584, -1.43131652],
[ 0.98808332, 2.09246627],
[-0.0625611 , 0.68295315],
[-0.0625611 , -0.2273574 ],
[ 0.98808332, 2.0043717 ],
[ 0.31949142, 0.27184516],
[-0.0625611 , 0.2424803 ],
[ 0.12846516, 1.88691227],
[ 1.08359645, 0.56549373],
[ 1.65667523, -0.9027491 ],
[-0.0625611 , 0.21311545],
[-0.25358736, -0.37418169],
[-0.15807423, -0.19799255],
[ 0.41500455, 0.09565602],
[ 0.51051768, 1.24088543],
[ 0.70154394, 0.27184516],
[ 0.79705706, 1.38770971],
[ 1.94321462, -0.93211396],
[ 0.98808332, 0.12502088],
[-0.0625611 , 1.97500684],
[-0.0625611 , 0.27184516],
[ 0.22397829, -0.28608712],
[ 0.41500455, -0.46227625],
[ 1.27462271, 1.88691227],
[ 0.89257019, 1.27025028],
[-0.15807423, 1.62262856],
[ 0.03295203, -0.57973568],
[ 0.41500455, 0.00756145],
[ 0.12846516, 0.77104772],
[ 0.03295203, -0.57973568],
[ 1.08359645, 2.09246627],
[ 0.12846516, 0.27184516],
[ 0.12846516, 0.15438573],
[ 1.5611621 , 1.00596657],
[-0.25358736, -0.4329114 ],
[ 0.70154394, -1.1083031 ],
[-0.15807423, -0.28608712],
[ 1.37013584, 2.0043717 ],
[ 1.46564897, 0.35993973],
[ 0.31949142, -0.52100597],
[ 0.98808332, -1.16703281],
[ 0.98808332, 1.7988177 ],
[ 0.31949142, -0.28608712],
[ 0.31949142, 0.06629116],
[ 0.41500455, 0.15438573],
[-0.15807423, 1.41707457],
[ 0.89257019, 1.09406114],
[ 0.03295203, -0.55037082],
[ 0.98808332, 1.44643942],
[ 0.41500455, -0.13926283],
[ 0.22397829, -0.13926283],
[ 1.84770149, -0.28608712],
[-0.15807423, -0.46227625],
[ 1.94321462, 2.18056084],
[-0.25358736, 0.27184516],
[ 0.03295203, -0.4329114 ],
[ 0.12846516, 1.53453399],
[ 1.46564897, 1.00596657],
[-0.25358736, 0.15438573],
[ 0.03295203, -0.13926283],
[ 0.89257019, -0.55037082],
[ 0.89257019, 1.03533143],
[ 0.31949142, -0.19799255],
[ 1.46564897, 0.06629116],
[ 1.5611621 , 1.123426 ],
[ 0.12846516, 0.21311545],
[ 0.03295203, -0.25672226],
[ 0.03295203, 1.27025028],
[-0.0625611 , 0.15438573],
[ 0.41500455, 0.59485858],
[-0.0625611 , -0.37418169],
[-0.15807423, 0.85914229],
[ 2.13424088, -1.04957339],
[ 1.5611621 , 0.00756145],
[ 0.31949142, 0.06629116],
[ 0.22397829, 0.03692631],
[ 0.41500455, -0.46227625],
[ 0.51051768, 1.74008799],
[ 1.46564897, -1.04957339],
[ 0.89257019, -0.57973568],
[ 0.41500455, 0.27184516],
[ 0.41500455, 1.00596657],
[ 2.03872775, -1.19639767],
[ 1.94321462, -0.66783025],
[ 0.79705706, 0.53612887],
[ 0.03295203, 0.03692631],
[ 1.5611621 , -1.28449224],
[ 2.13424088, -0.69719511],
[ 2.13424088, 0.38930459],
[ 0.12846516, 0.09565602],
[ 2.03872775, 1.76945285],
[-0.0625611 , 0.30121002],
[ 0.79705706, -1.1083031 ],
[ 0.79705706, 0.12502088],
[ 0.41500455, -0.49164111],
[ 0.31949142, 0.50676401],
[ 1.94321462, -1.37258681],
[ 0.41500455, -0.16862769],
[ 0.98808332, -1.07893824],
[ 0.60603081, 2.03373655],
[ 1.08359645, -1.22576253],
[ 1.84770149, -1.07893824],
[ 1.75218836, -0.28608712],
[ 1.08359645, -0.9027491 ],
[ 0.12846516, 0.03692631],
[ 0.89257019, -1.04957339],
[ 0.98808332, -1.02020853],
[ 0.98808332, -1.07893824],
[ 0.89257019, -1.37258681],
[ 0.70154394, -0.72655996],
[ 2.13424088, -0.81465453],
[ 0.12846516, -0.31545197],
[ 0.79705706, -0.84401939],
[ 1.27462271, -1.37258681],
[ 1.17910958, -1.46068138],
[-0.15807423, -1.07893824],
[ 1.08359645, -0.99084367]])
In [27]:
new_df=pd.DataFrame(scale_arr,columns=['Age','EstimatedSalary'])
In [28]:
new_df.head()
Out[28]:
| Age | EstimatedSalary | |
|---|---|---|
| 0 | -1.781797 | -1.490046 |
| 1 | -0.253587 | -1.460681 |
| 2 | -1.113206 | -0.785290 |
| 3 | -1.017692 | -0.374182 |
| 4 | -1.781797 | 0.183751 |
In [29]:
from sklearn.model_selection import train_test_split
In [31]:
x_train,x_test,y_train,y_test=train_test_split(new_df,df['Purchased'],test_size=0.30,random_state=101)
In [32]:
from sklearn.svm import SVC
In [33]:
model=SVC()
In [34]:
model.fit(x_train,y_train)
C:\Users\AbhishekSingh\Anaconda3\lib\site-packages\sklearn\svm\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning. "avoid this warning.", FutureWarning)
Out[34]:
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3, gamma='auto_deprecated',
kernel='rbf', max_iter=-1, probability=False, random_state=None,
shrinking=True, tol=0.001, verbose=False)
In [35]:
y_pred=model.predict(x_test)
In [36]:
y_pred
Out[36]:
array([0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1,
0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,
1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1,
0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1,
1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
1, 1, 0, 0, 1, 0, 0, 0, 0, 1], dtype=int64)
In [37]:
from sklearn.metrics import confusion_matrix
In [38]:
confusion_matrix(y_pred,y_test)
Out[38]:
array([[73, 1],
[ 7, 39]], dtype=int64)
In [39]:
from sklearn import metrics
In [40]:
metrics.accuracy_score(y_test,y_pred)
Out[40]:
0.9333333333333333
In [ ]:
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