# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
"""
# SUV DATA PREDICTIONS
"""
import pandas as pd
import matplotlib as plt
import numpy as np
%matplotlib inline
df = pd.read_csv('../input/suv-data/suv_data.csv')
df
df.head(10)
"""
using 2nd and 3rd column to predict the dependent variable i.e purchase
Let's use X to represent independent quantities
and Y to represent dependent variable 
"""
X=df.iloc[:,[2,3]].values
Y=df.iloc[:,4].values

X
Y

"""
0 stands for didn't purchase
1 stands for purchase
"""
"""
Now we will divide data into training and testing subsets
"""
from  sklearn.model_selection import train_test_split
train_test_split
X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.20,random_state=0)
from sklearn.preprocessing import StandardScaler
"""
sc=StandardScaler()
X_train=sc.fit_transform(X_train)
X_test=sc.transform(X_test)
"""
from sklearn.linear_model import LogisticRegression
classifier=LogisticRegression(random_state=0)
classifier.fit(X_train,Y_train)
Y_prediction=classifier.predict(X_test)
from sklearn.metrics import accuracy_score

accuracy_score(Y_test,Y_prediction)*100