<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
 <a href='http
Step1:  Get the Data
Read in the advertising.csv file and set it to a data frame called ad_data.
Step2:  Check the head of ad_data
Step3:  Use info and describe() on ad_data
Step4:  Exploratory Data Analysis
Let's use seaborn to explore the data!
Try recreating the plots shown below!
 Create a histogram of the Age
Step5:  Create a jointplot showing Area Income versus Age.
Step6:  Create a jointplot showing the kde distributions of Daily Time spent on site vs. Age.
Step7:  Create a jointplot of 'Daily Time Spent on Site' vs. 'Daily Internet Usage'
Step8:  Finally, create a pairplot with the hue defined by the 'Clicked on Ad' column feature.
Step9:  Logistic Regression
Now it's time to do a train test split, and train our model!
You'll have the freedom here to choose columns that you want to train on!
 Split the data into training set and testing set using train_test_split
Step10:  Train and fit a logistic regression model on the training set.
Step11:  Predictions and Evaluations
 Now predict values for the testing data.
Step12:  Create a classification report for the model.
Step13:  Great Job!
<ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
Explanation: <a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>
Logistic Regression Project
In this project we will be working with a fake advertising data set, indicating whether or not a particular internet user clicked on an Advertisement. We will try to create a model that will predict whether or not they will click on an ad based off the features of that user.
This data set contains the following features:
'Daily Time Spent on Site': consumer time on site in minutes
'Age': cutomer age in years
'Area Income': Avg. Income of geographical area of consumer
'Daily Internet Usage': Avg. minutes a day consumer is on the internet
'Ad Topic Line': Headline of the advertisement
'City': City of consumer
'Male': Whether or not consumer was male
'Country': Country of consumer
'Timestamp': Time at which consumer clicked on Ad or closed window
'Clicked on Ad': 0 or 1 indicated clicking on Ad
Import Libraries
Import a few libraries you think you'll need (Or just import them as you go along!)
End of explanation
ad_data = pd.read_csv('advertising.csv')
Explanation: Get the Data
Read in the advertising.csv file and set it to a data frame called ad_data.
End of explanation
ad_data.head()
Explanation: Check the head of ad_data
End of explanation
ad_data.info()
ad_data.describe()
Explanation: Use info and describe() on ad_data
End of explanation
sns.distplot(ad_data['Age'],kde=False,bins=30,color='blue')
Explanation: Exploratory Data Analysis
Let's use seaborn to explore the data!
Try recreating the plots shown below!
 Create a histogram of the Age
End of explanation
sns.jointplot(data=ad_data,x='Age',y='Area Income')
Explanation: Create a jointplot showing Area Income versus Age.
End of explanation
sns.jointplot(data=ad_data,x='Age',y='Daily Time Spent on Site',kind='kde')
Explanation: Create a jointplot showing the kde distributions of Daily Time spent on site vs. Age.
End of explanation
sns.jointplot(data=ad_data,x='Daily Time Spent on Site',y='Daily Internet Usage',color='green')
Explanation: Create a jointplot of 'Daily Time Spent on Site' vs. 'Daily Internet Usage'
End of explanation
sns.pairplot(ad_data,hue='Clicked on Ad')
Explanation: Finally, create a pairplot with the hue defined by the 'Clicked on Ad' column feature.
End of explanation
ad_data.columns
from sklearn.model_selection import train_test_split
X = ad_data[['Daily Time Spent on Site', 'Age', 'Area Income','Daily Internet Usage','Male']]
y = ad_data['Clicked on Ad']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=101)
Explanation: Logistic Regression
Now it's time to do a train test split, and train our model!
You'll have the freedom here to choose columns that you want to train on!
 Split the data into training set and testing set using train_test_split
End of explanation
from sklearn.linear_model import LogisticRegression
logmodel = LogisticRegression()
logmodel.fit(X_train,y_train)
logmodel.coef_
Explanation: Train and fit a logistic regression model on the training set.
End of explanation
predictions = logmodel.predict(X_test)
Explanation: Predictions and Evaluations
 Now predict values for the testing data.
End of explanation
from sklearn.metrics import classification_report
print(classification_report(y_test,predictions))
Explanation: Create a classification report for the model.
End of explanation
from collections import OrderedDict
d = OrderedDict({'Daily Time Spent on Site': 500, 'Age': 18, 'Area Income':23000,'Daily Internet Usage': 160,'Male': 1})
df = pd.DataFrame(d,index=[0])
sample_predict = logmodel.predict(df)
print(sample_predict)
Explanation: Great Job!
End of explanation
<END_TASK>