StarBucks Analysis based on the reward programme

Introduction

This data set contains simulated data that mimics customer behavior on the Starbucks rewards mobile app. Once every few days, Starbucks sends out an offer to users of the mobile app. An offer can be merely an advertisement for a drink or an actual offer such as a discount or BOGO (buy one get one free). Some users might not receive any offer during certain weeks.

Not all users receive the same offer, and that is the challenge to solve with this data set.

Your task is to combine transaction, demographic and offer data to determine which demographic groups respond best to which offer type. This data set is a simplified version of the real Starbucks app because the underlying simulator only has one product whereas Starbucks actually sells dozens of products.

Every offer has a validity period before the offer expires. As an example, a BOGO offer might be valid for only 5 days. You'll see in the data set that informational offers have a validity period even though these ads are merely providing information about a product; for example, if an informational offer has 7 days of validity, you can assume the customer is feeling the influence of the offer for 7 days after receiving the advertisement.

You'll be given transactional data showing user purchases made on the app including the timestamp of purchase and the amount of money spent on a purchase. This transactional data also has a record for each offer that a user receives as well as a record for when a user actually views the offer. There are also records for when a user completes an offer.

Keep in mind as well that someone using the app might make a purchase through the app without having received an offer or seen an offer.

Scope

The scope of this project is to analyse the 3 different dataset of the user from Starbucks. The process involved in this project is covers all the traits of the Data Science.

• Data Exploration
• Data Wrangling
• Data Visualisation
• Data Cleaning
• Data Proceessing using ML

Table of Contents

1. Libraries
2. Data Blog
3. File Descriptions
4. Results
5. Reference

Libraries

This project uses lot of libraries if its not already installed, kindly install it prior to use. This project executed entirely in Python 3, and not tested in python2. List of all the libraries used, is given below for reference.

import pandas as pd
import numpy as np
import math
import json
from sklearn import model_selection
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.graph_objs as go

% matplotlib inline

#libraries for using machine learning algorithm 
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import KFold
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import ExtraTreesClassifier

Data Blog

The link will take you to the data blog, It is written for the technical audience, It has the detailed analysis with the visualisation and the Machine learning explained.

File Descriptions

a. starbuck_notebook.ipynb : Jupyter notebook b. Data Sets The data is contained in three files:

portfolio.json - containing offer ids and meta data about each offer (duration, type, etc.) profile.json - demographic data for each customer transcript.json - records for transactions, offers received, offers viewed, and offers completed Here is the schema and explanation of each variable in the files:

1. portfolio.json

• id (string) - offer id
• offer_type (string) - type of offer ie BOGO, discount, informational
• difficulty (int) - minimum required spend to complete an offer
• reward (int) - reward given for completing an offer
• duration (int) - time for offer to be open, in days
• channels (list of strings)

2. profile.json

• age (int) - age of the customer
• became_member_on (int) - date when customer created an app account
• gender (str) - gender of the customer (note some entries contain 'O' for other rather than M or F)
• id (str) - customer id
• income (float) - customer's income

3. transcript.json

• event (str) - record description (ie transaction, offer received, offer viewed, etc.)
• person (str) - customer id
• time (int) - time in hours since start of test. The data begins at time t=0
• value - (dict of strings) - either an offer id or transaction amount depending on the record

Reference

• reference from kaggle for ML
• reference for markdown
• reference for PiePlot
• Used Geek for Geeks on various python related queries
• My Data_Blog also more has reference used for the programming.
• Train_and_test