StarBucks-Capstone Challenge

                                    CLONE THE REPOSITORY using 'git clone https://github.com/yashmanuraj2/StarBucks-Capstone.git'
                                    Download the Transcripts file from the given Link https://drive.google.com/file/d/1NkL6wKn42av_Dsg-TFd-4lrW_JLkmfW3/view?usp=sharing
                                    Paste the transcript.json in the data folder and run the notebook

Table of Contents :

1. Project Description

2. Acknowledgement

3. AIM

4 . Libraries Used

5. Results

PROJECT DESCRIPTION

The Data contains 3 files

1. Profile.csv 2.Transcripts.csv 3.portfolio.csv

The description of the files is as follows : #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) #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 #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

Acknowledgement

I would Like to thanks Udacity for giving me this great project as the final Project for my nanodegree program. I would like to thank project partner Starbucks for sharing the data so that it is available to students of the classroom to study.

AIM OF THE PROJECT

To create a machine learning model to predict how user responds to the offers of buy one get one free and discount offer based on users demographic variables like age gender income ,number of days since member etc.

LIBRARIES USED

pandas numpy json matplotlib seaborn sklearn.grid_search sklearn.neighbors import KNeighborsClassifier sklearn.linear_model import LogisticRegression sklearn.tree import DecisionTreeClassifier sklearn.svm import SVC sklearn.ensemble import RandomForestRegressor sklearn.naive_bayes import GaussianNB sklearn.metrics import accuracy_score,f1_score sklearn.model_selection import train_test_split,GridSearchCV warnings.filterwarnings("ignore")

Results

The model was trained on various algorithms using default parameters( Tried GridSeacrhCv but was taking a lot of time and model gave a pretty good accuraacy on default parameters so i chose not to use gridSearch on my model) The DecisionTreeClassifier gave the best results with an accuracy of 99% and 96% on bogo and discount offers