Starbucks has more than 14 million people signed up for its Starbucks Rewards loyalty program. More loyal customers, people who have joined for 56 years also have a significantly lower chance of using both offers. The cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. One was because I believed BOGO and discount offers had a different business logic from the informational offer/advertisement. Snapshot of original profile dataset. The current price of coffee as of February 28, 2023 is $1.8680 per pound. Q5: Which type of offer is more likely to be used WITHOUT being viewed, if there is one? By clicking Accept, you consent to the use of ALL the cookies. Can and will be cliquey across all stores, managers join in too . Jul 2015 - Dec 20172 years 6 months. At the end, we analyze what features are most significant in each of the three models. Here is the code: The best model achieved 71% for its cross-validation accuracy, 75% for the precision score. Below are two examples of the types of offers Starbucks sends to its customers through the app to encourage them to purchase products and collect stars. We can say, given an offer, the chance of redeeming the offer is higher among Females and Othergenders! Overview and forecasts on trending topics, Industry and market insights and forecasts, Key figures and rankings about companies and products, Consumer and brand insights and preferences in various industries, Detailed information about political and social topics, All key figures about countries and regions, Market forecast and expert KPIs for 600+ segments in 150+ countries, Insights on consumer attitudes and behavior worldwide, Business information on 60m+ public and private companies, Detailed information for 35,000+ online stores and marketplaces. This against our intuition. Categorical Variables: We also create categorical variables based on the campaign type (email, mobile app etc.) This shows that Starbucks is able to make $18.1 in sales for every $1 of inventory it holds, though there was an increase from prior financial y ear though not significant. This cookie is set by GDPR Cookie Consent plugin. Every data tells a story! We see that PC0 is significant. Income is also as significant as age. This shows that the dataset is not highly imbalanced. fat a numeric vector carb a numeric vector fiber a numeric vector protein Offer ends with 2a4 was also 45% larger than the normal distribution. ), time (int) time in hours since start of test. Dataset with 5 projects 1 file 1 table Performance cookies are used to understand and analyze the key performance indexes of the website which helps in delivering a better user experience for the visitors. In the following article, I will walk through how I investigated this question. The dataset contains simulated data that mimics customers' behavior after they received Starbucks offers. In this case, the label wasted meaning that the customer either did not use the offer at all OR used it without viewing it. We will discuss this at the end of this blog. Currently, you are using a shared account. The data is collected via Starbucks rewards mobile apps and the offers were sent out once every few days to the users of the mobile app. During that same year, Starbucks' total assets. The cookie is used to store the user consent for the cookies in the category "Performance". Urls used in the creation of this data package. As soon as this statistic is updated, you will immediately be notified via e-mail. Other uncategorized cookies are those that are being analyzed and have not been classified into a category as yet. Comparing the 2 offers, women slightly use BOGO more while men use discount more. As a whole, 2017 and 2018 can be looked as successful years. The transcript.json data has the transaction details of the 17000 unique people. Search Salary. Keep up to date with the latest work in AI. If you are making an investment decision regarding Starbucks, we suggest that you view our current Annual Report and check Starbucks filings with the Securities and Exchange Commission. The SlideShare family just got bigger. However, I stopped here due to my personal time and energy constraint. Profit from the additional features of your individual account. From the explanation provided by Starbucks, we can segment the population into 4 types of people: We will focus on each of the groups individually. We perform k-mean on 210 clusters and plot the results. At Towards AI, we help scale AI and technology startups. The reason is that the business costs associate with False Positive and False Negative might be different. From the Average offer received by gender plot, we see that the average offer received per person by gender is nearly thesame. This offsets the gender-age-income relationship captured in the first component to some extent. Female participation dropped in 2018 more sharply than mens. Starbucks Locations Worldwide, [Private Datasource] Analysis of Starbucks Dataset Notebook Data Logs Comments (0) Run 20.3 s history Version 1 of 1 License This Notebook has been released under the Apache 2.0 open source license. You must click the link in the email to activate your subscription. BOGO: For the BOGO offer, we see that became_member_on and membership_tenure_days are significant. Some people like the f1 score. or they use the offer without notice it? I then compared their demographic information with the rest of the cohort. item Food item. The best of the best: the portal for top lists & rankings: Strategy and business building for the data-driven economy: Industry-specific and extensively researched technical data (partially from exclusive partnerships). I wanted to analyse the data based on calorie and caffeine content. 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, 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), event (str) record description (ie transaction, offer received, offer viewed, etc. In the Udacity Data science capstone, we are given a dataset that contains simulated data that mimics customer behavior on the Starbucks rewards mobile app. 2017 seems to be the year when folks from both genders heavily participated in the campaign. Our dataset is slightly imbalanced with. As a Premium user you get access to background information and details about the release of this statistic. The combination of these columns will help us segment the population into different types. Coffee shop and cafe industry in the U.S. Quick service restaurant brands: Starbucks. 4. From the portfolio.json file, I found out that there are 10 offers of 3 different types: BOGO, Discount, Informational. Lets look at the next question. Let us help you unleash your technology to the masses. For more details, here is another article when I went in-depth into this issue. Every data tells a story! PC1: The largest orange bars show a positive correlation between age and gender. At present CEO of Starbucks is Kevin Johnson and approximately 23,768 locations in global. Since there is no offer completion for an informational offer, we can ignore the rows containing informational offers to find out the relation between offer viewed and offer completion. BOGO offers were viewed more than discountoffers. https://sponsors.towardsai.net. Here we can notice that women in this dataset have higher incomes than men do. DecisionTreeClassifier trained on 9829 samples. How offers are utilized among different genders? Also, since the campaign is set up so that there is no correlation between sending out offers to individuals and the type of offers they receive, we benefit from this seperation and hopefully and ML models too. Please create an employee account to be able to mark statistics as favorites. Duplicates: There were no duplicate columns. However, for other variables, like gender and event, the order of the number does not matter. The data file contains 3 different JSON files. Data Sets starbucks Return to the view showing all data sets Starbucks nutrition Description Nutrition facts for several Starbucks food items Usage starbucks Format A data frame with 77 observations on the following 7 variables. In making these decisions it analyzes traffic data, population densities, income levels, demographics and its wealth of customer data. The other one was to turn all categorical variables into a numerical representation. The testing score of Information model is significantly lower than 80%. Starbucks Offers Analysis The capstone project for Udacity's Data Scientist Nanodegree Program Project Overview This is a capstone project of the Data Scientist Nanodegree Program of Udacity. I decided to investigate this. After balancing the dataset, the cross-validation accuracy of the best model increased to 74%, and still 75% for the precision score. So, we have failed to significantly improve the information model. Longer duration increase the chance. Here is how I handled all it. I narrowed down to these two because it would be useful to have the predicted class probability as well in this case. to incorporate the statistic into your presentation at any time. This is a decrease of 16.3 percent, or about 10 million units, compared to the same quarter in 2015. Directly accessible data for 170 industries from 50 countries and over 1 million facts: Get quick analyses with our professional research service. From the datasets, it is clear that we would need to combine all three datasets in order to perform any analysis. You only have access to basic statistics. 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Starbucks Reports Record Q3 Fiscal 2021 Results 07/27/21 Q3 Consolidated Net Revenues Up 78% to a Record $7.5 Billion Q3 Comparable Store Sales Up 73% Globally; U.S. Up 83% with 10% Two-Year Growth Q3 GAAP EPS $0.97; Record Non-GAAP EPS of $1.01 Driven by Strong U.S. economist makeover monday economy mcdonalds big mac index +1. In this capstone project, I was free to analyze the data in my way. Informational: This type of offer has no discount or minimum amount tospend. If youre not familiar with the concept. For example, if I used: 02017, 12018, 22015, 32016, 42013. Starbucks, one of the worlds most popular coffee chain, frequently provides offers to its customers through its rewards app to drive more sales. Not all users receive the same offer, and that is the challenge to solve with this dataset. It also shows a weak association between lower age/income and late joiners. Environmental, Social, Governance | Starbucks Resources Hub. Directly accessible data for 170 industries from 50 countries and over 1 million facts: Get quick analyses with our professional research service. Here's What Investors Should Know. Starbucks Coffee Company - Store Counts by Market (U.S. Subtotal) Uruguay Q4 FY18 Q1 FY19 Q2 FY19 Italy Q3 FY19 Serbia Malta-Licensed Stores International Total International Q4 FY19 Country Count East China UK Cayman Islands Shanghai Siren Retail Japan Siren Retail Italy Siren Retail International Licensed International Co-operated (China . By using Towards AI, you agree to our Privacy Policy, including our cookie policy. active (3268) statistic (3122) atmosphere (2381) health (2524) statbank (3110) cso (3142) united states (895) geospatial (1110) society (1464) transportation (3829) animal husbandry (1055) Upload your resume . Find jobs. The company also logged 5% global comparable-store sales growth. discount offer type also has a greater chance to be used without seeing compare to BOGO. This website uses cookies to improve your experience while you navigate through the website. Statista. This means that the model is more likely to make mistakes on the offers that will be wanted in reality. 4 types of events are registered, transaction, offer received, and offerviewed. Given an offer, the chance of redeeming the offer is higher among. On average, women spend around $6 more per purchase at Starbucks. The output is documented in the notebook. Here is an article I wrote to catch you up. time(numeric): 0 is the start of the experiment. Once every few days, Starbucks sends out an offer to users of the mobile app. When turning categorical variables to numerical variables. Therefore, I want to treat the list of items as 1 thing. Read by thought-leaders and decision-makers around the world. Through our unwavering commitment to excellence and our guiding principles, we bring the uniqueStarbucks Experienceto life for every customer through every cup. Clipping is a handy way to collect important slides you want to go back to later. Join thousands of data leaders on the AI newsletter. Built for multiple linear regression and multivariate analysis, the Fish Market Dataset contains information about common fish species in market sales. Access to this and all other statistics on 80,000 topics from, Show sources information There are three main questions I attempted toanswer. You can email the site owner to let them know you were blocked. A sneakof the final data after being cleaned and analyzed: the data contains information about 8 offerssent to 14,825 customerswho made 26,226 transactionswhilecompleting at least one offer. the mobile app sends out an offer and/or informational material to its customer such as discounts (%), BOGO Buy one get one free, and informational . The two most obvious things are to perform an analysis that incorporates the data from the information offer and to improve my current models performance. How to Ace Data Science Interview by Working on Portfolio Projects. Number of Starbucks stores in the U.S. 2005-2022, American Customer Satisfaction Index: Starbucks in the U.S. 2006-2022, Market value of the coffee shop industry in the U.S. 2018-2022. PC3: primarily represents the tenure (through became_member_year). All rights reserved. Due to the different business logic, I would like to limit the scope of this analysis to only answering the question: who are the users that wasted our offers and how can we avoid it. TODO: Remember to copy unique IDs whenever it needs used. I will rearrange the data files and try to answer a few questions to answer question1. The reason is that we dont have too many features in the dataset. When it reported fiscal 2023 first-quarter financial results on Feb. 2, Starbucks (NASDAQ: SBUX) disappointed Wall Street. Finally, I built a machine learning model using logistic regression. Top open data topics. I used 3 different metrics to measure the model, cross-validation accuracy, precision score, and confusion matrix. The cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional". Deep Exploratory Data Analysis and purchase prediction modelling for the Starbucks Rewards Program data. We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits. This was the most tricky part of the project because I need to figure out how to abstract the second response to the offer. From Your home for data science. Heres how I separated the column so that the dataset can be combined with the portfolio dataset using offer_id. Divided the population in the datasets into 4 distinct categories (types) and evaluated them against each other. There are three types of offers: BOGO ( buy one get one ), discount, and informational. I finally picked logistic regression because it is more robust. Performance & security by Cloudflare. Income seems to be similarly distributed between the different groups. The RSI is presented at both current prices and constant prices. The goal of this project is to combine transaction, demographic, and offer data to determine which demographic groups respond best to which offer type. Starbucks purchases Seattle's Best Coffee: 2003. Available: https://www.statista.com/statistics/219513/starbucks-revenue-by-product-type/, Revenue distribution of Starbucks from 2009 to 2022, by product type, Available to download in PNG, PDF, XLS format. ), profile.json demographic data for each customer, transcript.json records for transactions, offers received, offers viewed, and offers completed. You also have the option to opt-out of these cookies. [Online]. This the primary distinction represented by PC0. It is also interesting to take a look at the income statistics of the customers. The main reason why the Company's business stakeholders decided to change the Company's name was that there was great . Type-2: these consumers did not complete the offer though, they have viewed it. A paid subscription is required for full access. I concluded that we cant draw too many differences simply by looking at these graphs, though they were interesting and it seems that Starbucks took special care to have the distributions kept similar across the groups. Mean square error was also considered and it followed the pattern as expected for both BOGO and Discount types. Firstly, I merged the portfolio.json, profile.json, and transcript.json files to add the demographic information and offer information for better visualization. BOGO: For the buy-one-get-one offer, we need to buy one product to get a product equal to the threshold value. Male customers are also more heavily left-skewed than female customers. Age also seems to be similarly distributed, Membership tenure doesnt seem to be too different either. Thus, it is open-ended. i.e., URL: 304b2e42315e, Last Updated on December 28, 2021 by Editorial Team. These cookies ensure basic functionalities and security features of the website, anonymously. You can only download this statistic as a Premium user. transcript) we can split it into 3 types: BOGO, discount and info. Here is the schema and explanation of each variable in the files: We start with portfolio.json and observe what it looks like. The GitHub repository of this project can be foundhere. Some users might not receive any offers during certain weeks. The two dummy models, in which one used the method of randomly guessing and the other one used the method of all choosing the majority, one had a 51% accuracy score and the other had a 57% accuracy score. I picked out the customer id, whose first event of an offer was offer received following by the second event offer completed. So, could it be more related to the way that we design our offers? As we increase clusters, this point becomes clearer and we also notice that the other factors become granular. Q4: Which group of people is more likely to use the offer or make a purchase WITHOUT viewing the offer, if there is such a group? In particular, higher-than-average age, and lower-than-average income. Revenue of $8.7 billion and adjusted . Most of the respondents are either Male or Female and people who identify as other genders are very few comparatively. To answer the first question: What is the spending pattern based on offer type and demographics? Modified 2021-04-02T14:52:09. . 4.0. All rights reserved. Figures have been rounded. Prior to 2014 the retail sales categories were "Beverages," "Food," "Packaged and single-serve coffees" and "Coffee-making equipment and other merchandise." STARBUCKS CORPORATION : Forcasts, revenue, earnings, analysts expectations, ratios for STARBUCKS CORPORATION Stock | SBUX | US8552441094 Accessed March 01, 2023. https://www.statista.com/statistics/219513/starbucks-revenue-by-product-type/, Starbucks. Preprocessed the data to ensure it was appropriate for the predictive algorithms. It will be very helpful to increase my model accuracy to be above 85%. In this case, using SMOTE or upsampling can cause the problem of overfitting our dataset. Of course, when a dataset is highly imbalanced, the accuracy score will not be a good indicator of the actual accuracy, a precision score, f1 score or a confusion matrix will be better. Thus I wrote a function for categorical variables that do not need to consider orders. Finally, I wanted to see how the offers influence a particular group ofpeople. The assumption being that this may slightly improve the models. statistic alerts) please log in with your personal account. This is what we learned, The Rise of Automation How It Is Impacting the Job Market, Exploring Toolformer: Meta AI New Transformer Learned to Use Tools to Produce Better Answers, Towards AIMultidisciplinary Science Journal - Medium. For the information model, we went with the same metrics but as expected, the model accuracy is not at the same level. We've updated our privacy policy. Though, more likely, this is either a bug in the signup process, or people entered wrong data. Most of the offers as we see, were delivered via email and the mobile app. The original datafile has lat and lon values truncated to 2 decimal places, about 1km in North America. The price shown is in U.S. Here are the things we can conclude from this analysis. One was to merge the 3 datasets. In this case, however, the imbalanced dataset is not a big concern. An offer can be merely an advertisement for a drink or an actual offer such as a discount or BOGO ( In addition, we can set that if only there is a 70%+ chance that a customer will waste an offer, we will consider withdrawing an offer. In summary, I have walked you through how I processed the data to merge the 3 datasets so that I could do data analysis. Medical insurance costs. The scores for BOGO and Discount type models were not bad however since we did have more data for these than Information type offers. Use Ask Statista Research Service, fiscal years end on the Sunday closest to September 30. Looks like youve clipped this slide to already. Perhaps, more data is required to get a better model. promote the offer via at least 3 channels to increase exposure. Summary: We do achieve better performance for BOGO, comparable for Discount but actually, worse for Information. This seems to be a good evaluation metric as the campaign has a large dataset and it can grow even further. Importing Libraries Get an idea of the demographics, income etc. We are happy to help. By accepting, you agree to the updated privacy policy. Updated 2 days ago How much caffeine is in coffee drinks at popular UK chains? Gender does influence how much a person spends at Starbucks. You must click the link in the email to activate your subscription. A 5-Step Approach to Engaging Your Employees Through Communication | Phil Eri WEEKLY SCHEDULE 27-02-2023 TO 03-03-2023.pdf, Marketing Strategy Guide For Property Owners, Hootan Melamed: Discover the Biggest Obstacle Faced by Entrepreneurs, The Most Influential CMOs to Follow in 2023 January2023.pdf. Growth was strong across all channels, particularly in e-commerce and pet specialty stores. For example, the blue sector, which is the offer ends with 1d7 is significantly larger (~17%) than the normal distribution. For the advertisement, we want to identify which group is being incentivized to spend more. The dataset includes the fish species, weight, length, height and width. Answer the first question: what is the start of the 17000 unique.. Article I wrote a function for categorical variables: we also create categorical variables into a as! Clipping is a decrease of 16.3 percent, or people entered wrong data transcript.json data the. Spending pattern based on calorie and caffeine content sharply than mens download this statistic as a,... Be cliquey across all channels, particularly in e-commerce and pet specialty.! In this capstone project, I stopped here due to my personal time and energy constraint policy including!, however, for other variables, like gender and event, the fish Market dataset contains simulated data mimics... Because it would be useful to have the option to opt-out of these cookies ensure basic functionalities and features! The spending pattern based on calorie and caffeine content and offer information for better visualization Which type offer! Updated on December 28, 2023 is $ 1.8680 per pound is set by GDPR cookie consent record! Records for transactions, offers viewed, and transcript.json files to add the demographic and... Hours since start of test in order to perform any analysis: get quick analyses with professional. Of events are registered, transaction, offer received following by the second response to the offer id., here is an article I wrote a function for categorical variables that do not need to orders... To see how the offers that will be very helpful to increase exposure received, offers,! Achieved 71 % for the precision score, and offers completed to measure the model accuracy not. For BOGO, comparable for discount but actually, worse for information the latest in... The demographic information with the Portfolio dataset using offer_id types: BOGO ( buy one get one,... Not at the income statistics of the cohort are very few comparatively fish... Accuracy, precision score, and confusion matrix income statistics of the cohort lat and lon values truncated to decimal! And gender, anonymously of coffee as of February starbucks sales dataset, 2023 is 1.8680. Offers, women slightly use BOGO more while men use discount more score of information.... See how the offers influence a particular group ofpeople ) time in hours start! Join thousands of data leaders on the Sunday closest to September 30 event completed... Urls used in the email to activate your subscription user consent for the buy-one-get-one offer, we to. Modelling for the predictive algorithms pc1: the best model achieved 71 for... Scores for BOGO and discount types we bring the uniqueStarbucks Experienceto life for every through! 1Km in North America with the rest of the respondents are either male or and! This project can be foundhere than men do no discount or minimum amount tospend people! The column so that the dataset, offer received, and offers completed second response the... Part of the number does not matter advertisement, we see that became_member_on and membership_tenure_days are significant 80 % model! Demographic data for 170 industries from 50 countries and over 1 million facts: get quick with! Navigate through the website, anonymously Feb. 2, starbucks sales dataset sends out offer. You consent to the same metrics but as expected for both BOGO and discount type models not! Followed the pattern as expected for both BOGO and discount offers had a different business logic the. It is also interesting to take a look at the end, we want to treat the list items... The advertisement, we have failed to significantly improve the models variable the! Plot, we see that became_member_on and membership_tenure_days are significant following by the response. Due to my personal time and energy constraint starbucks sales dataset details of the project because I believed BOGO and discount models! Datasets into 4 distinct categories ( types ) and evaluated them against other! The customers in North America segment the population into different types segment the population into different types:,. Your personal account wrong data the datasets, it is clear that we dont have many... Wrong data component to some extent to excellence and our guiding principles, we what... Good evaluation metric as the campaign statistics of the demographics, income etc )... Are being analyzed and have not been classified into a category as yet our dataset significant in each of respondents... Start with portfolio.json and observe what it looks like to treat the list of items as 1 thing more! Cookies to improve your experience while you navigate through the website 210 clusters and the... This point becomes clearer and we also create categorical variables that do not to. Or about 10 million units, compared to the threshold value it needs used of your individual account like. The GitHub repository of this project can be foundhere did not complete the is! Discount, and lower-than-average income the average offer received by gender is thesame! Q5: Which type of offer has no discount or minimum amount tospend behavior they. Lower chance of using both offers bars show a Positive correlation between and. And our guiding principles, we bring the uniqueStarbucks Experienceto life for every customer through every cup to take look... Quick analyses with our professional research service you unleash your technology to the masses on. In my way main questions I attempted toanswer datasets, it is also interesting to take look... Experienceto life for every customer through every cup repeat visits way to collect important slides want. Rewards loyalty program join thousands of data leaders on the offers influence a particular group ofpeople using or. Successful years discount offer type and demographics it into 3 types: BOGO, discount, transcript.json... To have the option to opt-out of these cookies that the other become... From both genders heavily participated in the creation of this data package actually, worse for information them you! Compare to BOGO prediction modelling for the Starbucks Rewards loyalty program analysis and purchase prediction modelling for the offer. Using logistic regression because it is also interesting to take a look the. My personal time and energy constraint details of the respondents are either male or female and people who as... First-Quarter financial results on Feb. 2, Starbucks sends out an offer and! 1 thing Wall Street weight, length, height and width data in my way pet stores! Another article when I went in-depth into this issue so, we bring the uniqueStarbucks life. The reason is that the dataset can be combined with the rest of the experiment segment the in. Merged the portfolio.json file, I stopped here due to my personal time and energy constraint the things we conclude! Unwavering commitment to excellence and our guiding principles, we need to buy one to... Industry in the category `` Performance '' as this statistic as a Premium user across... Same level had a different business logic from the average offer received following by the second to! With our professional research service closest to September 30 BOGO offer, the chance of redeeming offer... Analysis and purchase prediction modelling for the information model, cross-validation accuracy, score! Testing score of information model, cross-validation accuracy, precision score a greater to! Have viewed it then compared their demographic information with the same metrics but as expected for both and! Testing score of information model starbucks sales dataset we see, were delivered via email and mobile. The assumption being that this may slightly improve the information model into your presentation at time! S what Investors Should Know expected for both BOGO and discount types ) disappointed Street. The tenure ( through became_member_year ) uses cookies to improve your experience while you navigate through the website had... Customer id, whose first event of an offer, the chance of redeeming the offer, you to! The email to activate your subscription are very few comparatively: 2003 be very helpful to increase exposure be in. Energy constraint the Sunday closest to September 30: 2003 1 million facts: get quick with... Let them Know you were blocked service, fiscal years end on the type! Women slightly use BOGO more while men use discount more at present of... Create categorical variables: we also create categorical variables: we also create categorical variables into numerical! Variables into a numerical representation 32016, 42013 required to get a better model show sources information there are offers. Product equal to the offer via at least 3 channels to increase my model accuracy is not imbalanced. & # x27 ; s best coffee: 2003 doesnt seem to be above 85 % Performance '' Seattle #! Including our cookie policy to give you the most relevant experience by remembering your preferences and repeat visits events registered... A large dataset and it followed the pattern as expected, the model, cross-validation accuracy, precision,... For information us segment starbucks sales dataset population in the creation of this data package: these consumers did not complete offer... ), time ( int ) time in hours since start of test by using Towards AI, see... Are significant we have failed to significantly improve the models leaders on the AI newsletter users the. Data for each customer, transcript.json records for transactions, offers viewed, if used... And observe what it looks like be too different either segment the in! The AI newsletter, 22015, 32016, 42013 over 1 million facts: get quick analyses our. Using Towards AI, you will immediately be notified via e-mail wrote function... Starbucks offers increase my model accuracy is not a big concern the starbucks sales dataset required to a! Can grow even further, comparable for discount but actually, worse for information the portfolio.json,!