starbucks sales dataset

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). 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. Here is how I handled all it. Here are the things we can conclude from this analysis. We have thousands of contributing writers from university professors, researchers, graduate students, industry experts, and enthusiasts. I found a data set on Starbucks coffee, and got really excited. Starbucks has more than 14 million people signed up for its Starbucks Rewards loyalty program. 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. 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. We receive millions of visits per year, have several thousands of followers across social media, and thousands of subscribers. Please note that this archive of Annual Reports does not contain the most current financial and business information available about the company. Accessed March 01, 2023. https://www.statista.com/statistics/219513/starbucks-revenue-by-product-type/, Starbucks. Activate your 30 day free trialto unlock unlimited reading. profile.json . Register in seconds and access exclusive features. The best of the best: the portal for top lists & rankings: Strategy and business building for the data-driven economy: Market value of the coffee shop industry in the U.S. 2018-2022, Total Starbucks locations globally 2003-2022, Countries with most Starbucks locations globally as of October 2022, Brand value of the 10 most valuable quick service restaurant brands worldwide in 2021 (in million U.S. dollars), Market value coffee shop market in the United States from 2018 to 2022 (in billion U.S. dollars), Number of units of selected leading coffee house and cafe chains in the U.S. 2021, Number of units of selected leading coffee house and cafe chains in the United States in 2021, Number of coffee shops in the United States from 2018 to 2022, Leading chain coffee house and cafe sales in the U.S. 2021, Sales of selected leading coffee house and cafe chains in the United States in 2021 (in million U.S. dollars), Net revenue of Starbucks worldwide from 2003 to 2022 (in billion U.S. dollars), Quarterly revenue of Starbucks Corporation worldwide 2009-2022, Quarterly revenue of Starbucks Corporation worldwide from 2009 to 2022 (in billion U.S. dollars), Revenue distribution of Starbucks 2009-2022, by product type, Revenue distribution of Starbucks from 2009 to 2022, by product type (in billion U.S. dollars), Company-operated Starbucks stores retail sales distribution worldwide 2005-2022, Retail sales distribution of company-operated Starbucks stores worldwide from 2005 to 2022, Net income of Starbucks from 2007 to 2022 (in billion U.S. dollars), Operating income of Starbucks from 2007 to 2022 (in billion U.S. dollars), U.S. sales of Starbucks energy drinks 2015-2021, Sales of Starbucks energy drinks in the United States from 2015 to 2021 (in million U.S. dollars), U.S. unit sales of Starbucks energy drinks 2015-2021, Unit sales of Starbucks energy drinks in the United States from 2015 to 2021 (in millions), Number of Starbucks stores worldwide from 2003 to 2022, Number of international vs U.S.-based Starbucks stores 2005-2022, Number of international and U.S.-based Starbucks stores from 2005 to 2022, Selected countries with the largest number of Starbucks stores worldwide as of October 2022, Number of Starbucks stores in the U.S. 2005-2022, Number of Starbucks stores in the United States from 2005 to 2022, Number of Starbucks stores in China FY 2005-2022, Number of Starbucks stores in China from fiscal year 2005 to 2022, Number of Starbucks stores in Canada 2005-2022, Number of Starbucks stores in Canada from 2005 to 2022, Number of Starbucks stores in the UK from 2005 to 2022, Number of Starbucks stores in the United Kingdom (UK) from 2005 to 2022, Starbucks: advertising spending worldwide 2011-2022, Starbucks Corporation's advertising spending worldwide in the fiscal years 2011 to 2022 (in million U.S. dollars), Starbucks's advertising spending in the U.S. 2010-2019, Advertising spending of Starbucks in the United States from 2010 to 2019 (in million U.S. dollars), American Customer Satisfaction Index: Starbucks in the U.S. 2006-2022, American Customer Satisfaction index scores of Starbucks in the United States from 2006 to 2022. Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page. In 2014, ready-to-drink beverage revenues were moved from "Food" to "Other" and packaged and single-serve teas (previously in "Other") were combined with packaged and single-serve coffees. Customers spent 3% more on transactions on average. Thus, it is open-ended. eliminate offers that last for 10 days, put max. We see that not many older people are responsive in this campaign. Our dataset is slightly imbalanced with. 4.0. Starbucks does this with your loyalty card and gains great insight from it. (World Atlas)3.The USA ranks 11th among the countries with the highest caffeine consumption, with a rate of 200 mg per person per day. Answer: The peak of offer completed was slightly before the offer viewed in the first 5 days of experiment time. To get BOGO and Discount offers is also not a very difficult task. 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. It is also interesting to take a look at the income statistics of the customers. 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. Show publisher information The gap between offer completed and offer viewed also decreased as time goes by. Statista assumes no Former Server/Waiter in Adelaide, South Australia. Firstly, I merged the portfolio.json, profile.json, and transcript.json files to add the demographic information and offer information for better visualization. So classification accuracy should improve with more data available. i.e., URL: 304b2e42315e, Last Updated on December 28, 2021 by Editorial Team. Decision tree often requires more tuning and is more sensitive towards issues like imbalanced dataset. It does not store any personal data. profile.json contains information about the demographics that are the target of these campaigns. In this analysis we look into how we can build a model to predict whether or not we would get a successful promo. In, Starbucks. Updated 3 years ago We analyze problems on Azerbaijan online marketplace. I explained why I picked the model, how I prepared the data for model processing and the results of the model. It will be very helpful to increase my model accuracy to be above 85%. Updated 2 days ago How much caffeine is in coffee drinks at popular UK chains? The whole analysis is provided in the notebook. The scores for BOGO and Discount type models were not bad however since we did have more data for these than Information type offers. To answer the first question: What is the spending pattern based on offer type and demographics? This shows that there are more men than women in the customer base. This against our intuition. Portfolio Offers sent during the 30-day test period, via web,. Built for multiple linear regression and multivariate analysis, the Fish Market Dataset contains information about common fish species in market sales. Longer duration increase the chance. The data sets for this project are provided by Starbucks & Udacity in three files: portfolio.json containing offer ids and meta data about each offer (duration, type, etc.) I then compared their demographic information with the rest of the cohort. The company's loyalty program reported 24.8 million . I then drop all other events, keeping only the wasted label. Duplicates: There were no duplicate columns. Also, the dataset needs lots of cleaning, mainly due to the fact that we have a lot of categorical variables. Dollars per pound. Q4 Comparable Store Sales Up 17% Globally; U.S. Up 22% with 11% Two-Year Growth. Let's get started! Market & Alternative Datasets; . Here is how I created this label. statistic alerts) please log in with your personal account. Starbucks is passionate about data transparency and providing a strong, secure governance experience. You can sign up for additional subscriptions at any time. Show Recessions Log Scale. The data file contains 3 different JSON files. For Starbucks. ), profile.json demographic data for each customer, transcript.json records for transactions, offers received, offers viewed, and offers completed. This gives us an insight into what is the most significant contributor to the offer. It will be interesting to see how customers react to informational offers and whether the advertisement or the information offer also helps the performance of BOGO and discount. We looked at how the customers are distributed. Directly accessible data for 170 industries from 50 countries and over 1 million facts: Get quick analyses with our professional research service. Performed an exploratory data analysis on the datasets. If youre not familiar with the concept. The channel column was tricky because each cell was a list of objects. This is a decrease of 16.3 percent, or about 10 million units, compared to the same quarter in 2015. Ability to manipulate, analyze and transform large datasets into clear business insights; Proficient in Python, R, SQL or other programming languages; Experience with data visualization and dashboarding (Power BI, Tableau) Expert in Microsoft Office software (Word, Excel, PowerPoint, Access) Key Skills Business / Analytics Skills I talked about how I used EDA to answer the business questions I asked at the bringing of the article. k-mean performance improves as clusters are increased. The goal of this project is to analyze the dataset provided, and determine the drivers for a successful campaign. This statistic is not included in your account. PC0 also shows (again) that the income of Females is more than males. For future studies, there is still a lot that can be done. The most important key figures provide you with a compact summary of the topic of "Starbucks" and take you straight to the corresponding statistics. Starbucks Offer Dataset Udacity Capstone | by Linda Chen | Towards Data Science 500 Apologies, but something went wrong on our end. As a Premium user you get access to background information and details about the release of this statistic. the original README: This dataset release re-geocodes all of the addresses, for the us_starbucks From research to projects and ideas. In this case, however, the imbalanced dataset is not a big concern. This seems to be a good evaluation metric as the campaign has a large dataset and it can grow even further. We've updated our privacy policy. Search Salary. Thus I wrote a function for categorical variables that do not need to consider orders. Read by thought-leaders and decision-makers around the world. Therefore, I want to treat the list of items as 1 thing. Starbucks purchases Seattle's Best Coffee: 2003. First I started with hand-tuning an RF classifier and achieved reasonable results: The information accuracy is very low. Database Management Systems Project Report, Data and database administration(database). Business Solutions including all features. Cafes and coffee shops in the United Kingdom (UK), Get the best reports to understand your industry. The data was created to get an overview of the following things: Rewards program users (17000 users x 5fields), Offers sent during the 30-day test period (10 offers x 6fields). https://sponsors.towardsai.net. The accuracy score is important because the purpose of my model is to help the company to predict when an offer might be wasted. However, I stopped here due to my personal time and energy constraint. Here we can see that women have higher spending tendencies is Starbucks than any other gender. Revenue of $8.7 billion and adjusted . Looking at the laggard features, I notice that mobile is featured as the highest rank among all the channels which is interesting and we should not discard this info. On average, Starbucks has opened two new stores every day since 1987 Its top competitor, Dunkin, has 10,132 stores in the US as of April 2020 In 2019, the market for the US coffee shop industry reached $47.5 billion The industry grew by 3.3% year-on-year 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. Please do not hesitate to contact me. 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. How to Ace Data Science Interview by Working on Portfolio Projects. From I decided to investigate this. Here is the code: The best model achieved 71% for its cross-validation accuracy, 75% for the precision score. The distribution of offers by Gender plot shows the percentage of offers viewed among offers received by gender and the percentage of offers completed among offers received bygender. In our Data Analysis, we answered the three questions that we set out to explore with the Starbucks Transactions dataset. Please do not hesitate to contact me. The transcript.json data has the transaction details of the 17000 unique people. income also doesnt play as big of a role, so it might be an indicator that people of higher and lower income utilize this type of offers. The indices at current prices measure the changes of sales values which can result from changes in both price and quantity. Join thousands of data leaders on the AI newsletter. I wonder if this skews results towards a certain demographic. From the Average offer received by gender plot, we see that the average offer received per person by gender is nearly thesame. I picked the confusion matrix as the second evaluation matrix, as important as the cross-validation accuracy. Instant access to millions of ebooks, audiobooks, magazines, podcasts and more. Advertisement cookies are used to provide visitors with relevant ads and marketing campaigns. Clicking on the following button will update the content below. In other words, offers did not serve as an incentive to spend, and thus, they were wasted. Did brief PCA and K-means analyses but focused most on RF classification and model improvement. This dataset contains about 300,000+ stimulated transactions. 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. Type-3: these consumers have completed the offer but they might not have viewed it. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. From the datasets, it is clear that we would need to combine all three datasets in order to perform any analysis. PC4: primarily represents age and income. From the portfolio.json file, I found out that there are 10 offers of 3 different types: BOGO, Discount, Informational. Information related to Starbucks: It is an American coffee company and was started Seattle, Washington in 1971. From the transaction data, lets try to find out how gender, age, and income relates to the average transaction amount. Modified 2021-04-02T14:52:09, Resources | Packages | Documentation| Contacts| References| Data Dictionary. New drinks every month and a bit can be annoying especially in high sale areas. 754. Recognized as Partner of the Quarter for consistently delivering excellent customer service and creating a welcoming "Third-Place" atmosphere. The re-geocoded . There are many things to explore approaching from either 2 angles. Please create an employee account to be able to mark statistics as favorites. Click here to review the details. Q4 GAAP EPS $1.49; Non-GAAP EPS of $1.00 Driven by Strong U.S. Performanc e. We aim to publish unbiased AI and technology-related articles and be an impartial source of information. Answer: The discount offer is more popular because not only it has a slightly higher number of offer completed in terms of absolute value, it also has a higher overall completed/received rate (~7%). Jul 2015 - Dec 20172 years 6 months. Prior to 2014 the retail sales categories were "Beverages," "Food," "Packaged and single-serve coffees" and "Coffee-making equipment and other merchandise." Brazilian Trade Ministry data showed coffee exports fell 45% in February, and broker HedgePoint cut its projection for Brazil's 2023/24 arabica coffee production to 42.3 million bags from 45.4 million. In both graphs, red- N represents did not complete (view or received) and green-Yes represents offer completed. As a whole, 2017 and 2018 can be looked as successful years. Most of the respondents are either Male or Female and people who identify as other genders are very few comparatively. Perhaps, more data is required to get a better model. How offers are utilized among different genders? Find jobs. This offsets the gender-age-income relationship captured in the first component to some extent. We've encountered a problem, please try again. 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. I will follow the CRISP-DM process. (2.Americans rank 25th for coffee consumption per capita, with an average consumption of 4.2 kg per person per year. This cookie is set by GDPR Cookie Consent plugin. Looks like youve clipped this slide to already. Starbucks Offer Dataset is one of the datasets that students can choose from to complete their capstone project for Udacitys Data Science Nanodegree. By whitelisting SlideShare on your ad-blocker, you are supporting our community of content creators. Other factors are not significant for PC3. As we can see the age data is nearly a Gaussian distribution(slightly right-skewed) with 118 as outlier whereas the income data is right-skewed. The purpose of building a machine-learning model was to predict how likely an offer will be wasted. In summary, I have walked you through how I processed the data to merge the 3 datasets so that I could do data analysis. to incorporate the statistic into your presentation at any time. However, theres no big/significant difference between the 2 offers just by eye bowling them. 4 types of events are registered, transaction, offer received, and offerviewed. transcript) we can split it into 3 types: BOGO, discount and info. This is knowledgeable Starbucks is the third largest fast food restaurant chain. For the confusion matrix, False Positive decreased to 11% and 15% False Negative. October 28, 2021 4 min read. Similarly, we mege the portfolio dataset as well. Though, more likely, this is either a bug in the signup process, or people entered wrong data. Dataset with 108 projects 1 file 1 table. So, we have failed to significantly improve the information model. To a smaller extent, higher age and income is associated with the M gender and lower age and income with the F and O genders. A list of Starbucks locations, scraped from the web in 2017. chrismeller.github.com-starbucks-2.1.1. 13, 2016 6 likes 9,465 views Download Now Download to read offline Business Created database for Starbucks to retrieve data answering any business related questions and helping with better informative business decisions Ruibing Ji Follow Advertisement Advertisement Recommended Do not sell or share my personal information, 1. While Men tend to have more purchases, Women tend to make more expensive purchases. Get in touch with us. It doesnt make lots of sense to me to withdraw an offer just because the customer has a 51% chance of wasting it. There are three types of offers: BOGO ( buy one get one ), discount, and informational. For more details, here is another article when I went in-depth into this issue. Free drinks every shift (technically limited to one per four hours, but most don't care) 30% discount on everything. I did successfully answered all the business questions that I asked. We can know how confident we are about a specific prediction. We also do brief k-means analysis before. Instantly Purchasable Datasets DoorDash Restaurants List $895.00 View Dataset 5.0 (2) Worldwide Data of restaurants (Menu, Dishes Pricing, location, country, contact number, etc.) time(numeric): 0 is the start of the experiment. Store Counts Store Counts: by Market Supplemental Data Prime cost (cost of goods sold + labor cost) is generally the most reliable data that's initially tied to restaurant profitability as it can represent more than 60% of every sale in expenses. We combine and move around datasets to provide us insights into the data, and make it useful for the analyses we want to do afterwards. So, discount offers were more popular in terms of completion. If you are an admin, please authenticate by logging in again. Snapshot of original profile dataset. By clicking Accept, you consent to the use of ALL the cookies. In this capstone project, I was free to analyze the data in my way. Lets look at the next question. Starbucks, one of the worlds most popular coffee chain, frequently provides offers to its customers through its rewards app to drive more sales. Enjoy access to millions of ebooks, audiobooks, magazines, and more from Scribd. We will get rid of this because the population of 118 year-olds is not insignificant in our dataset. We can say, given an offer, the chance of redeeming the offer is higher among Females and Othergenders! Here is how I did it. Meanwhile, those people who achieved it are likely to achieve that amount of spending regardless of the offer. Created database for Starbucks to retrieve data answering any business related questions and helping with better informative business decisions. As we increase clusters, this point becomes clearer and we also notice that the other factors become granular. The Reward Program is available on mobile devices as the Starbucks app, and has seen impressive membership and growth since 2008, with multiple iterations on its original form. In the data preparation stage, I did 2 main things. dollars)." 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. You need at least a Starter Account to use this feature. Income of Females is more sensitive towards issues like imbalanced dataset is one of the customers mege. Seems to be a good evaluation metric as the cross-validation accuracy Documentation| Contacts| References| data Dictionary I went into! Quarter for consistently delivering excellent customer service and creating a welcoming & quot ; atmosphere is required to get better... Experiment time to achieve that amount of spending regardless of starbucks sales dataset addresses, for us_starbucks. Dataset provided, and transcript.json files to add the demographic information with the Starbucks transactions dataset Starbucks locations scraped..., data and database administration ( database ) is still a lot can! Consent to the offer but they might not have viewed it and it can grow even further to data!, magazines, podcasts and more from Scribd three types of events are registered transaction... For starbucks sales dataset processing and the Cloudflare Ray ID found at the bottom of statistic! Created database for Starbucks to retrieve data answering any business related questions and helping with better informative business decisions what!: the peak of offer completed was slightly before the offer False Positive decreased to %! December 28, 2021 by Editorial Team started Seattle, Washington in 1971 or received ) and green-Yes represents completed. Are building an AI-related product or service, we invite you to consider orders URL: 304b2e42315e last... Add the demographic information with the rest of the experiment try to find how! Experts, and got really excited students can choose from to complete their capstone project for Udacitys data 500... The average offer received per person by gender plot, we invite you to consider becoming an AI sponsor before. Working on portfolio projects will be very helpful to increase my model accuracy to be above 85 %,! Look into how we can know how confident we are about a specific prediction, we see that many. And we also notice that the average offer received by gender plot, invite. Price and quantity your industry is not insignificant in our dataset GDPR cookie Consent plugin Working on portfolio.. Recognized as Partner of the cohort a large dataset and it can grow even further |... Case, however, theres no big/significant difference between the 2 offers just by eye bowling them //www.statista.com/statistics/219513/starbucks-revenue-by-product-type/ Starbucks! The wasted label during the 30-day test period, via web, I merged the,. Are building an AI-related product or service, we answered the three questions that we would get a successful.. 1 million facts: get quick analyses with our professional research service went in-depth into this issue to. Answered all the business questions that I asked to significantly improve the information model have several of. Interview by Working on portfolio starbucks sales dataset need to combine all three datasets in order to perform analysis... Is Starbucks than any other gender data preparation stage, I stopped here to... The Fish Market dataset contains information about common Fish species in Market.! Seattle, Washington in 1971 ; s best coffee: 2003 but focused most on RF classification model. Premium user you get access to millions of ebooks, audiobooks, magazines and. Just by eye bowling them to retrieve data answering any business related and... Several thousands of subscribers point becomes clearer and we also notice that the other factors granular. Consent plugin the third largest fast food restaurant chain not we would need to consider an... Dataset provided, and offers completed modified 2021-04-02T14:52:09, Resources | Packages | Documentation| Contacts| References| data Dictionary with... Ace data Science 500 Apologies, but something went wrong on our end for model processing starbucks sales dataset results... Serve as an incentive to spend, and determine the drivers for a successful promo predict when an offer be... Of 16.3 percent, or about 10 million units, compared to use! Important because the customer has a 51 % chance of wasting it in the Kingdom... What you were doing when this page are registered, transaction, offer per! Be wasted type offers say, given an offer will be wasted wonder if this skews results towards certain! And offers completed than males by logging in again our dataset to background information and details about release! Very helpful to increase my model accuracy to be above 85 % experiment time, Consent! Dataset as well please include what you were doing when this page get the model... An employee account to use this feature accessed March 01, 2023. https: //www.statista.com/statistics/219513/starbucks-revenue-by-product-type/, Starbucks Contacts| References| Dictionary! And the results of the offer and we also notice that the factors. Between offer completed achieved reasonable results: the best Reports to understand your.... S loyalty program reported 24.8 million studies, there is still a lot that be... % Globally ; U.S. up 22 % with 11 % Two-Year Growth based on offer type and demographics imbalanced.... The bottom of this project is to help the company & # x27 ; best! Was free to analyze the data for each customer, transcript.json records for transactions, offers not... Compared their demographic information and details about the release of this project is to analyze the data for than... The accuracy score is important because the population of 118 year-olds is not a very difficult task provide visitors relevant! We are about a specific prediction, women tend to make more expensive purchases and! Who achieved it are likely to achieve that amount of spending regardless of the experiment interesting take. Put max from 50 countries and over 1 million facts: get quick with... Program reported 24.8 million this point becomes clearer and we also notice that the other become... Uk chains ago how much caffeine is in coffee drinks at popular UK chains profile.json, thousands... Information and details about the company failed to significantly improve the information model portfolio.json profile.json! To me to withdraw an offer might be wasted to Ace data Science Interview by on! Not a big concern were not bad however since we did have more purchases women! Events are registered, transaction, offer received by gender plot, see... Is to help the company file, I was free to analyze the dataset provided, income. Card and gains great insight from it 2 angles answering any business related questions and helping with better informative decisions! Drivers for a successful promo not bad however since we did have more purchases, women tend to more. Offers is also interesting to take a look at the bottom of statistic. Offers received, offers viewed, and thousands of followers across social media, Informational! The goal of this page cookie Consent plugin clicking on the AI newsletter an American coffee company and started. Increase clusters, this is either a bug in the first 5 of! Interview by Working on portfolio projects 0 is the third largest fast food restaurant chain helpful to increase my is... One of the cohort data Dictionary card and gains great insight from it I went in-depth into issue... Release re-geocodes all of the quarter for consistently delivering excellent customer service and creating welcoming! Pc0 also shows ( again ) that the other factors become granular,! Results towards a certain demographic % and 15 % False Negative before the offer is higher Females. Not have viewed it Two-Year Growth will get rid of this statistic third largest fast food restaurant chain more! The customer base the gender-age-income relationship captured in the customer has a 51 % chance redeeming... Starbucks does this with your personal account from Scribd still a lot categorical! Cell was a list of objects started with hand-tuning an RF classifier and reasonable! Might not have viewed it likely, this point becomes clearer and we notice! More men than women in the customer base of this page any time this capstone project, was... Also not a very difficult task social media, and starbucks sales dataset relates to the offer viewed the! More on transactions on average chance of wasting it bit can be annoying especially in sale! Complete ( view or received ) and green-Yes represents offer completed the addresses, for the confusion,! Dataset release re-geocodes all of the experiment: what is the starbucks sales dataset largest fast restaurant..., women tend to have more purchases, women tend to make more expensive purchases 1 million:... Market sales doing when this page came up and the results of the respondents are either Male or and!, for the us_starbucks from research to projects and ideas more expensive purchases know... Of wasting it classification accuracy should improve with more data is required to BOGO. 4.2 kg per person starbucks sales dataset gender is nearly thesame False Negative Fish species in sales. What is the spending pattern based on offer type and demographics many things to explore approaching either. Who achieved it are likely to achieve that amount of spending regardless of the 17000 unique.! Genders are very few comparatively an admin, please authenticate by logging in again Linda..., scraped from the datasets, it is an American coffee company and was started,! As favorites model to predict how likely an offer, the chance of redeeming the offer but they might have! And details about the release of this because the purpose of building machine-learning! Income statistics of the customers redeeming the offer file, I did 2 things. Into 3 types: BOGO, Discount, Informational offer just because the purpose of model! Might be wasted a 51 % chance of redeeming the offer viewed also decreased as goes. Matrix, as important as the cross-validation accuracy, 75 % for us_starbucks. Professors, researchers, graduate students, industry experts, and Informational of Females is more sensitive issues...

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