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Darren Gidado

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Below is a dataset with single bets made by gambling customers. Our task is to aggregate them into sessions, group sessions into length groups and calculate the average margin by session length group. For example: Session LengthAvg Margin (GGR divided by Turnover)< 15minX15 – 30 minY30 – 45 minZ…..… Description of the dataset: Player_id – unique player identifierGame_End_Timestamp – indicator when bet was placed (aggregated to full minute)Turnover – amount customer staked in that minuteGGR – gross gaming revenue (turnover – wins), casino revenue in that minute Plan of Action This dataset consists of log entries that contain a player_id, timestamp, and betting information. Our first step would be to group all of the actions that a user takes within a given session. A session can be defined as any continuous entry of logs from a specific user, where the gap between the previous timestamp isn’t more than x minutes.…

As part of the Safer Gambling initiative, it is important to make sure customers do not gamble above their financial means. One of the indicators of a financially strained customer is having their deposits declined due to insufficient funds. Our task is to design this journey and estimate this journey. Essentially, we want to fill in the tables: Impacted customers“Freeze” periodNumber of consecutive failed deposits6h12h24h2a34b5 For example, if we lock deposits for 6h after 2 consecutive failed deposits “a” customers will be impacted, and “x” deposits (in GBP) will be lost. Alternatively, if we lock deposits for 12h after 4 consecutive failed deposits “b” customers will be impacted and “y” deposits will be lost. Impacted deposits (GBP)“Freeze” periodNumber of consecutive failed deposits6h12h24h2x34y5 We want our solution to be scalable, meaning we can easily calculate what is the exposure if we change parameters to (for example) 6 consecutive failed deposits and…

Introduction ? You’re a marketing analyst and you’ve been told by the Chief Marketing Officer that recent marketing campaigns have not been as effective as they were expected to be. You need to analyze the data set to understand this problem and propose data-driven solutions. The data set marketing_data.csv consists of 2,240 customers with data on: Customer profilesProduct preferencesCampaign successes / failuresChannel performance Section 1: Exploratory Data Analysis Are there any null values or outliers? How will you wrangle/handle them?Are there any variables that warrant transformations?Are there any useful variables that you can engineer with the given data?Do you notice any patterns or anomalies in the data? Can you plot them? Section 2: Statistical Analysis What factors are significantly related to the number of store purchases?Does US fare significantly better than the Rest of the World in terms of total purchases?Your supervisor insists that people who buy gold are more conservative. Therefore,…

Introduction A customer receives a drink from Starbucks barista millions of times each week, but each interaction is unique. This is just a moment in time, but nevertheless a connection. How does this customer behave, and what prompts them to make a purchase? To find out, we can use a simulated dataset that mimics customer behaviour 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 offers during certain weeks. Not all users receive the same offer, and that is the challenge to solve with this data set. Our task is to combine transactions, demographics and offer data to determine which demographic groups respond best to which offer…

Introduction Airbnb is a popular way for homeowners to make money by renting out their properties or even spare rooms in their own home. More people are considering joining Airbnb to profit by investing in new properties to transform into Airbnbs. However, how will they know what to consider to make their property an attractive proposition for customers? How will they identify which variables can increase their listing price and profit? There is a problem though, you see. Hosts remove their listings for various reasons such as a lack of bookings or if the property is currently occupied. This means we must find a way to predict that data and recommend them a reasonable price so they can attract more guests. Before we can answer those questions, we need to find relevant variables to use. Since 2008, guests and hosts have used Airbnb to travel in a more unique, personalized…

Big Query requires a special package bigquery that allows us to connect to the database via an API using our credentials. We will be able to see things about any database we select and run queries against it using SQL to extract information. This is the most popular way of working with BigQuery datasets in Python, where we can subsequently able to do further analysis on it. This page shows how to get started with the Cloud Client Libraries for the BigQuery API. Read more about the client libraries for Cloud APIs, including the older Google APIs Client Libraries, in Client Libraries Explained. Step 1: Installing the BigQuery API Client Library First, we need to create a Google BigQuery account, then we need to install the library via Anaconda Prompt. pip install –upgrade google-cloud-bigquery Next, we must create a service account. Once we have done that and downloaded our API key we…

Introduction The dataset that we will be wrangling (and analyzing and visualizing) is the tweet archive of Twitter user @dog_rates, also known as WeRateDogs. WeRateDogs is a Twitter account that rates people’s dogs with a humorous comment about the dog. These ratings almost always have a denominator of 10. The numerators, though? Almost always greater than 10. 11/10, 12/10, 13/10, etc. Why? Because “they’re good dogs, Brent.” WeRateDogs has over 4 million followers and has received international media coverage. There are many insights we can get from this dataset on WeRateDogs but first, we have to do a final check and save our new dataframes to their master variables. Dictionary: pupper: Puppy, a small doggo and usually younger.puppo: A transitional phase between pupper and doggo. Easily understood as the dog equivalent of a teenager.doggo: Dog, usually older.floofer: Very fluffy dog or a dog with excess fur. Comical amounts of fur on a dog will certainly earn…

Table of Contents ? IntroductionStep 1: Importing LibrariesStep 2: Gathering DataStep 3: Univariate ExplorationStep 4: Bivariate ExplorationStep 5: Multivariate ExplorationStep 6: Random ExplorationStep 7: Conclusion Introduction ? What is the ASA? ASA stands for ‘American Statistical Association’, ASA is the main professional organisation for statisticians in the United States. The organization was formed in November 1839 and is the second oldest continuously operating professional society in the United States. Every other year, at the Joint Statistical Meetings, the Graphics Section and the Computing Section join in sponsoring a special Poster Session called The Data Exposition, but more commonly known as The Data Expo. All of the papers presented in this Poster Session are reports of analyses of a common data set provided for the occasion. In addition, all papers presented in the session are encouraged to report the use of graphical methods employed during the development of their analysis and…

Data scraping data, also known as web scraping is the process of extracting data from a website programmatically. The destination of the extracted data can vary, in some cases to channel that data to another website but is commonly saved to a spreadsheet or local file saved on your computer. It’s one of the most efficient ways to get data from the web aside from directly querying a REST API. We are going to be extracting Premier League data such as: All-time Top Scorers2020-21 – League Table2020/21 Top Scorers Table of Contents ? Importing LibrariesMethod 1: HTML Table ScrapingMethod 2: Beautiful Soup ScrapingMethod 3: Using an API and JSONs Importing Libraries ? We are going to be using the official Premier League website to extract the data we need. The libraries we are using are as follows: Pandas: This will be used to generate our dataframesNumpy: Numpy will be used to calculate our numeric…