I collected the data largely using Open AI.

Celebrity - Their stage name.

Name - Their birth name.

Nationality - Where they were born, using the 2 letter country code standards.

Gender - Their gender.

Estimated Net Worth - This was not gathered using AI. I used Google and if it returned an estimated range like 80 million to 100 million, I chose the lowest amount given, or 80 million in the example.

Age at End of 2023 - Their age on 12/31/23.

Birth Date - Their birthday in mm/dd/yyyy format.

Birth Month - The month they were born in.

Birth Day - The day of the month they were born on.

Birth Year - The year they were Born.

Industry - What Industries they operate in.

What you can analyze:

• What the average male and female celebrity net worth and age then compare the two.
• What the average net worth of a celebrity under 30 is compared to ones over 30.
• The proportions of men and women that have a net worth over a certain amount, are over a certain age, or are of each nationality.
• What the average celebrity's net worth within each industry is and then compare between industries.
• If you like astrology, you can check to see what the average astrological sign's net worth is and then compare amongst the other astrological signs.
• Also, if you like astrology, see which astrological sign is most common amongst celebrities.
• Comparing the average age, net worth, or most common industry occurred in any nation then compare amongst other nations.
• Comparing any combination of these demographics to other combinations of these demographics. (For the purpose of example: Comparing what the average net worth of a female celebrity born before 1980 in Great Britain makes compared to the average net worth of a male celebrity born after 1980 in the US. Though you may not have any logical reason to compare these 2 things, it shows how you can add more and more conditions then compare in order to get more accurate conclusions.)

Any form of currency that only exists digitally relying on cryptography to prevent counterfeiting and fraudulent transactions is defined as cryptocurrency. Bitcoin was the very first Cryptocurrency. It was invented in 2009 by an anonymous person, or group of people, who referred to themselves as Satoshi Nakamoto. When someone sends a bitcoin (or a fraction of a bitcoin) to someone else, “miners” record that transaction in a block and add the transaction to a digital ledger. These blocks are collectively known as the blockchain – an openly accessible ledger of every transaction ever made in bitcoin. Blockchains are distributed across many computers so that the record of transactions cannot be altered. Only 21 million bitcoins can ever be mined and about 17 million have been mined so far. Bitcoin is mined, or created, by people (miners) getting their computers to solve mathematical problems, in order to update and verify the ledger.

The value of bitcoin is determined by what people are willing to pay for it, and is very volatile, fluctuating wildly from day to day. In April 2013, the value of 1 bitcoin (BTC) was around $100 USD. At the beginning of 2017 its value was $1,022 USD and by the 15th of December it was worth $19,497. As of the 3rd of March 2018, 1 BTC sells for $11,513 USD. So, the time series analysis of bitcoin series is very challenging.

The following dataset is the daily closing price of bitcoin from the 27th of April 2013 to the 3rd of March 2018. Source: coinmarketcap.com

The dataset is focused on has gathered from coinmarketcap.com (https://coinmarketcap.com/). includes he daily closing price of bitcoin from the 27th of April 2013 to the 3rd of March 2018 and is available in the csv file Bitcoin_Historical_Price.csv

This Model includes a mean absolute scaled error (MASE), for each of model fits and forecasts. Using the real values of daily bitcoin for 10 days of forecast period (4th - 13th of March 2018).

This model is used to analyze the data, accurately predict the value of bitcoin for the next 10 days. The model includes descriptive analysis, proper visualization, model specification, model fitting and selection, and diagnostic checking.

Money Supply M0 in the United States increased to 53615000 USD Million in October from 5478000 USD Million in September of 2025. This dataset provides - United States Money Supply M0 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Data Set Description This dataset simulates a retail environment with a million rows and 100+ columns, covering customer information, transactional data, product details, promotional information, and customer behavior metrics. It includes data for predicting total sales (regression) and customer churn (classification).

Detailed Column Descriptions Customer Information:

customer_id: Unique identifier for each customer. age: Age of the customer. gender: Gender of the customer (e.g., Male, Female, Other). income_bracket: Income bracket of the customer (e.g., Low, Medium, High). loyalty_program: Whether the customer is part of a loyalty program (Yes/No). membership_years: Number of years the customer has been a member. churned: Whether the customer has churned (Yes/No) - Target for classification. marital_status: Marital status of the customer. number_of_children: Number of children the customer has. education_level: Education level of the customer (e.g., High School, Bachelor's, Master's). occupation: Occupation of the customer. Transactional Data:

transaction_id: Unique identifier for each transaction. transaction_date: Date of the transaction. product_id: Unique identifier for each product. product_category: Category of the product (e.g., Electronics, Clothing, Groceries). quantity: Quantity of the product purchased. unit_price: Price per unit of the product. discount_applied: Discount applied on the transaction. payment_method: Payment method used (e.g., Credit Card, Debit Card, Cash). store_location: Location of the store where the purchase was made. Customer Behavior Metrics:

avg_purchase_value: Average value of purchases made by the customer. purchase_frequency: Frequency of purchases (e.g., Daily, Weekly, Monthly, Yearly). last_purchase_date: Date of the last purchase made by the customer. avg_discount_used: Average discount percentage used by the customer. preferred_store: Store location most frequently visited by the customer. online_purchases: Number of online purchases made by the customer. in_store_purchases: Number of in-store purchases made by the customer. avg_items_per_transaction: Average number of items per transaction. avg_transaction_value: Average value per transaction. total_returned_items: Total number of items returned by the customer. total_returned_value: Total value of returned items. Sales Data:

total_sales: Total sales amount for each customer over the last year - Target for regression. total_transactions: Total number of transactions made by each customer. total_items_purchased: Total number of items purchased by each customer. total_discounts_received: Total discounts received by each customer. avg_spent_per_category: Average amount spent per product category. max_single_purchase_value: Maximum value of a single purchase. min_single_purchase_value: Minimum value of a single purchase. Product Information:

product_name: Name of the product. product_brand: Brand of the product. product_rating: Customer rating of the product. product_review_count: Number of reviews for the product. product_stock: Stock availability of the product. product_return_rate: Rate at which the product is returned. product_size: Size of the product (if applicable). product_weight: Weight of the product (if applicable). product_color: Color of the product (if applicable). product_material: Material of the product (if applicable). product_manufacture_date: Manufacture date of the product. product_expiry_date: Expiry date of the product (if applicable). product_shelf_life: Shelf life of the product (if applicable). Promotional Data:

promotion_id: Unique identifier for each promotion. promotion_type: Type of promotion (e.g., Buy One Get One Free, 20% Off). promotion_start_date: Start date of the promotion. promotion_end_date: End date of the promotion. promotion_effectiveness: Effectiveness of the promotion (e.g., High, Medium, Low). promotion_channel: Channel through which the promotion was advertised (e.g., Online, In-store, Social Media). promotion_target_audience: Target audience for the promotion (e.g., New Customers, Returning Customers). Geographical Data:

customer_zip_code: Zip code of the customer's residence. customer_city: City of the customer's residence. customer_state: State of the customer's residence. store_zip_code: Zip code of the store. store_city: City where the store is located. store_state: State where the store is located. distance_to_store: Distance from the customer's residence to the store. Seasonal and Temporal Data:

holiday_season: Whether the transaction occurred during a holiday season (Yes/No). season: Season of the year (e.g., Winter, Spring, Summer, Fall). weekend: Whether the transaction occurred on a weekend (Yes/No). Customer Interaction Data:

customer_support_calls: Number of calls made to customer support. email_subscription...

Income of individuals by age group, sex and income source, Canada, provinces and selected census metropolitan areas, annual.

In the eight years since he became the world’s highest-paid athlete for the first time, much has changed for Cristiano Ronaldo. The 39-year-old Portuguese soccer star went from lighting up the Bernabéu with Real Madrid to stints with Juventus and Manchester United, until finally landing at his current home, Al Nassr of the Saudi Pro League. But no matter the location, one thing has remained constant—Ronaldo is still drawing outsized paydays. He earned an estimated $260 million over the last 12 months, making him the highest-paid athlete in the world for the fourth time in his career. It estimates Ronaldo’s contract with Al Nassr earned him $200 million this season. And as one of the sports world’s most successful pitchmen, Ronaldo earned another $60 million off the field from an endorsement portfolio that includes Nike, Binance and Herbalife, among others.

The KALAHI-CIDSS program was set up in 2002 to alleviate rural poverty in the Philippines. It provides resources to poor rural municipalities to invest in public goods and by reviving local institutions to enhance people’s participation in governance. The project targeted the poorest 25 percent of municipalities in each of the poorest 42 provinces. The government of the Philippines committed $82 million to the project, which was complemented by a $100 million loan from the World Bank. As of December 2010, the project had covered 4,583 barangays (villages) in 200 municipalities and supported 5,645 subprojects, worth Php 5.7 billion and benefiting about 1.26 million households. The program's impact evaluation was designed in 2003 to evaluate general impacts on poverty reduction, social capital, empowerment, and governance. The team collected quantitative and qualitative data before, during, and after project implementation in a sample of KALAHI-CIDSS municipalities that received support ("treatment" municipalities) and from comparable municipalities that did not receive support ("control" municipalities). The quantitative baseline survey was carried out in September-October 2003, the quantitative midterm in October-November 2006 and the quantitative endline survey in February-March 2010. Data were collected on a broad range of indicators: service delivery (access to health, education), poverty (employment, per capita consumption, self-rated poverty), empowerment and governance (group membership, participation in barangay assemblies, collective action). The quantitative sample includes 2,400 households in 135 barangays in 16 municipalities in 4 provinces.

Greetings , fellow analysts !

(NOTE : This is a random dataset generated using python. It bears no resemblance to any real entity in the corporate world. Any resemblance is a matter of coincidence.)

REC-SSEC Bank is a govt-aided bank operating in the Indian Peninsula. They have regional branches in over 40+ regions of the country. You have been provided with a massive excel sheet containing the transaction details, the total transaction amount and their location and total transaction count.

The dataset is described as follows :

1. Date - The date on which the transaction took place. 2.Domain - Where or which type of Business entity made the transaction. 3.Location - Where the data is collected from 4.Value - Total value of transaction
2. Count of transaction .

For example , in the very first row , the data can be read as : " On the first of January, 2022 , 1932 transactions of summing upto INR 365554 from Bhuj were reported " NOTE : There are about 2750 transactions every single day. All of this has been given to you.

The bank wants you to answer the following questions :

1. What is the average transaction value everyday for each domain over the year.
2. What is the average transaction value for every city/location over the year
3. The bank CEO , Mr: Hariharan , wants to promote the ease of transaction for the highest active domain. If the domains could be sorted into a priority, what would be the priority list ?
4. What's the average transaction count for each city ?

The global number of Facebook users was forecast to continuously increase between 2023 and 2027 by in total 391 million users (+14.36 percent). After the fourth consecutive increasing year, the Facebook user base is estimated to reach 3.1 billion users and therefore a new peak in 2027. Notably, the number of Facebook users was continuously increasing over the past years. User figures, shown here regarding the platform Facebook, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

In 2023, Meta Platforms had a total annual revenue of over 134 billion U.S. dollars, up from 116 billion in 2022. LinkedIn reported its highest annual revenue to date, generating over 15 billion USD, whilst Snapchat reported an annual revenue of 4.6 billion USD.

Cristiano Ronaldo has one of the most popular Instagram accounts as of April 2024.

              The Portuguese footballer is the most-followed person on the photo sharing app platform with 628 million followers. Instagram's own account was ranked first with roughly 672 million followers.

              How popular is Instagram?

              Instagram is a photo-sharing social networking service that enables users to take pictures and edit them with filters. The platform allows users to post and share their images online and directly with their friends and followers on the social network. The cross-platform app reached one billion monthly active users in mid-2018. In 2020, there were over 114 million Instagram users in the United States and experts project this figure to surpass 127 million users in 2023.

              Who uses Instagram?

              Instagram audiences are predominantly young – recent data states that almost 60 percent of U.S. Instagram users are aged 34 years or younger. Fall 2020 data reveals that Instagram is also one of the most popular social media for teens and one of the social networks with the biggest reach among teens in the United States.

              Celebrity influencers on Instagram
              Many celebrities and athletes are brand spokespeople and generate additional income with social media advertising and sponsored content. Unsurprisingly, Ronaldo ranked first again, as the average media value of one of his Instagram posts was 985,441 U.S. dollars.

Facebook received 73,390 user data requests from federal agencies and courts in the United States during the second half of 2023. The social network produced some user data in 88.84 percent of requests from U.S. federal authorities. The United States accounts for the largest share of Facebook user data requests worldwide.