Aderinsola_Portfolio

Project: World Energy Consumption(Tableau): Project Overview

Energy consumption globally, energy use in various nations.

This dataset is a compilation of important metrics that Our World in Data keeps track of. It contains information on energy consumption (primary energy, per capita, and growth rates), energy mix, electricity mix, and other pertinent metrics, and it is updated on a regular basis.

Project: Shark Attacks(PowerBi): Project Overview

Shark Attacts

shark attacks documented during the previous 100 years, including location, activity, and victim information (name, shark species, gender, age, etc.

Project: Restaurant Ratings(PowerBi): Project Overview

Restaurant Ratings

Restaurant Ratings of Mexican restaurants from 2012 made by actual customers, including extra details on each establishment’s cuisines and the tastes of the individual customers.

Project: NYC Taxi Project(PowerBi): Project Overview

NYC Taxi Project

Regarding the dataset

This dataset includes a geographic map in the TopoJSON and Shapefile formats along with six tables in the CSV format. 28 million Green Taxi journeys in New York City were recorded between 2017 and 2020, according to the 4 Taxi journeys tables. Each record corresponds to a single trip, and its fields include information on the pick-up and drop-off times and places, travel lengths, prices, passengers, and other factors. The Taxi & Limousine Commission’s fiscal calendar (2017-2020) is included in the 454 Calendar table, which has fields for the date, fiscal year, quarter, month, and week. Information on 265 zone sites in New York City, including the location id, borough, and service zone, are included in the Taxi Zones table.

Project: COVID-19’s Impact on Airport Traffic(Tableau): Project Overview

Analysis of Traffic Volume Post-COVID: Effect of COVID-19 on Airport Traffic

This dataset displays traffic to and from the airport as a percentage of overall traffic volume throughout the reference period. From 1 February to 15 March 2020 serves as the baseline period for computing this measure. , and service zone. The information is updated every month.

Project: Brazilian E-Commerce Public Dataset by Olist(PowerBi): Project Overview

Olist’s Brazilian E-Commerce Public Dataset

Welcome! This is a publicly available Brazilian eCommerce order dataset from the Olist Store. The dataset contains details on 100k orders placed between 2016 and 2018 on several Brazilian marketplaces. Its capabilities enable examining an order from a variety of angles, including customer location, product qualities, order status, pricing, payment, and freight performance. A geolocation dataset that links Brazilian zip codes to lat/lng coordinates was also made available. References to the corporations and partners in the review language have been swapped out for the names of the great houses from Game of Thrones. This is actual commercial data that has been anonymised.

Project: Airline Flight Delays(Tableau): Project Overview

Airline Flight Delays

Delays with Airline Flights For the U.S. DOT Air Travel Consumer Report, records for more than 5,000,000 commercial airline flights were assembled in 2015. The airline name, flight number, origin and destination airports, flight duration, scheduled and actual departure and arrival times, and flight distance are all included in each record, which represents a single trip.

Project: Historical Stock Price of 10 Popular Companies(PowerBi): Project Overview

Historical Stock Price of 10 Popular Companies

The stock prices of the top ten businesses (Apple, Amazon, Netflix, Microsoft, Google, Facebook, Tesla, Walmart, Uber, and Zoom) are the focus of this dataset. Content The dataset contains the following information: The following information is provided: - The date - “Date” - The opening price of the stock - “Open” - The high price for that day - “High” - The low price for that day - “Low” - The closing price for that day - “Close” - The total number of shares traded on that day - “Volume” The stock’s closing price adjusted to reflect any dividends or corporate actions that take place before the next day’s open - “Adjusted Close” Time range: 2015 to 2021 (day level).

Project: Bitcoin Historical Data(PowerBi): Project Overview

Bitcoin Historical Data

From January 2012 to March 2021, Bitcoin data at 1-min intervals from a few exchanges. For the period from January 2012 through December March 2021, content CSV files for specific bitcoin exchanges are available. They include minute-by-minute updates of the OHLC (Open, High, Low, Close), volume in BTC and the specified currency, and the weighted bitcoin price. Unix time is used for timestamps. Timestamps with no transactions or other activity have NaN values in their data fields. The exchange (or its API) may have been down, it may not have existed, or there may have been some other unanticipated technical problem in data reporting or collection if a timestamp is missing or if there are jumps. Every attempt has been made to eliminate duplicate entries and confirm the contents are correct and complete to the best of my ability, but obviously trust at your own risk.

Project: Cholera Outbreak, Stockholm, 1853(Tableau): Project Overview

Cholera Outbreak, Stockholm, 1853

Context

This is a product of study on Stockholm’s nineteenth-century history. Since Sweden’s church archives are in good condition, it is possible to find out specific details on occurrences like pandemics. I’ll begin this data collection by learning more about the significant cholera epidemic that occurred in late August 1853. Within a few weeks, 3% of the population had passed away. St. Catherine parish, one of the city’s poorer neighborhoods, was particularly heavily impacted.

Content

The information is gathered from manually compiled church records. Up until about 1870, the church was in charge of all population registration. The only data examined were death records. Since cholera has a 40% CFR, we may presume that there are more people who are afflicted yet survive.

Inspiration

I’m hoping the data will be able to demonstrate the pace of a cholera pandemic in the 19th century in a city with very poor sanitation and the terrible consequences it had on society, along the with people.

Project: NCDC COVID-19 NIGERIA ANALYSIS(PowerBi): Project Overview

Nigeria Centre for Disease Control and Prevention(NCDC) COVID-19 NIGERIA ANALYSIS

Recommendations

Aside the basic health and hygiene practices, such as handwashing, preventive measure to ensure control of Covid-19 and use of hand sanitizer, it is imperative to note that there are other preventive measures to ensure control of Covid-19. States with emerging case should emphasize the importance of getting vaccinated because vaccines are highly effective at preventing COVID-19. • Wearing of nose mask in public areas help to prevent the spread or the virus from infected individuals to others. • Physical distancing from others who are not in your household, especially in crowded spaces. • Wash your hands regularly: Wash your hands frequently with soap and water for at least 20 seconds, or use hand sanitizer that contains atleast 60% alcohol. • Avoid large gatherings: Avoid crowded indoor spaces, especially those without good ventilation, and limit your attendance at large events or gatherings. • Stay home if you’re sick: If you have symptoms of COVID-19, stay home and isolate yourself from others, and get tested for the virus. • Practice good respiratory hygiene: Cover your mouth and nose with a tissue or your elbow when you cough or sneeze, and dispose of tissues properly. • Keep surfaces clean: Clean and disinfect frequently touched surfaces and objects regularly, especially in public spaces.

Project: WHO COVID-19 GLOBAL ANALYSIS(Tableau): Project Overview

WHO COVID-19 GLOBAL ANALYSIS

Project: STORE SALES ANALYSIS(Excel): Project Overview

STORE SALES ANALYSIS

Summary

This report shows a breakdown of products sold over a two-year period in four (4) regions with eight (8) salespeople, According to the breakdown, sales in 2018 generated more money than they did in 2019. Additionally, accessories were the top-selling item overall, selling 16% more than apparel, the second-best-selling item, which sold 24.63% more. Texas generated the most income for the shop, totaling $532k, while New Mexico generated the least, totaling $493K across all five areas. The top-performing salespeople were Anna Weber and Laura Larsen, who each earned $276k, and Kim Fishman, who earned $232k,the salesperson who performs the worst. The most revenue was earned by Company S in 2018 with a total of $74K, while Company C earned the most in 2019 with a total of $64K. However, Company D reported the greatest aggregate revenue for the two years, $123K.

Recommendations

Emphasis should be given to the best-selling item across the board, as this would increase customer happiness, sales, and money. A reward system should be in place to inspire and motivate sales staff to improve their performance and bring in more clients. To increase sales, more money should be invested in low-revenue areas. Increased social media marketing and advertising can do this by raising target audience awareness.

Project: CYCLISTIC BIKE-SHARE ANALYSIS(Tableau): Project Overview

CYCLISTIC BIKE-SHARE ANALYSIS

Identifying the use habits of Annual members and Casual riders will help marketing teams create a new marketing campaign strategy to turn Casual riders to Annual members.

SUMMARY

The season has an impact on both yearly members and casual riders overall journey lenght. The winter months have the lowest overall trip duration of any season since fewer people are ready to venture outside and those who must travel often for work opt to use alternative public transportation.

In terms of overall trip duration from May to September for two user group, we observe a considerable difference across time. This has considerably revealed their usage pattern as a result of their preferences and it becomes much more obvious when we consider the overall number of trips made throughout each hour of the day.

The annual member exhibit two peak hours, which are at Morning and Afternoon, suggest they are most likely office employees. The overall trip duration for casual riders and annual members are influenced by this, the afternoon is when there are the most casual riders. The top 20 stations that casual riders frequent are largely tourist destinations and the casual riders are probably a couple, family, student, retiree, or tourist.