Emmanuel Olorunbogun's Projects
In this project, SQL was used for the data analysis of sellers' listings on an online marketplace. This involved the study the behavior of sellers on their listing form online. By analyzing this dataset, insights into which sellers are excelling and driving business growth were gained.
Whether or not you like football, the Super Bowl is a spectacle. There's a little something for everyone at your Super Bowl party. Drama in the form of blowouts, comebacks, and controversy for the sports fan. There are the ridiculously expensive ads, some hilarious, others gut-wrenching, thought-provoking, and weird. The half-time shows with the biggest musicians in the world, sometimes riding giant mechanical tigers or leaping from the roof of the stadium. It's a show, baby. And in this notebook, we're going to find out how some of the elements of this show interact with each other. After exploring and cleaning our data a little, we're going to answer questions like: What are the most extreme game outcomes? How does the game affect television viewership? How have viewership, TV ratings, and ad cost evolved over time? Who are the most prolific musicians in terms of halftime show performances?
SQL Analysis of international breweries sales data for a duration of three years to aid in better decision-making in order to maximize profit and reduce loss to the lowest minimum.
For this project we will use a dataset with a combined nearly 1.5 million records, which represent traffic crashes reported in Chicago from 2013 to 2021 and the vehicles involved in them. The dataset was created in February 2021 and contains data sourced from the City of Chicago.
This takes a lot at the correlation between the budget and Gross Earning of a Movie Dataset.
This project was used to explore the current state of COVID-19 in Nigeria, Africa and the world at large.
In this project, I transformed the raw Nashville housing data by cleaning it and making it more usable for analysis.
Data Exploration of FordBike System is the final project of the Udacity Data Analyst Nanodegree. The dataset includes information about individual rides made in a bike-sharing system covering the greater San Francisco Bay area. It was requested to investigate this dataset to get at least some insights.
In this project, you must analyze demographic data using Pandas. You are given a dataset of demographic data that was extracted from the 1994 Census database.
Dr. Ignaz Semmelweis was a Hungarian physician born in 1818 and active at the Vienna General Hospital. If Dr. Semmelweis looks troubled it's probably because he's thinking about childbed fever: A deadly disease affecting women that just have given birth. He is thinking about it because in the early 1840s at the Vienna General Hospital as many as 10% of the women giving birth die from it. He is thinking about it because he knows the cause of childbed fever: It's the contaminated hands of the doctors delivering the babies. And they won't listen to him and wash their hands. In this notebook, we're going to reanalyze the data that made Semmelweis discover the importance of handwashing. Let's start by looking at the data that made Semmelweis realize that something was wrong with the procedures at Vienna General Hospital.
Config files for my GitHub profile.
This is my first project for the Udacity's Data Analyst Nanodegree. The of the project aim is to carry out an investigation which analyzes at least one dependent variable and three independent variables.
Create a function named calculate() that uses Numpy to output the mean, variance, standard deviation, max, min, and sum of the rows, columns, and elements in a 3 x 3 matrix. The input of the function should be a list containing 9 digits. The function should convert the list into a 3 x 3 Numpy array, and then return a dictionary containing the mean, variance, standard deviation, max, min, and sum along both axes and for the flattened matrix.
In this project, you will visualize and make calculations from medical examination data using matplotlib, seaborn, and pandas. The dataset values were collected during medical examinations.
In this project, Facebook Prophet was used for forecasting the sales of the individual merchant of a franchise to predict their sales for every day for the next three months.
This project describes how to scrape the trading summary of any company, in this case study, Netflix, Inc. (NFLX), form the Yahoo Finance webpage. The scraping was done with Python's requests and BeautifulSoup libraries. The data scraped was then converted into a DataFrame using pandas.
This project involves gathering, wrangling and analyzing data from the archives of a twitter account called WeRateDogs, a page that rates peoples dog in their tweets.
Youtube is the second most visited page in the world with approximately 1 billion hours of content is watched per day. This means that Youtube attracts 48% of all internet users. So Youtube generates a large amount of data and these data are going to be analyzed in this project. First of all, the sentiment analysis on the Youtube text is going to be performed, then the wordcloud representation of the sentiments will also be done. Finally, exploratory data analysis will be done for both the positive and negative sentences.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent