business-summary_dashboard_power_bi

A report that summarizes the highest severity alarms for the current dashboard. This report is context-sensitive and appears as a dashlet in multiple dashboards. The information that appears in this report is filtered based on your current selection and type of dashboard. In the context of the Summary dashboard, the report displays information for your environment.

caseconverter

I need to write a long piece of text. Suddenly, it turned out that it has to be in the upper case. Do you really need to rewrite the entire text? Of course not. Let's create a tool that automatically changes the case. Let's start with implementing the first HTML elements.

chinedu.obi

Config files for my GitHub profile.

customer_segementation

This is a data analysis portfolio project that will allow understand to performance of customers based on segmentation of mall customers

data_pipeline

diabetes_prevalence

Learning About Our Dataset Diabetes The increasing prevalence of diabetes in the 21st century is a problem. Patients have symptoms like unusual thirst frequent urination extreme fatigue Diabetes can also lead to more serious complications like stroke, blindness, loss of limbs, kidney failure, and even heart attack. Discovery of Insulin In the 1920s, insulin was discovered by Frederick Banting. Most of the food we eat is turned to glucose, or sugar, for our bodies to use for energy. The pancreas, an organ near the stomach, makes a hormone called insulin, to help glucose get into the cells of our bodies. When you have diabetes, your body either doesn't make enough insulin or can't use its own insulin as well as it should. And this causes sugars to build-up in the blood. With Banting's discovery of insulin, pharmaceutical companies began large-scale production of insulin. Although it doesn't cure diabetes, it's one of the biggest discoveries in medicine. When it came, it was like a miracle. Challenges with Insulin The default method of administration is by a needle, multiple times a day. Insulin pumps are a more recent invention. These are insulin delivering devices that are semi-permanently connected to a diabetic's body. The Future: Oral Insulin? Wouldn't it be great if diabetics could take insulin orally? This is an active area of research, but historically the roadblock is getting insulin through the stomach's thick lining. Our dataset: Auralin and Novodra Trials We will be looking at the phase two clinical trial data of 350 patients for a new innovative oral insulin called Auralin - a proprietary capsule that can solve this stomach lining problem. Phase two trials are intended to: Test the efficacy and the dose response of a drug Identify adverse reactions In this trial, half of the patients are being treated with Auralin, and the other 175 being treated with a popular injectable insulin called Novodra. By comparing key metrics between these two drugs, we can determine if Auralin is effective. Why do we need Data Cleaning? Healthcare data is notorious for its errors and disorganization, and its clinical trial data is no exception. For example, human errors during the patient registration process mean we can have duplicate data missing data inaccurate data You're going to take the first step in fixing these issues by assessing this data sets quality and tidiness, and then cleaning all of these issues using Python and Pandas. Our goal is to create a trustworthy analysis. DISCLAIMER: This Data Isn't "Real" The Auralin and Novodra are not real insulin products. This clinical trial data was fabricated for the sake of this course. When assessing this data, the issues that you'll detect (and later clean) are meant to simulate real-world data quality and tidiness issues. That said: This dataset was constructed with the consult of real doctors to ensure plausibility. This clinical trial data for an alternative insulin was inspired and closely mimics this real clinical trial for an inhaled insulin called Afrezza. The data quality issues in this dataset mimic real, common data quality issues in healthcare data. These issues impact the quality of care, patient registration, and revenue. The patients in this dataset were created using this fake name generator and do not include real names, addresses, phone numbers, emails, etc. The video above is only a short preview of the dataset that is intended to motivate. If you're not comfortable with the meanings of each column in each table, please revisit the Visual Assessment: Acquaint Yourself page in Lesson 3: Assessing Data. Descriptions of each column as well as the Auralin clinical trial, as a whole, are presented there.

diamond_dataset

Information on the Diamond Dataset In this lesson, you'll be working with a dataset regarding the prices and attributes of approximately 54,000 round-cut diamonds. You'll go through the steps of an explanatory data visualization, systematically starting from univariate visualizations, moving through bivariate visualizations, and finally multivariate visualizations. Finally, you'll work on polishing up selected plots from the analysis so that their main points can be clearly conveyed to others. You can find a copy of the dataset in the Resources tab of the classroom; it will automatically be available to you in the workspaces of this lesson. The dataset consists of almost 54,000 rows and 10 columns: price: Price in dollars. Data were collected in 2008. carat: Diamond weight. 1 carat is equal to 0.2 grams. cut: Quality of diamond cut, affects its shine. Grades go from (low) Fair, Good, Very Good, Premium, Ideal (best). color: Measure of diamond coloration. Increasing grades go from (some color) J, I, H, G, F, E, D (colorless). clarity: Measure of diamond inclusions. Increasing grades go from (inclusions) I1, SI2, SI1, VS2, VS1, VVS2, VVS1, IF (internally flawless). x, y, z: Diamond length, width, and depth, respectively, in mm. table: Ratio of the width of the top face of diamond to its overall width, as a percentage. depth: Proportional depth of the diamond, as a percentage. This is computed as 2 * z / (x + y), or the ratio of the depth to the average of length and width. For the case study, we will concentrate only on the variables in the top five bullet points: price and the four 'C's of diamond grade. Our focus will be on answering the question about the degree of importance that each of these quality measures has on the pricing of a diamond. You can see an example report covering all of the variables in the project information lesson.

dominos_pizza

A study that will collect and summarize data to be used in a marketing advertisement comparing Dominos Pizza to thier main competitor.

fuel_economy

If we want to inspect the relationship between two numeric variables, the standard choice of plot is the scatterplot. In a scatterplot, each data point is plotted individually as a point, its x-position corresponding to one feature value and its y-position corresponding to the second.

gdp_and_energy_used_visualization

Now imagine this scenario you're working as a data analyst for a policy research institute for your current project. You need to create a visualization that shows the CO2 emissions per capita for each country from 2000-2011. You'll also provide information about each country's population GDP and energy use. All right, let's get started.

home_schooling_and_pyschological_stress

Predicting the relationship between Time spent on childcare or home-schooling during the UK’s coronavirus lockdown and psychological distress.

mavenmoviesproject_1

The Company insurance policy is up for renewal and the insurance company's underwriter needs some updated Information from us before they will issue a new policy

mavenmoviesproject_2

Me and my business partner were recently approached by another local business owner who is interested in purchasing maven movies. he primarily owns a restaurant and bars, so he has alot of questions for me about the business and the rental business in general. His offer seems very generous, so you are going to entertain his questions

openspace

Have you ever dreamt about exploring deep space? In this project, you'll create a simple web game where you launch a rocket from an uninhabited planet. You don't have to be a rocket scientist to complete this project: we will start from the ground up by learning how to implement an HTML skeleton of a page and use CSS. Then, we'll learn how to make the game interactive with JS, and by the end of the project, you'll have a firm knowledge foundation for developing your frontend skills.

profitabilitydashboard-

Profit-focused dashboards are considered financial visualization tools and are often used by executives and board members to analyze budget variances and trends related to profitability. Key functionality in this type of dashboard displays column charts to compare actual gross and net profit

simplechattybot

you will introduce yourself to the bot. It will greet you by your name and then try to guess your age using arithmetic operations.

sql_data_cleaning

SQL is an extremely in demand skill. Tons of jobs use SQL, and in the this project i will be learning how to utilize SQL to analyze data and answer business questions. Project

students-performance-classification-using-ml

Compare the performance of Scikit-learn, TensorFlow, and PyTorch implementations in terms of accuracy, precision, recall, and confusion matrix and make recommendations with respect to their strengths and weakness

targeted-promotion-for-telecom-offer

develop a machine learning model using the experiment results to identify the most receptive customers from the full customer database for the new offer. This will enable the team to maximize campaign effectiveness by targeting the most likely customers to adopt the offer and optimize marketing spend by focusing on a high-potential segment.

tmdb_movie_case_study

Introduction Dataset Description 1: This dataset contains information about 10,866 movies collected from The Movie Database (TMDb) which is (cleaned from original data on Kaggle), Certain columns, like ‘cast’ and ‘genres’, contain multiple values separated by pipe (|) characters. Read through the description available on the homepage-links present here to understand more about the dataset

zookeeper

I make my software ready for the zoo staff to use. My program should understand the habitat numbers, show the animals, and be able to work continuously without having to be restarted.