Tunde Doherty (@thetundedoherty)
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    7
  • Global Rank 1,193,538 (Top 42 %)
  • Followers 1
  • Following 4
  • Registered over 7 years ago
  • Most used languages
    Java
    25.0 %
  • Location ๐Ÿ‡ณ๐Ÿ‡ฌ Nigeria
  • Country Total Rank 4,042
  • Country Ranking
    Java
    998

Top repositories

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OSHA-COVID-Inspections

Microsoft Power BI analysis and visualization on Occupational Safety and Health Administration Coronavirus-Related Inspections in the United States with Violations between July 2020 and December 2021
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thetundedoherty

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JavaDev

An application that fetches the list of Java Developers in Lagos with the Github API.
Java
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Severe_Injury

Python Pandas Data Series, NumPy, Matplotlib and Seaborn packages to do an Exploratory Data Analysis of Severe Injury recorded by OSHA between January 2015 - July 2021.
Jupyter Notebook
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Investigation-on-medical-appointment-records-in-Brazil

This project is an investigation of medical appointment records, it indicates whether a patient show up for an appointment or not, the dataset records information from more than 100,000 medical appointments in Brazil which include ScheduleDay, Neighboorhood, gender, age, AppointmentDay, and the kind of ailment each patient has that may prevent showing up for an appointment. The ailment are categorized into Hypertension, Diabetes, Alcoholism and Handicap. This dataset also include weather SMS was received or not and weather they are enrolled in brasilian welfare program Bolsa Familia, which has been mentioned as one factor contributing to the reduction of poverty in Brazil, The Economist described Bolsa Famรญlia as an anti-poverty scheme invented in Latin America that is winning converts worldwide. This study aims at providing many answers to questions including, but not limited to, The gender with the most missed appointment. Whether a particular neighboorhood is notorious for not adhering or non-challant to medical appointment. The mean age of those who missed appointments. Whether lack of scholarship 'Bolsa Familia'is a factor influencing decision of patient to meet up with an appointment. Whether a reminder in form of SMS influences a decision to keep to an appointment. The ailment that may hinder a patient from attending a medical appointment. The number of patients that did not show up for medical appointment. The number of male and female that did not show up for medical appointment. The number of patients that received an SMS. The number of male and female that received an SMS. The mean age of those who received an SMS
Jupyter Notebook
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ProsperLoan

This data set is a loan data records from prosper Loan in the United States, it contains 113,937 loans with 81 variables on each loan, including loan amount, interest rate, current loan status, borrower income, and many others. The report in this part would be structured to provide summary of simple univariate relationships to multivariate relationships, this research provides answers to various questions like whether the monthly loan payment has a correlation or any relationship between loan original,amount, what is the spread of lterm of loan in loan status, identifying the frequency of the categorical variables; Term of loan, borrower's employment status, year of loan, and loan status, are there differences between loans depending on how the loan term large the original loan amount was. Key insights would be generated from this to be able to make a presentation with it. In spite of the fact that the dataframe has 81 features, this study is only interested in few of the features, it would be appropriate to shrink the dataframe to the useful columns for the purpose of this study. The data set consist of 113,937 rows and 81 columns, implying 113,937 recorded observations with 81 features. The main features of interest to this study include but not limited to the following; loan status, loan term, employment Status, is borrower a homeowner or not, borrower state, income verifiable or not and occupation. To get a better understanding of how this features of interest would be investigated a number of features would support this study which include the following features original loan amount, loan origination date, monthly loan payment, loan current days of delinquency, stated monthly income, investors and recommendations. In total 11 features were pulled together and form into a new dataframe to be reference for exploration and analysis
Jupyter Notebook
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