This repository is an aggregation of all the Data Analysis projects I have done for work, fun, or academic purposes.
The IT team has provided historical information containing the data of applicants and the status of their loan application process. You are expected to automate the process by building a machine learning model to predict the outcome of the lending process if the credit facility process was completed via e-process, that is E-Signed or not.
With the use of certian python libraries, and in collaboration with 2 colleagues of mine, I was able to carry out Exploratory Data Analysis of a dataset containing the information of applicants and the status of their loan application process.
I was also able to build a Python Machine Learning Model to predict the outcome of the lending process if the credit facility process was completed via e-process, that is E-Signed or not using certain python libraries.
In this project, I performed an analysis on COVID-19 data from Nigeria. I used Microsoft excel and SQL for the data wrangling, also used SQL to perform the data exploratory analysis in order to draw key insights, and used Tableau for visualization.
In this project, I performed exploratory analysis on two datasets; The FBI NICS Firearm Background Check Dataset, and the US census dataset. I used Microsoft Excel to correct some errors in the dataset such as converting one of the datasets from the .xlsx format to .csv, but majority of the entire analysis process was carried in Jupyter Notebooks with the Numpy, Pandas and Matplotlib python libraries.
In this project, I focused on honing my data wrangling skills. This includes data gathering, data assessing, and data cleaning and trimming. The project was done primarily using python to download data from multiple sources, including scrapping data from the twitter API. Python was also used to assess and clean the data, as well as derive some summary insights from the data.
In this project, I performed a standard exploratory analysis of the Ford GoBike system data. Mainly focused on data exploration using Univariate, Bivariate and Multivariate visualizations.