Forem: Kadoon Verinumbe

Data Analysis with Microsoft Excel

Kadoon Verinumbe — Mon, 14 Apr 2025 12:12:13 +0000

Introduction
This Dataset was cleaned, visualized and analyzed using only Microsoft excel.
Data Structure
This dataset originated from the healthcare sector. The dataset contains Heart Attack Incident of Patients with different medical history, social economic, Family backgrounds etc
Data Cleaning and Preparation
The data cleaning and preparation phase was done to ensure dataset was free of blank spaces

1) Age and Gender of Heart Attack Patient
In the dataset the gender depicted as others had higher heart attack incidence for the Adult age group, while the Male and female which showed the same heart attack incidence for the adult age group. Lastly all the youths from the different gender had the same heart attack incidence

2) Physical Activity Level of each BMI
In the dataset the Physical activity level of each BMI was assessed, the Patients with High physical activity level had low BMI, the Patients with medium BMI had Medium BMI and Lastly Patients with Low physical activity level had high BMI.

3) Gender of Heart Attack Patient
In the dataset the 14013,34% of the patients with heart attack identified as other in the gender section, female Patients were 13602,33% and Male patients were also13603, 33%.

4) Smoking Status of Heart Attack Patients
In the dataset former smoker had higher heart attack incident, seconded by non-smokers and lastly smokers indicated the least amount of heart attack incidence.

Dashboard:

Data Analysis with Python

Kadoon Verinumbe — Wed, 09 Apr 2025 22:26:22 +0000

Introduction
The dataset for this project contains records of the world population in 2023
Data cleaning, analysis and visualization was done using python. The analysis provides answers to some important questions and to get an understanding of the dataset.

Data Structure
Columns in the dataset include; County, population, yearly change, density, land area, net migrants, fertility rate, median age, population urban and world share.

The necessary python libraries needed to carry out this analysis was imported into python IDLE (Jupyter Notebook), and the dataset was loaded in to begin analysis.

Total number of column and rows present in the dataset are 234 while total number of rows are 11.

Data Cleaning
Data cleaning was done using the python pandas library in order to clean the dataset and prepare it for analysis.
Checking duplicate value in the dataset

The image above shows there are no duplicate values

Checking missing values in the dataset

The image above shows there 20 missing values the dataset

The image above shows that the missing data was replaced with zero

Data Analysis and Exploration
To Ten Countries By Population
The visualization shows the top ten countries ranked by their population. The world's population is highly concentrated in a few region with India and China leading.

Bottom five Countries in Population
This visualization is for the five countries with least population. Showing some small island nations and territories have extremely low population.

Distribution of Fertility Rate Across Countries
The visualization shows the correlation between countries fertility rates, with regions experiencing rapid decline.

Data Analysis Using SQL

Kadoon Verinumbe — Wed, 09 Apr 2025 09:57:27 +0000

INTRODUCTION
In this project I carried out an exploratory analysis on the Human Resource dataset of an organization.
DATA PREPARATION AND CLEANING
The data was first loaded into Microsoft excel in order to clean the dataset, remove outliers and checked for consistency in the dataset.

Here is a picture of the dataset

A database was created in MYSQL workbench in order to begin analysis. Picture of the dataset after being imported into the SQL workbench.

ANALYSIS
Analysis was done to answer some necessary questions and get an understanding of the dataset.
1) Total Number of Employee

In the dataset the total number of employees is 1009.
2)Number of Employee Promoted in the last 5 years;

There were only 3 employees promoted in the last 5 years.
3)Average Monthly Hourly worked by Employees

The average monthly hourly worked by employee is 203.2131.
4) Employees with the Highest Number of Projects

Top 5 employees with highest number of projects.
5)Number of Employees with Work Accident;

Employees with work accidents are 87
6)Number of Employee at each Salary Level;

Employees at low salary level were 622, while Employees at Medium Salary level were 367 and lastly which is the least the employee at high salary level were 20.

Data Analysis with Power BI

Kadoon Verinumbe — Wed, 09 Apr 2025 09:06:14 +0000

Introduction
The retail dataset contains the sales at a retail store. The data includes the following details: Cost of goods sold, customer assessment of sales rep, Discount applied, cost of goods etc
Data Cleaning and Preparation

The dataset was loaded into power BI’s power query editor for cleaning in preparation for analysis. Initially the dataset contained empty rows and column, which were removed. Additionally, misspellings and duplicates were corrected or removed to ensure only accurate values were used.
Analysis and Insights
The analysis revealed that the total cost of goods by the retail store is 90K with the total sales price as 128K and Total Discount being 40.04.

Cost of Goods Sold by Product Category
The product category with the most cost in this retail store is the electronic category while automotive parts has the least cost.

Cost of Goods sold by Customer Segment
In the dataset retail customer had the highest cost of goods sold while institutional customers had the least cost of goods sold.

Sales Amount by Customer assessment of sale rep
In the dataset customers assessed as positive had the highest sales amount of 63K(49.35%) while the ones assessed as negative had the lowest sales of 31K(24.59%).

Cost of Goods Sold by Customer Assessment of Sales Rep
In the dataset the customers assessed Positive had higher cost of product sold of 44k(48.76%) while the customers that were assessed as Negative has the least cost of goods sold of 22K(24.78%).

Here is an overview of the analysis: