blog

Hi! Welcome to My Blog. I’m Victor Omondi, a Data Analyst at Maisha Meds, A Computer Technology graduate from Multimedia University of Kenya. I like manipulating, exploring, visualizing and modeling data to obtain insights that can be used in future decision making.

To preprocess wikipedia-vectors.csv into the format in which you used it in the exercises, you have to take its transpose:

Post With Code

news

code

analysis

This is a post with executable code.

Welcome To My Blog

news

This is the first post in a Quarto blog. Welcome!

Tristan O’Malley

Hyperparameter Tuning with Python

machine-learning

hyperparameter-tuning

perform hyperparameter tuning techniques to your most accurate model in an effort to achieve optimal predictions.

Data Wrangling in Python

data-wrangling

python

cit-club

CIT Club event blog & Notebook. The aim of this blog/notebook was to introduce CIT Club members in data wrangling.

Writing Reusable Code using Functions in Python

python

Learn how to create and use functions in python

A Quick Tour of Variables and Data Types in Python

python

data-types

This tutorial is the second in a series on introduction to programming using the Python language. These tutorials take a practical coding-based approach, and the best way to learn the material is to execute the code and experiment with the examples.

Cluster Analysis in R

machine-learning

cluster-analysis

r

build a strong intuition for how they work and how to interpret hierarchical clustering and k-means clustering results

Introduction and Exproling Raw data

No one likes missing data, but it is dangerous to assume that it can simply be removed or replaced. Sometimes missing data tells us something important about whatever it is that we’re measuring (i.e. the value of the variable that is missing may be related to - the reason it is missing). Such data is called Missing not at Random, or MNAR.

Appliances Energy Prediction data

energy

machine-learning

- author: Victor Omondi

Visualizing the Gender Gap in College Degrees

There are seventeen degrees, to generate line chart, we’ll use a subplot grid layout of 6 rows by 3 columns.

Dataset

this plot does not show a linear relationship between GDP and percent literate, countries with a lower GDP do seem more likely to have a lower percent of the population that…

Weight

The distribution of weights of medalists in gymnastics and in rowing in the 2016 Olympic games for a comparison between them

Cluster Analysis in Python

cluster-analysis

unsupervised-learning

exploring unsupervised learning through clustering using the SciPy library in Python

Avocado

Bar plots are great for revealing relationships between categorical (size) and numeric (number sold) variables, but manipulation on the data first is needed in order to get the numbers needed for plotting.

Analyzing Police Activity with Pandas

Exploring the Stanford Open Policing Project dataset and analyzing the impact of gender on police behavior.

Weather Visualization

data-visualization

weather

5 rows × 68 columns

Analyzing Hacker News Dataset

data-analysis

hacker-news

we were able to calculate that 444 of the 20,100 Hacker News stories in our dataset contain tags. What if we wanted to find out what the text of these tags were, and how…

Visualizing Women in USA Degrees

data-visualization

women

Explore how we can communicate the nuanced narrative of gender gap using effective data visualization. Author:

Working with Missing Data

missing-data

data-analysis

we’ll handle missing data without having to drop rows and columns using data on motor vehicle collisions released by New York City and published on the NYC OpenData website. There is data on over 1.5 million collisions dating back to 2012, with additional data continuously added. We’ll work with an extract of the full data: Crashes from the year 2018.

Working With Dates and Times in Python

dates

times

python

How to work with Dates and Times in python

Writing Functions in Python

functions

python

useful tricks, like how to write context managers and decorators

Working with Dates and Times in R

dates

times

r

the essentials of parsing, manipulating, and computing with dates and times in R. By VICTOR OMONDI

Unsupervised Learning in Python

machine-learning

unsupervised-learning

Unsupervised learning encompasses a variety of techniques in machine learning, from clustering to dimension reduction to matrix factorization. In this blog, we’ll explore the fundamentals of unsupervised learning and implement the essential algorithms using scikit-learn and scipy.

Supervised Learning with scikit-learn

machine-learning

supervised-learning

scikit-learn

Supervised learning, an essential component of machine learning. We’ll build predictive models, tune their parameters, and determine how well they will perform with unseen data—all while using real world datasets. We’ll be learning how to use scikit-learn, one of the most popular and user-friendly machine learning libraries for Python.

Statistical Thinking in Python (Part 2)

statistical-thinking

hypothesis-testing

data-science

Expanding and honing hacker stats toolbox to perform the two key tasks in statistical inference, parameter estimation and hypothesis testing.

Statistical Thinking in Python (Part 1)

statistical-thinking

eda

data-science

Building the foundation you need to think statistically, speak the language of your data, and understand what your data is telling you.

Dr. Semmelweis and the Discovery of Handwashing

ignaz

data-analysis

handwashing

Reanalyse the data behind one of the most important discoveries of modern medicine: handwashing.

Analyzing Police Activity with Pandas

Exploring the Stanford Open Policing Project dataset and analyzing the impact of gender on police behavior.

Exploring Boston Weather Data

We’ve confirmed that the object is a data frame with 286 rows and 35 columns.

Fastpages Notebook Blog Post

jupyter

A tutorial of fastpages for Jupyter notebooks.