Hello! 👋🏾

I'm Kenechukwu Ajufo, a software engineer based in Dublin, Ireland. I am passionate about

Get in touch 👉🏿 kenechukwu.ajufo@gmail.com

Background

I am a Frontend Software Engineer building seamless and aesthetically pleasing web applications at FWI | Poppulo.

Prior to FWI | Poppulo, I worked as a Frontend Developer at bxp software and then part time as a Computing Lab Assistant at the National College of Ireland where I assisted lecturers and supported students with computing tutorials.

Love working with Python and I am comfortable with R. I have gained academic experience building machine learning and deep learning algorithms for multiple applications including Signal Processing, Education, Bank Marketing and Natural Language Processing. I also have experience with Descriptive and Inferential Statistics, Hypothesis testing, visualizing data, and creating dashboards with PowerBI, and finally working with ERP and CRM tools.

I also have solid knowledge of Vanilla JavaScript (ES6), jQuery, and Web Frameworks like React, Redux, GraphQL. I understand of the principles of Responsive Design, Grids, Layout, & Typography, and use these programming languages and principles in building web applications

When I am not at my Computer Screen, I am listening to music on Music or , listening to an audiobook, on Twitter or taking a walk somewhere (when it's summer).

Skills

Experience

FWI | Poppulo
Software Development Engineer II
August 2022 - present
bxp software
Frontend Developer
January 2022 - July 2022
National College of Ireland
Computing Lab Assistant
September 2020 - April 2022
Chams PLC
Software Developer
January - October 2019
Precise Financial Systems
System Developer Intern
November 2017 – October 2018
GlaxoSmithKline Nigeria PLC
IT Intern
May – August 2013
View My Web Developer CV View My Data Analyst CV
Data Science Projects
Music Genre Classification

For my Master's Thesis, I built two Deep Learning models to automatically classify music genres.

Python Google Colab Librosa TensorFlowKerasKeras Tuner PandasNumPyscikit-learn matplotlibscipyseaborn
Sentiment Analysis of Hotel Reviews
using Deep Learning

(Team of three) performed a sentiment analysis on Trip Advisor Reviews dataset. The project focused on using two Deep Learning models (LSTM and BERT) to predict the hotel’s rating using it’s reviews.

Python Google Colab TensorFlow scikit-learn PyTorchKeras
Analysis on the Relationship
between Population Growth Rate,
Life Expectancy and
the Gross National Income

(Team of three) analyzed three public datasets from ArcGIS Hub & Datahub. The objective of the project was to demonstrate the team’s knowledge of data wrangling (transforming data from one form to another) and Relational (PostgreSQL) & NoSQL (MongoDB) databases.

Python Pandas NumPy pymongo psycopg2 requests
Exploring Student Academic
Performance in Portugal
using Machine Learning

Built a C5.0 Decision Tree Model to predict student academic performance.

R R Studio C5.0 printr
SQL Practice

Mastering PostgreSQL.

SQL
Analysis of Bank Marketing Campaign,
Predicting Outcomes of Customer
Purchase Intention and
Household Electricity Consumption
using Machine Learning

Built various Machine Learning Models for three domains: Banking, Customer Purchase Intention, Household Energy Consumption

Python R sklearn Pandas NumPy