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As a data scientist with a strong background in Python, Machine Learning, Microsoft Excel, Tableau, and Power BI, I am committed to leveraging data-driven insights to inform strategic decision-making. With experience building and deploying machine learning models, I have a deep understanding of statistical analysis and data visualization techniques. I am skilled in data wrangling and data cleaning, allowing me to work with large data sets and produce meaningful insights. My ability to communicate technical concepts to non-technical stakeholders enables me to collaborate effectively across departments. As a lifelong learner, I am passionate about staying up-to-date with the latest developments in data science and applying new techniques to solve complex business challenges.

Data Scientist


  • Birthday: March 29
  • City: Abuja, Nigeria
  • Email: abdulraqibshakir03@gmail.com
  • Freelance: Available


Some of my skills and the technologies I use.

Machine Learning

Supervised & Unsupervised learning, Statistics, Tensorflow, Keras, Natural Language Processing.

Programming and Scripting

Python, Object oriented programming, Data Structures and Algorithms.

Data Manipulation

Data processing, Data wrangling, Data Analysis, Data mining.

Data Visualization

Pandas, Matplotlib, Seaborn, Plotly, Power BI, Tableau

Database Management

SQL, MySQL, BigQuery, PostgreSQL, Microsoft SQL Server

Version Control

Git, GitHub


Microsoft Excel, Google Sheets

Soft Skills

Teamwork, Leadership, Communication, Report Writing


Google Colab, Linear Algebra, Mathematics, Command Line Interface


Here, you can find a showcase of my previous projects and works, which highlight my technical and creative abilities in areas such as data analytics, data science and machine learning.

  • ALL
  • APP

This simple, yet powerful tool allows you to transcribe audio files or recording to text effortlessly. Users have the option of choosing between two different methods of text transcription that suits them; Either Uploading an Audio File in formats including MP3, WAV, OGG, & AAC or Recording Audio on the App. The second option allows for speaking and getting transcription results in real-time leveraging the app's Built-In Audio Recorder. This project illustrates the practical usability of AI technologies in real-life applications.

This web-app is a book recommender system that uses a content-based approach, leveraging book features to recommend similar books to users based on features of the books or characteristics of the items and the user's preferences. Aspects such as plot, characters, and themes that truly engages the user are taken into consideration. Subsequently, book recommendations are provided that align with the user's tastes. The book data was scraped from Google Books using API calls and then extensive data preprocessing was performed to implement the recommender algorithm.

This fun app is an intuitive WhatsApp Chats Analysis tool. It provides a simple, enjoyable way to analyze your WhatsApp conversations. Explore and uncover insights into your messaging history, from active group members to peak activity times. More than just a utility, it offers an exciting journey through your messages.

Addressing a significant industry challenge, this project effectively predicts customer churn in the telecom sector using machine learning. The top-performing model, an XGBoost classifier, achieved an impressive 92% Recall Score in predicting positive cases of "churn" with other impressive performances in other evaluation metrics used too. Insights from this initiative empower the industry to implement strategic retention measures, mitigating churn and boosting customer satisfaction in a sector grappling with multimillion-dollar losses each month due to customer attrition.

This web app has the capability of detecting whether user-entered text has an underlying Positive, Neutral or Negative sentiment. The text classification model was trained on Feedback survey data collected from 300 level Undergraduate Computer Engineering Students at the University of Ilorin (who are my classmates). The model underwent fine-tuning using the BERT model, bert_tiny_en_uncased_sst2 and KerasNLP techniques, resulting in an impressive accuracy score of 96%. The data was subsequently evaluated using a RoBERTa-based model which is a transformer-based model and it also showed strong performances in analyzing sentiments accurately. This web app also offers an intuitive visualization experience.

This project allows businesses to customize their offerings, products and services to different customer segments, instead of utilizing a generic approach. Through techniques like unsupervised learning, I gain a better understanding of customers, facilitating adjustments to products based on specific needs, behaviors, and concerns of various customer types. For instance, instead of allocating resources to market a new product to every customer in the company's database, I can analyze which customer segment is most likely to make a purchase and then concentrate marketing efforts on that particular segment.

This project harnesses the power of data and AI to forecast and analyze employee attribution trends. Gradient Boosting Algorithms including XGBoost, CatBoost, LightGBM and an ensemble of these models were used to develop robust predictive models that provide insights into employee retention, identify patterns, and contribute to strategic talent management.

The project aims to perform sentiment analysis on customer satisfaction tweets related to Nigerian banks using NLP techniques and models like XGBoost, Support Vector Machine Classifier, BERT, and KerasNLP. The goal is to extract insights, identify improvement areas, and enable data-driven decisions for banks regarding customer service and concerns.

I wrangled, analyzed and visualized the tweet archive of Twitter user @dogrates also known as WeRateDogs. WeRateDogs is a Twitter account that rates people's dogs with a humorous comment about the dog. I wrangled WeRateDogs Twitter data to create interesting and trustworthy analysis and visualizations.
The Twitter archive is great, but it only contains very basic tweet information. I then got additional information by querying Twitter's API to gather more data.

This project involves analyzing WhatsApp chat data for my class by exporting chats to a text file and then using Python to wrangle and analyze the data. The analysis includes exploring the chat data, cleaning and preparing the data, and performing visualizations to gain insights into the data.
The project provides a better understanding of the patterns and trends in WhatsApp chats and can be used to gain insights into communication habits, sentiment analysis, and other text-based analyses.

In this project, I create an animated GIF maker using Streamlit, enabling users to upload images and transform them into custom animated GIFs. The goal is to offer a user-friendly tool for crafting animated GIFs for both personal and professional applications, including simple animations for social media or presentations. This project showcases the fusion of user interface design with data analysis and visualization, highlighting Streamlit's versatility in a fun and practical manner.

In this project, I analyze the Prosper loan dataset using Python to create insightful visualizations. The process involves thorough exploration, data cleaning, and preparation to extract meaningful insights into borrower behavior and loan performance. The objective is to comprehend patterns and trends in the Prosper loan data, covering loan origination rates, borrower credit scores, and critical metrics. These insights are invaluable for informed lending decisions, risk management strategies, and financial analysis. The project serves as a testament to the effectiveness of data visualization in simplifying complex data for a broader audience's understanding.

This project was part of my Data Analyst Nanodegree Programme at Udacity. The soccer database, sourced from Kaggle, is ideal for data analysis and machine learning, containing comprehensive data on matches, players, and teams across European countries from 2008 to 2016. It involved thorough data analysis using SQL and Python, including data wrangling, querying with SQL, and further analyses using Python. Through this project, I gained valuable insights into soccer data patterns and trends, encompassing player performance, team rankings, and other vital metrics. These insights are instrumental in guiding coaching decisions, player recruitment, and other critical aspects of soccer management.

This is a part of the projects I did in the Data Analyst Nanodegree Programme at Udacity The data set contains observation of about 10,000 movies collected from the Movie Database (TMDb), including user ratings and revenue. You can find more about the dataset here. In this project, I analyzed the TMDB dataset, which contains information on movies and TV shows. The analysis includes exploring the dataset, cleaning and preparing the data, and performing visualizations to gain insights into the data. The project provides a better understanding of the trends and patterns in the movie and TV industry, and can be used to inform business decisions and future content creation.

As a football fan, it was always clear that I would apply data science in some way to the sport. This marks my first venture into web scraping. In this project, I'm scraping data from the La Liga football league website using Python's Beautiful Soup library. The scraped data encompasses player statistics, team rankings, and various other crucial metrics tied to the league. The main aim of this project is to collect data for further analysis and to gain insights into player and team performance throughout the season. It's a great showcase of the potential of web scraping for efficiently gathering large volumes of data, presenting a valuable tool for football enthusiasts and analysts alike.


Here, you can find a detailed summary of my professional experience and skills, as well as my education and certifications. You can also learn more about my interests and achievements, including any publications or projects I have been involved in. Please feel free to browse through my resume and contact me if you have any questions or opportunities you would like to discuss. Click here to browse through My Resume ✅.


Here, you can check out some articles and contents I have written.

Navigating the Data Seas: My Year-Long Voyage into the World of Data Science


How to reach out to me..


Abuja, Federal Capital Territory, Nigeria

Call / WhatsApp:

(+234) 7025 9659 22

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