Data Science and Machine Learning Portfolio

Overview

Welcome to my Data Science and Machine Learning Portfolio, showcasing a collection of projects that exemplify my dedication to mastering Python and Rust programming languages. As an AI engineer deeply passionate about machine learning, I aim to provide efficient and innovative solutions. Each project reflects a commitment to fundamentals, efficiency, and the application of machine learning in solving real-world challenges.

Feel free to explore the diverse projects presented here, each designed to demonstrate the impact of machine learning in various domains. From predicting crop yield for smallholder farmers in India to classifying faults in Huawei's telecom network, these projects showcase my dedication to making a positive impact through AI technology.

Projects

1. Air Quality Trailblazers

   • Overview: Predicting PM2.5 particulate matter concentration using Sentinel 5P satellite data.
   • Aim: Tracking air quality changes in areas without ground-based sensors.
   • Technologies: Python, Machine Learning.

2. Bank Transactions Categorization

   • Overview: Categorizing banking transactions into categories and subcategories.
   • Aim: Creating a model for banking transaction categorization for analytics and customer insights.
   • Technologies: Python, Machine Learning.

3. Cryptojacking Prediction

   • Overview: Classifying network activity from various websites as cryptojacking or not.
   • Aim: Identifying illegal cryptocurrency mining using website activity.
   • Technologies: Python, Machine Learning.

4. Digital Green Yield Estimation

   • Overview: Predicting the crop yield per acre of rice or wheat crops in India.
   • Aim: Empowering smallholder farmers and breaking the cycle of poverty and malnutrition.
   • Technologies: Python, Machine Learning.

5. Expresso Churn

   • Overview: Developing a machine learning model to predict Expresso customer churn.
   • Aim: Understanding and addressing customer churn for improved service.
   • Technologies: Python, Machine Learning.

6. Fault Impact Analysis Huawei

   • Overview: Predicting an NE's average data rate change when a fault occurs in a telecom network.
   • Aim: Enhancing fault management and network service quality.
   • Technologies: Python, Machine Learning.

7. Financial Inclusion

   • Overview: Creating a machine learning model to predict individuals most likely to have or use a bank account.
   • Aim: Providing insights into financial inclusion in Kenya, Rwanda, Tanzania, and Uganda.
   • Technologies: Python, Machine Learning.

8. Laduma Analytics

   • Overview: Predicting the outcome of football matches based on historical match and player data.
   • Aim: Widening the types of services offered by Laduma Analytics.
   • Technologies: Python, Machine Learning.

9. Netflix Appetency

   • Overview: Classifying consumers according to their appetite to subscribe to Netflix.
   • Aim: Developing a model with anonymized and augmented data for classification.
   • Technologies: Python, Machine Learning.

10. Nigeria Insurance

    • Overview: Predicting the probability of a building having an insurance claim during a certain period.
    • Aim: Determining the probability of insurance claims based on building characteristics.
    • Technologies: Python, Machine Learning.

11. Song Popularity

    • Overview: Predicting the popularity of a song based on features like acousticness, danceability, etc.
    • Aim: Enhancing accuracy in predicting song popularity for the music industry.
    • Technologies: Python, Machine Learning.

12. SuperLender Loan Default

    • Overview: Predicting if a loan application is likely to be good or bad, where "good" means the applicant is likely to repay the loan.
    • Aim: Assisting SuperLender in making informed decisions about loan applications.
    • Technologies: Python, Machine Learning.

13. Tanzania Tourism AI4D

    • Overview: Developing a machine learning model to classify the range of expenditures a tourist spends in Tanzania.
    • Aim: Aiding tour operators and the Tanzania Tourism Board in estimating tourist expenditure.
    • Technologies: Python, Machine Learning.

14. Uganda Air-Quality

    • Overview: Using satellite radar data from Sentinel 5P to predict air quality readings from AirQo’s sensors.
    • Aim: Expanding air quality predictions to areas without air quality sensor devices.
    • Technologies: Python, Machine Learning.

15. Umoja Hack 2023

    • Overview: Predicting carbon emissions using open-source CO2 emissions data from Sentinel 5P.
    • Aim: Enabling governments to estimate carbon emission levels across Africa.
    • Technologies: Python, Machine Learning.

16. Vehicle Insurance Claim

    • Overview: Developing a predictive model for AutoInland to determine if a customer will submit a vehicle insurance claim.
    • Aim: Streamlining financial planning and improving customer service.
    • Technologies: Python, Machine Learning.

17. Zimnat Insurance

    • Overview: Predicting the value of future insurance claims per client to better forecast annual costs.
    • Aim: Aiding Zimnat Insurance in being better prepared to address claims, improving customer satisfaction.
    • Technologies: Python, Machine Learning.

18. Zindi User Engagement

    • Overview: Predicting if a new user from a selected cohort will continue to use Zindi in their second month.
    • Aim: Aiding Zindi in establishing and tracking user engagement performance.
    • Technologies: Python, Machine Learning.

Future Developments

Continuing to explore innovative solutions in data science and machine learning, I am committed to advancing AI technology for impactful applications. Stay tuned for updates and new projects that push the boundaries of what's possible in the realm of data science and machine learning.

Thank you for exploring my Data Science and Machine Learning Portfolio!