- Assisting in image data collection of leaves of plants such as Maize, Tomatoes, and Cassava from Kenyan farms.- Assisting in data preprocessing while collaborating with other team members on GitHub and dagshub in ensuring the collected images are properly cleaned for model development.- Developing Machine Learning models after training the images using sklearn libraries for prediction purposes.- Checking on the model’s accuracy and ensuring it is at 95% as directed by the project lead. - Defining and refining the data pipeline to support real-time disease detection.

• Gathering data from primary and secondary sources such as Kaggle and Tableau Data Sources and utilizing them to create visualizations and data dashboards in Microsoft Power BI and Tableau.• Cleaning data using Power Query in Power BI and Microsoft Excel in preparation for visualizations and analyses.• Conducting statistical analysis such as Hypothesis and ANOVA Tests using Microsoft Excel to come up with inferences useful in data-driven decision making.• Utilizing SSMS and MySQL Relational Database Management Systems in creating and managing databases to conduct CRUD operations.• Utilizing python libraries such as SQL Alchemy, Pandas, Numpy, matplotlib, and Seaborn in Exploratory Data Analysis useful in providing data-driven insights.

My achievements include:• Using Beautifulsoup and Requests python libraries to scrape data from websites.• Processing the scraped data by cleaning, organizing, and extracting information.• Incorporating automation by scheduling the scripts to run at different intervals to keep the dataset up-to-date.• Creating documents using Jupyter Notebook extensions on VS Code to explain the purpose of the script, how to run it, and if there are any dependencies.• Developing scripts that reads datasets, processes the data, and performs operations such as calculatingsummary statistics (mean, median, mode, standard deviation), data filtering based on specific criteria, and generating data visualizations.• Automating the scripts to ensure that it can be run multiple times with different datasets, making it a versatile tool for data processing.• Documenting the script using Jupyter Notebook to explain how to use the script, its functions, and anydependencies.

Dealt with drawing Telecommunication maps using survey software, including Autocad, Global mapper, Qgis, and Google earth. The main aim of the maps was to show the extent of the client's environs in order to determine the bill of materials required for Fiber installation.