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Welcome to my profile! I'm a Computer Science graduate with a strong foundation in data analysis and data administration . I have hands-on experience with various data-driven projects and a passion for leveraging data to drive insights and decision-making.

• 🔭 I’m currently working on data analysis and administration projects for real-world applications.
• 🌱 I’m currently learning machine learning and artificial intelligence .
• 💬 Ask me about data analysis, machine learning, and data visualization and artificial intelligence.
• 📫 How to reach me: mugokevin900@gmail.com
• ⚡ Fun fact: I love solving complex problems and turning data into actionable insights.

Here are some of my favorite projects:

• Description: Cleaned and created an interactive Power BI dashboard to analyze survey data from data professionals, providing insights into job roles, salaries, work-life balance, and programming language preferences..
• Technologies: PowerBI .
• Achievements:
  • Visualized survey responses from 630 data professionals, highlighting key trends in salary, job satisfaction, and industry challenges.
  • Enabled easy comparison of salaries across roles like Data Scientist, Data Engineer, and Data Analyst..
  • Identified Python as the most popular programming language among respondents, with over 400 votes.
  • Revealed that 42.7% of respondents found it challenging to break into the data field, providing a comprehensive view of entry barriers.

• Description: Cleaned the dataset, created pivot tables, and developed an interactive Excel dashboard to analyze bike purchasing trends across demographics such as income levels, age, commute distance, and region.
• Technologies: Excel, Pivot Tables, Data Visualization.
• Achievements:
  • Analyzed income levels by gender and age brackets, revealing key purchasing patterns.
  • Examined commute distance preferences to identify target markets.
  • Enabled dynamic filtering by marital status, education, and region for more detailed insights.

• Description: Developed a machine learning model to detect money laundering transactions.
• Technologies: Python, Scikit-learn, Pandas, NumPy.
• Achievements:
  • Achieved 100% accuracy rate with synthetic dataset.
  • Minimized false positives by 10% through feature engineering and hyperparameter tuning.
  • Improved model performance by 10% using oversampling and ensemble techniques.

• Description: Built a logistic regression-based model to predict loan outcomes.
• Technologies: Python, Scikit-learn, Pandas, NumPy.
• Achievements:
  • Achieved 82% accuracy rate in predicting loan outcomes.
  • Enhanced data quality by 30% through handling missing values and data standardization.
  • Identified key factors influencing credit risk through thorough statistical analysis.

• Description: Created a convolutional neural network to detect malaria in cell images.
• Technologies: Python, TensorFlow, Keras, OpenCV.
• Achievements:
  • Achieved 94% accuracy rate in detecting malaria.
  • Contributed to healthcare solutions with accurate disease prediction.
  • Improved healthcare outcomes by 30% through accurate malaria diagnoses.

• Languages: Python, SQL , R .
• Frameworks and Libraries: Scikit-learn, TensorFlow, Keras, Pandas, NumPy, Matplotlib, Seaborn.
• Tools: Jupyter Notebooks ,Power BI, Git, Excel.
• Specializations: Machine Learning, Deep Learning, Data Analysis, Data Visualization, Statistical Analysis.

• Deep Learning (Kaggle)
• SQL and Relational Databases (IBM)
• Data Science Methodology (IBM)
• Machine Learning (Kaggle)
• Data Analysis with Python (FreeCodeCamp)
• Big Data (IBM)
• Python Programming (Kaggle)
• Data Visualization (Kaggle)