I'm Morris Musee, a dedicated and innovative Data Scientist/Analyst passionate about transforming complex business challenges into actionable, data-driven solutions.

With a strong foundation in data analysis and machine learning, I thrive on uncovering insights that drive impactful decision-making.

Expertise: Skilled in leveraging Python, SQL, and PowerBI to perform in-depth data analysis, create compelling visualizations, and deliver actionable insights.

Current Focus: Actively advancing my expertise in designing, implementing, and deploying machine learning models to solve real-world business problems.

Passion: Committed to exploring datasets, identifying trends, and crafting solutions that generate tangible business value.

Collaboration: Enthusiastic about engaging in collaborative data science projects to enhance my skills and contribute to innovative initiatives.

Data Analysis & Visualization: Statisical analysis using Python (Pandas, NumPy, Matplotlib, Seaborn), PowerBI

Machine Learning: Scikit-learn, feature engineering, model development

Database Management: SQL

Other Interests: Exploratory data analysis, data preprocessing, and linear algebra for data science

Streamlining Operations: Python|Regression(LightGBM Regressor)|Classification(LightGBM Classifier)

This project streamlines the tedious and labor-intensive loan approval process by utilizing efficient ensemble machine learning models to evaluate customer profiles and determine loan approval or denial. For approved applicants, the system calculates an appropriate loan amount, optimizing efficiency and accuracy.

Targeted and Informed Marketing Python|Segmentation(KMeans Clustering)|Classification(RandomForest Classifier)

A comprehensive analysis of iFood(a leading food delivery app in Brazil) customer response data from past marketing campaigns was conducted to identify key factors influencing customer reactions, whether positive or negative. Segmentation and classification machine learning models were employed to cluster customers based on their profiles and predict their responses, enabling more targeted and effective future marketing campaigns.

Business Forecasting Prophet|Python

This project leverages Meta AI's Prophet, a robust forecasting tool, to analyze time series data and uncover trends, seasonal patterns, and holiday effects. By modeling historical data, Prophet identifies recurring patterns, such as weekly or yearly seasonality, and accounts for holiday-driven fluctuations, enabling accurate predictions.

• Machine learning models deployments to address complex business challenges.
• Exploring advanced techniques in data preprocessing, feature engineering, and visualization to uncover meaningful insights.
• Seeking opportunities to collaborate on impactful data science projects.

I’m always open to discussing data science, machine learning, or potential collaborations.

• Reach out to me at:Email: [email protected]

Let's connect and create data-driven solutions that make a difference!