Real-Estate Workforce HR Dashboard
Built HR dashboard visualising headcount, gender mix and seniority. Revealed payroll and staffing trends, cutting project turnaround to 61 hours with 97.45% task completion.
          Data Scientist, Data analyst
I'm a data scientist specialised in transforming unstructured data into useful information that inturn will increase organisations revenue. I handle the full process from defining problems and preparing data to developing models and delivering business recommendations. I combine technical expertise with business understanding to create practical data solutions. I work with Excel, Statistics, Power BI, Python and SQL to clean, analyse and visualise data to uncovering trends and building machine learning models that align with business goals.
I value clear communication and translate intricate analysis into dashboards, reports and presentations that support decision-making. I approach work with an eye of reason, commitment to quality and focus on business problem solving. I value collaboration in team environments and maintain a strong commitment to continuous learning.
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              Handling missing values, outliers, encoding, normalisation and ensuring data quality for trustworthy analysis in order to clean and prepare raw data.
              Utilising Matplotlib, Seaborn, Plotly, Power BI and Excel to create interactive dashboards and lucid charts that facilitate decision-making and reveal insights.
              Deep Learning and Machine Learning tools for constructing and training ML and DL neural networks I design systems that can mimic human intelligence.
              Enhancing model performance by creating meaningful variables from existing data through transformations, aggregations and domain-specific computations.
              Composing concise reports and making presentations that inform stakeholders, both technical and non-technical of findings and suggestions.
              Delivering continuous support for urgent data requirements across time zones, dashboard maintenance, model updates and data bug issues.
Mount Kenya Ewaso Water Partnership
Conducted a rural water access project, gathering data on household water use, interviewing community members and promoting sustainable usage and resilience in water-scarce areas.
African Leadership Experience
Developed practical skills in statistical analysis, machine learning and data visualisation using Python, SQL, Excel and Power Bi that have enhanced my analytical and problem-solving abilities for practical projects.
Excel
80%SQL
90%PowerBI
80%Python
85%Python
90%NLP
80%Machine Learning
85%Deep Learning
85%
                
                Built HR dashboard visualising headcount, gender mix and seniority. Revealed payroll and staffing trends, cutting project turnaround to 61 hours with 97.45% task completion.
                
                Developed LSTM model for GOOG stock prediction using 20-year data, technical indicators and volatility analysis. Deployed in Streamlit dashboard for accurate forecasts.
                
                Analysed 20k+ tracks to identify audio features driving popularity. Found energy, danceability and loudness key for viral success. Compared Spotify/YouTube to guide release strategy.
                
                Analysed 2,000+ US retail transactions to optimise sales strategy. Electronics drove revenue, Beauty gave top margins and seasonal peaks plus high-value customer segments informed targeted campaigns
                
                Analysed 100k+ Olist orders to boost sales and logistics. Found regional growth, flagged delivery delays and segmented customers for targeted retention campaigns.
                
                Used K-means clustering to analyse e-commerce customers by spending, returns and behavior. Identified high-value customers to improve targeted marketing and boost retention and enhancing overall sales.
                
                Processed data on 16k+ players to identify performance patterns. Built similarity algorithms using K-Means and hierarchical clustering to support scouting and team selection.
                
                Built a dashboard to track driver income, trips, expenses, cargo types and compliance. Enabling managers to optimise routes and schedules through monthly cost and performance insights.
                
                Built dashboard tracking service KPIs across banking products, raising SLA compliance to 94% and enabling teams to prioritise issues through dynamic trend analytics.
                
                Developed a collaborative filtering recommendation system that predicts user preferences by evaluating user ratings. Implemented as a customised recommendation web application with Streamlit.
                
                Analysed 5,000 Hilton London reviews with NLP and machine learning, built Logistic Regression, Random Forest, XGBoost and LSTM models, revealed 72% positive sentiment and flagged service and cleanliness issues.
                
                Developed a machine learning model that can forecast telecom customer attrition, allowing for proactive retention tactics to lower attrition and boost income.
                
                Developed a model predicting Indian flight ticket prices from historical data. Deployed a Streamlit app enabling users to estimate costs for different travel scenarios.
                
                Developed a dashboard that shows 562K+ SAR payouts for 560 requests broken down by service type, region and month. Riyadh's 23% share was flagged and daily requests were reduced to 4.38.
                
                Conducted RFM and market basket analysis on 500K+ transactions to segment customers and recommend products. Identified segments driving 80% of revenue and uncovered product affinities for cross-selling.
            October 30, 2025
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