Kundai Nelly Semu

• Supported staff members in their daily tasks, reducing workload burden and allowing for increased focus on higher-priority assignments.
• Gained valuable experience working within a specific industry, applying learned concepts directly into relevant work situations.
• Analyzed problems and worked with teams to develop solutions.
• Contributed to a positive team environment by collaborating with fellow interns on group projects and presentations.
• Sorted and organized files, spreadsheets, and reports.
• Gained hands-on experience in various software programs, increasing proficiency and expanding technical skill set.
• Explored new technologies and approaches to streamline processes.

• Managed and monitored MySQL/PostgreSQL databases supporting client web applications to ensure high availability and optimal performance.
• Performed routine database maintenance tasks including backups, restorations, indexing, and performance tuning.
• Optimized SQL queries and improved database efficiency by analyzing execution plans and implementing indexing strategies.
• Assisted development teams in database schema design, normalization, and deployment of database updates to production environments.
• Implemented user access controls and role-based permissions to maintain data security and integrity.
  Supported cloud-based database environments (e.g., AWS RDS) including configuration and monitoring.
• Troubleshot and resolved database-related issues, minimizing downtime and ensuring seamless application functionality.
• Maintained technical documentation for database architecture, backup procedures, and security protocols.
• Contributed to data migration, testing, and version control processes during client project deployments.

• Collected, cleaned, and analyzed structured and unstructured data to support client digital and web-based projects.
• Developed interactive dashboards and reports using Power BI / Tableau to track website performance, user engagement, and campaign metrics.
• Wrote complex SQL queries to extract, transform, and validate data from relational databases.
• Performed exploratory data analysis (EDA) to identify trends, patterns, and actionable insights for business decision-making.
• Supported SEO and digital marketing analytics by analyzing traffic, conversion rates, and customer behavior data.
• Automated routine data reporting processes using Python (Pandas, NumPy) to improve efficiency and reduce manual effort.
• Collaborated with developers and project managers to translate business requirements into data-driven solutions.
• Ensured data accuracy and integrity through validation, data quality checks, and documentation of processes.
• Assisted in A/B testing analysis and performance measurement for client web applications.

• Facilitated stakeholder communication, providing updates, scheduling project meetings and maintaining strong client relations.
• Oversaw end-to-end website development, ensuring key milestone delivery, gathering client requirements, and translating client needs into development tasks.
• Contributed to UI/UX design and web development, applying design principles and advanced coding skills to support overall web development

• Designed, deployed, and maintained Windows and Linux server infrastructure to support internal operations and multiple client environments.
• Installed, configured, and managed network devices, firewalls, and virtualisation platforms to ensure high system availability and performance.
• Monitored system performance and proactively resolved hardware, software, and network issues, reducing downtime and improving service reliability.
• Implemented backup, disaster recovery, and data protection solutions to safeguard critical business information and ensure business continuity.
• Applied system updates, security patches, and hardening measures to maintain compliance with security best practices and minimise vulnerabilities.
• Provided second- and third-level technical support to clients, troubleshooting complex infrastructure and application issues.
• Automated routine administrative tasks using scripting tools (e.g., PowerShell) to improve operational efficiency and reduce manual workload.
• Produced and maintained detailed technical documentation, including system configurations, procedures, and incident reports.

Predictive Model for assessing Loan Default risk

• Dataset Used: Credit_risk_dataset.csv(Tse, 2020)
• Goal: Developed a machine learning model for accurately predict the probability of loan defaults for financial institution clients, aiming to enhance risk managemt and decision-making.
• Technologies: Python, Pandas, Numpy, Scikit-learn, Matplotlib, Seaborn, Decision Tree Classifier
• Results: Preprocessed data, engineered features, and used SMOTE. Achieved 90% accuracy (Decision Tree). Noted high precision, but low recall (57%) on defaults for future focus.

Employee Attrition Analysis

• Dataset Used: WA_Fn_UseC_-HR-Employee_Attrition
• Technologies Used : Python, Pandas, Seaborn, Matplotlib, Scikit-Learn Goal/Problem: To analyze and predict employee attrition, identify key factors contributing to turnover, and provide actionable insights for HR decision-making.
• Action: Cleaned and preprocessed the HR dataset, mapped categorical variables, calculated attrition metrics, performed exploratory data analysis with visualizations, created derived features, encoded data for machine learning, built and evaluated a Decision Tree model to predict attrition, identified key factors influencing turnover, and calculated attrition probabilities for individual employees.
• Key Insights: Overall attrition was highest in Sales (20.63%) and Human Resources (19.05%), with certain roles like Sales Representatives (39.76%) and Laboratory Technicians (23.94%) most affected Attrition was inversely related to income and Tenure, with low-income employees (29.27%) and those with 0 - 2 years at the company (28.86%) most likely to leave. Top factors influencing attrition included Monthly Income, Overtime, Daily Rate, Marital Status, Distance From Home, and Job Satisfaction . Decision Tree model achieved 77% overall accuracy , highlighting high-risk employees and providing actionable insights for retention strategies.