Trained students in complex problem-solving and algorithmic logic, including the use of Python
Designed interactive teaching materials

Extracted, cleaned, and analysed data from multiple sources (APIs, web scraping)
Built statistical models and explored data to identify correlations, anomalies, and trends
Designed interactive dashboards for data visualisation and reporting
Communicated insights through data storytelling to support business decision-making

Managed commercial activity using performance indicators (revenue, margin, customer satisfaction)
Analysed client portfolios to identify growth opportunities and service optimisation
Utilised office and reporting tools to monitor results and support decision-making

Provided one-to-one tutoring in logic, quantitative reasoning, and problem-solving
Developed teaching materials based on progress assessment

Analysed client needs based on behavioural and financial data
Monitored commercial targets using dashboards and reporting tools

Cleaned and analysed a database of debts
Identified statistical trends
Formulated recommendations based on quantitative results

This study explores the key success factors behind crowdfunding campaigns on the Kickstarter platform and provides actionable recommendations for investors.
Using a rich dataset covering hundreds of thousands of campaigns, the analysis combines exploratory methods, predictive modelling (XGBoost), and clustering segmentation.
The findings highlight strategies for investors such as favouring projects with engaged communities, setting realistic funding goals, focusing on historically successful categories, and using early traction indicators to refine project selection.
The research also opens avenues for further work, including semantic analysis of project descriptions and cross-platform comparisons in crowdfunding dynamics.