Daniel Jaoui

Data Analyst skilled in Python, R, and SQL, with a background in actuarial sciences. Experienced in statistical analysis and machine learning to optimize risk assessment and financial planning.

Developed predictive models improving claims forecasting and reserve estimation. Focused about data-driven decision-making in insurance and finance.

• Jupyter Notebook

• Google Colab

• VS Code

Churn Modelling – Data Analysis Project (Python)

• Objective: Understand and identify key factors influencing customer churn using statistical and machine learning techniques.
• Key Contributions:
• Analyzed salary differences between churned and retained customers.
  Studied the distribution of account balance and credit scores across both customer segments (churn vs. non-churn).
  Applied statistical tests and bootstrap methods to assess the reliability and significance of observed differences.
  Provided actionable insights to support customer retention strategies.

Taylor Swift Songs analysis

• Collected and cleaned song lyrics data across all albums using

web scraping and public APIs.

• Visualized lyrical complexity, emotional shifts, and stylistic changes across the artist's career using Python (matplotlib, seaborn, wordcloud).
• Applied unsupervised machine learning (clustering algorithms such as K-Means) to group songs by musical style and popularity trends.
• Demonstrated potential use of data-driven segmentation for music marketing, audience targeting, and content recommendation strategies.