CELIN BIJU

Motivated Associate Information Analyst and MSc Data Science graduate with a strong analytical and problem‑solving mindset, experienced in data analysis, reporting and stakeholder communication across airline, healthcare, finance and logistics environments. Adept at transforming business ideas into clear data requirements, user stories and practical reporting solutions using SQL, Excel and Power BI. Proven track record of maintaining data quality, supporting data governance and collaborating with cross‑functional teams to deliver accurate, insight‑driven outputs that support better decisions.

• CMI Level 7 Certificate in Strategic Management and Leadership Practice – Coventry University / Chartered Management Institute, 2024.
• PCAP – Python Certification Course, KodeKloud, 2024.
• Python Basics, KodeKloud, 2024.
• Data Analyst Career Programme, ITonlinelearning Academy, UK, 2024–2025.

• Right to Work: I have full right to work in the UK and can provide documentation upon request.
• Commuting: Fully able and willing to commute to the Vitality Bournemouth office at least 2 days per week as required

Airline Passenger Satisfaction Prediction

• Analysed 100,000+ airline survey records to model passenger satisfaction using GBT, Random Forest and Logistic Regression, achieving strong predictive performance on held‑out data.
• Implemented data cleaning, feature engineering and model training in PySpark, identifying key satisfaction drivers such as service quality, seat comfort and punctuality.
• Delivered recommendations that could help an airline reduce dissatisfaction complaints by focusing on the top 3 identified service attributes.

Stock Market Price Prediction Using Deep Learning

• Developed deep learning models (CNNs, LSTMs, RNNs) on National Stock Exchange data to forecast short‑term price movements.
• Evaluated models using RMSE and MAE and optimised hyperparameters to improve forecasting accuracy, supporting better‑informed investment decisions and risk management scenarios.

Weather Forecast Analysis Using Machine Learning

• Integrated historical and real‑time meteorological data, implemented models including LSTM, ANN, Gradient Boosting and Random Forest and improved short‑term forecast accuracy.
• Produced timely forecast outputs that could enhance decision‑making for agriculture, disaster management and urban planning use cases.

Cardiovascular Disease & Lung Cancer Prediction (Big Data & ML)

• Led data management and governance activities to improve the quality and integrity of heart failure and lung cancer prediction datasets while ensuring alignment with UK GDPR.
• Conducted feature engineering, maintained data inventories and applied predictive models that supported early‑detection use cases and better clinical decision‑making simulations.