Junior Data Scientist & AI Engineer with a strong focus on data analysis, applied machine learning, and AI-driven solutions. Hands-on experience in data preparation, exploratory analysis, feature engineering, and buildin…
Junior Data Scientist & AI Engineer with a strong focus on data analysis, applied machine learning, and AI-driven solutions. Hands-on experience in data preparation, exploratory analysis, feature engineering, and building analytical dashboards through academic projects and professional internships. Strong Python background, analytical rigor, and ability to translate data into actionable insights within collaborative environments.
Built an analytical dashboard to visualize key fishery indicators for researchers and decision-makers.
Processed and consolidated data from multiple sources for analysis and reporting purposes.
Conducted requirements analysis and contributed to basic UI design aligned with user needs.
Implemented ETL workflows to ensure data consistency and usability.
Technologies: Power BI, SQL, Talend Open Studio, ETL, MongoDB, Express.js, Node.js, React.
Prepared and analyzed clinical data (patients, appointments, operational activity), Developed Python scripts for data cleaning, transformation, and exploratory data analysis, Designed basic KPIs and indicators to monitor operational performance, Collaborated with medical and administrative staff to translate needs into analytical outputs, Built simple analysis and reporting pipelines to support operational decision-making
Built an analytical dashboard to visualize key fishery indicators for researchers and decision-makers, Processed and consolidated data from multiple sources for analysis and reporting purposes, Conducted requirements analysis and contributed to basic UI design aligned with user needs, Implemented ETL workflows to ensure data consistency and usabilityTechnologies: Power BI, SQL, Talend Open Studio, ETL, MongoDB, Express.js, Node.js, React.
Designed an intelligent medical chatbot to assist patients with appointments and symptom management.
Implemented natural language processing model (NLP) components for intent understanding.
Integrated backend services to handle patient interactions and data storage.
Contributed to frontend interfaces to improve user interaction and accessibility.
Technologies: Python, Django, TensorFlow, React, MySQL.
Developed a basic employee management system.
Participated in database design and implementation of CRUD functionalities.
Technologies: SQL Server, .NET.
Designed an intelligent medical chatbot to assist patients with appointments and symptom management, Implemented natural language processing model (NLP) components for intent understanding, Integrated backend services to handle patient interactions and data storage, Contributed to frontend interfaces to improve user interaction and accessibility
Developed a basic employee management system, Participated in database design and implementation of CRUD functionalitiesTechnologies: SQL Server, .NET.
Prepared and analyzed clinical data (patients, appointments, operational activity) for data-driven use cases.
Developed Python scripts for data cleaning, transformation, and exploratory data analysis.
Designed basic KPIs and indicators to monitor operational performance.
Collaborated with medical and administrative staff to translate needs into analytical outputs.
Built simple analysis and reporting pipelines to support operational decision-making.
Technologies: Python, Pandas, NumPy, SQL, MySQL.
A hybrid movie recommender system (Content-Based + Collaborative Filtering) built using Python, Streamlit, Scikit-learn, and interactive visualizations. Ideal for learning data science workflows: EDA → Modeling → Deployment.
This is a Streamlit-based web application that uses face recognition to match your photo with a Hollywood celebrity.Upload your photo via the web interfaceMatches your face with a celebrity from the datasetUses facial embeddings generated via face_recognition and dlibSimple and interactive UI using Streamlit
Built a predictive dashboard for mortality risk and hospital stay duration for heart failure patients.Technologies: Python, Pandas, Scikit-learn, Streamlit, Plotly.
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