Data Analytics undergraduate, currently seeking a 12 weeks internship from 19/01/26 to 17/04/26. Eager utilizing
data analysis and visualization skills, where expertise in statistical analysis and database management, al…
Data Analytics undergraduate, currently seeking a 12 weeks internship from 19/01/26 to 17/04/26. Eager utilizing
data analysis and visualization skills, where expertise in statistical analysis and database management, along with
strong analytical and communication skills, can be used to derive actionable insights and improve data-driven
decision-making
Assist in installation and troubleshooting of related system (CCTV/Access/Barrier Gate/Alarm System).
Perform in surveillance software programming and adjusting AI assistance technologies.
Assist in project management tasks, including progress monitoring, documentation, and reporting.
Perform other ad hoc tasks informed by the management or superior.
Assist in installation and troubleshooting of related system (CCTV/Access/Barrier Gate/Alarm System), Perform in surveillance software programming and adjusting AI assistance technologies, Assist in project management tasks, including progress monitoring, documentation, and reporting, Perform other ad hoc tasks informed by the management or superior
Assist in preparing and packaging TCM herbal medicines (e.g., decoctions, granules) under supervision.
Organize, stock, and monitor TCM herbs and products, storing them in the right way and restocking.
Learn to understand the core TCM ideas and typical herbs for adequate support.
Assist in preparing and packaging TCM herbal medicines (e.g., decoctions, granules) under supervision, Organize, stock, and monitor TCM herbs and products, storing them in the right way and restocking, Learn to understand the core TCM ideas and typical herbs for adequate support
This project is aimed at answering the core question of current e-commerce: forecasting if online consumers will finalize a purchase within their browsing sessions. With a rapidly growing virtual market, customer purchasing behavior intelligence and forecasting have gained significance to enhance marketing effectiveness, minimize cart abandonment, and maximize conversions. The project employs machine learning algorithms to examine behavioral trends using web session information with the goal of delivering actionable intelligence for real-time decision-making at e-commerce companies.The paper is an end-to-end machine learning study examining online buying behavior with the help of the UCI Online Shoppers Purchasing Intention Dataset containing 12,330 user sessions with diverse behavioral and temporal features. The paper employs and compares six different classification models programmed in Python, which are Logistic Regression, Decision Tree, Random Forest, Support Vector Classifier (SVC), XGBoost, and LightGBM. The data set has the challenge of severe class imbalance because purchases occur in just 15.5% of sessions, and the project remedies this through the application of Synthetic Minority Over-sampling Technique (SMOTE). The study utilizes 18 distinct features such as page visit lengths, bounce rates, exit rates, proximity to special days, visitor types, and other session-based metrics to develop predictive models capable of recognizing likely buyers.
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