Pramiti Sreesha

Results-driven Master's in Business Analytics professional with a strong academic foundation in Statistics, Data Management, and Human Resource Analytics. Proficient in SQL, Tableau, KNIME, Power BI, and Python, demonstrating a solid aptitude for data mining and data-driven decision-making. Recognized for innovative problem-solving skills through successful application of analytical techniques in academic projects and competitive environments such as 'Datathons' and 'Hackathons.' Notable achievements include a featured published patent, underscoring a commitment to innovation and applied research, with a keen desire to leverage analytical expertise to address complex business challenges and enhance strategic decision-making in fast-paced settings.

Led and implemented a project 'Outdoor Tour Guiding System,' which resulted in a patent publication from the Intellectual Property Rights (IPR) Government of India (2023), A comprehensive data collection survey was conducted, allowing for the analysis of more than 1,000 data points. The insights obtained were crucial in enhancing government initiatives aimed at promoting the development of Micro, Small, and Medium Enterprises (MSMEs) across various regional markets (2023)

Housing Price Prediction – Statistical Analysis

• Applied regression models on Austin housing dataset to identify key pricing factors and support property valuation.

Technologies: R, ggplot2

Term Deposit Subscription Analysis – Marketing Campaign Insights

• Analysed customer behaviour from a real-world banking dataset to identify key factors influencing subscription decisions.

• Applied regression modelling, feature engineering, and bivariate analysis to derive insights.
Technologies: Python, SQL | Concepts: Feature Engineering, Cluster Analysis, LDA, Regression

Airline Customer Recommendation Prediction

• Built hybrid ML models using text and numeric features to predict customer satisfaction and recommendation likelihood.

Technologies: Python, Machine Learning

Customer Segmentation using RFM & Cluster Analysis

• Segmented customers of a UK-based online retailer using RFM scoring and clustering techniques to identify behaviour patterns.

• Applied Linear Discriminant Analysis (LDA) to validate cluster distinctiveness and build customer profiles.

Technologies: Python, R, LDA (Linear Discriminant Analysis)

QUB Datathon 2024 – Tackling Deprivation & Sustainability in NI

• Selected as Top 9 out of 30 teams. Analysed inequality across health, education, income, crime, and environment in Northern Ireland.

• Proposed actionable policy recommendations using advanced analytics for sustainability and equity.

Technologies: Python, Alteryx