NANCY SHARMA

Self-starter and detail-focused Business Insights and Analytics post-graduate student at Humber college with more than five years of hands-on expertise in data analytics. Skilled in utilizing SQL, R, Microsoft Excel, Pythonand Power BI to develop data-driven actionable insights and proficient in presenting recommendations to stakeholders in an impactful way.

• Salary Prediction: Executed a Salary Prediction project using Polynomial Regression in Python, specifically, leveraging Scikit-Learn. Implemented a meticulous leveraging train-test spilt to ensure robust model training and testing. Additionally, incorporated data visualization techniques to showcase model accuracy through informative graphs, emphasizing the practical application of machine learning in salary forecasting.
• Customer Segmentation for Targeted Marketing: Executed a Customer Segmentation project employing K-Means Clustering to categorize customers based on age and salary. Applied the Elbow Method to determine the optimal number of clusters, ensuring accurate and meaningful segmentation. Operated on unsupervised data, showcasing proficiency in unsupervised machine learning techniques. Visualized the clustered data, providing valuable insights into customers demographics, preferences, and aiding strategic decision.
• Utilized SQL to analyze an extensive music-related dataset and developed innovative data-driven insights like identification of top customers by country, analysis of sales data by artist, ranking customers by spending, determining top-selling artists, and understanding customer spending patterns. These actionable insights empowered the business to make informed decisions, optimize marketing strategies, tailor promotions, and enhance overall customer satisfaction, contributing to improved business performance and profitability.
• Displayed proficiency in ERD and SQL by leading a comprehensive data analytics project to address declining customer retention at a fictional company. Developed descriptive, diagnostic, predictive, and prescriptive analytics questions, designed an ERD diagram, performed data manipulation, and implemented BI solutions to provide actionable insights for improving customer retention and driving business growth, resulting in a final grade of 92%.
• Spearheaded a team of six members to discover insights and trends; presented insightful recommendations to improve the sustainability of the business model of a Canadian retail store by conducting strategic analysis and applying a range of data analytic frameworks.
• Sentiment Classification Model: Build a sentiment classification model using Naïve Bayes and other natural language processing techniques to analyze customer reviews from restaurant surveys and predict their sentiment as positive, or negative. This project aimed to automate the sentiment analysis process, enabling restaurants to gain valuable insights from customer feedback more efficiently.