Deekshya Bidyanta

Highly motivated and detail-oriented individual passionate about using data to improve business performance and customer experience. Skilled at leveraging data to develop actionable solutions to business challenges and utilizing data mining and data visualization to create meaningful insights. Excellent technical aptitude and knowledge of programming languages, data analytics and data visualization.

• UNDERSTADING OPINIONS TOWARDS E-COMMERCE COMPANY 2021, PYTHON:

Conducted sentiment analysis on a dataset of over 1,000 real-time customer reviews for an e commerce company's service. Developed machine learning sentiment classification algorithms using Multinomial Naïve Bayes and Multinomial Logistic Regression to classify reviews into three sentiment categories. Leveraged VADER's lexicon and AFINN approach for sentiment analysis to gain additional insights into customer sentiment. Employed NLP techniques for topic modeling to identify key customer concerns, revealing that 'product' was the most frequently occurring term, indicating a focus on product features and specifications. Network to accurately categorize queries into positive, negative, and neutral sentiments. Implemented Data Visualization to assess user satisfaction rates based on sentiments.

• NBA TEAM COMPOSITION 2022, SAS, TABLEAU, EXCEL:

Classified college basketball players by GBPM, Biometrics, Offensive and Defensive stats. Performed various linear regression models in SAS to find optimal players who can play for the NBA. Compared our model to real NBA player data to get an accuracy of 68%.

• PATIENT CLASSIFICATION 2022, R, RSTUDIO:

Visualized and analyzed real-world hospital patient data., Utilized PCA analysis to cluster patients effectively., Implemented diverse classification methods: Classification Tree, SVM, Neural Network, Random Forest, KNN, Naive BAYES, and LDA. Evaluated algorithm performance based on accuracy, sensitivity, and ROC curves. Concluded that simpler methods like Classification Tree can outperform others, achieving 81% accuracy.

• RETAIL MANAGEMENT SYSTEM 2023, PYTHON, SQL:

Designed and implemented a comprehensive Retail Management System (RMS) using Python programming language integrated with an SQL database for seamless management of retail store operations. Developed a user-friendly graphical user interface (GUI) to facilitate easy navigation and execution of key functions including sorting, filtering, and performing CRUD operations (Create, Read, Update, Delete) for managing books inventory. Implemented robust database management features using SQLite, ensuring efficient storage, retrieval, and manipulation of data related to store operations, employee management, product inventory, and billing processes. Streamlined various aspects of retail store management including inventory management, employee scheduling and tracking, product catalog management, and billing operations, thereby enhancing overall operational efficiency and productivity.

• ERP-SAP-Sales & Distribution Module Workshop Certification
• Google Data Analytics Professional Certification