Welcome to Sunehra-Tazreen-Data-Analytics-Portfolio

           Through these projects, I highlight my passion for turning complex data into clear stories that support smarter business strategies.

Projects

Excel: Sales Performance and Profitability Analysis

 

Tools: Excel, PivotTables, VBA
Dataset: Retail sales data (2013–2016)

Objective: Analyze regional sales performance, profit margins, and automate repetitive reporting using Excel VBA

Tableau: Covid-19 Global Analysis

 

Tools: Tableau
Dataset: Johns Hopkins & WHO COVID-19 data (2020–2021)

Objective: Visualize and analyze the spread of COVID-19 globally, focusing on time-series trends and country comparisons.

RStudio: Production Optimization & Simulation

Tools: R, lpSolve, ggplot2
Dataset: Manufacturing resource allocation & profitability data

Objective: Use prescriptive analytics to optimize production planning and evaluate investment opportunities.

JMP: Consumer Preferences Analysis

 

Tools: JMP
Dataset: Consumer Preferences.xlsx

Objective: Understand consumer dental-hygiene behavior (brushing, flossing) and related demographics using JMP

SAS Viya: Customer Segmentation & Loan Prediction

Tools: SAS Viya (Clustering, Logistic Regression, Decision Tree)
Dataset: Banking customer demographic & transaction data

Objective: Segment banking customers into distinct groups and build models to predict loan approval likelihood

PulseSync Digital Marketing Campaign


Tools: Excel, Mailchimp, Powerpoint
Dataset: Marketing Campaign Data (Self-made)

Objective: End-to-end campaign strategy simulation with data analysis deliverables

 

Predictive Analytics with Python

Real-World Applications in Retail, Housing, and Healthcare

  • Programming Language: Python

  • Environment: Jupyter Notebook

  • Libraries: Pandas, NumPy, scikit-learn, Matplotlib, Seaborn

  • Techniques: Data cleaning & transformation, feature engineering, one-hot encoding, regression modeling, classification (Logistic Regression, kNN), cross-validation, model evaluation (RMSE, R², confusion matrix, recall, accuracy)

MySQL: Cineplex
Ticketing Database

Tools: MySQL, Microsoft SQL Server, ERDplus

Skills: Database Design, ER Modeling, Relational Schema, Data Integrity Control, SQL Queries (JOIN, Aggregation, Filtering), Data Normalization, Transaction Management

Objective: Designed and implemented a relational database system to manage Cineplex’s customer sign-up, movie ticket booking, transactions, and payment processes. Built tables with integrity constraints, developed E-R models and relational schemas, and wrote SQL queries for customer insights, movie preferences, and transaction tracking.

BIG DATA: Medical Insurance Fraud Detection

Tools: Python, Jupyter Notebook

Skills: Data Cleaning, Feature Engineering, Machine Learning (Classification), Model Evaluation

Objective: Medical Insurance Fraud Detection within the U.S. healthcare system, where fraud accounts for nearly 10% of Medicare spending. 

Includes- Logistic Regression, Gaussian Naïve Bayes, Random Forest, Extra Trees, and Gradient Boosting. The Random Forest Classifier achieved the best performance with 94% accuracy, 72% AUC, though recall remained  at 7%.

I hope you enjoyed exploring my projects!
I’m continuously working to improve them and add new ones.
If you have suggestions to help me grow, or if you’d like to connect about opportunities, please don’t hesitate to reach out!

                                                                                                   

                                                                                                LinkedIn: https://www.linkedin.com/in/sunehra-t/ 

                                                                                                                   Email: sunehratazreen@gmail.com

Contact Me

Always happy to connect over opportunities!