PROJECTS
Each project highlights my skills and expertise in handling complex data sets, deriving insights, and providing valuable recommendations. Feel free to explore and learn more about my work.
Credit Card Complaints
Tableau Project
Project Overview:
Developed a Credit Card Complaints Dashboard to analyze complaint trends, resolutions, and submission channels, offering actionable insights for customer service improvement.
Business Impact:
Improved resolution efficiency, achieving a 98.90% timely resolution rate and identifying key issues, contributing to enhanced customer satisfaction.
Data Integration:
Consolidated data from multiple channels with high accuracy, applying rigorous cleaning techniques to ensure data reliability and meaningful analysis.
Key Performance Metrics:
Created custom KPIs to track weekly complaint trends (current average: 310 per week), resolution outcomes (20.73% monetary relief), and top complaint categories (billing disputes, identity theft/fraud, account cancellations).
User-Centric Design:
Designed an interactive and intuitive dashboard with a heat map visualization for geographic trends and daily/weekly patterns, optimizing usability and facilitating stakeholder decision-making.
US HealthCare Dynamics
Tableau Project
Project Overview:
Developed the "U.S. Healthcare Dynamics" project, a dashboard providing an in-depth analysis of trends in the U.S. healthcare system during 2019-2020, focusing on hospital performance, patient demographics, and payer-provider dynamics.
Business Impact:
Enabled healthcare stakeholders to identify strategic opportunities for operational and financial improvements, driving better patient outcomes and system efficiency.
Data Integration:
Integrated patient-centric data, institutional metrics, and financial records into a unified dashboard. Applied rigorous data cleaning and preprocessing to ensure high-quality analysis.
Key Performance Metrics:
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Monthly Healthcare Expenses: Identified trends and anomalies in expense patterns.
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Treatment Outcomes: Evaluated hospital performance with an average improvement rate of 12% in key outcomes.
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Payer-Provider Dynamics: Analyzed collaboration trends, uncovering opportunities for cost-saving initiatives.
Technology-Centric Design:
Developed the dashboard using Tableau and Python, incorporating interactive visualizations for patient demographics, expense trends, and performance metrics. The tool provided an intuitive interface for policymakers and decision-makers to derive actionable insights.
Strategic Recommendations:
Proposed patient-centric strategies and financial collaborations to improve healthcare system efficiency, effectiveness, and compassion, aligning with policy objectives and stakeholder goals.
Breast Cancer Classification
Python - ML Project
Project Overview:
Developed a Breast Cancer Prediction model to classify cases as benign or malignant, leveraging machine learning to support early diagnosis and improve patient outcomes.
Business Impact:
Enhanced diagnostic accuracy, contributing to earlier detection and improved treatment planning, with the potential to reduce mortality rates associated with breast cancer.
Data Integration:
Processed historical biopsy data, incorporating features such as tumor size, cell shape, and mitotic count. Applied rigorous data preprocessing techniques, including handling missing values, feature scaling, and categorical encoding, to ensure data quality and reliability.
Key Performance Metrics:
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Accuracy: 97.5%
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Precision: 95.8%
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Recall: 96.2%
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F1 Score: 96.0%
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Reduced misclassification rates, improving the model’s reliability in clinical settings.
Technology-Centric Design:
Utilized Python and libraries such as scikit-learn, TensorFlow, and Pandas to train and evaluate multiple machine learning algorithms, including Support Vector Machines (SVM), Random Forest, and Neural Networks. The final model delivered high performance and interpretability, empowering healthcare professionals with precise and timely diagnostic insights.
Walmart Superstore Retail Analysis
MS Excel
Project Overview:
Developed the Walmart Superstore Retail Performance Analyzer, an Excel-based dashboard that provides actionable insights into sales, profitability, and growth for data-driven decision-making.
Business Impact:
Enabled the optimization of inventory and marketing strategies by identifying trends and top-performing categories, contributing to increased profitability and improved resource allocation.
Data Integration:
Analyzed multi-dimensional retail data across customer segments, product categories, and regions. Ensured data accuracy and consistency through rigorous cleaning and preprocessing.
Key Performance Metrics:
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Total Sales: Highlighted an annual growth rate of 8.5%.
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Profit Margins: Identified variations across regions, with top-performing areas exceeding 22% margin.
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Category Analysis: Determined top-performing categories contributing to 65% of total revenue.
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Geographic Insights: Visualized sales distribution and market share for targeted expansion planning.
Technology-Centric Design:
Utilized Excel for data analysis and visualization, incorporating pivot tables, charts, and conditional formatting to create an intuitive and interactive dashboard.
Strategic Recommendations:
Proposed targeted inventory adjustments and regional marketing campaigns based on performance trends and market share insights, driving strategic growth and operational efficiency.
Car Feature Segmentation Analysis
Python Project
Project Overview:
Developed regression and classification models to predict car prices and classify vehicles by price range, enabling strategic market positioning and targeted marketing efforts.
Business Impact:
Achieved an 87% prediction accuracy for car prices, supporting sales teams in refining market strategies and improving customer targeting.
Data Integration:
Processed and analyzed vehicle features, such as engine size, mileage, and brand, using Python and scikit-learn. Applied robust feature engineering to enhance data quality and model precision.
Key Performance Metrics:
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Prediction Accuracy: 87% for price predictions, ensuring reliable market insights.
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Price Range Classification: Improved accuracy by 22%, enabling precise targeting in marketing campaigns.
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Model Precision: Enhanced by 18% through advanced feature selection techniques.
Technology-Centric Design:
Leveraged Python and libraries like scikit-learn and Matplotlib for model development, evaluation, and visualization. Built regression models to estimate prices and classification models to categorize vehicles by price range.
Insight Delivery:
Visualized key factors influencing car prices, such as mileage, brand, and engine type, providing actionable insights to sales and marketing teams for strategic decision-making.
Retail Vendor Analysis
MS Excel - Statical Analysis - Tableau
Project Overview:
Conducted a comprehensive Retail Vendor Analysis for a retail organization to evaluate vendor performance and support strategic decision-making.
Business Impact:
Identified a 34% higher quality rating for the top-performing vendor, enabling improved vendor selection and optimizing inventory management, ultimately enhancing profitability.
Data Integration:
Analyzed a multi-year dataset of vendor metrics, employing data cleaning and transformation techniques in Excel to ensure consistency and accuracy.
Key Performance Metrics:
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Delivery Accuracy: Benchmarked vendor performance with an average accuracy rate of 92.4%.
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Quality Ratings: Highlighted top vendor performance with a 34% higher rating compared to the average.
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Timeliness: Measured and compared delivery timeliness across vendors, revealing trends in supply chain efficiency.
Analytical Approach:
Applied statistical methods, including t-tests and ANOVA, to identify significant differences in vendor performance. Designed actionable Tableau visualizations to compare vendors across key metrics, driving data-driven insights for management.
Strategic Recommendations:
Proposed targeted strategies for vendor selection and relationship management, fostering improved supply chain efficiency and reducing operational risks.