Welcome to my Python Projects Portfolio! This repository showcases my proficiency in Python programming through a diverse collection of data analysis, visualization, and analytics projects. Each project demonstrates my ability to work with real-world datasets, extract meaningful insights, and present findings using industry-standard Python libraries and techniques.
As a Business Analytics graduate student at Roosevelt University, these projects reflect my hands-on experience in data manipulation, exploratory data analysis (EDA), statistical analysis, and data visualization—skills essential for Business Intelligence and Data Analyst roles.
- Comprehensive Python Projects covering various analytical techniques
- Jupyter Notebook Format for interactive, reproducible analysis
- Real-World Applications in market analysis, customer behavior, and data cleaning
- Professional Code Quality with clear documentation and structured approaches
- Business-Focused Insights demonstrating value-driven analysis
Files: Conversion_Funnel.ipynb, Conversion_Funnel_Analysis2.ipynb
Description:
Analyzed customer conversion funnels to identify drop-off points and optimize conversion rates. This project examines user behavior through various stages of the customer journey, from initial engagement to final conversion.
Key Learnings:
- Understanding customer journey stages and conversion metrics
- Identifying bottlenecks in conversion processes
- Calculating conversion rates and funnel performance metrics
- Data visualization techniques for funnel analysis
- Strategic recommendations based on funnel insights
Technologies: Python, Pandas, Matplotlib, Seaborn, NumPy
File: Data_Cleaning_Project.ipynb
Description:
Comprehensive data cleaning and preprocessing project demonstrating essential data quality techniques. This project addresses common data issues including missing values, duplicates, inconsistencies, and outliers.
Key Learnings:
- Identifying and handling missing data using various imputation techniques
- Detecting and removing duplicate records
- Standardizing data formats and fixing inconsistencies
- Outlier detection and treatment strategies
- Data type conversions and validation
- Creating clean, analysis-ready datasets
Technologies: Python, Pandas, NumPy
Files: Data_Visualization_Project.ipynb, Data_Visualization_Project2.ipynb
Description:
Data visualization projects showcasing various chart types and visualization techniques to communicate insights effectively. These projects demonstrate best practices in visual storytelling and data presentation.
Key Learnings:
- Creating impactful visualizations (bar charts, line graphs, scatter plots, heatmaps)
- Selecting appropriate chart types for different data scenarios
- Customizing plots for professional presentation
- Using color theory and design principles in data visualization
- Building comprehensive dashboards with multiple visualizations
- Communicating data insights through visual narratives
Technologies: Python, Matplotlib, Seaborn, Pandas, Plotly
Files: Demand vs Supply analysis.ipynb, Demand&Supply_Analysis.ipynb
Description:
Market analysis projects examining the relationship between demand and supply dynamics. These analyses provide insights into market equilibrium, pricing strategies, and resource allocation.
Key Learnings:
- Understanding supply and demand relationships
- Analyzing market trends and patterns
- Identifying supply-demand gaps and opportunities
- Forecasting and predictive analysis techniques
- Creating actionable business recommendations from market data
Technologies: Python, Pandas, Matplotlib, NumPy, Statistical Analysis
File: EDA Using Python.ipynb
Description:
Comprehensive exploratory data analysis project demonstrating systematic approaches to understanding datasets. This project showcases statistical analysis, pattern recognition, and hypothesis generation techniques.
Key Learnings:
- Conducting thorough statistical summaries and descriptive statistics
- Identifying data distributions, patterns, and relationships
- Detecting correlations and multicollinearity
- Creating comprehensive EDA reports
- Generating data-driven hypotheses for further analysis
- Using advanced Pandas techniques for data exploration
Technologies: Python, Pandas, NumPy, Matplotlib, Seaborn, SciPy
File: GoogleSearch_Analysis.ipynb
Description:
Analysis of Google search trends and patterns to understand user search behavior, trending topics, and search volume patterns. This project demonstrates web analytics and trend analysis capabilities.
Key Learnings:
- Analyzing search trends over time
- Identifying seasonal patterns in search behavior
- Understanding user intent through search queries
- Time series analysis for trend forecasting
- Deriving actionable insights from search data
Technologies: Python, Pandas, Matplotlib, Time Series Analysis
Files: Market_Basket_Analysis.ipynb, Market_Basket_Analysis1.ipynb
Description:
Market basket analysis projects using association rule mining to discover purchasing patterns and product relationships. These analyses help identify cross-selling opportunities and optimize product placement strategies.
Key Learnings:
- Implementing association rule mining algorithms (Apriori, FP-Growth)
- Calculating support, confidence, and lift metrics
- Identifying frequently purchased product combinations
- Creating product recommendation strategies
- Optimizing product bundling and cross-selling opportunities
- Visualizing association rules and relationships
Technologies: Python, Pandas, mlxtend, NetworkX, Matplotlib
File: ReviewsAnalysis.ipynb
Description:
Sentiment analysis and text analytics project examining customer reviews to extract insights about product performance, customer satisfaction, and improvement opportunities.
Key Learnings:
- Text preprocessing and cleaning techniques
- Sentiment classification and polarity analysis
- Extracting key themes and topics from reviews
- Identifying common pain points and positive attributes
- Creating word clouds and frequency distributions
- Generating actionable insights from customer feedback
Technologies: Python, Pandas, Natural Language Processing (NLP), TextBlob/NLTK, Matplotlib, WordCloud
File: SupplyRatio vs Driver.ipynb
Description:
Logistics and operations analysis examining the relationship between supply capacity ratios and driver availability. This project provides insights for supply chain optimization and resource planning.
Key Learnings:
- Analyzing operational efficiency metrics
- Understanding supply chain capacity constraints
- Identifying resource allocation optimization opportunities
- Creating predictive models for resource planning
- Developing data-driven recommendations for logistics improvement
Technologies: Python, Pandas, Matplotlib, Statistical Analysis, NumPy
File: Financial_Variance_Analysis.ipynb
Description: Comprehensive ETL (Extract, Transform, Load) pipeline for financial variance analysis used in corporate finance and budget management. This project demonstrates automated budget performance monitoring by comparing actual spending against budgeted amounts across departments and time periods, identifying spending anomalies and generating executive-ready reports.
Key Learnings:
- Building end-to-end ETL pipelines for financial data
- Calculating variance metrics (absolute dollar difference and percentage deviation)
- Creating automated flagging systems for high-variance departments
- Implementing business rules and thresholds for financial monitoring
- Generating department-level budget performance dashboards
- Monthly variance trend analysis and visualization
- Data transformation techniques for financial reporting
- Handling division-by-zero scenarios in financial calculations
Technologies: Python, Pandas, NumPy, Matplotlib, Seaborn, ETL Pipeline Development
File: Webscraping Using Beautiful Soup.ipynb
Description: Systematic web scraping project demonstrating data extraction from SEO tutorial websites for content analysis. This project showcases data engineering skills including HTTP requests, HTML parsing, data transformation, and Natural Language Processing (NLP) for keyword extraction. The scraped data is structured into analyzable datasets for competitive content analysis and SEO research.
Key Learnings:
- Sending HTTP requests and retrieving HTML content from web servers
- Parsing HTML structure using BeautifulSoup for data extraction
- Extracting structured data (links, titles, URLs) from web pages
- Data transformation from raw HTML to pandas DataFrames
- Text preprocessing and cleaning techniques for NLP
- Implementing stopword removal and tokenization
- Keyword frequency analysis and trend identification
- Exporting scraped data to CSV for reusability
- Understanding ethical web scraping practices and rate limiting
- Building production-ready scraping frameworks with error handling
Technologies: Python, Requests, BeautifulSoup, Pandas, Natural Language Processing (NLP), Text Analytics
- Python: Core programming, data structures, functions, and object-oriented programming
- Pandas: Data manipulation, aggregation, merging, and transformation
- NumPy: Numerical computing, array operations, and statistical calculations
- Matplotlib & Seaborn: Data visualization, customization, and styling
- Plotly: Interactive visualizations and dashboards
- Jupyter Notebooks: Interactive development and documentation
- Exploratory Data Analysis (EDA): Statistical summaries, distributions, correlations
- Data Cleaning: Handling missing values, duplicates, outliers, and inconsistencies
- Statistical Analysis: Descriptive statistics, hypothesis testing, probability
- Time Series Analysis: Trend analysis, seasonality, forecasting
- Association Rule Mining: Market basket analysis, recommendation systems
- Text Analytics: Sentiment analysis, NLP, text preprocessing
- Customer Behavior Analysis: Conversion funnels, purchase patterns, segmentation
- Market Analysis: Demand-supply dynamics, trend analysis, competitive insights
- Operational Analytics: Resource optimization, efficiency metrics, logistics
- Product Analytics: Reviews analysis, market basket analysis, recommendations
- Performance Metrics: KPIs, conversion rates, operational efficiency indicators
- Chart Types: Bar charts, line graphs, scatter plots, heatmaps, histograms, box plots
- Advanced Visualizations: Correlation matrices, pair plots, distribution plots, network graphs
- Dashboard Creation: Multi-chart layouts, interactive elements, professional styling
- Storytelling: Creating narrative-driven visualizations that communicate insights effectively
These Python projects demonstrate my ability to:
✅ Transform Raw Data into Actionable Insights: Clean, analyze, and interpret complex datasets to drive business decisions
✅ Identify Business Opportunities: Discover patterns, trends, and relationships that reveal growth opportunities
✅ Optimize Processes: Analyze operational data to improve efficiency and resource allocation
✅ Understand Customer Behavior: Leverage analytics to understand customer journeys, preferences, and pain points
✅ Communicate Effectively: Present technical findings in clear, visual formats accessible to stakeholders
✅ Drive Data-Driven Decision Making: Provide evidence-based recommendations supported by rigorous analysis
Explore my other professional work:
- 📊 Power BI Projects - Interactive dashboards and business intelligence solutions
- 🗃️ SQL Data Analysis Projects - Database querying and SQL-based analysis
- 📜 Certificates - Professional certifications and credentials
- 📈 Excel Portfolio - Advanced Excel analysis and modeling
- Programming: Python (Pandas, NumPy, Matplotlib, Seaborn), SQL
- Business Intelligence: Power BI, Data Visualization, Dashboard Development
- Data Analysis: Statistical Analysis, EDA, Predictive Analytics, Data Cleaning
- Tools: Jupyter Notebooks, Git/GitHub, Excel, Database Management
- Business Skills: Problem-solving, Critical Thinking, Stakeholder Communication, Project Management
I'm always open to connecting with fellow data professionals, discussing opportunities, or collaborating on interesting projects!
This repository is for educational and portfolio purposes. Please feel free to explore the projects and reach out if you have any questions or collaboration ideas!
⭐ If you find these projects helpful or interesting, please consider starring this repository!
Last Updated: January 2025