Hi, I'm Suhas Abhare, a data analyst and aspiring data scientist with a passion for uncovering insights from complex datasets. I specialize in transforming raw data into meaningful stories that drive strategic decisions. With a strong foundation in statistics, machine learning, and data visualization, I thrive on solving real-world problems through data.
When I'm not analyzing data, you'll find me exploring new tools, contributing to open-source projects, or mentoring others in the data community.
Welcome to my portfolio of Python-based data analysis projects. Each repository showcases the use of powerful libraries and statistical techniques to extract insights, visualize patterns, and support decision-making across diverse domains—from finance and logistics to entertainment and fitness.
- Core Libraries:
NumPy,Pandas,Matplotlib,Seaborn,SciPy - Statistical Techniques: Hypothesis Testing, ANOVA, Chi-Square, T-Test, Kruskal-Wallis, Shapiro-Wilk, Mann-Whitney U
- Feature Engineering: Encoding, Normalization, Standardization, Box-Cox Transformation
- Visualization: Plotly, Matplotlib, Seaborn
- Other: Regex, Central Limit Theorem, Confidence Intervals
| Project | Tools Used | Area of Analysis | Description |
|---|---|---|---|
| Zomato Data Analysis | NumPy, Pandas, Matplotlib, Seaborn, SciPy | Data Cleaning, EDA, Visualization, Feature Engineering | Demonstrates handling of new datasets (new_data) with preprocessing, exploratory analysis, and visualization to uncover meaningful insights. |
| Credit Score | NumPy, Pandas, Matplotlib, Seaborn, Regex, SciPy, Normalization | Data Cleaning, EDA, Visualization, Feature Engineering | Computes a hypothetical credit score using weighted averages, feature selection, and preprocessing. Offers insights for banks and credit companies. |
| Delhivery Logistics | NumPy, Pandas, Seaborn, SciPy, Hypothesis Testing, Encoding | EDA, Visualization, Probability & Stats, Feature Engineering | Analyzes ACTUAL vs OSRM time-distance metrics to uncover logistical patterns and improve delivery precision. |
| Yulu Bike Rentals | NumPy, Pandas, Seaborn, SciPy, Box-Cox, ANOVA, Chi-Square, T-Test | Data Analysis, Visualization, Hypothesis Testing | Explores customer behavior and rental patterns using statistical tests to inform strategic decisions. |
| Walmart Black Friday | NumPy, Pandas, Matplotlib, Seaborn, CLT, Confidence Intervals | EDA, Visualization, Statistics | Applies Central Limit Theorem and confidence intervals to analyze purchase behavior during Black Friday sales. |
| Aerofit Fitness | NumPy, Pandas, Matplotlib, Seaborn, Statistics | EDA, Descriptive Statistics, Probability | Provides insights into fitness product trends using descriptive statistics and probability analysis. |
| Netflix OTT | NumPy, Pandas, Matplotlib, Seaborn | Data Cleaning, EDA, Visualization | Analyzes content trends and user behavior on Netflix to uncover streaming patterns and preferences. |
| National Park Biodiversity Analysis | NumPy, Pandas, Matplotlib, Seaborn | EDA, Visualization | conservation_status has many missing values. In the species file it was sparse (191 non-null out of 5824), but after merging (because observations.csv contains many rows per species across parks) the count of non-null conservation_status rows changes — be mindful whether you want species-level counts (unique scientific_name) or observation-level counts (rows of merged).Vascular plants dominate the species counts; raw counts can hide relative differences for smaller categories. |
| Generating Word Cloud | NumPy, Pandas, Matplotlib, Seaborn, SciPy | Data Cleaning, EDA, Visualization, Feature Engineering | Demonstrates handling of new datasets (new_data) with preprocessing, exploratory analysis, and visualization to uncover meaningful insights. |
This section highlights my SQL-based data analysis projects, showcasing my ability to write efficient queries, clean and explore data, and extract actionable insights from real-world business scenarios.
- Databases: MySQL, Google BigQuery
- Techniques: Data Cleaning, Data Exploration, Business Case Analysis, Insight Generation
| Project | Tools Used | Area of Analysis | Description |
|---|---|---|---|
| MySQL Case Studies | MySQL | Data Analysis, Data Cleaning, Data Exploration | Solutions to selected case studies from the #8WeekSQLChallenge, demonstrating advanced SQL query writing, data wrangling, and analytical thinking. |
| Supermarket Business Case | Google BigQuery | Data Analysis, Data Exploration | A business case study for Target (Brazil), showcasing SQL-based data exploration, insight generation, and strategic recommendations for retail operations. |
This portfolio showcases a series of interactive dashboards and analytical visualizations built using Tableau, Power BI, Excel, and BigQuery. Each project demonstrates data cleaning, exploration, and storytelling to support strategic decision-making across finance, retail, entertainment, and public policy domains.
- Visualization Tools: Tableau, Power BI
- Data Sources: Microsoft Excel, Google BigQuery
- Techniques: Data Cleaning, EDA, Forecasting, Dashboard Design, Storytelling, Video Presentation
| Project | Tools Used | Area of Analysis | Description |
|---|---|---|---|
| Fintech Cash Flow | Tableau, Excel | Data Cleaning, Analysis, Visualization, Dashboard | Financial dashboard analyzing cash flow and balance sheet of a fintech company using Tableau. |
| Telangana Economic Growth | Excel, BigQuery, Tableau | Data Cleaning, Analysis, Visualization, Dashboard, Story, Video | Interactive dashboard presenting a comprehensive analysis of Telangana state’s economy to support sustainable growth. |
| META Stock Forecast | Tableau, Excel | Data Cleaning, Analysis, Visualization, Forecasting | Dashboard analyzing META’s stock prices over five years and forecasting future trends using quantile regression. |
| Sara Fashion Boutique | Power BI, Excel | Data Cleaning, Analysis, Visualization, Dashboard | Financial and budget analysis for Sara Fashion Boutique with strategic recommendations for growth. |
| Netflix Growth Analysis | Tableau, Excel | Data Cleaning, Analysis, Visualization, Dashboard, Story | Dashboard analyzing Netflix’s growth trends and offering strategic insights for future expansion. |
- 📈 Interactive dashboards with real-time insights
- 📊 Forecasting and regression modeling in Tableau
- 🧠 Strategic recommendations based on financial and behavioral data
- 🎥 Storytelling and video presentations for public policy and business growth
Each project is available as a standalone repository. Click the project name above to explore the dashboards, visualizations, and insights.
Here are some of the platforms where I practice and sharpen my skills:
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Customer Segmentation with K-Means
Clustered customer data to identify behavioral patterns for targeted marketing. -
Sales Forecasting using ARIMA & Prophet
Built time series models to predict future sales trends. -
Twitter Sentiment Analysis
Applied NLP techniques to classify public sentiment on trending topics. -
Interactive Retail Dashboard
Designed a Power BI dashboard to visualize KPIs and sales performance.
🔗 LinkedIn: Suhas Abhare • 📧 Email: suhasabhare@outlook.com