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95% of small businesses struggle with inventory management — losing an average of $47,000 a year to stockouts and overstocking. That's not bad luck. That's a data problem.
Most freelancers hand you a clean file and disappear. I build systems that stay clean — automated data cleaning that refreshes itself every time new data drops in, so you're never paying someone to redo the same job every month.
Clean data → real analysis → visuals you can act on. Dashboards are the next layer I'm actively building toward.
Currently focused on e-commerce and small business owners — helping you know what's actually selling and what's actually working, before the next decision gets made blind. Not limited to one sector though — if your numbers feel untrustworthy, that's usually the first thing worth fixing.
The network for creativity
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Creatives on Contra have earned over $150M and we are just getting started
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The image is a visual comparison of the customer-lead data before and after cleanup for TechNova Solutions.
Upper section: Messy data
The upper section shows the information exactly as it was collected from business cards and signup sheets. It contains inconsistent capitalization, different phone-number formats, duplicate entries, incomplete fields, and possible spelling variations. For example, “ravi kumar” and “RAVI KUMAR” represent the same person, while Farhan Ali appears twice with slightly different company and phone formatting.
Lower section: Organized and cleaned data
The lower section is the CRM-ready version. Duplicate leads have been removed, names and company names have been standardized, email addresses have been converted to lowercase, and phone numbers have been formatted using the +91-XXXXXXXXXX pattern. A new Status column has also been added.
Status
Meaning
Ready
The lead has the required email and phone information.
Needs Follow-up
Important information, such as an email address or phone number, is missing or incomplete.
The blank cells in the cleaned table are intentional. They indicate information that was not available in the original data; no details were invented. Overall, the image demonstrates how unstructured lead information can be transformed into a more consistent and usable CRM database.
This sample demonstrates how I can take business data, organise and analyse it, and turn it into clear information that can support better business decisions.
The project includes data cleaning, spreadsheet analysis, KPI reporting, revenue and profit analysis, trend analysis and data visualisation.
The aim is to make business data easier to understand and highlight the areas that may require attention.
Work demonstrated:
Data cleaning and organisation
Spreadsheet analysis
KPI reporting
Revenue and profit analysis
Trend analysis
Data visualisation
Business insights
Note: All data used in this project is fictional and was created specifically for portfolio demonstration purposes. No client results are being claimed.
Analyzed insurance data using Python to identify key factors affecting medical costs and built machine learning models to predict insurance charges. The project includes data cleaning, exploratory data analysis, visualization, feature analysis, and model evaluation using Scikit-learn.
