You will get an AI-Powered Product & Record Matching System
Rising Talent
Project details
Messy, inconsistent datasets slowing you down? I’ll build you a smart matching solution that automatically cleans and aligns your records. Whether they’re products, customer names, or survey entries.
Using AI/NLP methods (TF-IDF, SBERT, custom rules), the system recognises:
• Misspellings & typos
• Synonyms & variations (e.g., “NY” vs. “New York”)
• Units & quantities (e.g., “0.5kg” = “500g”)
• Duplicate or inconsistent entries
This ensures your data is clean, unified, and ready to use.
What You’ll Receive
• A working Python tool or lightweight web app (Gradio UI)
• Clean, matched output with confidence scores
• Support for multiple datasets
• Optional reporting (accuracy metrics, charts)
• Documentation or walkthrough for future use
Who This Is For
• Businesses consolidating product catalogs
• Researchers cleaning free-text survey responses
• Startups unifying records from multiple sources
• Developers preparing training datasets
With my background in AI, NLP, and data automation, I’ll turn your messy data into structured, actionable insights
Using AI/NLP methods (TF-IDF, SBERT, custom rules), the system recognises:
• Misspellings & typos
• Synonyms & variations (e.g., “NY” vs. “New York”)
• Units & quantities (e.g., “0.5kg” = “500g”)
• Duplicate or inconsistent entries
This ensures your data is clean, unified, and ready to use.
What You’ll Receive
• A working Python tool or lightweight web app (Gradio UI)
• Clean, matched output with confidence scores
• Support for multiple datasets
• Optional reporting (accuracy metrics, charts)
• Documentation or walkthrough for future use
Who This Is For
• Businesses consolidating product catalogs
• Researchers cleaning free-text survey responses
• Startups unifying records from multiple sources
• Developers preparing training datasets
With my background in AI, NLP, and data automation, I’ll turn your messy data into structured, actionable insights
Machine Learning Tools
BERT, ChatGPT, MLflow, NLTK, NumPy, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, SciPy, SQL, TensorFlowWhat's included
| Service Tiers |
Starter
$50
|
Standard
$100
|
Advanced
$200
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | |
Number of Graphs/Charts | 0 | 1 | |
Model Validation/Testing | - | ||
Model Documentation | |||
Data Source Connectivity | - | - | |
Source Code |
Optional add-ons
You can add these on the next page.
Extra Dataset
(+ 2 Days)
+$40
API Integration
(+ 3 Days)
+$70
Custom Reporting Dashboard
(+ 3 Days)
+$80Frequently asked questions
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jb
james b.
Dec 18, 2025
Imagine a more fun way to design a mix and match game
She is absolutely the best and creative game design with knowledge in every field which contributes to gaming
About Millie
Python, CSV & Excel Data Cleaning Specialist
Reading, United Kingdom - 9:08 am local time
Clean, reliable data makes better decisions possible. Whether you have duplicate rows, inconsistent formats, mismatched columns, or multiple CSV and Excel files that need combining, the result is one accurate, well-structured dataset that's ready to use.
Clients hire me when data quality matters and manual spreadsheet work has become a bottleneck.
Services
- Data cleaning
- File merging
- Data standardisation
- Data validation
- CSV processing
- Excel processing
- Python automation
Every project starts with understanding the goal, then delivering a clear, repeatable solution with defined outcomes. You'll receive a realistic timeline before work begins.
Written communication. Efficient delivery. No unnecessary meetings.
Steps for completing your project
After purchasing the project, send requirements so Millie can start the project.
Delivery time starts when Millie receives requirements from you.
Millie works on your project following the steps below.
Revisions may occur after the delivery date.
I review your dataset(s) and goals.
- Review & Confirm Requirements - We agree on the matching approach (basic, AI-enhanced, or custom).
Data Preparation
- I clean and normalise the input data. - Handle missing values, duplicates, and inconsistencies.