Freelancers using NLTKFreelancers using NLTK
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USMAN AKINTOBI's avatar
USMAN AKINTOBI
Nigeria
Making Sense of Data to Drive Decisions
5.0
Rating
6
Followers
Making Sense of Data to Drive Decisions
Cover image for Context-Aware Fintech User Feedback Classification
Context-Aware Fintech User Feedback Classification & Urgency Detection Most feedback tools stop at sentiment; positive or negative. This project goes further by building a decision-driven NLP system that classifies fintech user feedback by issue type and detects operational urgency, enabling smarter triage and escalation. What was built: Two machine learning models trained on manually collected and annotated app store reviews from real fintech platforms like Revolut, Wise, and Monzo: Issue Classifier: categorizes feedback into 9 classes including transaction issues, account security, KYC, refunds, app performance, and fraud Urgency Detector: binary classifier that flags high-risk feedback requiring immediate attention, optimized for recall to minimize missed critical cases
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85
Cover image for DSP Asset Managers
DSP Asset Managers
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30
Cover image for Housing Dashboard
Housing Dashboard
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8
Cover image for Energy Dashboard
Energy Dashboard
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12
NLTK(1)
Laksmi Wulandiari's avatar
Laksmi Wulandiari
Indonesia
Data Analyst and Visualization Expert
Data Analyst and Visualization Expert
Cover image for Hotel Review Sentiment Analysis
Hotel Review Sentiment Analysis
1
13
Cover image for CUSTOMER GAIN PREDICTION DASHBOARD
CUSTOMER GAIN PREDICTION DASHBOARD
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34
Cover image for ELECTRONIC SALES PERFORMANCE DASHBOARD
ELECTRONIC SALES PERFORMANCE DASHBOARD
0
28
Cover image for UI DESIGN | green cycle
UI DESIGN | green cycle
0
34
NLTK(1)
Ciro 's avatar
Ciro
Buenos Aires, Argentina
Go to sleep, may your sweet dreams come true...
271
Followers
Go to sleep, may your sweet dreams come true...
Cover image for Sentiment Analysis of IMDB Movie Reviews
Sentiment Analysis of IMDB Movie Reviews
2
12
Cover image for Heritage Soil Station: A NZ Website
Heritage Soil Station: A NZ Website
5
19
Cover image for Gardens of the World: Full-Stack Web App/Website
Gardens of the World: Full-Stack Web App/Website
6
21
Cover image for Folktales Around the World: Full-Stack Web App/Website
Folktales Around the World: Full-Stack Web App/Website
6
17
NLTK(1)
Axel Gusto's avatar
Axel Gusto
Paris, France
Fractional CPO/CTO for HealthTech & AI Startups
Fractional CPO/CTO for HealthTech & AI Startups
Cover image for MIND — AI Medical Billing
MIND — AI Medical Billing Assistant MIND automates medical billing coding for French physicians. The system reads clinical notes and suggests the correct CCAM codes in real time — eliminating manual lookup and reducing billing errors. Built solo from scratch: — NLP pipeline for clinical note analysis — Secure architecture for sensitive medical data — Go-to-market strategy targeting independent practitioners Stack: Python, NLP, cloud-native, HIPAA & GDPR-compliant data architecture. Role: Sole founder — product, engineering, and distribution.
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54
Cover image for Cloud Infrastructure & DevOps Engineering Designed
Cloud Infrastructure & DevOps Engineering Designed and deployed cloud infrastructure for early-stage startups — from bare VPS to containerized production environments. Delivered across 2 client engagements: — GCP infrastructure setup and configuration — Docker containerization and orchestration with Kubernetes — Linux server hardening and production deployment — CI/CD pipeline implementation — Monitoring and incident response setup Each engagement ran 2–4 weeks, fully remote, delivered autonomously from architecture to production handoff. Stack: GCP · Docker · Kubernetes · Linux · VPS
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35
Cover image for 2 Peer-Reviewed Publications — ML
2 Peer-Reviewed Publications — ML for Biomedical Classification Co-authored two peer-reviewed papers on applied machine learning and deep learning in clinical and biological contexts, working with CNRS, CEA, and Université Côte d'Azur. Paper 1 — Bioinformatics: Introduced PD-CR, a novel primal-dual classification method outperforming SVM, Random Forests, and PLS-DA on cancer metabolomics datasets (lung & brain tumors). Real clinical data from university hospitals of Nice and Montpellier. Paper 2 — IEEE/ACM Transactions on Computational Biology and Bioinformatics: Non-invasive live cell cycle monitoring using a supervised autoencoder on Quantitative Phase Imaging data. Achieved 98.6% classification accuracy on 80,000+ cells. Both projects used real clinical datasets. No simulations.
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Cover image for MailSwap — AI Email Classifier
MailSwap — AI Email Classifier for Shopify MailSwap automatically classifies incoming merchant emails — refunds, support, billing, shipping — and routes them to the right team without human triage. Built end-to-end: — Custom ML classifier trained on e-commerce email data — Shopify App integration — Production deployment with confidence scoring Achieved 94%+ classification accuracy in production. Role: Sole founder — model training, backend engineering, Shopify App Store launch.
3
109
NLTK(1)
Venkata Anirudh Parakala's avatar
Venkata Anirudh Parakala
Fort Lauderdale, USA
AI & ML Engineer | Automation & Data Solutions
AI & ML Engineer | Automation & Data Solutions
Cover image for Geographical Understanding of Twitter Health Topics using NLP
Geographical Understanding of Twitter Health Topics using NLP
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16
Cover image for AI Calorie and Nutrition Estimator Development
AI Calorie and Nutrition Estimator Development
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5
Cover image for Sneaker Authenticity Classifier Demo
Sneaker Authenticity Classifier Demo
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4
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Arham Malik's avatar
Arham Malik
Rawalpindi, Pakistan
Backend & AI engineer who ships systems fast and scale.
New to Contra
Backend & AI engineer who ships systems fast and scale.
Cover image for This was a really cool
This was a really cool EdTech project. The goal was to automate grading and student verification. I built a Flask backend that hooks into NLP models, speech to text, text to speech, and even computer vision with OpenCV. Students can give live demos, and the system checks their face, asks them questions out loud, and grades everything automatically. No more manual grading or worrying about cheating. It’s 100% automated, and teachers love how transparent it is. The AI responds in under a second, so it feels almost magical. For more details, please visit: https://arham-nexus.vercel.app/work/evaluasysai
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Cover image for This is a classic enterprise
This is a classic enterprise academic portal, but built solid. It uses  Web Forms, ASP.NET, C#, and SQL Server. The main challenge was role based access control because you have admins, faculty, TAs, and lab demonstrators all needing different permissions. I designed a three tier architecture with stored procedures and optimistic concurrency control so data doesn’t get messed up when people edit at the same time. Over 200 users per semester use it for task assignments and progress tracking. It’s not flashy, but it works perfectly. For more details, please visit: https://arham-nexus.vercel.app/work/talabportal
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Cover image for Skill Swap is a peer
Skill Swap is a peer to peer learning platform where you can be both a student and a teacher. I built the backend with Java Spring Boot, real time chat with WebSockets, and video calls with WebRTC. The matching algorithm finds people based on skills, ratings, and availability. You can switch roles anytime. It also has a trust based review system so fake reviews don’t ruin it. This was an MVP, but it proved that real time learning communities can work really well. For more details, please visit: https://arham-nexus.vercel.app/work/skillswap
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Cover image for This is a native Android
This is a native Android app I built with Kotlin and Firebase. It connects tourists with verified local guides in real time. You open the app, see nearby guides, book a tour, and then you can track each other’s location live using Google Maps. I also added offline support because travel doesn’t always have perfect internet. Over 100 active users ended up using it,during the MVP. The best part? Battery usage was optimized so your phone doesn’t die halfway through the day. Really proud of how smooth it turned out. For more details, please visit: https://arham-nexus.vercel.app/work/raaheraast
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66
NLTK(1)
Sowmya Lanka's avatar
Sowmya Lanka
Bengaluru, India
Data Scientist | ML, NLP & GenAI Solutions
New to Contra
Data Scientist | ML, NLP & GenAI Solutions
Cover image for What I built Developed a Natural
What I built Developed a Natural Language Processing (NLP) solution to detect DI Flag issues by analyzing and classifying text descriptions. How it works Cleaned text data by removing hashtags, HTML tags, special characters, and numeric values. Applied text preprocessing techniques including tokenization, stop-word removal, stemming, and lemmatization. Converted text into numerical features using Bag of Words (BoW) and TF-IDF. Trained and evaluated the model using accuracy and confusion matrix metrics. Performed 30 days of validation testing before production deployment. Technologies Python · NLP · Scikit-learn · TF-IDF · Bag of Words · Pandas · NumPy Outcome Built a text classification solution that helped identify DI Flag issues from descriptions and validated the model's performance before deployment.
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Cover image for What I built Developed an automated
What I built Developed an automated clinical text summarization solution using Google Gemini AI and prompt engineering to generate concise summaries from lengthy clinical chart notes, transcripts, and unstructured medical text. How it works Processed lengthy clinical notes and transcripts. Used prompt engineering to guide Gemini in identifying important medical information. Extracted key details such as symptoms, diagnoses, medications, and treatment plans. Generated concise, readable summaries from the original clinical text. Technologies Google Gemini AI · Python · Prompt Engineering · NLP · Generative AI Outcome Reduced clinical chart review time by approximately 30%, helping healthcare professionals identify important information more quickly and efficiently.::
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Cover image for What I built Developed a machine
What I built Developed a machine learning solution to predict Buy Box and sales prices using historical analytical data and Linear Regression. How it works Analyzed the data for linear relationships, autocorrelation, multicollinearity, homoscedasticity, and normally distributed errors. Applied Principal Component Analysis (PCA) for dimensionality reduction and to improve model performance. Identified key business factors influencing price prediction. Evaluated the model using R², Adjusted R², RMSE, MAE, and MSE metrics. Technologies Python · Pandas · NumPy · Scikit-learn · Linear Regression · PCA Outcome Built a price prediction model that identified important business drivers and evaluated prediction performance using multiple regression metrics.
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Cover image for AI-Powered Policy Q&A Assistant Built a
AI-Powered Policy Q&A Assistant Built a Retrieval-Augmented Generation (RAG) chatbot using Azure OpenAI GPT to answer policy-related questions with accurate, context-aware responses. What I built Extracted and structured information from policy documents into a knowledge base. Used Azure Cosmos DB for scalable information storage and retrieval. Implemented RAG to retrieve relevant policy information based on user queries. Integrated Azure OpenAI GPT to generate natural-language, context-aware answers. Technologies Python · Azure OpenAI · GPT · RAG · Azure Cosmos DB · LangChain Outcome The solution enables users to quickly find relevant information from policy documents without manually searching through lengthy documents.
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