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Sanket Sabharwal, PhD's avatar
Sanket Sabharwal, PhD
maxGenoa, Italy
Senior Software & ML Engineer | Zero to One Product Builder
6x
Hired
5.0
Rating
50
Followers
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Senior Software & ML Engineer | Zero to One Product Builder
Cover image for Machine Learning for Sports Betting - NCAA College Basketball
Machine Learning for Sports Betting - NCAA College Basketball
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Cover image for Web Scraping Systems - Large-Scale Data Extraction Pipelines
Web Scraping Systems - Large-Scale Data Extraction Pipelines
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62
Cover image for BI Dashboards - Retail Analytics & Forecasting
BI Dashboards - Retail Analytics & Forecasting
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41
Cover image for Computer Vision for Manufacturing - Defect Detection & QA
Computer Vision for Manufacturing - Defect Detection & QA
2
44
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Ugo Chukwu's avatar
Ugo Chukwu
proDubai - United Arab Emirates
Fractional AI & Finance Automation Lead | Model Fine-tuning
$10k+
Earned
6x
Hired
5.0
Rating
47
Followers
BUILDSHIP_EXPERT

expert

Fractional AI & Finance Automation Lead | Model Fine-tuning
Cover image for ML Evaluation Infrastructure for Fraud Detection
ML Evaluation Infrastructure for Fraud Detection
0
15
Cover image for LLM Fine-Tuning & Evaluation for Financial Risk
LLM Fine-Tuning & Evaluation for Financial Risk
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4
Cover image for VC Fund Management Platform Development
VC Fund Management Platform Development
0
6
Cover image for AI Agents Multimodal Video Pipeline
AI Agents Multimodal Video Pipeline
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19
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Anurag Nagare's avatar
Anurag Nagare
Mumbai, India
I’m an AI & Machine Learning engineer with expertise in deve
I’m an AI & Machine Learning engineer with expertise in deve
Cover image for I’m excited to share AuditFlow AI – AI-powered continuous au...
I’m excited to share AuditFlow AI – AI-powered continuous auditing platform built specifically for Chartered Accountants and audit firms. CA's practices today are drowning in manual sampling, 40–60 hour audit cycles, talent shortages, and rising client pressure for faster delivery with lower fees. Most frauds and GST/TDS errors go undetected until the assessment stage because traditional methods check only 2–5% of transactions. AuditFlow AI changes that completely: upload any ledger/Excel/CSV and in under 10 seconds it scans 100% of transactions, flags duplicates, round-figure entries, weekend fraud, high-value anomalies, and vendor loops – with plain-English AI explanations for every red flag. Tech stack: Python, Flask, XGBoost, Isolation Forest, scikit-learn, Bootstrap 5, and trained on 5,000+ synthetic + real-world patterns
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80
Cover image for One Gate: Voice-controlled Gmail, Calendar,
One Gate: Voice-controlled Gmail, Calendar, and Drive that can't fire the wrong thing Every "AI agent" demo shows a voice command turning into a sent email like magic, but almost nobody shows the part that actually matters: what stops it from sending the wrong thing. I built proof: an agent that runs your Gmail, Calendar, Contacts, and Drive by voice reply to a thread, schedule a meeting, archive an email, find a file hands-free end to end, but never fires anything irreversible without you saying so. The honest hard part isn't getting an LLM to sound smart, it's this: a transcript goes to LLM against a strict JSON schema and comes back as an ordered plan, every step tagged reversible or not and exactly one thing in the whole system is allowed to check that flag. Finding a thread, drafting a reply, creating a calendar event: those just run. The result covers real ground without ever feeling like it's guessing: forward or reply to email with the original quoted underneath, archive/label/trash, resolve a name to a real address through your Contacts first and your mail history as fallback, schedule an event that creates quietly and only emails the invite after a second confirmation, answer "what did John say" or "what's on my calendar Thursday" grounded in content actually fetched from your account not hallucinated. No backend, no server anywhere in the loop
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Cover image for What every "AI gesture control"
What every "AI gesture control" demo quietly leaves out Everyone films a hand waving at a screen and calls it AI. Almost nobody shows what's underneath that there's usually no gesture model at all. A "grab" is one distance crossing a line. Here's proof. I built a jigsaw puzzle you solve with your bare hands no mouse, no controller, no gesture classifier, no training.
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Cover image for Everyone's building AR filters and
Everyone's building AR filters and calling it "computer vision magic." Almost nobody's asking what's actually happening underneath — that most of these effects are just clever masking, not detection. Here's proof. I built an invisibility cloak that runs entirely in the browser, no green screen, no chroma key, no model training. https://github.com/AnuragNagare/Ghost-frame
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2
271
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Ruslan Sheikh's avatar
Ruslan Sheikh
Hong Kong
AI Engineer | LLM Agents, Automation & RAG Solutions
New to Contra
AI Engineer | LLM Agents, Automation & RAG Solutions
Cover image for AI-Powered Insurance Fraud Detection Platform I
AI-Powered Insurance Fraud Detection Platform I designed and built an end-to-end AI-powered platform for detecting and investigating suspicious health insurance claims. The system combines machine learning, XGBoost, anomaly detection and behavioral analytics with a multi-stage RAG and document intelligence pipeline for AI-assisted claims investigation. I also developed an interactive React + D3.js dashboard for exploring fraud networks, geographic patterns, anomalies and explainable model outputs. This project took me through the complete AI product lifecycle from data analysis and feature engineering to LLM orchestration, frontend development and interactive data visualization. Built with: Machine Learning · XGBoost · RAG · LLMs · Document Intelligence · React · D3.js · Explainable AI
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47
Cover image for AI-Powered Customer Support & Information
AI-Powered Customer Support & Information Assistant I designed and developed an AI-powered conversational assistant that automated information retrieval and customer enquiries for an online marketplace. The assistant connected users with structured listing and account data through a conversational interface, allowing information to be retrieved without manually navigating multiple parts of the platform. I built the supporting backend using Node.js, Express.js, MongoDB and REST APIs, integrating application data with automated enquiry workflows and the AI assistant. I owned the project across the full development lifecycle, including system architecture, backend development, database design, API integration, testing, debugging, deployment and iterative product improvement. The project demonstrated how AI could be integrated into an existing digital product to make business information more accessible while reducing repetitive enquiry workflows.
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20
Cover image for Strategy Consultant Chatbot I engineered an
Strategy Consultant Chatbot I engineered an LLM-powered strategic intelligence agent designed to search, synthesize and deliver insights from complex business information. I worked on the agent's Generative AI workflows, prompt engineering, contextual reasoning and conversational AI capabilities, helping transform large volumes of information into source-linked, decision-ready responses for strategic use cases. The system was designed to handle complex, multi-dimensional questions while providing transparent, attributable insights. This project gave me hands-on experience developing an enterprise AI agent around a real business use case from LLM workflow and prompt design to contextual reasoning and executive-facing delivery.
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Cover image for AI-Powered Retail Operations & Management
AI-Powered Retail Operations & Management Platform I designed and developed a full-stack retail operations platform that centralized the day-to-day processes of a real operating business into one connected system. The platform brought together POS and sales processing, inventory and shelf management, stock transfers, customer management, promotions, loyalty rewards, employee shifts and alerts, online orders, analytics, and reporting within a single web application. I built the solution end-to-end — translating real business requirements into features, designing the system architecture and database, developing the frontend and backend, implementing business logic and APIs, and handling testing and deployment. I also incorporated AI-assisted capabilities and automation into the wider platform to reduce repetitive operational work, surface useful business information, and support faster decision-making. The result replaced fragmented workflows with a centralized digital system, giving the business better visibility over sales, inventory, employees, customers, and day-to-day operations, while creating the technical foundation for its e-commerce operations.
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38
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Usman Haider's avatar
Usman Haider
Lahore, Pakistan
AI/ML & Data Solutions Engineer
New to Contra
AI/ML & Data Solutions Engineer
Cover image for Built an intelligent inventory management
Built an intelligent inventory management system that automates stock ordering using machine learning and AI agents. Leveraging an XGBoost-based forecasting model, the system predicts future inventory demand and proactively places purchase orders when shortages are detected. The backend is powered by Django, integrated with AWS-hosted datasets for scalability and real-time data access. AI agents handle autonomous procurement decisions, reducing manual oversight and streamlining supply chain operations for greater efficiency and accuracy.
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102
Cover image for Retail Knowledge Graph In this project,
Retail Knowledge Graph In this project, we built a semantic knowledge graph tailored to the retail industry. The pipeline involved developing AI agents to transform heterogeneous data into standardized formats. Ontologies were created to represent domain knowledge accurately. Using Gemini models and LangChain, user queries were converted into Cypher queries to retrieve insights from a Neo4j database. We utilized an MCP server for orchestration and LangSmith for secure login and audit trails. This system enhances complex data exploration for non-technical users.
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141
Cover image for Student Medical Chatbot Built a chatbot
Student Medical Chatbot Built a chatbot to assist MBBS students in navigating medical literature. Leveraged Llama Index and fine-tuned language models to ensure accuracy. Embeddings were stored in OpenSearch, hosted on AWS. The Django backend included secure authentication and session management for a robust user experience.
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77
Cover image for Prompt Engineering Mini-Academy is a
Prompt Engineering Mini-Academy is a digital learning product built using Kajabi. It helps users learn how to write better AI prompts and use AI tools for daily tasks such as writing, research, summarization, and productivity. The problem it solves is that many people use AI tools without a proper structure, which leads to weak or generic results. This product gives users a clear learning path, practical prompt templates, and workflow examples to improve the quality of their AI outputs. I used Kajabi to create the landing page, email capture form, downloadable prompt resource, product offer, checkout page, and course structure. A sample video is attached to demonstrate the product flow and user experience.
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100
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Christians Steven Zoe's avatar
Christians Steven Zoe
Denpasar, Indonesia
Data Scientist | Solving Business Problems with Data & ML
Data Scientist | Solving Business Problems with Data & ML
Cover image for The reports folder contains model
The reports folder contains model evaluation outputs generated during the machine learning workflow. These reports provide insights into model performance, feature importance, and predictive capabilities, helping stakeholders understand both the effectiveness and business implications of the solution. 1. Feature Importance Report Feature importance analysis was performed to identify the variables that contributed most to customer churn predictions, providing valuable to business insights. 2. ROC Curve Report ROC-AUC analysis was used to compare multiple machine learning models and identify the model with the strongest predictive performance. 3. Confusion Matrix Report A confusion matrix was generated to evaluate classification outcomes and understand the strengths and limitations of the predictive model.
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Cover image for # 📊 Dashboard for Small
# 📊 Dashboard for Small Businesses (UMKM) A simple and user-friendly Excel dashboard designed to help small business owners monitor their business performance and make better decisions through data. --- ## 🚀 Project Overview This project demonstrates how sales data can be transformed into meaningful business insights using Microsoft Excel. The dashboard provides a clear overview of: - Monthly revenue - Monthly expenses - Profit tracking - Cashflow trends - Business performance visualization --- ## ✨ Features ✅ Sales Dashboard ✅ Cashflow Monitoring ✅ Profit & Loss Summary ✅ Interactive Charts ✅ Clean and Easy-to-Understand Layout ## 📸 Dashboard Preview Dashboard screenshots are available in the `screenshots` folder. --- ## 🛠 Tools Used - Microsoft Excel - Google Sheets - Git & GitHub --- ## 💡 Business Value Small business owners often struggle to understand their financial performance because their data is scattered and difficult to interpret. This dashboard simplifies business reporting and helps users: - Track revenue growth - Monitor expenses - Identify profit trends - Make data-driven decisions --- ## 👨‍💻 Created By Christians Steven Zoe Aspiring Data Analyst & Freelance Data Specialist GitHub: https://github.com/stevendsml01-blockchain
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Cover image for Data Cleaning and Sales Analysis ##
Data Cleaning and Sales Analysis ## Project Overview This project demonstrates an end-to-end data cleaning and exploratory data analysis (EDA) workflow using Python. The dataset was intentionally generated with multiple data quality issues to simulate real-world business scenarios commonly encountered by Data Analysts and Data Scientists. --- ## Objectives - Identify data quality issues. - Handle missing values. - Remove duplicate records. - Standardize mixed date formats. - Perform exploratory data analysis. - Generate business insights. - Create visualizations for decision-making. --- ## Dataset Issues The raw dataset contained several intentional problems: - Missing values in `Qty` - Missing values in `Harga` - Duplicate transactions - Mixed date formats - Inconsistent category naming --- ## Data Cleaning Process The following steps were performed: 1. Loaded and profiled the raw dataset. 2. Identified missing values and duplicate records. 3. Removed duplicate transactions. 4. Filled missing values using median imputation. 5. Investigated mixed date formats. 6. Built a custom date parser to standardize dates. 7. Saved the cleaned dataset. --- ## Results ### Before Cleaning | Metric | Value | |----------|---------| | Total Records | 1009 | | Missing Qty | 8 | | Missing Harga | 5 | | Duplicate Records | 10 | # After Cleaning | Metric | Value | |----------|---------| | Total Records | 999 | | Missing Qty | 0 | | Missing Harga | 0 | | Duplicate Records | 0 | | Failed Date Parsing | 0 | --- ## Business Insights ### Best-Selling Products Kopi Arabica was the top-selling product, followed by Teh Hijau and Mouse. ### Sales by City Bandung generated the highest sales volume, indicating strong market potential compared to Surabaya and Jakarta. ### Category Performance Electronics dominated sales performance. An inconsistency between `Makanan` and `makanan` was discovered, highlighting the importance of data standardization before analysis. ### Revenue The total revenue generated was: Rp 13,593,130,000 ## Technologies Used - Python - Pandas - NumPy - Matplotlib
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Cover image for Image 1 – Project Overview
Image 1 – Project Overview & Dataset Information Customer Churn Prediction Using Random Forest This project focuses on predicting customer churn using machine learning techniques to help businesses proactively identify customers who are likely to discontinue their services. The predictive solution was developed using a structured approach involving Random Forest classification, SMOTE oversampling for handling class imbalance, GridSearchCV for hyperparameter optimization, and threshold tuning to improve recall performance. The dataset contains customer demographic and behavioral attributes, including: Age, Membership Years, Lifetime Value, Total Purchases, Days Since Last Purchase, Average Order Value, Returns Rate, Cart Abandonment Rate The target variable is customer churn status, where: 0 = Active Customer, 1 = Churned Customer Business Objective: The primary objective of this project is to identify customers at risk of churn so businesses can implement preventive retention strategies and reduce customer attrition. Image 2 – Machine Learning Pipeline End-to-End Machine Learning Workflow: A comprehensive machine learning pipeline was designed to ensure robustness, reproducibility, and business relevance throughout the modeling process. The workflow consisted of: 1. Data Cleaning Prepared and validated the dataset by handling inconsistencies and ensuring data quality. 2. Exploratory Data Analysis (EDA) Investigated customer behavior patterns and feature distributions to understand underlying trends. 3. Baseline Random Forest Modeling Established an initial benchmark using Random Forest classification. 4. SMOTE Oversampling Addressed class imbalance to improve the model's ability to detect churned customers. 5. Hyperparameter Tuning Optimized model performance using GridSearchCV. 6. Threshold Tuning Adjusted classification thresholds to maximize business-oriented objectives, particularly recall. 7. Model Evaluation Assessed predictive performance using multiple evaluation metrics. Professional Value This structured workflow demonstrates adherence to industry best practices rather than relying solely on default machine learning configurations. Image 3 – Correlation Heatmap Exploratory Correlation Analysis A correlation heatmap was generated to identify relationships between customer attributes and churn behavior. The analysis revealed several noteworthy insights: Customers with longer periods since their last purchase exhibited a stronger tendency to churn. Higher cart abandonment rates were moderately associated with increased churn risk. Demographic variables such as age showed minimal correlation with churn outcomes. Key Insight The strongest relationship with churn was observed in: Days Since Last Purchase (correlation = 0.312) suggesting that customer inactivity is a meaningful indicator of potential attrition. Business Relevance Understanding these relationships enables organizations to focus their retention initiatives on the factors most strongly associated with customer loss. Image 4 – Key Insights, Recommendations & Technologies Used Key Insights Several actionable findings emerged from the analysis: 1. Customers with extended inactivity periods are more likely to churn. 2. Elevated cart abandonment behavior may signal disengagement. 3. Improving recall is critical because accurately identifying potential churners aligns directly with the business objective. Business Recommendations: Based on the findings, the following strategies are recommended: Target High-Risk Customers: Deploy retention campaigns aimed at customers identified as likely to churn. Personalize Customer Communication: Develop personalized email and promotional initiatives to improve engagement. Strengthen Loyalty Programs: Offer incentives and rewards to reactivate inactive customers. Monitor Behavioral Indicators: Continuously track customer activity metrics to detect early warning signs of churn. Technologies Used The project was implemented using the following technologies: Python Pandas NumPy Matplotlib Seaborn Scikit-Learn Imbalanced-Learn
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Mary Kurt's avatar
Mary Kurt
İstanbul, Turkey
Analytics, insights, impact - all just one hire away.
Analytics, insights, impact - all just one hire away.
Cover image for AutoML Pipeline
AutoML Pipeline
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2
Cover image for ETF Analytics Dashboard
ETF Analytics Dashboard
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6
Cover image for Bestseller Narrative Analytics Platform
Bestseller Narrative Analytics Platform
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7
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