Freelance ML Engineers in Johar TownFreelance ML Engineers in Johar Town
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Waqas Ali's avatar
Waqas Ali
Lahore, Pakistan
AI Developer Data Analyst and ML Expert
AI Developer Data Analyst and ML Expert
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Spam_Email
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3
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Road Traffic Accident
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3
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Wild Blueberry Yield Prediction
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5
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ML Engineer(3)
Hammad Tahir's avatar
Hammad Tahir
Lahore, Pakistan
AI Developer & ML Engineer: Top-notch Expertise
AI Developer & ML Engineer: Top-notch Expertise
Cover image for Yolo v10 - Object Detection and tracking
Yolo v10 - Object Detection and tracking
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314
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AI Agents workflow
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Cover image for RAG (Retrieval Augmented Generation) Pipeline
RAG (Retrieval Augmented Generation) Pipeline
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Video Virtual Tryon
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22
ML Engineer(5)
Saud Saleem's avatar
Saud Saleem
pro
Lahore, Pakistan
Top Rated Plus Freelancer & Top 1% Talent
$5k+
Earned
2x
Hired
5.0
Rating
14
Followers
Top Rated Plus Freelancer & Top 1% Talent
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AI Function Calling Agent
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Business Development AI Workflow
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ADHD-Friendly AI Automation Workflows
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Anchor Down - Transport & Logistics Platform
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ML Engineer(1)
KAZIM HAIDER SYED's avatar
KAZIM HAIDER SYED
Lahore, Pakistan
Data Analyst | Logo Design | Video Editing | Frontend Dev |
New to Contra
Data Analyst | Logo Design | Video Editing | Frontend Dev |
Cover image for Predictive Analytics Dashboard Dataset: DataCo Smart
Predictive Analytics Dashboard Dataset: DataCo Smart Supply Chain Dataset Size: ~180,519 Rows | 53 Columns 🛠️ Phase 1: Tech Stack & Tools Used This project was completed using industry-standard tools: Data Manipulation: Pandas, NumPy Visualization: Matplotlib, Seaborn (for static EDA) and Plotly Express (for interactive dashboards) Machine Learning: Scikit-learn (for preprocessing and metrics), XGBoost (for advanced regression) Forecasting: Facebook Prophet (for seasonality) and XGBoost (for demand volume prediction) Deployment: Streamlit (to build the live BI dashboard) 🧹 Phase 2: Major Hurdles & Data Cleaning The dataset was heavily corrupted, presenting several challenges: Misplaced Data: City names like “São Paulo,” “Rio de Janeiro,” and “Grande del Norte” were incorrectly placed in the Order Status column. Missing Statuses: Many rows had empty Order Status fields, while the actual status was found in Order State. Solution: I developed a custom Restoration Engine that cleaned columns and relocated misplaced data to their correct fields (Order Region). 💰 Phase 3: Profit & Strategy Analysis Beyond visualization, the project supported business decision-making: Profit Analysis: Calculated profit margins for each product. Price Optimization: Suggested a 5% price increase for high-selling products with margins below 10% to improve profitability. 🚀 Phase 4: Modeling & Predictions (Next 5 Months) Advanced XGBoost models were used instead of basic regressions: Demand Forecast: Predicted order volumes for the next five months to enhance inventory management. Sales Trends: Optimized models to capture seasonality and trend effects on future sales. 🏭 Phase 5: The Final BI Dashboard Built a complete interactive system using Streamlit: 11 Industry-Level Visualizations: Demand trends, top-selling products, regional sales, and late delivery root causes. Interactive System: Management can use live filters to extract insights from over 100,000 rows of data. 🧠 I’ll help you extract business insights through data analysis, dashboards, and forecasting. Here’s my portfolio: kazimhaidersyedportfolio.lovable.app (http://kazimhaidersyedportfolio.lovable.app)
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Cover image for Dashboard Overview: A Three-Pillar System The
Dashboard Overview: A Three-Pillar System The interface is divided into three functional tabs, providing a comprehensive view of the product lifecycle: The Home Tab (Product Showcase): * Features a sleek carousel of iconic models like Air Jordan 1 Low SE, Jordan Aerospace 720, and Air Max 97. Provides high-quality product imagery alongside narrative descriptions of the shoe's heritage and design features. The Review Tab (Sales Performance): Displays critical KPIs: Total Profit (reaching up to $1.21M for the Air Jordan 5 Retro), Total Units Sold, and User Ratings/Reviews. Uses a "Sold Out" badge to visually highlight high-demand inventory. The Analyse Tab (Geographic Insights): Visualizes regional performance through "Units Sold by Region" line graphs and "Profit by Region" bar charts (categorized into South, Northeast, Southwest, and Midwest). Key Insights for Portfolio Posting If you are listing this project on Contra or LinkedIn, highlight these technical and business strengths: Integrated UX/UI Design: The dashboard seamlessly transitions from a retail catalog to a data-heavy analytics tool, maintaining a consistent brand aesthetic. Data-Driven Decision Making: By linking specific shoe models to regional profit data, the dashboard allows managers to see exactly where a product is trending (e.g., higher profit in the "South" vs. "Midwest"). Interactive Navigation: The bottom carousel serves as a global filter, allowing the user to switch products and instantly update the sales and geographic data across all tabs. Technical Skillset Demonstrated Data Visualization: Mastery of multi-series charts and KPI cards. Frontend Design: Clean, "Nike-inspired" minimalist layout with high attention to white space and typography. Business Intelligence: Creating a closed-loop system where product features are directly tied to financial outcomes.
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Built an interactive sales dashboard to analyze Nike product performance. Used Power BI to visualize trends, revenue, and customer insights. Enabled data-driven decision-making through clear and actionable visuals.
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Cover image for Project Title: Pizza Sales Insights
Project Title: Pizza Sales Insights Dashboard Tools Used: Power BI | Excel | SQL | Data Cleaning Project Overview I transformed a complex dataset of pizza transactions into a high-impact, interactive dashboard designed to optimize restaurant operations. The goal was to identify peak ordering times, popular products, and sales trends to improve inventory management and staffing. Key Features & Insights Sales Performance Tracking: Real-time visibility into Total Sales ($152K) and order volumes to track growth against business goals. Peak Time Analysis: Identified that orders peak significantly during the evening hours (51.87%), allowing for better staff scheduling. Product Popularity: Segmented sales by category, showing that "Classic" pizzas drive the highest volume (15K orders). Customer Behavior: Analyzed order patterns by day of the week, pinpointing high-demand days to optimize marketing spend. The Technical Edge To build this, I performed extensive Data Cleaning and transformation to ensure accuracy across thousands of rows. I focused on a "clean-UI" design approach to ensure that even non-technical stakeholders can find answers in seconds. Why this matters for your business: "Data is just numbers until you can see the story. I build dashboards that help you stop guessing and start growing based on facts."
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ML Engineer(1)
Irtaza Ahmed Khan's avatar
Irtaza Ahmed Khan
Lahore, Pakistan
Machine Learning Engineer
Machine Learning Engineer
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Time Series Forecasting
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Cover image for Wasail: Demand Forecasting System
Wasail: Demand Forecasting System
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Natural Language Processing
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ML Engineer(1)
Fariha Muazzam's avatar
Fariha Muazzam
Lahore, Pakistan
I build production-ready AI systems that actually ship
New to Contra
I build production-ready AI systems that actually ship
Cover image for Satellite imagery is the ultimate
Satellite imagery is the ultimate stress test for Computer Vision. It is not just about detection; it is about architecting precision across massive, high-resolution datasets where every pixel counts. I have been building AI pipeline to segment parking lots, walkways, and structures from aerial data. The challenge is not just the mode, it is the Infrastructure: - Tiling & Inference: Processing massive dimensions without losing small-object context. - Robust Pre-processing: Using OpenCV to handle varying lighting and atmospheric "noise." - Cloud Orchestration: Turning heavy segmentation models into responsive, scale-ready tools. It is about turning raw pixels into a reliable data layer for the business. Currently opening 20 hours/week for long-term partnerships in Computer Vision & AI Systems.
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AI-based Video Generation Pipeline
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Cover image for I designed and delivered an
I designed and delivered an end-to-end MVP for a real-time AI talking avatar system, where live browser speech is converted into an AI-generated response and rendered through a lip-synced avatar video on GPU hardware. The objective was to validate technical feasibility, latency characteristics, and perceived real-time interaction before committing to production hardening. The system integrates speech-to-text, LLM-based reasoning, text-to-speech, and video synthesis into a single, runnable pipeline, deployed on an A100 GPU.
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Cover image for At Formulatrix, I led the
At Formulatrix, I led the development of a computer vision pipeline for RockMaker, a biotech product used in crystallization experiments. I built and deployed object detection and image classification models that improved scoring accuracy by 13%, reducing manual effort for scientists and increasing customer satisfaction. The solution was productionized with Docker and CI/CD pipelines, ensuring scalability and reliability across client sites.
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ML Engineer(1)
Wasiq Malik's avatar
Wasiq Malik
Lahore, Pakistan
🚀 Cutting-Edge AI & Fullstack Engineer ✨
🚀 Cutting-Edge AI & Fullstack Engineer ✨
Cover image for Collision Monitor System for iw.hub
Collision Monitor System for iw.hub
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7
Cover image for GPT-2 from fine-tuning to production
GPT-2 from fine-tuning to production
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Cover image for Soccer Player Identification in Live Streams
Soccer Player Identification in Live Streams
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1
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ML Engineer(1)