You will get a High-Performance Machine Learning Model for Predictive Analytics
Project details
Stop guessing and start predicting. I turn your raw data into high-performance Machine Learning models built for real results.
Most ML projects fail due to poor data, not poor algorithms. My data-centric approach focuses on deep preprocessing, metadata extraction, and model ensembling to maximize accuracy, not just running a standard model.
From customer churn prediction to transaction classification and lead scoring, I build systems that are robust, explainable, and production-ready.
What sets my work apart:
Advanced Feature Engineering, I extract hidden signals and create new features to boost performance.
Ensemble Modeling, Using Stacking & Boosting (XGBoost, LightGBM, Random Forest) to combine multiple models into one powerful predictor.
Clean, Transparent Code, Well-documented Python (Scikit-Learn, Pandas) you can understand and maintain.
Best for:
• Tabular Classification (Fraud, Spam, Risk)
• Regression (Pricing, Forecasting)
• Data Cleaning & Auto-Labeling Pipelines
Tech Stack: Python, Pandas, Scikit-Learn, XGBoost, Snorkel
Most ML projects fail due to poor data, not poor algorithms. My data-centric approach focuses on deep preprocessing, metadata extraction, and model ensembling to maximize accuracy, not just running a standard model.
From customer churn prediction to transaction classification and lead scoring, I build systems that are robust, explainable, and production-ready.
What sets my work apart:
Advanced Feature Engineering, I extract hidden signals and create new features to boost performance.
Ensemble Modeling, Using Stacking & Boosting (XGBoost, LightGBM, Random Forest) to combine multiple models into one powerful predictor.
Clean, Transparent Code, Well-documented Python (Scikit-Learn, Pandas) you can understand and maintain.
Best for:
• Tabular Classification (Fraud, Spam, Risk)
• Regression (Pricing, Forecasting)
• Data Cleaning & Auto-Labeling Pipelines
Tech Stack: Python, Pandas, Scikit-Learn, XGBoost, Snorkel
Machine Learning Tools
NumPy, pandas, Python, Python Scikit-Learn, scikit-learn, SciPy, XGBoostWhat's included
| Service Tiers |
Starter
$75
|
Standard
$165
|
Advanced
$245
|
|---|---|---|---|
| Delivery Time | 3 days | 6 days | 10 days |
Number of Revisions | 2 | 2 | 6 |
Number of Model Variations | 1 | 2 | 5 |
Number of Scenarios | 1 | 3 | 5 |
Number of Graphs/Charts | 2 | 5 | |
Model Validation/Testing | - | ||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code |
Optional add-ons
You can add these on the next page.
Additional Model Variation
(+ 2 Days)
+$40
Additional Scenario
(+ 3 Days)
+$60
Additional Graph/Chart
(+ 1 Day)
+$35
Model Validation/Testing
(+ 2 Days)
+$40
Model Documentation
+$30Frequently asked questions
About Abdul
AI/ML Engineer | Computer Vision, Agentic AI & Lean Automation
Lahore, Pakistan - 10:40 am local time
Computer Vision (The "Eyes"):
— Multi-stage pipelines combining YOLO (v8-v11), RetinaFace, DeepFace, and MediaPipe for detection, face verification, and liveness/anti-spoofing
— Few-shot classification via CLIP embeddings and clustering, built a system that classifies fine-grained categories using embedding similarity rather than large labeled datasets
— Prior work with ControlNet and OpenPose for pose-guided image manipulation
Agentic AI & Automation (The "Hands"):
— Voice AI agents (VAPI, ElevenLabs) handling real inbound/outbound calls, checking live calendar availability, and completing bookings end to end
— Workflow automation with n8n, connecting AI models to real business tools
— Backend architecture for LLM-powered systems: FastAPI, PostgreSQL, LangChain-based RAG pipelines, built with graceful fallback handling so provider failures never surface as raw errors to end users
Cost-Conscious ML:
— Weak supervision (Snorkel) for programmatic labeling instead of manual annotation
— Classical ML (XGBoost, Scikit-learn) where it beats an LLM call on tabular data, cheaper, faster, more interpretable
Stack: Python (daily), SQL, FastAPI, PostgreSQL, YOLO, CLIP, DeepFace, RetinaFace, MediaPipe, LangChain, n8n, VAPI, ElevenLabs, React.
I'm early in my professional career, but not new to shipping, I build production AI/backend systems daily, and everything above is work I've actually built and can walk you through in real detail, not a keyword list. If you need a CV pipeline that handles messy real-world input, or an agent that actually finishes tasks instead of just chatting, let's talk.
Steps for completing your project
After purchasing the project, send requirements so Abdul can start the project.
Delivery time starts when Abdul receives requirements from you.
Abdul works on your project following the steps below.
Revisions may occur after the delivery date.
Data Health Check
I audit your dataset for missing values, outliers, and inconsistencies.
Preprocessing & Metadata Extraction
I clean the data and engineering new features from timestamps, text, or logs.