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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 Automation Specialist
Lahore, Pakistan - 5:53 pm local time
What I Bring to Your Project:
- Computer Vision (The "Eyes"):
--> Expertise in YOLO (v8-v11), MediaPipe, and RetinaFace for real-time object detection and facial recognition.
--> Experience with generative CV tools like ControlNet and OpenPose for high-fidelity image manipulation.
--> Specialization in Few-Shot/No-Shot learning for specialized datasets where labeled data is scarce.
---
- Agentic AI & Automation (The "Hands"):
--> Specializing in Agentic Wrokflow tools like n8n and make .com
--> I build autonomous agents that connect your AI models to your business tools (Shopify, Amazon SP-API, Google Sheets) to automate end-to-end workflows.
---
- Cost-Optimized Machine Learning:
--> Weak Supervision (Snorkel): I replace manual labeling with programmatic labeling functions, cutting data prep time by 70%.
--> Metadata-Driven Models: Utilizing Scikit-learn and Pandas to build high-speed, interpretable classifiers that outperform LLMs on tabular data.
---
Technical Stack:
- Languages: Python (Expert), SQL.
- Frameworks: Scikit-learn, YOLO, MediaPipe, ONNX, FastAPI, Pydantic.
- Automation: n8n, make .com, Agentic Frameworks.
Currently serving as a Professional AI/ML Engineer Intern, I provide well-documented, production-ready code that is built to scale.
Let’s build something smarter. Click "Invite" to discuss your project!
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.