You will get Machine Learning Model Development | Algorithm Comparison |
Project details
Build and optimize ML models that actually work.
The Challenge:
With dozens of algorithms available, which one is best for YOUR data?
Testing them manually takes weeks. You need systematic model comparison.
My Approach:
I test multiple algorithms scientifically and show you exactly which one
performs best with your data.
What I Do:
✓ Implement 5-7 different algorithms (RandomForest, XGBoost, Gradient Boosting, etc.)
✓ Hyperparameter tuning for each algorithm
✓ 5-fold cross-validation for robust evaluation
✓ Comprehensive performance metrics
✓ Feature importance analysis
✓ Side-by-side model comparison
What You Get:
✓ Best model (trained and ready to deploy)
✓ Complete comparison report (all models ranked)
✓ Python code with documentation
✓ Feature importance visualizations
✓ Usage guide and prediction examples
Result: You'll have a data-proven best model, not a guess.
Delivery: Trained model + comparison report + reproducible code
Timeline: 5-6 days
The Challenge:
With dozens of algorithms available, which one is best for YOUR data?
Testing them manually takes weeks. You need systematic model comparison.
My Approach:
I test multiple algorithms scientifically and show you exactly which one
performs best with your data.
What I Do:
✓ Implement 5-7 different algorithms (RandomForest, XGBoost, Gradient Boosting, etc.)
✓ Hyperparameter tuning for each algorithm
✓ 5-fold cross-validation for robust evaluation
✓ Comprehensive performance metrics
✓ Feature importance analysis
✓ Side-by-side model comparison
What You Get:
✓ Best model (trained and ready to deploy)
✓ Complete comparison report (all models ranked)
✓ Python code with documentation
✓ Feature importance visualizations
✓ Usage guide and prediction examples
Result: You'll have a data-proven best model, not a guess.
Delivery: Trained model + comparison report + reproducible code
Timeline: 5-6 days
Machine Learning Tools
GitHub Copilot, Microsoft Excel, MLflow, NumPy, pandas, Python, Python Scikit-Learn, scikit-learn, TensorFlow, XGBoostWhat's included
| Service Tiers |
Starter
$50
|
Standard
$70
|
Advanced
$150
|
|---|---|---|---|
| Delivery Time | 2 days | 3 days | 6 days |
Number of Revisions | Unlimited | Unlimited | Unlimited |
Number of Model Variations | 5 | 8 | 10 |
Number of Graphs/Charts | 4 | 10 | 20 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | - | - | - |
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$20 - $50About Affan
Machine Learning Engineer | Data Science & Agentic AI
Karachi, Pakistan - 11:09 pm local time
I help small businesses and startups turn messy data into working, deployable ML and analytics solutions not just a Jupyter notebook, but something you can actually use.
What I can do for you:
✓ Clean and analyze your data (Python, SQL, Pandas) to answer specific business questions
✓ Build predictive models (regression, classification) with clear accuracy metrics
✓ Turn a model into something usable: a Flask/Streamlit app, dashboard, or simple API
✓ Deploy it to the cloud (AWS) so it's live, not stuck on my laptop
I'm newer to Upwork but not new to building I've completed several full pipelines end-to-end (data → model → cloud deployment), including a CI/CD deployment system and a student performance prediction app deployed on AWS. You can see the code and a full write-up on my GitHub.
Because I'm building my Upwork track record, I'm offering my first few clients a lower rate and extra attention to make sure you're fully happy with the result before anything is marked complete.
Let's talk about what you're trying to solve happy to give you a quick, honest read on scope before you commit to anything.
Steps for completing your project
After purchasing the project, send requirements so Affan can start the project.
Delivery time starts when Affan receives requirements from you.
Affan works on your project following the steps below.
Revisions may occur after the delivery date.
Data Preparation & Train-Test Split
I prepare your cleaned dataset for modeling: verify feature completeness, handle any remaining issues, and create train/test splits . I ensure proper data separation to avoid data leakage. The dataset is now ready for fair algorithm comparison.
Algorithm Implementation & Training
I implement 5-7 different algorithms (Random Forest, XGBoost, Gradient Boosting, Linear Regression, etc). Each model is trained on the training dataset with appropriate configurations. I use cross-validation to ensure robust performance estimates