You will get a complete machine learning regression model for your dataset
Project details
Turn your structured data into a reliable, well-evaluated regression solution built in Python.
I will review your dataset, prepare numerical and categorical features, train the selected number of regression models, and evaluate performance using suitable metrics such as MAE, RMSE, and R². Depending on your package, the work may include cross-validation, hyperparameter tuning, model comparison, residual diagnostics, coefficient or feature analysis, and documentation.
My workflow is designed to prevent data leakage: preprocessing is learned from training data, while final performance is measured on unseen data. Suitable algorithms may include Linear Regression, Ridge, Lasso, Elastic Net, Random Forest, and XGBoost, depending on your dataset and objective.
You will receive clean source code or a reproducible Jupyter Notebook, model results, selected visualizations, and requested prediction files. Model performance depends on the available data quality and predictive signal, so I provide transparent evaluation instead of unrealistic accuracy guarantees.
If you are unsure which tier fits your dataset, please message me before placing the order.
I will review your dataset, prepare numerical and categorical features, train the selected number of regression models, and evaluate performance using suitable metrics such as MAE, RMSE, and R². Depending on your package, the work may include cross-validation, hyperparameter tuning, model comparison, residual diagnostics, coefficient or feature analysis, and documentation.
My workflow is designed to prevent data leakage: preprocessing is learned from training data, while final performance is measured on unseen data. Suitable algorithms may include Linear Regression, Ridge, Lasso, Elastic Net, Random Forest, and XGBoost, depending on your dataset and objective.
You will receive clean source code or a reproducible Jupyter Notebook, model results, selected visualizations, and requested prediction files. Model performance depends on the available data quality and predictive signal, so I provide transparent evaluation instead of unrealistic accuracy guarantees.
If you are unsure which tier fits your dataset, please message me before placing the order.
Machine Learning Tools
NumPy, pandas, Python Scikit-Learn, SciPy, SQL, XGBoostWhat's included
| Service Tiers |
Starter
$40
|
Standard
$100
|
Advanced
$200
|
|---|---|---|---|
| Delivery Time | 2 days | 5 days | 10 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 3 | 5 |
Number of Scenarios | 1 | 1 | 2 |
Number of Graphs/Charts | 5 | 8 | 15 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code |
Optional add-ons
You can add these on the next page.
Additional Revision
+$15
Additional Model Variation
(+ 1 Day)
+$25
Additional Scenario
(+ 2 Days)
+$40
Additional Graph/Chart
(+ 1 Day)
+$10
Data Source Connectivity
(+ 2 Days)
+$40Frequently asked questions
About Muhammad
Machine Learning Engineer | Python, LLMs, RAG & Computer Vision
Multan, Pakistan - 3:48 am local time
I build practical AI and machine learning solutions using Python. My work focuses on reproducible preprocessing, proper model validation, clean code, and results that are easy to understand and use.
WHAT I CAN HELP YOU WITH:
* Machine Learning: Regression, classification, feature engineering, model tuning, cross-validation, and evaluation
* Generative AI: RAG applications, document-based Q&A systems, embeddings, and LLM integration
* Computer Vision: Image classification, preprocessing, and deep learning prototypes
* Data Analysis: Data cleaning, exploratory analysis, visualization, and actionable reporting
* Automation: Excel, CSV, SQL, and repetitive data-processing workflows
* Deployment: Interactive prototypes using Streamlit and FastAPI
RECENT PROJECT RESULT:
Developed an end-to-end used-car price prediction solution using 2,059 real-world records. I built a leakage-safe preprocessing pipeline and compared Linear, Ridge, Lasso, and Elastic Net regression using cross-validation. The final model achieved an MAE of 297,854 and an R² of 0.8573, supported by residual diagnostics, coefficient analysis, visualizations, and complete documentation.
TECHNOLOGIES:
Python | Pandas | NumPy | Scikit-learn | TensorFlow | Keras | OpenCV | LangChain | FAISS | SQL Server | Streamlit | FastAPI | Git
WHAT YOU WILL RECEIVE:
* Clean and readable code
* Reproducible Jupyter Notebooks
* Properly evaluated machine learning models
* Clear visualizations and documented results
* Regular communication and structured delivery
Send me your dataset, business problem, or AI project requirements, and I will help you identify the right technical approach and deliver a practical solution.
Steps for completing your project
After purchasing the project, send requirements so Muhammad can start the project.
Delivery time starts when Muhammad receives requirements from you.
Muhammad works on your project following the steps below.
Revisions may occur after the delivery date.
Project Step 1
Review the dataset and confirm the regression objective
Project Step 2
Preprocess the data and engineer relevant model features