You will get I will build and optimize a machine learning classification model in Python
Project details
I will build, evaluate, and optimize a machine learning classification model using Python and your structured dataset.
The project can include data inspection, missing-value handling, categorical encoding, feature scaling, train/test splitting, model training, performance comparison, validation, and clear visualizations. Depending on the selected package, I can compare multiple algorithms such as Logistic Regression, Random Forest, Gradient Boosting, Extra Trees, and other suitable scikit-learn models.
You will receive clean and reproducible source code in a Jupyter Notebook or Python script, along with relevant metrics such as accuracy, precision, recall, F1-score, and a confusion matrix. Standard and Advanced packages also include model comparison and technical documentation.
This service is designed for tabular classification datasets provided as CSV, Excel, or JSON files. The exact workflow will be selected according to your dataset, target variable, class balance, and business objective.
Please contact me before ordering if your project requires deep learning, image classification, NLP, cloud deployment, a web application, a real-time API, or production MLOps.
The project can include data inspection, missing-value handling, categorical encoding, feature scaling, train/test splitting, model training, performance comparison, validation, and clear visualizations. Depending on the selected package, I can compare multiple algorithms such as Logistic Regression, Random Forest, Gradient Boosting, Extra Trees, and other suitable scikit-learn models.
You will receive clean and reproducible source code in a Jupyter Notebook or Python script, along with relevant metrics such as accuracy, precision, recall, F1-score, and a confusion matrix. Standard and Advanced packages also include model comparison and technical documentation.
This service is designed for tabular classification datasets provided as CSV, Excel, or JSON files. The exact workflow will be selected according to your dataset, target variable, class balance, and business objective.
Please contact me before ordering if your project requires deep learning, image classification, NLP, cloud deployment, a web application, a real-time API, or production MLOps.
Machine Learning Tools
NumPy, pandas, Python, Python Scikit-LearnWhat's included
| Service Tiers |
Starter
$55
|
Standard
$120
|
Advanced
$220
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 3 | 5 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 2 | 5 | 9 |
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
(+ 1 Day)
+$25
Additional Graph/Chart
(+ 1 Day)
+$10
Model Documentation
(+ 1 Day)
+$25
Data Source Connectivity
(+ 2 Days)
+$50
Model Validation/Testing
(+ 1 Day)
+$35Frequently asked questions
About Emre
Machine Learning & Data Science Specialist | Python, Computer Vision
Ankara, Turkey - 2:57 am local time
I can support your project with:
• Machine learning classification and regression models
• Data cleaning, preprocessing, and exploratory data analysis
• Feature engineering and model selection
• Model training, validation, and performance optimization
• Accuracy, precision, recall, F1-score, and confusion matrix analysis
• Computer vision model and dataset preparation
• Image classification and damage-detection workflows
• Jupyter Notebook and Google Colab development
• Python, Pandas, NumPy, Scikit-learn, Matplotlib, and OpenCV
• Existing machine learning code and notebook debugging
• Dataset quality checks and class-balance analysis
• Clear technical reports, documentation, and reproducible source code
You will receive organized code, transparent results, and deliverables that are easy to understand and continue developing.
I focus on practical solutions, clear communication, realistic expectations, and on-time delivery.
Please send me your dataset, project objective, target variable, current code, and expected output so I can recommend the most suitable approach.
Steps for completing your project
After purchasing the project, send requirements so Emre can start the project.
Delivery time starts when Emre receives requirements from you.
Emre works on your project following the steps below.
Revisions may occur after the delivery date.
Step 1 — Dataset and objective review
I review the submitted dataset, target variable, class definitions, project objective, and requested evaluation metric.
Step 2 — Data preparation
I inspect data quality and perform the agreed preprocessing, such as missing-value handling, encoding, scaling, and train/test splitting.