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I will benchmark and compare machine learning models using python
AIML Engineer, Data Science and Data Engineer
I’m Jashu, an AI/ML developer with hands-on experience in machine learning, data science, and data engineering. I work on developing machine learning algorithms, building predictive models, analyzing ...
Über diesen Service
Want to know which machine learning models perform best on your dataset?
I provide Python-based machine learning benchmarking to compare algorithms, evaluate predictive performance, and identify optimization opportunities.
My services include:
- Classification and regression model benchmarking.
- Comparing algorithms such as Random Forest, KNN, Logistic Regression, XGBoost, and LightGBM.
- Evaluating accuracy, precision, recall, F1-score, ROC-AUC, and other relevant metrics.
- Measuring training time and inference latency.
- Data preprocessing and feature engineering.
- Hyperparameter tuning and model optimization.
- Generating comparison tables, performance charts, and visualizations.
What you'll receive:
- Python source code or a Jupyter Notebook.
- Model comparison results and performance metrics.
- Clear visualizations and a concise technical report.
- Findings and recommendations based on your dataset.
I focus on fair comparisons, reproducible experiments, and reliable evaluation tailored to your dataset and requirements.
Whether you need to compare models for a project, evaluate an existing machine learning pipeline, or understand performance trade-offs, I can help.
Programmiersprache:
Python
Frameworks:
scikit-learn
•
PyTorch
Tools:
Jupyter-Notizbuch
Mein Portfolio
FAQ
1. What machine learning models can you benchmark?
I can compare models such as Random Forest, KNN, Logistic Regression, XGBoost, LightGBM, and other suitable algorithms for classification or regression or Clustering.
2. What do I need to provide to get started?
Please share your dataset, the target variable, and any specific models or evaluation metrics you want to compare. If you have no preferences, I can recommend a suitable benchmarking approach.
3. What will I receive after the benchmarking is complete?
You will receive model comparison results, relevant performance metrics, visualizations, Python source code or a Jupyter Notebook, and a concise report summarizing the findings.
