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English
$
USD
I will build machine learning models for classification and regression
Machine Learning, NLP Specialit
Ranked 200 th among 3000+ Expert
competitors in the Trace the Ace NLP competition.
My work focuses on end-to-end Machine Learning and NLP from project architecture, EDA and feature engineering to mo...
About this Gig
Need a reliable machine learning model for classification, regression, or prediction?
Portfolio: https://kawsar-portfolio-kawsar07ahmmed0712-5454.vercel.app/
I build clean, practical machine learning solutions for structured and tabular data using Python.
What I can help with:
- Data preprocessing and feature engineering
- Classification and regression models
- Model comparison and cross-validation
- Hyperparameter tuning and optimization
- Handling imbalanced datasets
- Evaluation with appropriate metrics
- Feature importance and model explainability
Tools I use:
Scikit-learn, XGBoost, LightGBM, CatBoost, Pandas, NumPy, Jupyter Notebook, and TensorFlow when needed.
You will receive clean source code, a trained model, evaluation results, visualizations, and clear documentation. Standard and Premium packages include deeper tuning and model comparison, while Premium can also include Flask API integration.
Please message me with your dataset and project goal before ordering so I can recommend the right package.
Thanks,
Kawsar Ahmmed
Programming language:
Python
•
SQL
•
Colab
Frameworks:
Scikit-learn
•
Keras
•
PyTorch
•
Panda
•
Other
Tools:
Jupyter Notebook
•
TensorFlow
•
Excel
•
Colab
My Portfolio
Other Data Science & ML Services I Offer
FAQ
What type of datasets can you work with?
I mainly work with structured or tabular datasets such as CSV and Excel files containing numerical or categorical features. If you are unsure whether your dataset is suitable, please message me before ordering.
Do I need to know whether my problem is classification or regression?
No. Simply explain what you want to predict, and I will review your target variable and recommend the most suitable machine learning approach.
How do you choose the right machine learning model for my data?
I compare suitable algorithms based on your dataset, problem type, and evaluation metrics. Depending on the project, I may use models such as Logistic or Linear Regression, Random Forest, XGBoost, LightGBM, CatBoost, SVM, or KNN.
What will I receive with my order?
Depending on the package, you will receive clean Python source code, a Jupyter Notebook, preprocessing workflow, trained model, evaluation results, relevant visualizations, and model documentation.
Can you work with messy or imbalanced datasets?
Yes. I can handle common issues such as missing values, categorical features, scaling, feature engineering, and class imbalance using appropriate preprocessing techniques.
Can you guarantee a specific accuracy or performance score?
No fixed accuracy can be guaranteed because model performance depends on the quality, size, balance, and predictive information in the dataset. I will use appropriate validation, metrics, and optimization methods to achieve a reliable result.
Do you provide API integration for the trained model?
Yes. The Premium package can include Flask API integration so the trained model can accept input and return predictions through an API. Cloud deployment is not included unless separately agreed upon.
