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Fiverr Pro
English
$
USD
I will build and optimize machine learning models using python
About this Gig
A strong machine learning project is not just about training a model. It starts with understanding the data, choosing the right validation strategy, and building a reliable workflow from baseline to final evaluation.
What I provide:
- Exploratory Data Analysis (EDA)
- Data cleaning & preprocessing
- Feature engineering & selection
- Classification, regression & clustering
- Train/validation/test strategy
- Model comparison & selection
- Hyperparameter tuning
- Overfitting & data leakage checks
- Model evaluation & error analysis
- Visualizations & result interpretation
- Clean, reproducible Python code
- Documentation in Standard & Premium
- Basic Flask API integration in Premium
I work mainly with Python, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, and suitable ML libraries.
My workflow focuses on strong baselines, systematic experimentation, diagnosing weak performance, and improving results through evidence-based decisions.
Please contact me before ordering, especially for large or custom projects, so I can confirm the scope, package, and delivery time.
Programming language:
Python
•
Colab
Frameworks:
Scikit-learn
•
Panda
Tools:
Jupyter Notebook
•
Colab
My Portfolio
FAQ
What do you need from me to start?
Please provide the dataset, problem statement, target variable if applicable, expected output, evaluation metric if known, and any existing code or previous results. For complex projects, please contact me before ordering so I can confirm the appropriate scope.
What types of machine learning problems do you work with?
I mainly work with structured/tabular machine learning problems including classification, regression, clustering, predictive modeling, anomaly detection, and related supervised or unsupervised tasks.
How do you choose the right model?
I do not select a model based on popularity alone. I first study the data and problem structure, establish a baseline, compare suitable algorithms, evaluate them using appropriate validation strategies, and optimize the strongest candidates.
What happens if the model performs poorly?
I diagnose poor performance using EDA, validation, error analysis, and model comparisons. If no modeling or implementation issue is found, I identify dataset-level limitations such as weak features, noisy labels, imbalance, inconsistencies, distribution shift, or limited predictive signal.
Can you guarantee a specific accuracy?
No fixed score can be guaranteed before analyzing the data. Performance depends on data quality, target quality, available signal, validation setup, and problem difficulty. I focus on reliable improvement, not artificial accuracy claims.
Do you perform error analysis?
Yes. I can analyze where and why the model fails, identify difficult samples or segments, and determine whether improvement requires better data, features, modeling, or changes to the problem formulation.
Do you provide API integration or deployment?
Flask API integration is available in the Premium package. Lightweight deployment can be discussed separately. Production MLOps or complex AWS, GCP, or Azure infrastructure is not included by default.
