I will build a python machine learning or sentiment analysis model for your data
About this Gig
Are you looking for a machine learning expert to build a prediction or classification model for your data? You are in the right place.
I build accurate and reliable machine learning models and NLP models using Python, Scikit-learn, PyTorch, and HuggingFace Transformers. Whether you need a sentiment analysis model, text classification, or a prediction model I will deliver clean, working, and well-documented code.
What I offer:
- Sentiment analysis model using BERT and HuggingFace
- Text classification and NLP models
- Customer behavior prediction model
- Classification and regression models
- Data preprocessing and feature engineering
- Model training, evaluation, and accuracy optimization
- Scikit-learn and PyTorch pipelines
- Confusion matrix, accuracy report, and visualizations
What you will receive:
- Fully working Python code
- Trained model with evaluation results
- Clean and well-commented code
- Accuracy report and performance metrics
- Support after delivery
Why choose me:
- BS in Mathematics strong statistical background
- Real project experience with BERT and Scikit-learn
- I understand the math behind machine learning models
- Fast delivery and clear communication
Message me before placing an order.
Programming language:
Python
Frameworks:
Scikit-learn
•
PyTorch
•
Panda
Tools:
Jupyter Notebook
•
Excel
FAQ
What type of data do you work with?
I work with text data, CSV files, Excel files, and structured datasets.
Do I need to provide labeled data?
Yes, labeled data is required for supervised learning models. Message me if you need help with this.
Which machine learning libraries do you use?
Scikit-learn, PyTorch, HuggingFace Transformers, Pandas, and NumPy.
Can you build a sentiment analysis model for my reviews?
Yes, this is one of my core services. Message me with your dataset details before ordering.
Will I receive the trained model file?
Yes, you will receive the full Python code and the trained model file.
