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I will train machine learning models using python scikit learn
AI Solutions Local LLM and Software Developer
Hi! I’m a Computer Science student specializing in AI/ML with experience building intelligent apps and system solutions. I work with Python, C++, Linux, Docker, and ML frameworks (RAG, GNNs, local LLM...
Sobre este Serviço
Are you looking for an expert to clean, preprocess, and analyze your raw data? You are in the right place!
As a Computer Science student specializing in AI and Machine Learning, I help clients transform messy, unstructured datasets into clean, actionable data ready for analysis and machine learning models.
Services I Offer:
- Data Cleaning & Formatting: Handling missing values, removing duplicates, and fixing data types.
- Exploratory Data Analysis (EDA): Statistical analysis and identifying key trends in your data.
- Data Preprocessing: Feature scaling, encoding categorical variables, and normalization.
- Data Visualization: Creating clean charts, plots, and heatmaps using Matplotlib and Seaborn.
- Machine Learning: Training regression and classification models using Scikit-Learn.
Tools & Technologies:
- Python, Pandas, NumPy
- Matplotlib, Seaborn
- Scikit-Learn, Jupyter Notebook, Google Colab
Why Choose Me?
- Clean, well-commented Python code
- Fast delivery and responsive communication
- High attention to detail
Please contact me before placing an order to discuss your project requirements!
Perguntas frequentes
What format should I provide my dataset in?
You can upload your data in CSV, Excel (.xlsx), JSON, or SQL format. If your data is unstructured or needs special extraction, please message me first so we can review it!
What deliverables will I receive upon project completion?
You will receive a clean dataset (CSV/Excel format), fully commented Python source code in a .ipynb (Jupyter Notebook / Google Colab) file, high-resolution visualization charts (if included in your package), and a clear summary report explaining the output.
Do you guarantee 100% machine learning accuracy?
No real-world machine learning model can guarantee 100% accuracy, as results depend heavily on the quality, volume, and patterns in your provided dataset. However, I promise to use best-practice data preprocessing, proper feature engineering, and model evaluation to give you the highest performance
