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model-deployment-in-the-cloud-using-streamlit

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DHANA5982 / Churn-Prediction-And-Customer-Segmentation

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🚀 End-to-end ML project: 93% accurate churn prediction + customer segmentation with Random Forest & K-Means. Features automated pipeline, Streamlit dashboard, comprehensive testing, and cross-platform deployment. Production-ready with 505K+ customer dataset analysis.

python data-science machine-learning cross-platform random-forest scikit-learn pandas pytest statistical-analysis data-analysis production-ready predictive-analytics kmeans-clustering churn-prediction business-analytics customer-segmentation customer-analytics automated-pipeline model-deployment-in-the-cloud-using-streamlit ene-to-end-machine-learning
  • Updated Aug 4, 2025
  • Jupyter Notebook

sreekanthpogula / Complete-LLM-Fine-Tuning

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A comprehensive guide and codebase for fine-tuning Large Language Models (LLMs). This repository provides step-by-step tutorials, example scripts, and best practices for fine-tuning LLMs using popular frameworks like PyTorch and Hugging Face Transformers.

deep-learning pytorch openai lora deeplearning-ai huggingface streamlit prompt-engineering qlora finetuning-llms llama2 peft-fine-tuning-llm model-deployment-in-the-cloud-using-streamlit context-engineering
  • Updated Jul 20, 2025
  • Jupyter Notebook

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