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classification-model

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Classifies the presence of key exogenous variables in projects subject to federal NEPA (National Environmental Policy Act) review using NEPA environmental permitting reports (EISs) as input, and LangChain, Pinecone, and OpenAI API (GPT-4) to parse the reports efficiently with low costs ($0.40/report, on average) and high (>85%) correctness.

  • Updated Jul 3, 2024
  • Jupyter Notebook

In this project, we aim to predict whether a particular customer will switch to another telecom provider or not, a process referred to as churning and not churning in telecom terminology.

  • Updated Jul 3, 2024
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This project analyzes tumor cell data from 550 patients using Python. It involves data cleaning, exploratory analysis, feature engineering, and machine learning to classify tumors as malignant or benign. Techniques include PCA, logistic regression, and k-fold cross-validation to ensure model accuracy and reliability.

  • Updated Jun 30, 2024
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