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amit4009/README.md

Amit Pandit

Data Scientist | Applied ML • Product Analytics • Risk Modeling • MLOps

I build practical data science systems that turn messy data into decisions — from credit risk models and churn analytics to NLP pipelines, experimentation frameworks, and business dashboards.

My work sits at the intersection of machine learning, product analytics, customer behavior, and decision intelligence.

What I Work On

  • Applied ML, model evaluation, and deployment-ready workflows
  • Product analytics, experimentation, and metric frameworks
  • Credit risk, loan analytics, churn, and retention modeling
  • NLP, sentiment analysis, text classification, and transformer models
  • SQL, BI dashboards, and executive-ready reporting

Core Stack

Python, SQL, Scikit-learn, TensorFlow, PyTorch, Hugging Face, MLflow, DVC, Docker, Flask, Power BI, Tableau, Snowflake, AWS, Azure

Current Focus

Building a portfolio around applied ML systems, experimentation, risk decisioning, NLP, and analytics products that are practical, measurable, and business-relevant.

Connect

LinkedIn · GitHub · Email

Pinned Loading

  1. Credit-Default-Mlops-Pipeline Credit-Default-Mlops-Pipeline Public

    End-to-end credit default prediction pipeline with threshold tuning, Flask deployment, and CI/CD workflow.

    Jupyter Notebook

  2. text-classification-mlops-pipeline text-classification-mlops-pipeline Public

    End-to-end NLP text classification pipeline with DVC, Docker, MLflow tracking, CI/CD workflow, and Flask deployment.

    Python

  3. Fake_news_Detection_Bert Fake_news_Detection_Bert Public

    BERT-based fake news classification project using news headlines, PyTorch, Hugging Face Transformers, and model evaluation.

    Jupyter Notebook

  4. customer-churn-prediction-dashboard customer-churn-prediction-dashboard Public

    Customer churn prediction and retention analytics project using Python, SQL, Power BI, and Random Forest.

    Jupyter Notebook

  5. content-based-movie-recommender content-based-movie-recommender Public

    Content-based movie recommender using TMDB metadata, CountVectorizer, and cosine similarity.

    Jupyter Notebook

  6. Sentiment_Analysis Sentiment_Analysis Public

    IMDb sentiment classification using neural networks, GloVe embeddings, LSTM, and Flask deployment.

    Jupyter Notebook