Extensive EDA of the IBM telco customer churn dataset, implemented various statistical hypotheses tests and Performed single-level Stacking Ensemble and tuned hyperparameters using Optuna.
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Updated
Nov 9, 2021 - HTML
Extensive EDA of the IBM telco customer churn dataset, implemented various statistical hypotheses tests and Performed single-level Stacking Ensemble and tuned hyperparameters using Optuna.
The goal was to perform predictive maintenance on commercial turbofan engine. The approach used here is a data-driven approach, meaning that data collected from the operational jet engine is used to perform predictive maintenance modeling. To be specific, to build a predictive model to estimate the Remaining Useful Life ( RUL) of a jet engine ba…
Utilized Jupyter Notebooks to scrape over 8,764 articles from a medical news site and applied clustering, LDA topic modeling, and classification techniques to find similar news topics and predict article sentiment
Predict and prevent customer churn in the telecom industry with our advanced analytics and Machine Learning project. Uncover key factors driving churn and gain valuable insights into customer behavior with interactive Power BI visualizations. Empower your decision-making process with data-driven strategies and improve customer retention.
For the kaggle project - Credit Card Lead Prediction, the repository contains the deployment details, project documentation file and project link.
This is my portfolio containing files I have used for my various projects
Machine Learning Class at Harding Univeristy
The project was prepared and submitted within the Brazilian "Bootcamp Data Science na prática" by Neuron (https://www.facebook.com/neuronDSAI/photos/a.1924971354499031/2664668797195946/?type=3&eid=ARCBaznRnMGbE-iFheLbf7HyZpHcxpz7vT-F8J9Yl9_BrqHtwnjLsmdbyaE4l4nbEJKXWdg2aLyGuj7B&ifg=1).
Capstone Project for the Data Scientist Nanodegree by Udacity.
Fraud Detection of a 6 million row dataset using AWS and Spark
Binary classification project in PySpark on an AWS-EMR cluster to predict customer churn.
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