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scikit-learn-python

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This repository shows the implementation of machine learning algorithms, data pipelines and data visualization with scikit-learn and python.

  • Updated Jul 16, 2021
  • Jupyter Notebook

The project analyzed Asana user data to determine adoption rate and factors influencing adoption. After data cleaning, an adoption rate of 12% was calculated. Predictor variables were extracted and modeled using Random Forest and Decision Tree classifiers. Both models performed well, with Random Forest achieving 87% accuracy.

  • Updated Oct 17, 2023
  • Jupyter Notebook
TweetyPy

Full-Stack application that allows client to use a predictive model to determine which user is more likely to have tweeted a given text. This project covers everything from API's to Predictive Modeling, SQLAlchemy database storage, Flask, along with other full-stack components. In the end it is deployed for online usage using Heroku.

  • Updated Feb 16, 2023
  • Python

This project predicts the burned area of forest fires using historical data and weather reports. It employs a neural network model built with TensorFlow, utilizing data preprocessing with Scikit-learn and analysis with Pandas and Matplotlib. The goal is to provide accurate, real-time predictions to aid in fire management and prevention.

  • Updated Sep 24, 2024
  • Jupyter Notebook

This Flask app predicts house prices using a RandomForestRegressor model trained on a housing dataset. It includes data pre-processing with pipelines and imputers, stratified train-test splitting, and a user input form. Predictions are displayed on the web page, making it ideal for learning basic machine learning deployment with Flask.

  • Updated Aug 30, 2024
  • Python

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