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This repository showcases my Kaggle projects in Data Science and Machine Learning fields, offering valuable resources and practical examples for the community. Feel free to explore and leverage the shared content.

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AlbertGallegoJimenez/Kaggle-Projects

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Kaggle

Kaggle Projects Repository

Welcome to my Kaggle Projects repository, where I store my personal solutions as part of my learning journey in data science and machine learning.

Repository Purpose

This repository serves as a record of my personal challenges on Kaggle, where I tackle different datasets and problems to enhance my skills in data analysis, predictive modeling, and other areas related to data science.

Current Projects

Here's a list of projects I have tackled to date (📊 and 🤖 denote whether the project contains Statistical Analysis and/or Modeling, respectively):

  1. 🐲Pokémon - 📊: Explored a Pokémon dataset, conducting in-depth statistical and visual analysis to unveil patterns and quirks in their characteristics.

  2. 🛳️Titanic - 🤖: Developed a binary classification model of the Titanic dataset to predict passenger survival of the shipwreck.

  3. 🏘️Housing price prediction - 📊🤖: Developed a regression model to predict housing prices based on various features.

  4. 🎥IMDb movies - 📊: Explored an IMDb movies dataset, conducting a comprehensive data pre-processing and an in-depth statistical analysis to find valuable patterns.

  5. Daily atmospheric data - 📊🤖: Developed a regression model capable of predicting atmospheric data with strong seasonality. Machine Learning (Random Forest), Deep Learning (LSTM and CNN+LSTM) and ARIMA models have been explored.

Contact

Thank you for exploring my repository! If you have any questions, suggestions, or just want to connect, feel free to reach out:

I hope you find the projects here helpful and inspiring for your own Kaggle challenges!

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This repository showcases my Kaggle projects in Data Science and Machine Learning fields, offering valuable resources and practical examples for the community. Feel free to explore and leverage the shared content.

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