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Kaggle's Titanic Machine Learning Challenge
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README.md
out.csv
test.csv
titanic.py
train.csv

README.md

Kaggle: Titanic Challenge

This is my take on the Kaggle Titanic Challenge, I used Sklearn's Logistic Regression to produce an accuracy of 76.5%. I performed mild preprocessing on the data, which included dividing the fares and ages into 4 categories, allowing for a competely numerical dataset. I dropped Name, Cabin, Ticket, and Embarked from the data, proving either too challenging or irrelevant for our purposes. The name could have potential to be used, and I plan to change that in later revisions.

Requirements

  • Pandas (Data handling)
  • Numpy (General Operations)
  • Scikit-Learn (Logistic Regression Model)

All the data and code for the project can be found in this repo, as well as my output.

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