---
title: "How to Predict %80 Accuracy in the Titanic Disaster Competition"
url: https://daily.dev/posts/how-to-predict-80-accuracy-in-the-titanic-disaster-competition-qpkmexpnm
source_url: https://python.plainenglish.io/how-to-predict-80-accuracy-in-titanic-disaster-competition-762f5c0f4bfb
type: article
source: "Python in Plain English"
published: 2024-03-25T04:18:01.007Z
updated: 2024-05-09T09:06:08.895Z
tags: ["machine-learning", "data-science", "logistic-regression", "random-forest", "xgboost", "feature-engineering"]
reading_time: 13
upvotes: 0
comments: 0
language: en
---

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# How to Predict %80 Accuracy in the Titanic Disaster Competition

**[Python in Plain English](https://daily.dev/sources/inPlainEngHQ)** · 13 min read · 0 upvotes · 0 comments

## Summary

This post explains how to achieve over 80% accuracy in the Kaggle Titanic Disaster Competition by using data manipulation, feature engineering techniques, and machine learning algorithms. It provides details on the problem, data analysis, missing value handling, feature engineering, and machine learning model implementation.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://python.plainenglish.io/how-to-predict-80-accuracy-in-titanic-disaster-competition-762f5c0f4bfb>

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---

Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#data-science](https://daily.dev/tags/data-science), [#logistic-regression](https://daily.dev/tags/logistic-regression), [#random-forest](https://daily.dev/tags/random-forest), [#xgboost](https://daily.dev/tags/xgboost), [#feature-engineering](https://daily.dev/tags/feature-engineering)

[View this post on daily.dev](https://daily.dev/posts/how-to-predict-80-accuracy-in-the-titanic-disaster-competition-qpkmexpnm)