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https://github.com/shridhar1504/titanic-survivor-prediction-datascience-classification-project

This projects predicts whether a passenger on the titanic survived or not using machine learning algorithms with the given details of the passenger data.
https://github.com/shridhar1504/titanic-survivor-prediction-datascience-classification-project

classification-algorithm data-analysis data-analytics data-cleaning data-preprocessing data-science data-visualization eda exploratory-data-analysis gradient-boosting jupyter-notebook machine-learning machine-learning-algorithms matplotlib naive-bayes-classifier predictive-modeling python3 scikit-learn seaborn supervised-learning

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This projects predicts whether a passenger on the titanic survived or not using machine learning algorithms with the given details of the passenger data.

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# Titanic-Survivor-Prediction-Datascience-Classification-Project

This projects predicts whether a passenger on the titanic survived or not using machine learning algorithms with the given details of the passenger data.

## Problem Statement:

The sinking of the Titanic was a tragedy that claimed the lives of over 1,500 people. One of the most important questions that has been asked since the disaster is: who was most likely to survive? This project attempts to answer this question by using machine learning to predict whether a passenger on the Titanic survived or not.

## Solution Approach:

The data for this project is the Titanic dataset from Kaggle. This dataset contains information on 419 passengers, including their age, gender, ticket price, and whether they survived. The solution approach is to use machine learning to train a model that can predict whether a passenger survived based on their personal characteristics. The following machine learning algorithms were used:

* Logistic Regression

* Support Vector Machine(SVC)

* KNeighbors(KNN)

* Decision Trees

* Random Forest

* Extra Trees

* Naive Bayes(Gaussian, Bernoulli, Multinomial)

* Gradient Boosting (XG Boost, CatBoost, LightGBM)

## Observation:

The following observations were made during the analysis of the Titanic dataset:

* Age: Passengers who were younger were more likely to survive.

* Gender: Females were more likely to survive than males.

* Pclass: Passengers in first class were more likely to survive than passengers in third class.

* Fare: Passengers who paid a higher fare were more likely to survive.

## Findings:

The following findings were made after training and evaluating the machine learning models:

* The best machine learning algorithm for this project was Gaussian Naive Bayes.

* The Gaussian Naive Bayes model achieved an accuracy of 78 % on the test set.

* The results of this project can be used to understand the factors that influenced survival and to develop strategies for improving survival rates in future disasters.

## Insights:

The insights from this project can be used to understand the factors that influenced survival on the Titanic and to develop strategies for improving survival rates in future disasters. For example, the findings suggest that it is important to prioritize women and children in rescue efforts. Additionally, the findings suggest that it is important to provide lifeboats for all passengers, regardless of their class.