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- titanic_dataset.csv · GitHub
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- titanic-dataset · GitHub Topics · GitHub
Data visualization tool for the Titanic dataset developed in...
- Likith25/Titanic_dataset
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Apr 9, 2024 · Clone this repository at <script src="https://gist.github.com/fyyying/4aa5b471860321d7b47fd881898162b7.js"></script> Save fyyying/4aa5b471860321d7b47fd881898162b7 to your computer and use it in GitHub Desktop.
Data visualization tool for the Titanic dataset developed in Unity3D for the course Interaction in Mixed Reality Spaces at the University of Konstanz.
An in-depth analysis of the Titanic dataset, exploring passenger demographics, survival rates, and other key metrics using Python. This repository contains code for data acquisition, preprocessing,...
- Data Analysis and Prediction of Survivors on the Titanic Dataset
- Note:
- Data Analysis:
- Data prepocessing:
- Missing value/data:
- Feature Scaling:
Dependencies:
This project addresses the following Data Analysis topics:
Data analysis is a statistical method used to describe variability among observed, correlated variables. The goal of performing factor analysis is to search for some unobserved variables called factors.
This dataset has passenger information who boarded the Titanic along with other information like survival status, Class, Fare, and other variables. The unfortunate event which was occurred on 15 April 1912, the Titanic sank after colliding with an iceberg, aboard 2224 peoples.
Data Exploration and Preparation Learn about data: Are there missing data? Is it categorical? if not, min , max, avg values? if yes, what are the categories? distribution of variables Duplicate entry. Data Representation and Transformation Few Passengers didn't pay for the ticket, so there may be a possibility that they didn't purchase a ticket or ...
It is a process to convert raw data into meaningful data using different techniques. Data preprocessing techinques are: 1. Data Cleaning 2. Data Integration 3. Data Reduction 4. Data Transformation 5. Data Discretization
•Data Cleaning: It is a technique for identifying the missing values, smooth out noise while identifying outliers, correcting inconsistencies in the data.
•Data Integration: It is a techinque to merges data from multiple sources into a coherent data store, such asa data warehouse.
•Data Reduction: It is a technique use to reduce the data size by aggregating, eliminating redundant features, or clustering, for instance.
•Data Transformation: It is a technique, data are transformed into appropriate forms for ML model training, such as normalization, may be applied where data are scaled to fall within a range 0 to 1 or -1 to 1.
•Data Discretization: It is a technique tranforms numeric data by mapping values to interval or concept labels.
There are the best 6 methods to handle missing data or values. It is the part of Data Preprocessing and this is the most important step to build Machine Learning/Data Science project. The following are the most popular methods to handle missing data.
•Ignore missing values row / Delete row
•Fill missing value manually
•Use global constant
•Measure of central tendency (Mean, Median & Mode)
•Measure of central tendency for each class
It is a method to scale numeric features in the same scale or range (-1 to 1, 0 to 1). This step is involved in data prepocessing and before ML model training. It is also called Data Normalization.
•we apply feature scaling on independent variables.
•those algorithms calculate distance and gradient descent based algorithms required Feature Scaling.
•tree based algorithms not required FS.
Jan 27, 2018 · Star 26. Code. Issues. Pull requests. Using Machine learning algorithm on the famous Titanic Disaster Dataset for Predicting the survival of the passenger.
Titanic Dataset¶ Kasey Cox / March 2017. Question: Which passenger characteristics correlate with survival?¶
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