Visualizing Prediction and Correlation

Data analysis to estimate the state of the existing data according to the desired category.

Background Problem

On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for everyone on board, resulting in the death of 1502 out of 2224 passengers and crew. what sorts of people were more likely to survive?

Steps in this project :

Libraries

1. pandas
2. matplotlib
3. seaborn

Exploratory Data Analysis (EDA)

• Understanding dataset
• view summary statistic dataset

Feature Engineering

• Checking & Handling missing values
• Checking & Handling Duplicated Data
• Making Sure Complete Dataset

Visualizing the Prediction

Visualizing what has been predicted on the dataset, this prediction is divided into several categories, namely :

1. Visualizing the distribution of Age
2. Visualizing the distribution of Fare
3. Visualizing the distribution of Survived
4. Survival rates based on Sex
5. Survival rates based on Pclass
6. Survival rates based on Embarked
7. Box Plot for Numerical Features

Plot the Correlation between each categories

to determine the correlation between variables in a data set. This heatmap can help identify variables that are strongly correlated or contradictory.

Insight

know more clearly what categories or types of people have survived in relation to existing data. analyzing by finding out whether there is a relationship between each category to see and become an insight that in the future it can be an evaluation of categories with these related variables that have a better chance of surviving.