DSF-Dibimbing

Data Science Fair 3.0 Dibimbing Portofolio - Analysis and Learning from titanic dataset

The purpose of this project is to master the exploratory data analysis (EDA) in titanic crash with Pandas framework.

Download Dataset file : https://www.kaggle.com/competitions/titanic

Goals of the Project:

1. Explore a titanic accident dataset with Pandas framework.
2. Build pivot tables.
3. Visualize the dataset with various plot types.

Outline

1. Materials and methods
2. General Part : Data Preprocessing, Data Cleaning, Data Visualization
3. Tasks

Materials and Methods

How To Learn with Cross-Industry Standard Process for Data Mining (CRISP-DM)

1. Business Understanding : The Business Understanding phase focuses on understanding the objectives and requirements of the project. In this project, we should know about what's happening, why it's happening and who is involved in Titanic Accident.

2. Data Understanding : Next is the Data Understanding phase. Adding to the foundation of Business Understanding, it drives the focus to identify, collect, and analyze the data sets that can help you accomplish the project goals. The data is consist of Name of Passengers, Age, Sex, Fare, Embarked, Class of ticket (Pclass) and so on.

3. Data Preparation : This phase, which is often referred to as “data munging”, prepares the final data set(s) for modeling. In this methods , we cleaning the data from anomali, null, not correlation data and any others missed from the data source.

4. Modeling : What is widely regarded as data science’s most exciting work is also often the shortest phase of the project. Here you’ll likely build and assess various models based on several different modeling techniques.

5. Evaluation : Whereas the Assess Model task of the Modeling phase focuses on technical model assessment, the Evaluation phase looks more broadly at which model best meets the business and what to do next

6. Deployment : A model is not particularly useful unless the customer can access its results. The complexity of this phase varies widely

Start Working

Download Libary that you need for analytics

import numpy as np # linear algebra import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

Read Data

used pandas library to read the data that we want to learn and analysis. Then, store it to data frame.

df = pd.read_csv("/kaggle/input/titanic/train.csv")

df #Show data frame from csv file

Statistik deskriptif dari data

Check Null value

Data Preprocessing

Fill Blank data on Age Columns with Median

Fill Blank data on Embarked Columns with Modus

Drop Cabin's Columns because too much empty value

Check null value as well

EDA

Import library for EDA

Analysis for Age Columns

Analysis for Fare Columns

Analysis for Pclass Columns

Analysis for Sex Columns

Correlation Matrix

Conclusion

• Children under 10 years have a better chance of surviving, perhaps because they are prioritized for rescue using lifeboats

• Pclass and Fare have a negative correlation, the lower the Pclass, the higher the Fare

• The feature that has the most influence on Survived is Pclass

• Passengers with high costs have a better chance of surviving than those with low costs

• The percentage of survivors in Pclass = 1 is greater than in Pclass 2 and 3

• The percentage of survivors in Sex = female is higher than male