Data Analysis on the Titanic Dataset 🚢
I recently completed a captivating data analysis project on the Titanic dataset, a widely recognized dataset in the field of data science. The Titanic dataset provides a unique opportunity to delve into machine learning and data analysis tasks, particularly predicting passenger survival based on various features.
✨ Project Highlights:
Exploratory Data Analysis : I began by conducting an in-depth exploration of the dataset, aiming to understand the variables and their distributions. This initial step allowed me to gain valuable insights into the dataset's structure and uncover potential patterns.
Missing Value Handling : The dataset presented missing values, and I carefully devised appropriate strategies to address this challenge. By applying effective imputation techniques, such as mean, median, or regression, I successfully filled in the missing values, ensuring a comprehensive dataset for analysis.
Insightful Dashboard Presentation : To showcase my findings, I developed a visually appealing and user-friendly dashboard. The dashboard served as an overview of the dataset, allowing me to highlight specific insights and trends discovered during the analysis. By leveraging visualization tools like pivot tables, I identified correlated data and trends, enabling a comprehensive understanding of the factors that influenced passenger survival.
Data-Driven Decision Making : Throughout the project, I focused on leveraging data to make informed decisions. By analyzing the relationships and trends within the dataset, I uncovered valuable insights that can contribute to making informed decisions in various domains, including safety measures, risk assessment, and emergency response planning.
To go through the observations and conclusion please take a look at the ppt attached.
For a detailed look of the dashboard please look at the png files and to take a look at the dashboard please follow the tableu public link attached below:
DashBoard1: https://public.tableau.com/views/DADraft1/Dashboard1?:language=en-US&:display_count=n&:origin=viz_share_link