https://github.com/mastermindromii/titanic-incident-analysis

Welcome to the Titanic Dataset EDA project! On my second day of practicing Exploratory Data Analysis (EDA) in data science. Building on the foundations laid during my first day's exploration of the Diwali Sales Data, I'm now turning my attention to the fascinating stories hidden within the Titanic dataset.
https://github.com/mastermindromii/titanic-incident-analysis

Last synced: 9 days ago
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Welcome to the Titanic Dataset EDA project! On my second day of practicing Exploratory Data Analysis (EDA) in data science. Building on the foundations laid during my first day's exploration of the Diwali Sales Data, I'm now turning my attention to the fascinating stories hidden within the Titanic dataset.

• Host: GitHub
• URL: https://github.com/mastermindromii/titanic-incident-analysis
• Owner: MasterMindRomii
• Created: 2023-10-17T12:32:00.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2023-10-17T17:13:53.000Z (about 1 year ago)
• Last Synced: 2024-11-10T21:17:28.107Z (9 days ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 70.3 KB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# EDA-DAY2

On this second day of my data science journey, I'm excited to dive into the famous Titanic dataset and perform Exploratory Data Analysis (EDA). The Titanic dataset is a classic in the world of data science and provides a great opportunity to explore, analyze, and draw insights from historical passenger data. Just like my first day, I'm enthusiastic about this step in my data science journey and the chance to apply my growing knowledge to real-world data.

Data Cleaning: Just like on my first day, I'll start by cleaning the data. This process involves handling missing values, dealing with outliers, and ensuring that the dataset is well-prepared for analysis.

Data Visualization: I'll create visualizations to better understand the relationships between variables. This will include creating histograms, scatter plots, and more to reveal insights and patterns in the data.

Data Insights: As I explore the data, I'll aim to draw meaningful insights. For example, I might analyze the age and gender distribution of passengers, the impact of passenger class on survival, and any other patterns that emerge from the data.

Just like on my first day, I expect challenges and learning opportunities along the way. I will document any errors I encounter and share my solutions with the community. It's all part of the learning process, and I'm committed to growing as a data scientist.

Stay tuned for updates on my Titanic dataset EDA journey! Day 3 awaits with more exciting challenges.