🚢 Titanic Dataset – Data Cleaning & Exploratory Data Analysis (EDA)

📌 Project Overview

This project focuses on performing data cleaning and exploratory data analysis (EDA) on the Titanic dataset from Kaggle. The goal is to understand the dataset, clean missing and inconsistent data, explore relationships between variables, and identify patterns and trends that influenced passenger survival.

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📂 Dataset

• Source: Kaggle – Titanic: Machine Learning from Disaster
• File Used: train.csv
• Records: Passenger demographic and travel information
• Target Variable: Survived (0 = Did not survive, 1 = Survived)

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🎯 Objectives

• Clean and preprocess the dataset
• Handle missing values and irrelevant features
• Perform univariate, bivariate, and multivariate analysis
• Identify key factors affecting survival
• Visualize insights using graphs and charts

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📈 Key Patterns & Trends Identified

• Female passengers had significantly higher survival rates than males
• First-class passengers were more likely to survive
• Children had higher survival probability compared to adults
• Higher fare-paying passengers showed better survival chances
• Third-class male passengers had the lowest survival rate

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🧰 Tools & Technologies Used

• Programming Language: Python
• Libraries:
  • Pandas
  • NumPy
  • Matplotlib
  • Seaborn
• Environment: Jupyter Notebook

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📚 Skills Demonstrated

• Data Cleaning & Preprocessing
• Exploratory Data Analysis (EDA)
• Data Visualization
• Pattern & Trend Identification
• Analytical Thinking

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📬 Contact

For any questions, feedback, or collaboration opportunities, feel free to reach out:

📧 Email: thoratom37@email.com

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