EDA Kaggle Titanic Dataset

📌 Overview

This project performs Explatory Data Analysis (EDA) on the Titanic dataset from Kaggle to uncover patterns, relationships, and insights that influence passenger survival

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🎯 Objectives

• Perform data cleaning and preprocessing
• Handle missing values and outliers
• Conduct univariate, bivariate, and multivariate analysis
• Analyze the target variable (Survived)

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🛠️ Tools & Technologies

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn

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📊 Dataset

Source: Kaggle Titanic Dataset 👉 https://www.kaggle.com/code/mariyamalshatta/masterclass-1-a-comprehensive-guide-for-eda

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🔍 Key Steps

1. Initial Data Exploration
   • Checked dataset structure using:
   • df.head()
   • df.info()
   • df.describe()
   • Identified missing values and duplicates

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1. Data Cleaning
   • Handled missing values in:
   • Age
   • Cabin
   • Embarked
   • Removed duplicates

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1. Univariate Analysis
   • Age distribution
   • Passenger class distribution
   • Fare distribution

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1. Bivariate Analysis
   • Survival vs Gender → Females had higher survival
   • Survival vs Pclass → 1st class had higher survival
   • Fare vs Pclass relationship

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1. Multivariate Analysis
   • Combined effects of:
   • Age + Pclass + Fare
   • Embarked + Pclass + Survival

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1. Outlier Handling
   • Fare outliers analyzed
   • Decision: (Explain what YOU chose — remove/cap/keep)

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1. Target Variable Analysis
   • Survival distribution (imbalanced dataset)
   • Key influencing factors:
   • Gender
   • Class
   • Age

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📈 Key Insights

• Females and 1st class passengers had higher survival rates
• Higher fare correlates with higher survival
• Younger passengers had slightly better survival chances

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🖼️ Screenshots

(Upload images to /assets/img/ and link them below)

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🚀 Outcome

The project successfully demonstrated how data analysis can be used to improve academic performance monitoring and decision-making.

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🔗 GitHub Repository

View Project Code

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📚 Lessons Learned

• Importance of data cleaning before analysis
• How to communicate insights using dashboards
• Practical use of Power BI for visualization