🌊Titanic Dataset Analysis 🚢

Let's dive into analyzing the Titanic dataset! We'll filter, transform, and explore different analyses to understand the factors affecting passenger survival. 🤓

1. Data Loading & Preparation 📂

• The dataset is loaded from a CSV file.
• Irrelevant columns are removed: SibSp, Parch, Name, PassengerId, Ticket, Cabin.
• 👨‍👩‍👧‍👦 A new column FamilySize is created to calculate the family size (including the passenger). ## 2. Converting Categorical Columns to Numeric🧮
• Sex is converted to numeric values: male → 0, female → 1.
• Embarked is converted to numeric values: C → 0, Q → 1, S → 2.

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3. Data Visualization 📊

Variable Distributions🔍

• Histograms for continuous variables: Age, Fare.
• Bar charts for categorical variables: FamilySize, Sex, Pclass, Embarked.

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4. Survival Analysis 🧑‍⚖️

• Analyzing survival rate based on:
  • Age (Age).
  • Ticket fare (Fare).
  • Family size (FamilySize).

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5. Categorical Survival Analysis 🏷️

• Comparing survival rates based on:
  • Gender (Sex).
  • Travel class (Pclass).
  • Embarkation port (Embarked).
• Displaying the data using countplot.

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6. Fare & Survival Exploration 💰

• Using boxplot and histplot to analyze the relationship between ticket fare and survival probability.
• Splitting the data into two groups:

  • Low-cost tickets (Fare < 50).
  • High-cost tickets (Fare >= 50).

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7. Pie Charts of Survival Rates 🍰

Finally, let’s wrap it up with pie charts for survival rates by Age and Fare groups. 🥧

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7. Correlation Heatmap 🔥

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8. Missing Values 💔

• Detecting missing values (NaN) using heatmap.
• Graphical representation of missing data extent.

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🎉 That's it! We now have a comprehensive Titanic dataset analysis, including visualizations and statistical insights. 💡