AI-ML-task-2

# Titanic Dataset - Exploratory Data Analysis (EDA)
## Overview
This project performs **Exploratory Data Analysis (EDA)** on the Titanic dataset using **Pandas, Seaborn, Matplotlib, and Plotly**.

## Objectives
- Understand data using statistics and visualizations.
- Identify patterns, trends, and anomalies.
- Generate insights into survival rates based on features.

## Steps Followed
1. **Data Loading & Cleaning**: Read and inspect the dataset.
2. **Summary Statistics**: Compute mean, median, std deviation, missing values.
3. **Visualizations**:
   - Histograms & Boxplots to analyze distributions.
   - Pairplots & correlation matrices to explore feature relationships.
4. **Feature Insights**: Analyze survival rates based on gender and class.

## Key Files
- `Titanic-Dataset.csv` - Raw dataset
- `titanic-eda.ipynb` - Jupyter Notebook with all analysis
- `README.md` - Project documentation

## How to Run
Clone the repository and open the Jupyter Notebook:
```bash
git clone https://github.com/YOUR_USERNAME/eda-titanic-analysis.git
cd eda-titanic-analysis
jupyter notebook titanic-eda.ipynb

Tools Used

• Pandas - Data manipulation
• Seaborn & Matplotlib - Data visualization
• Plotly - Interactive plots

Insights & Findings

• Survival rates were higher for females and first-class passengers.
• Fare distribution had outliers.
• Correlation between age and survival was weak.

---