Overview:-This project performs Exploratory Data Analysis (EDA) on the Titanic dataset to understand data patterns, correlations, missing values, and feature behavior. The goal is to analyze the dataset using statistical summaries and visualizations.
🎯 Objectives Generate summary statistics (mean, median, std, etc.) Visualize numeric features using histograms and boxplots Analyze feature relationships using a correlation matrix Detect patterns, trends, and anomalies Make basic feature-level inferences from the data 🧰 Tools & Libraries:-Python,Pandas,NumPy,Matplotlib,Seaborn (only for loading dataset or optional visuals) 📂 Dataset The project uses the Titanic dataset. You may load it through Seaborn or use a local titanic.csv. import seaborn as sns df = sns.load_dataset("titanic") Or place titanic.csv in the project folder. 📊 What This Project Includes:- ✔ Summary statistics (numeric & categorical) ✔ Missing value analysis ✔ Histograms for numeric variables ✔ Boxplots grouped by survival ✔ Correlation heatmap ✔ Scatter plots for key features ✔ Categorical value counts ✔ Basic insights like survival rate by sex and class STRUCTURE:- ├── eda_titanic.ipynb # Jupyter Notebook with full EDA ├── titanic.csv # Dataset (if used) ├── titanic_summary.csv # Generated summary file └── README.md # Project documentation
Install required libraries: pip install pandas numpy matplotlib seaborn Open the notebook: jupyter notebook Run all cells.
📈 Results The EDA provides: Key numerical distributions Relationship between passenger class, gender, age, and survival Data quality insights (missing values, outliers) Correlations among numeric features 📬 Author Project completed as part of a Data Analytics/ML learning task.