Titanic Dataset Python Analysis

I need a clear, reproducible Python workflow that takes the original Titanic CSV, cleans it thoroughly, uncovers the key statistical patterns, and presents the findings visually so anyone can see exactly how gender, age, and passenger class affected the odds of survival.

Data preparation comes first. Please write code that handles missing values intelligently (for instance, impute age rather than simply dropping rows), removes true duplicates, and corrects every inappropriate data type so numeric columns behave as numbers and categorical columns are treated consistently.

Once the frame is tidy, move into Exploratory Data Analysis. I want to see descriptive statistics that highlight central tendency and spread, a correlation analysis that makes the relationships between variables explicit, and an outlier detection step that flags anything that could distort the story of survival.

Visual storytelling is just as important. The final notebook should include bar charts that compare survival counts across categories, histograms that reveal age distribution by outcome, and scatter plots that bring continuous variables together so patterns pop out instantly.

Deliverables
• A well-commented Jupyter notebook (.ipynb) using pandas, NumPy, Matplotlib or Seaborn
• Cleaned Titanic dataset saved as a separate CSV
• All generated figures saved to a /figures directory
• A short written summary (Markdown cell is fine) interpreting the main insights

Acceptance criteria: the notebook runs end-to-end without manual edits, no missing or mismatched data types remain, and every visualization directly supports the narrative about how gender, age, and class influence survival.