This repository is about Data analysis with Python using libraries such as Pandas, Matplotlib, Seaborn, and NumPy to analysis prices of properties in given areas to predict future prices.

• The original file in which the analysis of data is called RAW_DATA in the repository.

By leveraging Pandas, we can efficiently load, clean, and manipulate datasets, ensuring data integrity.

Matplotlib allows us to create insightful visualizations, such as line plots, scatter plots, and bar charts, to visualize trends and patterns in property prices.

Seaborn enhances our visualizations with attractive statistical graphics, providing deeper insights into the data.

NumPy facilitates mathematical operations and statistical calculations, enabling us to analyze numerical aspects of property prices, such as means, medians, and standard deviations.

With these libraries at my disposal, I delved into comprehensive data analysis to uncover valuable information about property markets in targeted towns.