Performed Data Cleaning and Data Analysis of the California Housing Prices Dataset to find the relation between the housing prices at a block and the amenities and facilities stated in the dataset such as total number of rooms, ocean proximity etc.

The notebook conducts a data analysis of California housing prices, exploring various relationships within the dataset:

1. Data Cleaning:

   • Imports necessary libraries (numpy, pandas, matplotlib, seaborn).
   • Loads the dataset and examines the ocean_proximity column for unique values.
   • Checks for and removes NaN values, ensuring accurate analysis.

2. Exploratory Data Analysis (EDA):

   • Analyzes and visualizes several features:
     • Population by Ocean Proximity: Groups data by ocean_proximity and plots a bar graph with population on a logarithmic scale, showing higher population near locations labeled "<1H OCEAN."
     • Income and House Value Relationship: Uses a 2D histogram to show a positive correlation between median income and house value, with higher income concentrated in houses under $200,000.
     • House Age and Value Correlation: Uses a scatter plot to show that median house value and median housing age are not strongly correlated.
     • Housing Ocean Proximity Distribution: A pie chart reveals that most houses are within "<1H OCEAN," with "ISLAND" having the least presence.
     • House Age Frequency: A histogram shows that the majority of homes have a median age between 30 to 35 years.

The notebook effectively uses grouping, 2D histograms, pie charts, and scatter plots to illustrate patterns and relationships within the California housing market dataset.