https://github.com/aishwaryagm1999/california-housing-prices-data-analysis

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.
https://github.com/aishwaryagm1999/california-housing-prices-data-analysis

data-analysis data-visualization matplotlib numpy pandas python seaborn

Last synced: about 14 hours ago
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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.

• Host: GitHub
• URL: https://github.com/aishwaryagm1999/california-housing-prices-data-analysis
• Owner: aishwaryagm1999
• Created: 2022-11-24T12:54:19.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-11-01T07:08:06.000Z (18 days ago)
• Last Synced: 2024-11-01T08:18:16.847Z (17 days ago)
• Topics: data-analysis, data-visualization, matplotlib, numpy, pandas, python, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 786 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
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.