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Project: California Housing Price Prediction

Objective:

The goal of this project is to explore the California housing dataset and understand the relationship between various features (such as location, population, income levels, etc.) and the house prices. Using visualizations and data analysis techniques, we aim to explore key patterns in the data that can help in predicting house prices.

Dataset:

The dataset is taken from the California Housing Dataset, which contains various features for each block in California, such as:

  • longitude, latitude
  • housingMedianAge
  • totalRooms, totalBedrooms
  • population, households
  • medianIncome, medianHouseValue
  • oceanProximity (the distance of the house from the ocean)

Steps Taken:

  1. Data Loading and Exploration:

    • The fetch_california_housing function from sklearn.datasets was used to load the dataset.
    • Data was converted into a Pandas DataFrame for easier manipulation.
    • Features (columns) were labeled appropriately, and the target variable (house prices) was added to the dataset.

  2. Data Preprocessing:

    • Data was examined for missing values and outliers.
    • Features were understood based on their meaning and potential relationship with house prices.

  3. Data Visualization: Various plots were created to understand the relationships between features and house prices:

    • Scatter Plots: To show individual feature vs. price relationships.
    • Correlation Heatmap: To identify correlations between features and prices.

    ------- Interesting Aspects ---------

    • Pairplot: To examine pairwise relationships between features.
    • Boxplot & Violin Plot: To visualize distributions and detect outliers.
    • Histogram: To visualize the distribution of house prices.
    • KDE Plot: To estimate and visualize the probability distribution of prices.

  4. Modeling (if applicable):

    • Potential machine learning models could be applied to predict house prices using features such as linear regression, decision trees, or random forests.
    • The goal is to build a model that can accurately predict housing prices based on the available features.

Tools and Libraries Used:

  • Python: Main programming language.
  • Pandas: Data manipulation and analysis.
  • Matplotlib & Seaborn: For data visualization.
  • Scikit-learn: For loading the dataset and potential machine learning models.

Conclusion:

This project explores how various features impact house prices in California. By visualizing the data and analyzing relationships between features, we can uncover patterns and gain insights that can be useful in predicting future house prices in similar regions.

The visualizations give us an intuitive understanding of the relationships between house prices and features like location, income, and proximity to the ocean.