chirag314 / dl-aurelien-geron-book-practice

Project Overview:

• This project explores a dataset containing housing information to analyze features, visualize relationships, and prepare data for potential machine learning tasks.

Key Steps:

Data Loading and Preparation:

Downloads and extracts the housing dataset from a GitHub repository.
Splits the data into training and test sets using stratified sampling to ensure representative income distribution.
Creates a copy of the training set for exploration and further processing.

Data Exploration and Visualization:

Generates histograms of numerical attributes to visualize distributions.
Creates scatter plots to visualize geographical patterns and correlations.
Calculates and visualizes correlations between numerical attributes.

Feature Engineering:

Adds new features like rooms per house, bedrooms ratio, and people per house to potentially enhance predictive power.

Handling Missing Values:

Demonstrates different approaches to handle missing values:
Dropping rows with missing values in a specific attribute.
Dropping the entire attribute with missing values.
Filling missing values with the median using Scikit-Learn's SimpleImputer.'

Dependencies:

pandas
NumPy
matplotlib
tarfile
urllib.request
sklearn.model_selection
sklearn.impute

Further Exploration:

Experiment with different feature engineering techniques.
Apply various machine learning algorithms to predict housing values.
Evaluate model performance and identify key factors influencing housing prices.

Contribution:

Feel free to fork and contribute to this project by:

Performing more in-depth analysis and visualization. Implementing machine learning models for prediction tasks. Sharing insights and findings from your exploration.