https://github.com/nayankanaparthi/california-housing-price-prediction
https://github.com/nayankanaparthi/california-housing-price-prediction
Last synced: 8 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/nayankanaparthi/california-housing-price-prediction
- Owner: NayanKanaparthi
- Created: 2023-07-11T14:10:33.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-07-19T06:51:58.000Z (over 2 years ago)
- Last Synced: 2025-02-05T21:59:34.381Z (10 months ago)
- Language: Jupyter Notebook
- Size: 76.2 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# About the Dataset
Welcome to the California Housing Price Prediction dataset! This dataset is used in the second chapter of Aurélien Géron's book, 'Hands-On Machine learning with Scikit-Learn and TensorFlow.' It serves as an excellent introduction to implementing machine learning algorithms as it requires rudimentary data cleaning, has an easily understandable list of variables, and sits at an optimal size between being too toyish and too cumbersome.
The data contains information from the 1990 California census, focusing on the houses found in various California districts and some summary statistics about them. Although it may not help you predict current housing prices like the Zillow Zestimate dataset, it provides an accessible introductory dataset for teaching people about the basics of machine learning.
## Content
The dataset contains the following variables:
longitude: Longitude of the district.
latitude: Latitude of the district.
housing_median_age: Median age of houses in the district.
total_rooms: Total number of rooms in the district's houses.
total_bedrooms: Total number of bedrooms in the district's houses.
population: Total population in the district.
households: Total number of households in the district.
median_income: Median income of households in the district.
median_house_value: Median value of houses in the district (target variable).
ocean_proximity: Proximity of the district to the ocean.
Please note that the data is not fully cleaned, so some preprocessing steps will be required before building a machine learning model.
## Using the Dataset
You can use this dataset to explore various machine learning algorithms and regression techniques. In your research, you mentioned that you used an ensemble RandomForest Regressor to build your model. If you're new to machine learning, this dataset is an ideal starting point to gain hands-on experience.
To get started, you can follow these steps:
### Data Preprocessing:
Since the data requires some cleaning and preprocessing, you can explore techniques like data imputation, handling categorical variables (e.g., ocean_proximity), and feature scaling.
### Data Exploration:
exploratory data analysis to gain insights into the distribution of variables, correlations, and potential outliers.
### Feature Engineering:
Consider creating new features that might improve the predictive performance of your model.
### Model Building:
Utilize various regression models, such as Linear Regression, Decision Trees, Random Forest, Gradient Boosting, etc. You already mentioned using a RandomForest Regressor, but don't hesitate to experiment with other algorithms.
### Model Evaluation:
Assess the performance of your model using appropriate evaluation metrics like Mean Squared Error (MSE) or Root Mean Squared Error (RMSE).
### Predictions:
Once you have trained your model, you can use it to predict the median house values for new data points.