California House Price Prediction: Case Study

Download the dataset below to solve this Data Science case study on California House Price Prediction. (Dataset source: Kaggle)

California House Price Prediction: Case Study

Predicting prices of properties like housing and land is an application of Data Science in real estate. The price or rent of a housing property depends on many factors like location, number of rooms, and many other factors.

Here is a dataset based on the housing prices of California submitted by Cam Nugent on Kaggle. Below are all the features in the dataset:

1. longitude: The longitude of the housing property
2. latitude: The latitude of the housing property
3. housingMedianAge: The median age of the housing property
4. totalRooms: total number of rooms in the house
5. totalBedrooms: total number of bedrooms in the house
6. population: total number of people living in the area of the housing property
7. households: total number of households in the area of the housing property
8. medianIncome: The median income of households in the area of the housing property
9. medianHouseValue: The median value of households in the area of the housing property
10. oceanProximity: The location of the housing property

This problem aims to predict housing prices, so the medianHouseValue is the target variable. Perform regression analysis to find relationships about how different factors affect the price of a housing property.

References to Solve this Data Science Case Study

• End-to-End Machine Learning Project by Aurélien Géron
• California House Price Prediction by Akash Joshi