Abaout Dataset;
California Housing This is a dataset obtained from the StatLib repository. Here is the included description: S&P Letters Data We collected information on the variables using all the block groups in California from the 1990 Cens us. In this sample a block group on average includes 1425.5 individuals living in a geographically co mpact area. Naturally, the geographical area included varies inversely with the population density. W e computed distances among the centroids of each block group as measured in latitude and longitude. W e excluded all the block groups reporting zero entries for the independent and dependent variables. T he final data contained 20,640 observations on 9 variables. The dependent variable is ln(median house value). Bols tols
INTERCEPT 11.4939 275.7518
MEDIAN INCOME 0.4790 45.7768
MEDIAN INCOME2 -0.0166 -9.4841
MEDIAN INCOME3 -0.0002 -1.9157
ln(MEDIAN AGE) 0.1570 33.6123
ln(TOTAL ROOMS/ POPULATION) -0.8582 -56.1280
ln(BEDROOMS/ POPULATION) 0.8043 38.0685
ln(POPULATION/ HOUSEHOLDS) -0.4077 -20.8762
ln(HOUSEHOLDS) 0.0477 13.0792
The file contains all the the variables. Specifically, it contains median house value, med ian income, housing median age, total rooms, total bedrooms, population, households, latitude, and lo ngitude in that order.
Reference
Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions, Statistics and Probability Letters, 33 (1997) 291-297.
GOAL; Goal: The objective is to predict house prices using various features, such as the number of rooms, the size of the house, location, proximity to key amenities, and neighborhood characteristics. By analyzing these factors, the aim is to create a predictive model that estimates house prices accurately. This helps in understanding how different variables impact property values and provides insights for potential buyers, sellers, or real estate professionals to make informed decisions. The ultimate goal is to develop a reliable machine learning model that can generalize well to unseen data and offer accurate predictions based on the dataset.
ALGORİTHM
Linear Regression is a statistical method used for modeling the relationship between a dependent variable (target) and one or more independent variables (features). It assumes a linear relationship, meaning that changes in the independent variables result in proportional changes in the dependent variable.
Linear Regression Model Performance:
Mean Squared Error (MSE): 0.56
R-squared Score: 0.58
Mean Absolute Error (MAE): 0.53
Random Forest is an ensemble learning method primarily used for classification and regression tasks. It combines the predictions from multiple decision trees to improve the overall performance and robustness of the model
Random Forest Model Performance:
Mean Squared Error (MSE): 0.26
R-squared Score: 0.81
Mean Absolute Error (MAE): 0.33
Gradient Boosting Regressor is an ensemble learning technique that builds a predictive model in a stage-wise fashion from a collection of weak learners, typically decision trees. It is particularly effective for regression tasks, where it aims to predict a continuous target variable
Gradient Boosting Regressor Model Performance:
Mean Squared Error (MSE): 0.23
R-squared Score: 0.82
Mean Absolute Error (MAE): 0.32