housing-predictor-devaraj-saravana 0.0.1

california's housing price prediction

Median housing value prediction

The housing data can be downloaded from https://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data.

The following techniques have been used:

• Linear regression
• Decision Tree
• Random Forest with RandomizedSearchCV
• Random Forest with GridSearchCV

Steps performed

• We prepare and clean the data. We check and impute for missing values.
• Features are generated and the variables are checked for correlation.
• Multiple sampling techinuqies are evaluated. The data set is split into train and test.
• All the above said modelling techniques are tried and evaluated.
• Mean squared error, Root mean squaerd error, Mean absolute error metrics are used to evaluate the model

To excute the script's

There are three scripts need to run for evaluating the model

$ python ingest_data.py -p raw

you can run this script with specifying where you want to place the downloaded data and also with default arguments

$ python train.py -x housing_prepared.csv -y housing_labes.csv

you can run this script with specifying dependent and independent variables and also with no argument passed

$ python score.py -m final_model.pkl -d test_set.csv

you can run this script with specifying which ML model want to use and with what dataset to score metrics