housing-price-pred 0.0.3

Housing Price Prediction

Welcome to housing_price_pred's documentation!

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 - Both Randomized Search and Grid Search has been done for Hyperparameter Tuning.

The trained models are saved in a specified directory from where they can be used to check the performance on the test set.

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. The final metric used to evaluate is mean squared error.

Install the package

• Install the package using python3 -m python3 -m pip install housing_price_pred

Usage

• From Command Prompt type python3 to start Python
• To check availability inside python type help("modules"). The package should show up in the list.
• Package majorly contains 3 modules, ingest_data, train and score. Please note the train_data function from train module does not return any object, it simply trains the model on pre-processed data and stores the trained models as pickles inside specified directory. Incase returns are needed, Please raise and issue and this can be fixed.
• Import the modules from the package from housing_price_pred import ingest_data,train,score.
• Functions inside these modules can be used with appropriate arguments.
• For more information on the functions, run help(function_name).

Here is an usage example: ::

from housing_price_pred import ingest_data,train,score
housing, strat_train_set, strat_test_set = download_data(housing_url, housing_path)
train_data(input_folder=args.input_path,processed_folder,pickle_path)
lr_predictions, tr_predictions, rnd_forest_predictions, grd_forest_predictions = score_models(processed_folder=\
                                                                                              pickle_path,\
                                                                                              output_path
                                                                                              )

It is recommended to run train_data and score_models with default parameters if ingest_data is run with default parameters.

For Contributors and Development

• Fork the repo here https://github.com/sibashisc/mle-training/tree/fix/9%2Fml-workflow

• Create a dev environment using the .yml file

  • conda env create -f env.yml

• Activate environment

  • conda activate mle-dev

To excute the script

• python <scriptname.py>
• Each script ingest_data.py, train.py and score.py can take user arguments. For more information run python3 <scriptname.py> --help

License

• MIT License