HousePricePrediction-dakshinm 1.4.1

House Price Prediction package

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

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.

Project Description

● Created new issue in github repo.

● Created a new branch to make the required changes related to the issue specified.

● Fixed bugs to make the python executable run.

● Added comments to the changes made to the existing code to make others understand the fixes.

● Created a conda environment to execute the python code.

How to run the code:

Command to create an environment from the env.yml file you have generated.

conda env create -f env.yml

Command to create an environment from the env.yml file you have generated.

conda activate mle-dev

Command to run the python scripts.

1. Run the ingest_data.py file

python3 src/HousePricePrediction_dakshinm/ingest_data.py

If necessary include the arguments like this

python3 src/HousePricePrediction_dakshinm/ingest_data.py --output data/raw --logs-folder logs --log-level DEBUG

REFER BELOW DOCUMENTATION FOR OTHER OPTIONS AS WELL:

Download dataset from source and creates train and test datasets

options: -h, --help show this help message and exit

--output OUTPUT Output folder path where dataset will be stored.

--logs-folder LOGS_FOLDER Output folder path where logs are recorded

--log-level LOG_LEVEL Specify the log level (default: DEBUG)

--no-console-log Toggle whether or not to write logs to the console (default:False)

2. Run the train.py file

python3 src/HousePricePrediction_dakshinm/train.py

If necessary include the arguments like this

python3 src/HousePricePrediction_dakshinm/train.py --input data/processed --output artifacts/ --logs-folder logs --log-level DEBUG

REFER BELOW DOCUMENTATION FOR OTHER OPTIONS AS WELL:

usage: train.py [-h] [--input INPUT] [--output OUTPUT] [--logs-folder LOGS_FOLDER] [--log-level LOG_LEVEL] [--no-console-log]

Train the model(s).

options: -h, --help show this help message and exit

--input INPUT Input folder path where dataset is stored.

--output OUTPUT Output folder path where trained model(s) will be stored.

--logs-folder LOGS_FOLDER Output folder path where logs are recorded

--log-level LOG_LEVEL Specify the log level (default: DEBUG)

--no-console-log Toggle whether or not to write logs to the console (default:False)

3. Run the score.py file

python3 src/HousePricePrediction_dakshinm/score.py

If necessary include the arguments like this

python3 src/HousePricePrediction_dakshinm/score.py --input_data data/processed

--input_pickle_folder artifacts/ --logs-folder logs --log-level DEBUG

REFER BELOW DOCUMENTATION FOR OTHER OPTIONS AS WELL:

usage: score.py [-h] [--input_data INPUT_DATA] [--input_pickle_folder INPUT_PICKLE_FOLDER] [--logs-folder LOGS_FOLDER] [--log-level LOG_LEVEL] [--no-console-log]

Score the model(s).

options: -h, --help show this help message and exit

--input_data INPUT_DATA Input folder path where dataset(s) is stored.

--input_pickle_folder INPUT_PICKLE_FOLDER Input folder path where pickle file(s) are stored.

--logs-folder LOGS_FOLDER Output folder path where logs are recorded

--log-level LOG_LEVEL Specify the log level (default: DEBUG)

--no-console-log Toggle whether or not to write logs to the console (default:False)

How to package the code:

Step 1 : python -m build

Step 2 : This will create a .tar.gz and .whl file in the dist folder.

Step 3 : Install the package from the .whl file by running the command below

pip install dist/HousePricePrediction_dakshinm-1.4.1-py3-none-any.whl

or

pip install -i https://test.pypi.org/simple/ HousePricePrediction-dakshinm==1.4.1

How to run the test files:

Step 1 : Check if the installation of packages is successful by running the command below.

pytest tests/functional_tests/test_installation.py

Step 2 : Run the unit test cases for the files.

python3 -m unittest tests/unit_tests/test_data_ingestion.py

python3 -m unittest tests/unit_tests/test_train.py

python3 -m unittest tests/unit_tests/test_score.py

Step 3 : Run the functional test cases.

pytest tests/functional_tests/test_functional_tests.py

Where to find logs?:

Logs can be found in the logs/ folder inside the mle-training folder.

How to create documentations using Sphinx:

Step 1: Run the command : pip install sphinx

Step 2: Run the command : sphinx-quickstart

Step 3: Run the command : sphinx-apidoc -o docs/source/ src/

        This will make the docstrings as html documents

Step 4: Run the command : sphinx-build -b html docs/source docs/build

        This will create the html docs in the build folder