semvec/pypi-packages huggingface dataset:
housing-library-5507 0.2
Sample code for coding practice
Requires Python: >=3.11
Keywords: housing,data training

# 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.

## To excute the script
python < scriptname.py >
housing-library-5515 0.2
Sample code for coding practice
Requires Python: >=3.11
Keywords: housing,data training

# Median housing value prediction

The housing data can be downloaded from https://github.com/ageron/handson-ml/blob/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.
 -performed the tests

##sklearn pypi acount recreation 
-added the functional tests and unit tests
-performed test_training and tets_installation tests in functional testings
-performed test_data_ingestion test in unit testing


## To excute the script
python src/score.py

##To Excute the distribution file
unzip dist/housing_library_5515-0.1-py3-none-any.whl -d wheel_contents
 cd wheel_contents
python3 house_price_prediction/score.py


##To activate the environment
conda activate mle-dev
##To export the environment
conda env export > environment.yml
##To import the environment
 conda env create -f environment.yml
##LICENSE
This project is licensed under the MIT license - see the (LICENSE) file for details
pcloud-dl 0.1.8
pcloud downloader
Requires Python: >=3.6


# pcloud-dl 

![last release](https://badgen.net/github/release/JavSensei/pcloud-dl/stable)

pcloud-dl is a Python package that allows you to download public files from pCloud using their public link or code.

## Installation

You can install pcloud-dl using pip:

```
pip install pcloud-dl
```

## Usage

To download a file, simply run:

```
pcloud-dl <link/code>
```

where `<link/code>` is the public link or code of the file you want to download.

The downloaded file will be saved in the current directory with the same name as the original file.

## Example

To download a file with the public link `https://u.pcloud.link/publink/show?code=XZtTpwVZnMvzSRIY2vhs6zCQgfHtI8Oya3YV`, run:

```
pcloud-dl https://u.pcloud.link/publink/show?code=XZtTpwVZnMvzSRIY2vhs6zCQgfHtI8Oya3YV
```

This will download the file to your current directory with the same name as the original file.

## License

pcloud-dl is licensed under the MIT License. See [LICENSE](https://github.com/javsensei/pcloud-dl/blob/main/LICENSE) for more information.