semvec/pypi-packages huggingface dataset:
housing-price-prediction-NR 0.1.0
A package for predicting housing prices using machine learning models
Requires Python: >=3.7

# 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 nonstandardcode.py

## Installation
1. **Clone the repository:**

```bash
git clone https://github.com/yourusername/your-repo-name.git
cd your-repo-name

2. Setup conda environment

conda env create -f env.yaml

3. Activate the environment

conda activate myenv

4. Install the package

pip install -e .

Run tests - pytest tests/

5. Running the Code
## Running the Code

1. **Ingest Data:**

- Use the `ingest_data.py` script to download and create training and validation datasets.

```bash
python src/your_package/ingest_data.py --output ./data

2. Run the train.py script to train your model. 

python src/your_package/train.py --input ./data --output ./artifacts

3. Score the Model:

python src/your_package/score.py --model ./artifacts --data ./data --output ./results
vtt-to-srt2 0.2.0.4
vtt to srt subtitles converter package

# vtt_to_srt2
Convert vtt files to srt subtitle format
> For Python 2.x [you can get version for Python 3.x here](https://github.com/jsonzilla/vtt_to_srt3)

## Docs
[https://jsonzilla.github.io/vtt_to_srt2/](https://jsonzilla.github.io/vtt_to_srt2/)


## Installation
```shell
pip install vtt_to_srt2
```

```cmd
python -m pip install vtt_to_srt2
```

## Usage from terminal

```shell
usage: vtt_to_srt [-h] [-r] [-e ENCODING] [-rf] pathname

Convert vtt files to srt files

positional arguments:
  pathname              a file or directory with files to be converted

options:
  -h, --help            show this help message and exit
  -r, --recursive       walk path recursively
  -e ENCODING, --encoding ENCODING
                        encoding format for input and output files
  -rf, --remove_format  remove the format tags like bold & italic from output files
```

## Usage as a lib

Convert vtt file
```python
from vtt_to_srt.vtt_to_srt import ConvertFile

convert_file = ConvertFile("input_utf8.vtt", "utf-8")
convert_file.convert()
```

Recursively convert all vtt files in directory
```python
from vtt_to_srt.vtt_to_srt import ConvertDirectories

recursive = False
convert_file = ConvertDirectories(".", recursive, "utf-8")
convert_file.convert()
```

## Manual build

Generate wheel
```shell
python -m pip install --upgrade setuptools wheel build
python -m build
```

## Generate documentation

Generate documentation
```shell
python -m pip install pdoc3
pdoc --html vtt_to_srt/vtt_to_srt.py -o docs
mv docs/vtt_to_srt.html docs/index.html
rm -rm docs/vtt_to_srt
```