# 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
Raw data
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"author": "Neha Rawat",
"author_email": "neha.rawat@tigeranalytics.com",
"download_url": null,
"platform": null,
"description": "# Median housing value prediction\r\n\r\nThe 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. \r\n\r\nThe following techniques have been used: \r\n\r\n - Linear regression\r\n - Decision Tree\r\n - Random Forest\r\n\r\n## Steps performed\r\n - We prepare and clean the data. We check and impute for missing values.\r\n - Features are generated and the variables are checked for correlation.\r\n - Multiple sampling techinuqies are evaluated. The data set is split into train and test.\r\n - All the above said modelling techniques are tried and evaluated. The final metric used to evaluate is mean squared error.\r\n\r\n## To excute the script\r\npython nonstandardcode.py\r\n\r\n## Installation\r\n1. **Clone the repository:**\r\n\r\n```bash\r\ngit clone https://github.com/yourusername/your-repo-name.git\r\ncd your-repo-name\r\n\r\n2. Setup conda environment\r\n\r\nconda env create -f env.yaml\r\n\r\n3. Activate the environment\r\n\r\nconda activate myenv\r\n\r\n4. Install the package\r\n\r\npip install -e .\r\n\r\nRun tests - pytest tests/\r\n\r\n5. Running the Code\r\n## Running the Code\r\n\r\n1. **Ingest Data:**\r\n\r\n- Use the `ingest_data.py` script to download and create training and validation datasets.\r\n\r\n```bash\r\npython src/your_package/ingest_data.py --output ./data\r\n\r\n2. Run the train.py script to train your model. \r\n\r\npython src/your_package/train.py --input ./data --output ./artifacts\r\n\r\n3. Score the Model:\r\n\r\npython src/your_package/score.py --model ./artifacts --data ./data --output ./results\r\n\r\n\r\n",
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