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README.md

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-# Template for Machine Learning projects
+# Data Science Project Boilerplate
+
+This boilerplate is designed to kickstart data science projects by providing a basic setup for database connections, data processing, and machine learning model development. It includes a structured folder organization for your datasets and a set of pre-defined Python packages necessary for most data science tasks.
+
+## Structure
+
+The project is organized as follows:
+
+- `app.py` - The main Python script that you run for your project.
+- `utils.py` - This file contains utility code for operations like database connections.
+- `requirements.txt` - This file contains the list of necessary python packages.
+- `models/` - This directory should contain your SQLAlchemy model classes.
+- `data/` - This directory contains the following subdirectories:
+  - `interin/` - For intermediate data that has been transformed.
+  - `processed/` - For the final data to be used for modeling.
+  - `raw/` - For raw data without any processing.
+
+
+## Setup
+
+**Prerequisites**
+Ensure you have Python 3.6+ installed on your system. You will also need pip for installing the Python packages.
+
+**Installation**
+Clone the project repository to your local machine.
+
+Navigate to the project directory and install the required Python packages:
+
+```bash
+pip install -r requirements.txt
+```
+
+Create a .env file in the project root directory to store your environment variables, such as your database connection string:
+
+```makefile
+DATABASE_URL="your_database_connection_url_here"
+```
+
+## Running the Application
+
+To run the application, execute the app.py script from the root of the project directory:
+
+```bash
+python app.py
+```
+
+## Adding Models
+
+To add SQLAlchemy model classes, create new Python script files inside the models/ directory. These classes should be defined according to your database schema.
+
+Example model definition (`models/example_model.py`):
+
+```py
+from sqlalchemy.ext.declarative import declarative_base
+from sqlalchemy import Column, Integer, String
+
+Base = declarative_base()
+
+class ExampleModel(Base):
+    __tablename__ = 'example_table'
+    id = Column(Integer, primary_key=True)
+    name = Column(String)
+
+```
+
+## Working with Data
+
+You can place your raw datasets in the data/raw directory, intermediate datasets in data/interim, and the processed datasets ready for analysis in data/processed.
+
+To process data, you can modify the app.py script to include your data processing steps, utilizing pandas for data manipulation and analysis.
+
+