naren-api 0.0.5

A small python package

::: Base Skeleton for Data-Science Projects :::

Steps to run this Bare-Bones Template:

{Note}: Operates exclusively within the 'Git Bash' environment.

1. Run below command

python template.py

-|- Upon initiating the project, remove the 'temp.log' temporary log file.

-|- Include modules or packages in the requirements.txt file according to the specific requirements of your project.

-|- Modify the 'setup.cfg' and 'setup.py' files according to the specific requirements(project_name_description) of the project.

2. Run below command

bash init_setup.sh

To activate environment in bash:

source ~/Anaconda3/etc/profile.d/conda.sh
conda activate venv/

3. Run below command (optional)

pytest -v
tox

---

The base skeleton for a data science project typically includes the following components:

1. Project Structure:

   • Create a well-organized directory structure for your project.
   • Divide folders for data, code, documentation, and models.

2. Data Collection and Exploration:

   • Gather relevant data from various sources.
   • Explore and understand the data through descriptive statistics and visualizations.

3. Data Cleaning and Preprocessing:

   • Handle missing values, outliers, and inconsistencies in the data.
   • Transform and preprocess the data for analysis.

4. Feature Engineering:

   • Create new features or transform existing ones to improve model performance.

5. Model Building:

   • Select appropriate algorithms based on the problem.
   • Split the data into training and testing sets.
   • Train and evaluate models using appropriate metrics.

6. Model Deployment:

   • Deploy the chosen model for real-world use if applicable.

7. Documentation:

   • Provide clear documentation for your code, including comments and a README file.

8. Version Control:

   • Use version control (e.g., Git) to track changes and collaborate with others.

9. Testing and Validation:

   • Implement testing procedures to validate the correctness of your code.

10. Visualization and Reporting:

    • Create visualizations to communicate insights.
    • Prepare a report summarizing the findings and methodology.

This basic structure ensures a systematic and reproducible approach to data science projects.