This repository provides a foundational template for jumpstarting data science projects, designed specifically for seamless integration with Google Colab and GitHub. It includes essential library imports, a basic data workflow, and clear instructions for version control.
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Essential Library Imports: Pre-configured with pandas, numpy, matplotlib, seaborn, and core scikit-learn modules.
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Sample Data: Includes a dummy DataFrame for quick testing and demonstration.
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Basic Data Exploration: Examples of df.info(), df.describe(), and missing value checks.
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Simple Visualization: Demonstrates scatter plots and histograms using matplotlib and seaborn.
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Machine Learning Example: A basic linear regression model with data splitting, training, prediction, and evaluation using scikit-learn.
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GitHub Integration Guide: Step-by-step instructions for saving to GitHub, opening repositories, and committing changes directly from Google Colab.
Feel free to fork this repository, make improvements, and submit pull requests. Suggestions for more advanced examples, best practices, or additional integrations are welcome!