Welcome to the Watermelon Sweetness Prediction workshop! In this workshop, we'll explore the fascinating world of data science and machine learning using the example of predicting watermelon sweetness based on various features like size and color.
In this workshop, participants will learn about:
- The fundamentals of logistic regression and its assumptions.
- The concept of overfitting in regression models.
- Time series data analysis and decomposition.
By the end of the workshop, participants will have hands-on experience with real-world data analysis techniques and best practices.
- Basic knowledge of Python programming.
- Familiarity with Jupyter Notebooks.
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Clone the repo:
git clone https://github.com/abd1rahmane/Watermelon-Prediction-Workshop.git -
Install the required packages:
pip install -r requirements.txt
- Understand the assumptions underlying logistic regression.
- Learn how to check for a linear relationship between features and log odds.
- Visualize and understand homoscedasticity.
- Dive deep into the concept of overfitting.
- Understand the importance of splitting data into training and testing sets.
- Learn how to compare train/test scores to identify overfitting.
- Explore the fascinating world of time series data.
- Decompose time series data into its components: trend, seasonality, and residuals.
- Visualize and analyze residuals to understand the behavior of time series data.
Any contributions you make are greatly appreciated. Please read the CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests.
Distributed under the MIT License. See LICENSE for more information.