awesome-python-for-data-science

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Implement Feature Engineering Tutorial

7

### Description create a comprehensive tutorial on feature engineering to help both new and experienced team members understand and apply this crucial aspect of our data science work. ### Tasks...

rajveer43

Data Visualization with Matplotlib and Seaborn

1

### Description Craft a tutorial on data visualization using Matplotlib and Seaborn. Show beginners how to create various types of plots and charts to explore and present data. ### Acceptance...

miriamspsantos

Introduction to Classification with Scikit-Learn

1

### Description Develop a beginner-friendly tutorial on classification using Scikit-Learn. Explain the basics of classification algorithms and guide users through building their first classifier. ### Acceptance Criteria - [ ]...

miriamspsantos

Explore this comprehensive flowchart illustrating the architecture for developing a state-of-the-art fire detection model. This meticulously crafted guide follows a step-by-step methodology, providing clarity in the code development process for...

eniayejudaniel

A step by step comprehensive beginner's guide on how to deal with categorical data and how to convert it into the numerical form with the famous one hot encoding technique.

Axikop

Brief step-by-step guide and code for K-Means Clustering. Apply and understand the concept of K-Means Clustering for complete beginners to unsupervised learning.

Axikop

Clustering with K-Means

2

### Description Create a beginner-friendly tutorial introducing the concept of clustering using K-Means. ### Acceptance Criteria - [ ] Submit a Jupyter notebook containing the tutorial and the necessary datasets...

miriamspsantos

How to Encode Categorical Data

2

### Description Create a Jupyter Notebook tutorial that guides beginners through encoding categorical data for machine learning tasks. Cover techniques such as one-hot encoding and others to convert categorical variables...

miriamspsantos

### Description Develop a tutorial on common classification metrics in machine learning, such as accuracy, precision, recall, and F1-score. Explain when to use each metric and how to calculate them....

miriamspsantos

### Description Develop a beginner-friendly tutorial on data augmentation using the [ydata-synthetic](https://docs.synthetic.ydata.ai/1.3/) library. Explain how to generate synthetic data to increase the size of training dataset, improving model performance. ###...

miriamspsantos