Welcome to the DS_Collections wiki!

📊 DS_Collections: Data Science Projects Repository

Welcome to DS_Collections, a curated collection of data science projects and notebooks. This repository showcases various aspects of data analysis, machine learning, and data visualization using Python.

---

🧠 Projects Overview

Notebook Title	Description
Electric_Size_Market_.ipynb	Analysis of electric market size trends.
MLLearningWithPython.ipynb	Introduction to machine learning concepts.
Seaborn_Visualizations.ipynb	Data visualization using Seaborn library.
Sequential_model.ipynb	Implementation of sequential neural networks.
Timeseriesplot.ipynb	Time series data plotting and analysis.
Yoro_vs_Man_united_Center_Backs.ipynb	Comparative analysis of football center backs.
linearRegression.ipynb	Linear regression model implementation.
lineplot.ipynb	Line plot visualizations with sample data.
mnistdataset.ipynb	MNIST dataset exploration and modeling.
tensorTest.ipynb	Tensor operations and manipulations.

DS_Collections/ ├── .jupyter/

├── Electric_Size_Market_.ipynb

├── MLLearningWithPython.ipynb

├── Seaborn_Visualizations.ipynb

├── Sequential_model.ipynb

├── Timeseriesplot.ipynb

├── Yoro_vs_Man_united_Center_Backs.ipynb

├── linearRegression.ipynb

├── lineplot.ipynb

├── mnistdataset.ipynb

├── tensorTest.ipynb

├── LICENSE

└── README.md

---

🎯 Purpose

This repository serves as a learning and reference resource for:

• Understanding and applying data science techniques.
• Exploring machine learning models and their implementations.
• Visualizing data effectively to derive insights.

---

🤝 Contributions

Contributions are welcome! If you have suggestions, improvements, or additional projects to add, feel free to fork the repository and submit a pull request.

---

📜 License

This project is licensed under the GPL-3.0 License.

---

📬 Contact

For any inquiries or discussions, please open an issue on the repository.

---

Happy Data Exploring!

---

🛠️ Technologies Used

• Programming Language: Python
• Libraries:
  • Data Manipulation: pandas, numpy
  • Visualization: matplotlib, seaborn
  • Machine Learning: scikit-learn, tensorflow
• Tools: Jupyter Notebooks

---

📁 Repository Structure

---

Seaborn_Visualizations.ipynb

Code

# Anscombe Quartet

import seaborn as sns

sns.set_theme(style = "darkgrid")

Anscombe_Dt = sns.load_dataset("anscombe")

sns.lmplot(data= Anscombe_Dt, x= "x", y="y",
           hue= "dataset",col = "dataset", col_wrap=2, palette= "muted",
           ci=None,height=4, scatter_kws={"s": 50, "alpha": 1})

Anscombe_Dt.head()

• data: The DataFrame to use (in this case, Anscombe_Dt).

• x: The variable to be plotted on the x-axis.

• y: The variable to be plotted on the y-axis.

• hue: Grouping variable that will produce points with different colors. Here, it's based on the "dataset" column.

• col: Variable that will produce separate columns within the grid for different values. This will create separate plots for each "dataset".

• col_wrap: Number of columns in the grid before wrapping to a new row.

• palette: Color palette to use for the different levels of the hue variable.

• ci: Confidence interval for the regression estimate. None in this case.

• height: Height (in inches) of each facet.

• scatter_kws: Additional keyword arguments to pass to plt.scatter and plt.plot.

  ---