Exploratory Data Analysis (EDA) and Plotting Skills

Overview

This repository includes module assignments showcasing various skills in Exploratory Data Analysis (EDA) and Plotting Techniques. It covers a wide range of tasks, including data cleaning, feature selection, statistical analysis, and creating insightful visualizations. The projects demonstrate practical applications of EDA and data visualization to uncover trends and patterns in real-world datasets.

Key Skills Demonstrated

• Exploratory Data Analysis (EDA) Techniques:

  • Data cleaning (handling missing data, outliers, etc.)
  • Feature selection and transformation
  • Statistical analysis (mean, median, mode, standard deviation)
  • Correlation analysis
  • Identifying trends and patterns in the data

• Plotting and Visualization:

  • Scatter plots for analyzing relationships between variables
  • Box plots for visualizing distributions and outliers
  • Histograms to understand data distribution
  • Regression lines and other model-based visualizations
  • Visualizations for comparing categorical and numerical variables

Projects Included

• Avalanche Data Analysis: Scraping and analyzing avalanche data from the Utah Avalanche Center website, with a focus on the relationship between avalanche width and depth.
• Data Cleaning and Transformation: Demonstrating techniques for handling missing or inconsistent data, including imputation strategies and transformations.
• Visualizations: Creating various plots to represent the data effectively, including the use of regression lines and correlation analysis.

Requirements

To run the projects in this repository, you will need the following libraries:

• pandas
• numpy
• matplotlib
• seaborn
• requests
• beautifulsoup4

You can install these libraries using pip:

pip install pandas numpy matplotlib seaborn requests beautifulsoup4

How to Run

Clone the repository:

git clone https://github.com/yourusername/eda-and-plotting-skills.git
Navigate into the project directory:

cd eda-and-plotting-skills

Open the Jupyter notebooks or Python scripts and run the cells to explore the assignments.

License This repository is licensed under the MIT License. See LICENSE for more information.

Acknowledgements Data sources used in these projects (e.g., Utah Avalanche Center) Various open-source libraries like Pandas, Matplotlib, Seaborn, and BeautifulSoup that facilitated the analysis and visualization tasks.