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Data Analysis and Visualization

A small collection of Python scripts for exploratory data analysis (EDA) and data visualization using the classic Wine Quality (Red) dataset (winequality-red.csv).

This repository is meant to be easy to read and run: each script focuses on one concept (e.g., histograms, scatter plots, correlation) so you can learn EDA step-by-step.

What we’re planning to do (Project Goals)

This repo is an ongoing EDA + visualization walkthrough. The plan is to:

  1. Understand the dataset

    • Identify columns, data types, missing values, and basic summary statistics.

  2. Run univariate analysis (one variable at a time)

    • Central tendency and spread (mean/median/std, min/max, IQR)
    • Distribution shape (skewness and kurtosis)
    • Visual checks with histograms and box plots

  3. Run multivariate analysis (relationships between variables)

    • Scatter plots for feature-to-feature relationships
    • Pairwise plots to quickly scan multiple relationships
    • Correlation and covariance matrices to measure linear relationships

  4. Connect features to wine quality

    • See which physicochemical properties appear most related to the quality target
    • Use visuals + correlation to support conclusions (not just guesses)

  5. Make results easier to understand (planned improvements)

    • Add clearer plot titles/labels and consistent styling
    • Add short written interpretations after each analysis step
    • (Optional) Export plots to an outputs/ folder for easy viewing

What’s included

Scripts

  • histogram.py — histograms to view distributions.
  • boxplot.py — box plots to detect outliers and compare spread.
  • scatter_plot.py — scatter plots to explore relationships.
  • pairwise_plots.py — pair plots for quick multivariate scanning.
  • correlation_matrix.py — correlation heatmap / matrix.
  • covariance_matrix.py — covariance matrix.
  • skewness_and_kurtosis.py — distribution shape metrics.
  • Univariate_non_graphical.py — non-graphical summary statistics.

Dataset

  • winequality-red.csv — physicochemical properties of red wines plus a quality score (target).

Requirements

Install dependencies from requirements.txt:

pip install -r requirements.txt

How to run

git clone https://github.com/SGcpu/Data-Analysis-and-Visualization.git
cd Data-Analysis-and-Visualization
python histogram.py

Run any other script the same way, for example:

python correlation_matrix.py

Dataset columns (Wine Quality - Red)

  • Fixed Acidity
  • Volatile Acidity
  • Citric Acid
  • Residual Sugar
  • Chlorides
  • Free Sulfur Dioxide
  • Total Sulfur Dioxide
  • Density
  • pH
  • Sulphates
  • Alcohol
  • Quality (target variable)

License

MIT License (see LICENSE).

Author

Developed by SGcpu.