Numpy , Pandas And Matplot Lib Practice

The dataset has been created specifically for practicing Python, NumPy, Pandas, and Matplotlib. It is designed to provide a hands-on learning experience in data manipulation, analysis, and visualization using these libraries.

Specifics of the Dataset:

The dataset consists of 5000 rows and 20 columns, representing various features with different data types and distributions.
The features include numerical variables with continuous and discrete distributions, categorical variables with multiple categories, binary variables, and ordinal variables.
Each feature has been generated using different probability distributions and parameters to introduce variations and simulate real-world data scenarios.
The dataset is synthetic and does not represent any real-world data. It has been created solely for educational purposes.

One of the defining characteristics of this dataset is the intentional incorporation of various real-world data challenges:

Certain columns are randomly selected to be populated with NaN values, effectively simulating the common challenge of missing data.

• The proportion of these missing values in each column varies randomly between 1% to 70%.
• Statistical noise has been introduced in the dataset. For numerical values in some features, this noise adheres to a distribution with mean 0 and standard deviation 0.1.
• Categorical noise is introduced in some features', with its categories randomly altered in about 1% of the rows.
  Outliers have also been embedded in the dataset, resonating with the Interquartile Range (IQR) rule

*Context of the Dataset: *

The dataset aims to provide a comprehensive playground for practicing Python, NumPy, Pandas, and Matplotlib.
It allows learners to explore data manipulation techniques, perform statistical analysis, and create visualizations using the provided features.
By working with this dataset, learners can gain hands-on experience in data cleaning, preprocessing, feature engineering, and visualization.
Sources of the Dataset:

The dataset has been generated programmatically using Python's random number generation functions and probability distributions.
No external sources or real-world data have been used in creating this dataset.