Generate a summary of a dataset

1. Key Features

1. Number of Records: {Number of rows or records in the dataset.}
2. Number of Features: {Number of columns or attributes.}

2. Feature Types

• Numerical Features:
  • Examples: {List numerical features, e.g., age, salary.}
  • Characteristics: Continuous or discrete values.
• Categorical Features:
  • Examples: {List categorical features, e.g., gender, country.}
  • Characteristics: Nominal or ordinal data.
• Date/Time Features:
  • Examples: {List date/time features, e.g., transaction_date.}
  • Characteristics: Represent temporal data.
• Textual Features (if applicable):
  • Examples: {List text features, e.g., comments, descriptions.}
  • Characteristics: Unstructured data.

3. Missing Values

• Percentage of Missing Values:
  • {Percentage or count of missing values per feature.}
• Affected Features:
  • {List features with missing values.}

4. Summary Statistics

• Numerical Features:
  • Minimum, maximum, mean, and median values.
• Categorical Features:
  • Unique categories, frequency distribution.
• Date/Time Features:
  • Range of dates, time intervals.

5. Data Imbalance

• Target Variable (if applicable):
  • Distribution of classes or categories.
  • Example: {Class A: X%, Class B: Y%.}

6. Outliers and Anomalies

• Presence of Outliers:
  • Identified in features such as {List affected features.}
• Potential Anomalies:
  • Examples of extreme values or unexpected patterns.