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Extract useful information, patterns or insights from large volumes of data using statistics and machine learning.

Data mining definition:

Data mining is the process of extracting useful information, patterns or insights from large volumes of data using various statistical methods and machine learning algorithms. It is an integral component of the data pipeline, often occurring after data cleaning and preprocessing stages, and before data visualization and reporting stages. Data mining techniques are used to build predictive or descriptive models, perform segmentation, clustering, anomaly detection, or discover association rules, among other tasks, which support data-driven decision-making processes in various domains.

Note that, while we provide a definition above, The term "Data Mining" is sometimes used interchangeably with machine learning, statistics, or predictive analytics, but other times it is used to refer to a specific subset of techniques within those fields.

Example of data mining in Python

Let's use the Pandas library for data manipulation, the Scikit-Learn library for machine learning, and the Seaborn library for data visualization. For the purposes of this example, let's use the Iris dataset, a classic dataset in the field of machine learning that includes measurements for 150 iris flowers from three different species.

Please note that you need to have the necessary Python libraries installed in your Python environment to run this code.

# import necessary libraries
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
import seaborn as sns
import matplotlib.pyplot as plt

# load the iris dataset
from sklearn.datasets import load_iris
iris = load_iris()
data = pd.DataFrame(, columns=iris.feature_names)
data['target'] =

# explore the data

# visualize the data
sns.pairplot(data, hue="target")

# prepare data for model training
X = data.iloc[:, :-1]  # features
y = data['target']  # target

# split data into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# train a RandomForestClassifier
model = RandomForestClassifier(), y_train)

# evaluate the model
y_pred = model.predict(X_test)
print(classification_report(y_test, y_pred))

This example includes several steps common in data mining workflows:

  1. Loading and inspecting the data.
  2. Visualizing the data to understand relationships between features and the target variable.
  3. Preparing the data for machine learning, including splitting it into training and testing datasets.
  4. Training a machine learning model on the training data.
  5. Evaluating the model on the testing data to understand its performance.

It will also print out the data to the terminal:

   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  target
0                5.1               3.5                1.4               0.2       0
1                4.9               3.0                1.4               0.2       0
2                4.7               3.2                1.3               0.2       0
3                4.6               3.1                1.5               0.2       0
4                5.0               3.6                1.4               0.2       0
              precision    recall  f1-score   support

           0       1.00      1.00      1.00        10
           1       1.00      1.00      1.00         9
           2       1.00      1.00      1.00        11

    accuracy                           1.00        30
   macro avg       1.00      1.00      1.00        30
weighted avg       1.00      1.00      1.00        30

While this example provides a simple demonstration of data mining in Python, real-world data mining tasks are typically much more complex and often require significant data cleaning, feature engineering, and model tuning.

Other data engineering terms related to
Data Management:


Move rarely accessed data to a low-cost, long-term storage solution to reduce costs. store data for long-term retention and compliance.


Add new data or information to an existing dataset to enhance its value. Enhance data with additional information or attributes to enrich analysis and reporting.


Create a copy of data to protect against loss or corruption.


Select, organize and annotate data to make it more useful for analysis and modeling.


Identify and remove duplicate records or entries to improve data quality.


Analyzing the number of features or attributes in the data to improve performance.


Enhance data with additional information from external sources.


Extract data from a system for use in another system or application.


Create an optimized data structure for fast search and retrieval.


combine data from different sources to create a unified view for analysis or reporting.


Store the results of expensive function calls and reusing them when the same inputs occur again.


Combine data from multiple datasets into a single dataset.


Create a conceptual representation of data objects.


Track data processing metrics and system health to ensure high availability and performance.

Named Entity Recognition

Locate and classify named entities in text into pre-defined categories.


Interpret and convert data from one format to another.


Divide data into smaller subsets for improved performance.


Transform your data so it is fit-for-purpose.


Transform raw data before data analysis or machine learning modeling.


Create a copy of data for redundancy or distributed processing.


Increasing the capacity or performance of a system to handle more data or traffic.

Schema Mapping

Translate data from one schema or structure to another to facilitate data integration.


Ensure that data in different systems or databases are in sync and up-to-date.


Check data for completeness, accuracy, and consistency.


Maintain a history of changes to data for auditing and tracking purposes.