1. Data Mining 101 with Scikit-Learn
An informal introduction of data mining
Shuhan Yuan

2. What is data mining?
Data mining is the computing process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. (
Data mining is the process of discovering interesting knowledge from large amounts of data stored either in databases, data warehouses, or other information repositories. (Data Mining: Concepts and Techniques)

3. What is data mining?
Data mining is the computing process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. (
Data mining is the process of discovering interesting knowledge from large amounts of data stored either in databases, data warehouses, or other information repositories. (Data Mining: Concepts and Techniques)

4. What is data mining? Data Mining Knowledge Data (Models)
A naïve view of data mining
Data Mining
Data
Knowledge
(Models)
knowledge discovery from data

5. Six common classes of tasks
Classification – is the task of generalizing known structure to apply to new data. For example, an program might attempt to classify an as "legitimate" or as "spam".
Clustering – is the task of discovering groups and structures in the data that are in some way or another "similar", without using known structures in the data.
Regression – attempts to find a function which models the data with the least error that is, for estimating the relationships among data or datasets.
Summarization – providing a more compact representation of the data set, including visualization and report generation.
Anomaly detection (outlier/change/deviation detection) – The identification of unusual data records, that might be interesting or data errors that require further investigation.
Association rule learning (dependency modelling) – Searches for relationships between variables. For example, a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis.

6. Six common classes of tasks
Classification – is the task of generalizing known structure to apply to new data. For example, an program might attempt to classify an as "legitimate" or as "spam".
Clustering – is the task of discovering groups and structures in the data that are in some way or another "similar", without using known structures in the data.
Regression – attempts to find a function which models the data with the least error that is, for estimating the relationships among data or datasets.
Summarization – providing a more compact representation of the data set, including visualization and report generation.
Anomaly detection (outlier/change/deviation detection) – The identification of unusual data records, that might be interesting or data errors that require further investigation.
Association rule learning (dependency modelling) – Searches for relationships between variables. For example, a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis.

7. Classification Supervised Learning

8. Regression Supervised Learning

9. Clustering Unsupervised Learning Clustering Algorithms

10. Anomaly Detection

11. Association Rule Market Basket Analysis

12. Summarization Know your data

13. Pipeline for Data Mining
Data Preprocessing
Feature Engineering
Model
Training
Testing
Prediction

14. Linus Torvalds: “Talk is cheap. Show me the code.”

15. Python Ecosystem

16. Jupyter Notebook
Contain both computer code (e.g. python) and rich text elements (paragraph, equations, figures, links, etc...).

17. Scikit-Learn

18. http://peekaboo-vision. blogspot

19. Like this graph?
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20. Scikit-learn Simple and consistent API Instantiate the model
m = Model()
Fit the model
m.fit(train_data)
Predict
m.predict(test_data)
Evaluate
m.score(predict_y, target_y)

21. Classification: k-nearest neighbors (K-NN)

22. Decision tree

23. Clustering: k-means
Given a data set where each observed example has a set of features, but no labels