What is Data Mining and it’s techniques or Algorithms, Explaining with Python Code

#datamining #dataclassification #regression #clustering #datascience #machinelearning

#data

Hello Friends

Today’s Blog is for Data Mining, the first step before working in data science is Data Mining, every time we have to mine the data for further process. So python has number of different libraries for this task. Firstly, I will explain following things for general understanding.

· Data Mining

· Techniques of Data Mining

· Why mine the data

Data Mining

Let’s start with Data Mining, as per the definition data of mining, that is refer to find the valuable information from raw data. We can make this raw data to valuable information by formatting, modeling and manipulating it.

Techniques and algorithm for Data Mining

There are some basic techniques and algorithms for done this job efficiently. Below are mentioned few algorithms:

1. · Model Selection

2. · Regression

3. · Classification

4. · Clustering

Model Selection

First algorithm is Model Selection, In Model selection algorithms we can compare, validate and select the best parameters and data for use in Data science projects. First step is just describing the data model you choose. You cannot use all provided raw data because of their size and extra efforts. We have to choose valuable information for further use. There we have some techniques to describe the data model.

• Grid Search

• Cross Validation

• Metrics

Later on I will give you a brief introduction about all techniques and algorithm, I will also explain few practical examples for better understanding of these algorithms.

Regression

Regression involves creating a model that tries to comprehend the relationship between input and output data. For example, regression tools can be used to understand the behavior of stock prices.

Regression algorithms include:

• · SVMs

• · Ridge regression

• · Lasso

Classification

The classification tools identify the category associated with provided data. For example, they can be used to categorize email messages as either spam or not.

• Support vector machines (SVMs)

• Nearest neighbors

• Random forest

In Next Blog I will share how these algorithms working and how we can implement these algorithms using python Libraries. Apart from that I will also explain these all algorithms via a practical example for your better understanding of these concepts.

Hope you have gone through with my previously shared Blog, which is for your Anaconda Environment and how to create your first simple Web App using Streamlit Library from Python. I have shared all steps in easy form that everyone can understand these stuff easily and implement quickly.

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