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DATA MINING LIFE CYCLE PROCESS IN DATA SCIENCE

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Brief Description: : Data Mining is one of the crucial aspects in the data analysis process in Data Science. It is the process of extracting hidden insights from large sets of big data to uncover hidden patterns & to extract meaningful insights from it. Developing skills in Data Mining is very crucial for budding Data Scientists.

Life cycle of the data mining process:
Business Understanding:

The very step in the Data Mining process is to get a clear vision of business understanding. With in-depth understanding of business, Data Scientists can get to know what resources are required & what type of plan needs to be executed to find solution to problem in hand.

Data Understanding & Preparation:

This is the most important aspect of Data Mining process. Data Scientists need to have a clear understanding of the data in hand. Data Scientists would be collecting data from the relevant sources & then they would be analyzing if the collected data can help them in achieving the desired objectives. Data Anomalies would be treated to address the data quality problems.

Data Modeling:

After preparing the data, a relevant data model needs to be built. This model needs to be trained with relevant algorithms & known data sets. The data patterns would be analyzed by using mathematical models & data tools.

Evaluation:

Once the model is deployed, it needs to be evaluated before final deployment. This evaluation would help in knowing whether this model can achieve the predefined business objectives or not.

Deployment:

After evaluating the model, if desired results are achieved then it is successfully deployed.