ICT 706 | Data Mining Techniques in Python | Information Technology

Home Assignment Answers ICT 706 | Data Mining Techniques in Python | Information Technology

Data analytics

Data analytics is a mechanism of examining sets of data to conclude information they comprise, increasingly with the help of specialised software and system. The technologies of data analytics are used in industries for enabling enterprises to make informed decisions related to business. The initiatives of data analytics support businesses to improve revenue, increase operational efficiency, customer service efforts and optimise the marketing campaigns.

Data mining

Data mining is termed as a practice of automatically finding a large set of data to discover trends and patters that goes beyond simple analysis. It makes use of sophisticated mathematical algorithms for segmenting the data and analysing the possibility of future events. Crucial properties of data mining are given as beneath:

  • Generation of actionable information
  • Automatic pattern discovery
  • Predict likely outcomes
  • Concentrate on large databases

Data mining techniques

Association

This is well-known data mining technique. A pattern is discovered by relationships between items in the similar transaction. This is involved in the market-based analysis to determine the sequence of services that are purchased by customers.

Classification

It is classic data mining technique which is based on the machine learning. This is used for categorising every item in data set into a predefined sequence of groups or classes. It involves the use of mathematical methodologies like linear programming, decision trees, statistics and neural network.

Clustering

It is data mining techniques that involve a relevant cluster of objects having similar attributes through automatic technique. It defines classes and put entities in every class.

Prediction:

This is another data mining technique which discovers relationship existing between independent variables along with the relationship between both dependent and independent variables. It can be involved in the sale for predicting future profit when the sale is addressed as independent variable and profit as the dependent variable.

Sequential patterns

It is data mining technique that probes to identify or discover same patterns, period events or trends in the transition data across the business period.

Decision tree:

It is also commonly used the technique of data mining as its models can be easily comprehended by users. The root of the decision tree is a simple condition or query having multiple solutions.

Predictive model

 The predictive model is a process that involves data mining as well as the probability to predict outcomes. Every model is composed of a range of predictors that are variables being likely to affect future results. When data has been gathered for the relevant predictors, the statistical technique is formulated.

Naïve Bayes

Naïve Bayes categoriser technique is based on Bayesian theorem and is specifically appropriate when input dimensionality is higher. It can outperform highly sophisticated classification techniques.

Linear regression

It is commonly used a form of predictive analysis. The inclusive idea of regression is to identify two aspects i.e.

  • If a set of predictor variables perform a good job for predicting outcomes
  • Which variable in specific are significant predictors of outcome variable

These regression estimates are involved in describing the relationship among one dependent variable or multiple independent variables. Regression analysis is used to receive point estimates.

Innovative idea to improve profitability of company through data analytics

Areas where analytics can be used to convert their performance that boost revenue, improve customer satisfaction and increase profits.

  • Improved service level performance: Analytics enables to predict the ability to satisfy the consumer demands by understanding the influence of traffic patterns as well as average delivery time for suppliers. This helps to meet commitment and pass the business where they know delivery is not possible.
  • Better order fulfilment: Analysing performance of suppliers helps in identifying which supplier will provide a higher probability of success for orders based on the location that increases the process of order fulfilment.
  • Improved supplier management: Analysing the complaints of customers and refunding requests enable them to drop the poorly performing suppliers.
  • Driving down costs: Rotating the analytical direction inward has assisted business to significantly enhance staffing level foretell that assisted in handling the operational expense.
  • Better product management: Several products can be offered. Understanding the popular products or combination of the product is distinct across regions. The data is involved to assure the organisation to target the accurate product at right that assists in improving sales.

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