Data mining

Problem Definition

Market Analysis

Customer Profiling, Identifying Customer Requirements, Cross Market Analysis, Target Marketing, Determining Customer purchasing pattern

Corporate Analysis and Risk Management

Finance Planning and Asset Evaluation, Resource Planning, Competition

Fraud Detection

Customer Retention

Production Control

Science Exploration

> Data Preparation

Data preparation is about constructing a dataset from one or more data sources to be used for exploration and modeling. It is a solid practice to start with an initial dataset to get familiar with the data, to discover first insights into the data and have a good understanding of any possible data quality issues. The Datasets you are provided in these projects were obtained from kaggle.com.

Variable selection and description

Numerical – Ratio, Interval

Categorical – Ordinal, Nominal

Simplifying variables: From continuous to discrete

Formatting the data

Basic data integrity checks: missing data, outliers

> Data Exploration

Data Exploration is about describing the data by means of statistical and visualization techniques.

· Data Visualization:

· Univariate analysis explores variables (attributes) one by one. Variables could be either categorical or numerical.

Note: There are two types of numerical variables, interval and ratio. An interval variable has values whose differences are interpretable, but it does not have a true zero. A good example is temperature in Centigrade degrees. Data on an interval scale can be added and subtracted but cannot be meaningfully multiplied or divided. For example, we cannot say that one day is twice as hot as another day. In contrast, a ratio variable has values with a true zero and can be added, subtracted, multiplied or divided (e.g., weight).

· Bivariate analysis is the simultaneous analysis of two variables (attributes). It explores the concept of relationship between two variables, whether there exists an association and the strength of this association.

There are three types of bivariate analysis.

1.Numerical & Numerical

Scatter Plot, Linear Correlation …

2.Categorical & Categorical

Stacked Column Chart, Combination Chart, Chi-square Test

3.Numerical & Categorical

Line Chart with Error Bars, Combination Chart, Z-test and t-test

> Modeling

· Predictive modeling is the process by which a model is created to predict an outcome

· If the outcome is categorical it is called classification and if the outcome is numerical it is called regression.