# Chapter 2 Overview of the Data Mining Process

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Chapter 2 Overview of the Data Mining Process

Introduction Data Mining Data Base Methods Predictive analysis OLAP
Tasks of Classification & Prediction Core of Business Intelligence Data Base Methods OLAP SQL Do not involve statistical modeling

Core Ideas in Data Mining
Analytical Methods Used in Predictive Analytics Classification Used with categorical response variables E.g. Will purchase be made / not made? Prediction Predict (estimate) value of continuous response variable Prediction used with categorical as well Association Rules Affinity analysis – “what goes with what” Seeks correlations among data

Core Ideas in Data Mining
Data Reduction Reduce variables Group together similar variables Data Exploration View data as evidence Get “a feel” for the data Data Visualization Graphical representation of data Locate tends, correlations, etc.

Supervised Learning “Supervised learning" algorithms are those used in classification and prediction. Data is available in which the value of the outcome of interest is known. “Training data" are the data from which the classification or prediction algorithm “learns," or is “trained," about the relationship between predictor variables and the outcome variable. This process results in a “model” Classification Model Predictive Model

Supervised Learning Model is then run with another sample of data
“validation data" the outcome is known but we wish to see how well the model performs If many different models are being tried out, a third sample of known outcomes -“test data” is used with the final, selected model to predict how well it will do. The model can then be used to classify or predict the outcome of interest in new cases where the outcome is unknown.

Supervised Learning Linear regression analysis is an example of supervised Learning The Y variable is the (known) outcome variable The X variable is some predictor variable. A regression line is drawn to minimize the sum of squared deviations between the actual Y values and the values predicted by this line. The regression line can now be used to predict Y values for new values of X for which we do not know the Y value.

Unsupervised Learning
No outcome variable to predict or classify No “learning” from cases Unsupervised leaning methods Association Rules Data Reduction Methods Clustering Techniques

The Steps in Data Mining
1. Develop an understanding of the purpose of the data mining project It is a one-shot effort to answer a question or questions or Application (if it is an ongoing procedure). 2. Obtain the dataset to be used in the analysis. Random sampling from a large database to capture records to be used in an analysis Pulling together data from different databases. Internal (e.g. Past purchases made by customers) External (credit ratings). Usually the analysis to be done requires only thousands or tens of thousands of records.

The Steps in Data Mining
3. Explore, clean, and preprocess the data Verifying that the data are in reasonable condition. How missing data should be handled? Are the values in a reasonable range, given what you would expect for each variable? Are there obvious “outliers?" Data are reviewed graphically – For example, a matrix of scatter plots showing the relationship of each variable with each other variable. Ensure consistency in the definitions of fields, units of measurement, time periods, etc.

The Steps in Data Mining
4. Reduce the data If supervised training is involved separate them into training, validation and test datasets. Eliminating unneeded variables, Transforming variables Turning “money spent" into “spent > \$100" vs. “Spent · \$100"), Creating new variables A variable that records whether at least one of several products was purchased Make sure you know what each variable means, and whether it is sensible to include it in the model. 5. Determine the data mining task Classification, prediction, clustering, etc. 6. Choose the data mining techniques to be used Regression, neural nets, hierarchical clustering, etc.

The Steps in Data Mining
7. Use algorithms to perform the task. Iterative process - trying multiple variants, and often using multiple variants of the same algorithm (choosing different variables or settings within the algorithm). When appropriate, feedback from the algorithm's performance on validation data is used to refine the settings. 8. Interpret the results of the algorithms. Choose the best algorithm to deploy, Use final choice on the test data to get an idea how well it will perform. 9. Deploy the model. Integrate the model into operational systems Run it on real records to produce decisions or actions. For example, the model might be applied to a purchased list of possible customers, and the action might be “include in the mailing if the predicted amount of purchase is > \$10."

Preliminary Steps Organization of datasets Sampling from a database
Records in rows Variables in columns In supervised learning one of these will be the outcome variable Labels the first or last column Sampling from a database Use a samples to create, validate, & test model Oversampling rare events If response variable value is seldom found in data then sample size increase Adjust algorithm as necessary

Preliminary Steps (Pre-processing and Cleaning the Data)
Types of variables Continuous – assumes a any real numerical value (generally within a specified range) Categorical – assumes one of a limited number of values Text (e.g. Payments e {current, not current, bankrupt} Numerical (e.g. Age e {0 … 120} ) Nominal (payments) Ordinal (age)

Preliminary Steps (Pre-processing and Cleaning the Data)
Handling categorical variables If categorical is ordered then it can be used as continuous variable (e..G. Age, level of credit, etc.) Use of “dummy” variables when range of values not large e.g. Variable occupation e {student, unemployed, employed, retired} Create binary (yes/no) dummy variables Student – yes/no Unemployed – yes/no Employed – yes/no Retired – yes/no Variable selection The more predictor variables the more records need to build the model Reduce number of variables whenever appropriate

Preliminary Steps (Pre-processing and Cleaning the Data)
Overfitting Building a model - describe relationships among variables in order to predict future outcome (dependent) values on the basis of future predictor (independent) values. Avoid “explaining“ variation in the data that was nothing more than chance variation. Avoid mislabeling “noise” in the data as if it were a “signal” Caution - if the dataset is not much larger than the number of predictor variables, then it is very likely that a spurious relationship like this will creep into the model

Overfitting

Preliminary Steps (Pre-processing and Cleaning the Data)
How many variables & how much data A good rule of thumb is to have ten records for every predictor variable. For classification procedures At least 6xmxp records, Where m = number of outcome classes, and p = number of variables Compactness or parsimony is a desirable feature in a model. A matrix of x-y plots can be useful in variable selection. Can see at a glance x-y plots for all variable combinations. A straight line would be an indication that one variable is exactly correlated with another. We would want to include only one of them in our model. Weed out irrelevant and redundant variables from our model Consult domain expert whenever possible

Preliminary Steps (Pre-processing and Cleaning the Data)
Outliers Values that lie far away from the bulk of the data are called outliers no statistical rule can tell us whether such an outlier is the result of an error these are judgments best made by someone with “domain" knowledge if the number of records with outliers is very small, they might be treated as missing data.

Preliminary Steps (Pre-processing and Cleaning the Data)
Missing values If the number of records with missing values is small, those records might be omitted The more variables, the more records to dropped Solution - use average value computed from records with valid data for variable with missing data Reduces variability in data set Human judgment can be used to determine best way to handle missing data

Preliminary Steps (Pre-processing and Cleaning the Data)
Normalizing (standardizing) the data To normalize the data, we subtract the mean from each value, and divide by the standard deviation of the resulting deviations from the mean Expressing each value as “number of standard deviations away from the mean“ – the z-score Needed if variables are in different units e.G. Hours, thousands of dollars, etc. Clustering algorithms measure variables values in distance from each other – need a standard value for distance. Data mining software, including XLMiner, typically has an option that normalizes the data in those algorithms where it may be required

Preliminary Steps Use and creation of partition Training partition
The largest partition Contains the data used to build the various models Same training partition is generally used to develop multiple models. Validation partition Used to assess the performance of each model, Used to compare models and pick the best one. In classification and regression trees algorithms the validation partition may be used automatically to tune and improve the model. Test partition Sometimes called the “holdout" or “evaluation" partition is used to assess the performance of a chosen model with new data.

The Three Data Partitions and Their Role in the Data Mining Process

Simple Regression Example

Simple Regression Model
Make prediction about the starting salary of a current college graduate Data set of starting salaries of recent college graduates Data Set Compute Average Salary How certain are of this prediction? There is variability in the data.

Simple Regression Model
Use total variation as an index of uncertainty about our prediction Compute Total Variation The smaller the amount of total variation the more accurate (certain) will be our prediction.

Simple Regression Model
How “explain” the variability - Perhaps it depends on the student’s GPA Salary GPA

Simple Regression Model
Find a linear relationship between GPA and starting salary As GPA increases/decreases starting salary increases/decreases

Simple Regression Model
Least Squares Method to find regression model Choose a and b in regression model (equation) so that it minimizes the sum of the squared deviations – actual Y value minus predicted Y value (Y-hat)

Simple Regression Model
How good is the model? a= 4,779 & b = 5,370 A computer program computed these values u-hat is a “residual” value The sum of all u-hats is zero The sum of all u-hats squared is the total variance not explained by the model “unexplained variance” is 7,425,926

Simple Regression Model
Total Variation = 23,000,000

Simple Regression Model
Total Unexplained Variation = 7,425,726

Simple Regression Model
Relative Goodness of Fit Summarize the improvement in prediction using regression model Computer R2 – coefficient of determination Regression Model (equation) a better predictor than guessing the average salary The GPA is a more accurate predictor of starting salary than guessing the average R2 is the “performance measure“ for the model. Predicted Starting Salary = 4, ,370 * GPA

Building a Model - An Example with Linear Regression

Problems  Problem 2.11 Page 33