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    Powerpoint demo Powerpoint demo Presentation Transcript

    • XLMiner – a Data Mining Toolkit QuantLink Solutions Pvt. Ltd. www.quantlink.com www.xlminer.com
    • XLMiner – a quick tour
      • Here is a short demo of XLMiner.
      • Let us use a simple example:
      • a bank sends mailers to its customers, offering a special deal on Personal Loans. In its previous campaign, it got only about 9% positive response.
      • Objective : How to target customers for increased conversion rate.
      • In other words, the question to address is:
      • what profile indicates a high-potential customer?
    • XLMiner – a quick tour
      • Past campaign data will be used to train the data mining model
      • This is called supervised learning in DataMining terms
      • Let’s see how to build a model and use it for improving the response rate.
    • XLMiner Quick Tour Data description
      • Our past campaign data has the following customer attributes:
      • Customer ID
      • Customer’s Age
      • Professional Experience
      • Family Income
      • Credit Card average annual spending
      • Education Level
      • #appliances owned
      • Did this customer accept past campaign offer?
      • The last variable is the known outcome of the past campaign. Our Data Mining model will use this for Supervised Learning.
    • XLMiner Quick Tour A view of the data
      • This is what the data looks like:
      The variable labeled as “PersLoan?” is binary: 0 means the customer was not interested in the Personal Loan. 1 means the customer was interested.
    • XLMiner Quick Tour the Data Mining Process Partition the data into Training & Validation Partitions Fit the Model on Training Partition only Obtain results, see if they look good enough Check if they are good for Validation data too! Study the outputs for validation data Try out several alternative models Choose and deploy the best model
    • XLMiner Quick Tour Start the analysis
      • Let’s get going with XLMiner.
      • Notice that XLMiner is as easy to use as Excel!
      • All we need to do is use the friendly menus. We follow just three simple steps to fit a model and see the outputs!
    • XLMiner Quick Tour Step 1: Partition the data
      • We’ll create two partitions by choosing the records randomly.
      • The Training Partition will be used for fitting the model.
      • The Validation partition will be used for checking if the model gives a good fit for another piece of known data.
    • XLMiner Quick Tour Partitioned Data
      • XLMiner creates a Partition Sheet that shows the data split into Two partitions.
      Easy Hyperlinks on the Navigator facilitate viewing of either partition
    • XLMiner Quick Tour Step 2: Fitting the Model
      • This is a “ Classification ” Problem where we want to predict customers as likely / not likely to take a Personal Loan.
      • Let’s use one of the available techniques – Classification Tree .
      • Later we can use other Classification techniques.
      We select input (predictor) variables here… … and the outcome variable here
    • XLMiner Quick Tour Step 2: Fitting the Model
      • The model fit guides us through easy wizard-like steps.
      • In these steps we choose technique-specific parameters and the output options.
      • In the end, we click Finish to produce the results.
    • XLMiner Quick Tour Step 3: Understanding the Outputs
      • The friendly Output Navigator lets us go over all the outputs.
      The Summaries show us the classification error percentages – i.e. how well the model is predicting Other outputs (like the Tree here) will tell us the decision rules that the model is suggesting. Many other diagnostic outputs are available depending on options we choose.
    • XLMiner Quick Tour Output 1: Validation Summary
      • First, we look at how well the model predicted for the Validation data set
      In the Training data where we already knew the outcome, 156 “will buy” were predicted correctly, and 38 wrongly. 1801 “Won’t buy” were predicted correctly and merely 5 wrongly. Here are the corresponding error percentages. The errors are not very small but could still indicate a workable model.
    • XLMiner Quick Tour Output 2: the decision rule
      • Here is the Classification Tree that gives the easy-to-understand and implement Decision Rules
      Cut-off points for different variables decide whether to go Left or Right 0: not likely to buy 1: likely to buy
    • XLMiner Quick Tour the decision rule in table form
      • The same decision rule as shown visually, can be converted into the table below. This is useful for implementing it in your information systems.
    • XLMiner Quick Tour Output 3: more details
      • Each technique (Classification Tree in this case) has additional helpful outputs
      • The example here shows the “Prune Log” – how the percentage error reduced by “pruning” the tree
    • XLMiner Quick Tour Output 4: the Lift Chart
      • “ Lift” tells us how much better the model did compared to a random targeting of customers. This is one of the most important outputs.
      If customers were targeted randomly, we would expect this outcome. For instance, 1000 mailers would probably yield less than 100 customers. With our Tree model, we get a much superior result. In less than 500 mailers sent to high probability customers , we would get nearly 170 successes!
    • XLMiner Quick Tour Output 5: the Detailed report
      • The Validation data is “scored” in detail as shown below. Scoring means using the fitted model to classify each record of the data.
      Predicted values can be seen against the actuals here. Probability of success is computed for each record. This is what helps XLMiner suggest selective records (customers) to target.
    • XLMiner Quick Tour Try several techniques!
      • That was just one of the many techniques in XLMiner – Classification Tree.
      • A typical Data Mining exercise involves several alternative approaches on the same data. This can be either with different techniques, or with different parameters, or both.
      • Comparing multiple approaches lets us “assess” which model to finally choose for implementation.
    • XLMiner Quick Tour Rich repertoire of techniques!
      • XLMiner supports a comprehensive array of supervised learning procedures:
      • Multiple Linear Regression
      • Logistic Regression
      • Classification & Regression Trees
      • Neural Networks
      • k Nearest Neighbors
      • Naïve Bayes Classifier
      • Discriminant Analysis
    • XLMiner Quick Tour Rich repertoire of techniques!
      • ... and several other features in Unsupervised Learning, Data Reduction and Exploration:
      • Principal Components Analysis
      • k-means Clustering
      • Hierarchical Clustering
      • Self-organizing Maps (coming soon)
      • Affinity– Market Basket Analysis
      • Here are some sample outputs from these methods …
    • XLMiner Quick Tour sample output - Dendrogram
      • Hierarchical Clustering produces a dendrogram – an excellent visual representation of Cluster formation.
      Height of the bars is a measure of dissimilarity in the clusters that are merging into one. Smaller clusters “agglomerate” into bigger ones, with least possible loss of cohesiveness at each stage.
    • XLMiner Quick Tour sample output – cluster predictions
      • Cluster Analysis has many powerful uses like Market Segmentation. We can view individual record’s predicted cluster membership.
    • XLMiner Quick Tour sample output – BoxPlots
      • XLMiner supports powerful visualization . The example here shows BoxPlots of two variables.
      Cluster 2 clearly shows higher Income & Credit Card spend than Cluster 1. This is an excellent aid to characterizing the clusters
    • XLMiner Quick Tour sample output – Scatter Plots
      • Matrix Scatterplots in XLMiner give a visual insight into relationship among variables.
    • XLMiner Quick Tour sample output – Association Rules
      • For Market Basket Analysis XLMiner produces easy-to-read Association Rules
      Rules are explained in simple English! Each rule tells us which offerings will go well together
    • XLMiner Quick Tour … and that’s not all!
      • XLMiner has handy utilities for Data handling:
      • Missing data treatment
      • Transforming categorical data
      • Binning continuous data
      • Sampling from Databases
      • Scoring to Databases
    • XLMiner Quick Tour XLMiner => Versatility!
      • This was a quick demonstration of just a few things XLMiner can do.
      • It can do lots more. It is comprehensive in coverage, like the best DM products around.
      • Get your free download for evaluation at www.xlminer.ncom
    • XLMiner Quick Tour XLMiner => Simplicity!
      • Daryl Pregibon had said – Data Mining is “Statistics at Scale and Speed”.
      • You’ll find that XLMiner is Statistics at Scale, Speed and Simplicity !
      • If you know to use Excel, you already know XLMiner. You can get started in minutes .
    • XLMiner Quick Tour XLMiner => Great Value!
      • Several comprehensive DM products are many times more expensive.
      • For exploring how Data Mining will work for you, XLMiner provides a great start!
    • XLMiner Quick Tour What others say …
      • The American Statistician reviewed XLMiner along with other reputed products in the November 2003 issue
      • This is what it had to say:
      • “ An easy to use… an excellent, inexpensive add-on that greatly expands the capabilities of Excel.”
      • “ XLMiner’s documentation is remarkably good…”
    • XLMiner Quick Tour More Resources
      • For your initiation into Data Mining:
      • Free evaluation download
      • Online Courses at www.statistics.com
      • Case Book in the making
      • Technical references on product website
    • XLMiner Quick Tour Thank you for viewing this Demo!
      • www.xlminer.com
      XLMiner - the Data Mining Toolset for the Managers of Tomorrow