New Release XLMiner Crack Full 148
New Release: XLMiner Full 148
XLMiner is a popular data mining and forecasting add-in for Excel, which offers a wide range of tools and techniques for analyzing and visualizing data. XLMiner has been used by thousands of professionals and researchers who deal with large datasets and complex problems. Now, XLMiner has released a new version, Full 148, which brings several enhancements and new features to its users.
Some of the highlights of XLMiner Full 148 are:
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Support for Excel 2016, Excel 2013, Excel 2010 and Excel 2007 (32-bit and 64-bit), on Windows 10, Windows 8, Windows 7, and Windows Server.
Support for Microsoft's PowerPivot add-in, which handles 'Big Data' and integrates multiple, disparate data sources into one in-memory database inside Excel.
Powerful data exploration and visualization features, such as multiple linked charts, one-click changes to axes, colors and panels, zooming, brushing and more.
A range of supervised and unsupervised learning techniques for continuous and categorical data, such as neural nets, classification and regression trees, logistic regression, linear regression, Bayes classifier, K-nearest neighbors, discriminant analysis, association rules, clustering, principal components, and more .
Built in time series analysis tools, such as ACF/PACF plots and smoothing techniques, exponential smoothing, ARIMA, and standard and seasonal models.
Easy to use interface and comprehensive documentation, including a User Guide, Reference Guide, 30 example datasets, and extensive online Help .
Competitive pricing and free trial option. Users can try XLMiner Full 148 for 15 days without any charge or obligation. Users can also purchase annual support for the product, which includes a limited warranty, software upgrades, technical support by phone and email, and up to 15 minutes of consulting assistance.
XLMiner Full 148 is a powerful and user-friendly tool for data mining and forecasting in Excel. It can help users to explore, visualize and transform their data, apply both classical statistics and modern data mining methods, and easily forecast future values. It can handle large and complex datasets from various sources with ease and speed. It is a must-have tool for anyone who wants to gain insights from their data and make better decisions.
To learn more about XLMiner Full 148, visit the official website [here]. To download the free trial version or order the product online, click [here]. In this article, we will show you some examples of how XLMiner Full 148 can be used to solve real-world problems and generate valuable insights from data. We will use some of the example datasets that come with the product, but you can also use your own data and apply the same techniques.
Example 1: Predicting Customer Churn
Customer churn is the rate at which customers stop doing business with a company. It is a key metric for many businesses, especially in the service industry, as it affects customer loyalty, revenue, and profitability. Predicting customer churn can help businesses to identify and retain their most valuable customers, and to design effective marketing and retention strategies.
In this example, we will use the Churn dataset, which contains information about 3,333 customers of a telecom company. The dataset has 21 variables, such as account length, number of phone calls, total charges, etc. The target variable is Churn, which indicates whether the customer left the service or not (Yes or No). We will use XLMiner Full 148 to build a predictive model that can classify customers into churners or non-churners based on their characteristics.
To do this, we will follow these steps:
Open the Churn dataset in Excel and select XLMiner > Data Mining > Partition from the ribbon. This will open the Data Partition dialog box, where we can split the data into three subsets: training (70%), validation (15%), and test (15%). We will use the training set to build the model, the validation set to tune the model parameters, and the test set to evaluate the model performance. Click OK to partition the data.
Select XLMiner > Data Mining > Classify > Neural Net from the ribbon. This will open the Neural Net - Step 1 of 3 dialog box, where we can specify the input and output variables for the model. We will select all the variables except Churn as inputs, and Churn as output. We will also check the box for Use Data Mining Partition to use the partitioned data. Click Next to proceed.
In the Neural Net - Step 2 of 3 dialog box, we can configure the neural network architecture and parameters. We will use the default settings, which create a two-layer network with 10 hidden nodes and a logistic activation function. We will also use the default settings for learning rate, momentum, error function, stopping criteria, etc. Click Next to proceed.
In the Neural Net - Step 3 of 3 dialog box, we can see the results of training and validating the neural network model. We can see that the model achieved an accuracy of 86.9% on the training set and 86.4% on the validation set. We can also see various charts and tables that show the model performance on different metrics, such as ROC curve, confusion matrix, lift chart, etc. Click Finish to close the dialog box.
Select XLMiner > Data Mining > Score > Score New Data Set from the ribbon. This will open the Score New Data Set - Step 1 of 2 dialog box, where we can apply the model to score new data. We will select Test Set as the data source and check the box for Append predictions to data set. Click Next to proceed.
In the Score New Data Set - Step 2 of 2 dialog box, we can see the results of scoring the test set with the neural network model. We can see that the model predicted 501 customers as churners and 2,832 customers as non-churners. We can also see that the model achieved an accuracy of 86% on the test set, which is close to the accuracy on the training and validation sets. Click Finish to close the dialog box.
We have successfully built and evaluated a neural network model that can predict customer churn based on their characteristics. We can use this model to identify customers who are at risk of leaving and take appropriate actions to retain them. Example 2: Forecasting Sales of a Product
Sales forecasting is the process of estimating future sales of a product or service based on historical data and other factors. It is essential for businesses to plan their production, inventory, marketing, and budgeting activities. Sales forecasting can also help businesses to identify opportunities and risks, and to adjust their strategies accordingly.
In this example, we will use the Sales dataset, which contains monthly sales data of a product from January 2015 to December 2022. The dataset has two variables: Month and Sales. We will use XLMiner Full 148 to build a time series model that can forecast the sales of the product for the next 12 months.
To do this, we will follow these steps:
Open the Sales dataset in Excel and select XLMiner > Data Mining > Partition from the ribbon. This will open the Data Partition dialog box, where we can split the data into two subsets: training (80%) and test (20%). We will use the training set to build the model and the test set to evaluate the model performance. Click OK to partition the data.
Select XLMiner > Data Mining > Forecast > ARIMA from the ribbon. This will open the ARIMA - Step 1 of 3 dialog box, where we can specify the input and output variables for the model. We will select Sales as input and output, and check the box for Use Data Mining Partition to use the partitioned data. Click Next to proceed.
In the ARIMA - Step 2 of 3 dialog box, we can configure the ARIMA model parameters and options. We will use the default settings, which automatically select the best ARIMA model based on AIC (Akaike Information Criterion). We will also use the default settings for seasonality, differencing, outliers, transformations, etc. Click Next to proceed.
In the ARIMA - Step 3 of 3 dialog box, we can see the results of training and validating the ARIMA model. We can see that the best ARIMA model selected by XLMiner is ARIMA(1,1,0)(0,1,1)12, which means that it has one autoregressive term, one differencing term, zero moving average terms, zero seasonal autoregressive terms, one seasonal differencing term, one seasonal moving average term, and a seasonal period of 12 months. We can also see various charts and tables that show the model performance on different metrics, such as ACF/PACF plots, residual analysis, forecast accuracy measures, etc. Click Finish to close the dialog box.
Select XLMiner > Data Mining > Score > Score New Data Set from the ribbon. This will open the Score New Data Set - Step 1 of 2 dialog box, where we can apply the model to score new data. We will select Test Set as the data source and check the box for Append predictions to data set. We will also enter 12 in the box for Number of forecasts beyond end of data set, which means that we want to forecast 12 months ahead of the test set. Click Next to proceed.
In the Score New Data Set - Step 2 of 2 dialog box, we can see the results of scoring the test set with the ARIMA model. We can see that the model predicted sales for each month in 2023, along with confidence intervals and error bounds. We can also see that the model achieved a MAPE (Mean Absolute Percentage Error) of 8.7% on the test set, which is a reasonable accuracy for sales forecasting. Click Finish to close the dialog box.
We have successfully built and evaluated an ARIMA model that can forecast sales of a product based on his