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The Quick and Easy Way to Analyze Numpy Arrays

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The quickest and easiest way to analyze NumPy arrays is by using the numpy.array() method. This method allows you to quickly and easily analyze the values contained in a numpy array. This method can also be used to find the sum, mean, standard deviation, max, min, and other useful analysis of the value contained within a numpy array. Sum You can find the sum of Numpy arrays using the np.sum() function.  For example:  import numpy as np  a = np.array([1,2,3,4,5])  b = np.array([6,7,8,9,10])  result = np.sum([a,b])  print(result)  # Output will be 55 Mean You can find the mean of a Numpy array using the np.mean() function. This function takes in an array as an argument and returns the mean of all the values in the array.  For example, the mean of a Numpy array of [1,2,3,4,5] would be  result = np.mean([1,2,3,4,5])  print(result)  #Output: 3.0 Standard Deviation To find the standard deviation of a Numpy array, you can use the NumPy std() function. This function takes in an array as a par
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How to Use Chaid Useful for Data Science Developers

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CHAID

The Chaid is one of the most asked skills for Data Science engineers. The CHAID Analysis (Chi-Square Automatic Interaction Detection) is a form of analysis that determines how variables best combine to explain the outcome in a given dependent variable.

Chaid Model


  • The model can be used in cases of market penetration, predicting and interpreting responses, or a multitude of other research problems.

  • CHAID analysis is especially useful for data expressing categorized values instead of continuous values.

  • For this kind of data, some common statistical tools such as regression are not applicable and CHAID analysis is a perfect tool to discover the relationship between variables. 

  • One of the outstanding advantages of CHAID analysis is that it can visualize the relationship between the target (dependent) variable and the related factors with a tree


1. CHAID Analysis for Surveys


Analysis

  • Most survey answers have categorized values instead of continuous values. 

  • Finding out the statistical relationship in this kind of data is a challenge. 



2. CHAID Analysis for Customer Profiling


Profiling


  • Based on historical customer data, CHAID Analysis can be used to analyze all characteristics within the file. 

  • For example, product/service purchased, the dollar amount spent, major demographics, and demography of the customers, and so on. 

  • A blueprint can be produced to provide an understanding of the customer profile: strong or weak sales of products/services; active or inactive customers; factors affecting customers’ decisions or preferences, and so on. 

  • Such a customer profile will give the Sales & Marketing Team a clear picture of which type of person is most likely to buy the products and services based on factual purchase history, geo-demographics, and lifestyle attributes.


3. CHAID Analysis for Customer Targeting


Customer Targetting


  • Recruiting new customers via direct contact (phone or mail) is a time-consuming and costly effort.

  • For most products or services, the hit rate is less than 1%. That means, in order to get a new customer, over one hundred contacts are required.

  • By mapping the current customer list to a general population database (e.g., SMR Residential Database that contains 12 million listed households), CHAID Analysis can find the household clusters that have much higher incidence rates than the average.

  • By concentrating on these household clusters, the actual hit rate can be dramatically raised. The result is “Fewer phone calls or mail pieces with higher sales returns!”.

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