Monthly Archives: May 2018

Simpson’s Paradox

Simpson’s paradox for quantitative data: a positive trend ( blue line and red line  ) appears for two separate groups, whereas a negative trend (dotted line) appears when the groups are combined. Simpson’s paradox, or the Yule–Simpson effect, is a phenomenon in probability and statistics, in which a trend appears in several different groups of data but disappears or reverses […]

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Experimentation Key Steps

  Within webpages, nearly every element can be changed for a split test. Marketers and web developers may try testing: Visual elements: pictures, videos, and colors Text: headlines, calls to action, and descriptions Layout: arrangement and size of buttons, menus, and forms Visitor flow: how a website user gets from point A to B Some […]

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Attribution modeling

An attribution model is the rule, or set of rules, that determines how credit for sales and conversions is assigned to touchpoints in conversion paths. For example, the Last Interaction model in Analytics assigns 100% credit to the final touchpoints (i.e., clicks) that immediately precede sales or conversions. In contrast, the First Interaction model assigns 100% credit to touchpoints that initiate conversion […]

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Online lender Kabbage to launch payment services by year-end

NEW YORK (Reuters) – Kabbage Inc, a U.S. online lender for small businesses, plans to launch payment processing services by year-end, President Kathryn Petralia said on Monday, helping it to diversify and compete more directly with industry leaders PayPal Holdings Inc (PYPL.O) and Square Inc (SQ.N). The Atlanta-based startup will offer tools to enable clients, […]

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Comparing Performance of 6 Classification Models

In the example below 6 different algorithms are compared: Logistic Regression Linear Discriminant Analysis K-Nearest Neighbors Classification and Regression Trees Naive Bayes Support Vector Machines # Python Code # Compare Algorithms import pandas import matplotlib.pyplot as plt from sklearn import model_selection from sklearn.linear_model import LogisticRegression from sklearn.tree import DecisionTreeClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.discriminant_analysis […]

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Gradient Descent

Gradient descent is an optimization algorithm used to minimize some function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In machine learning, we use gradient descent to update the parameters of our model. Parameters refer to coefficients in Linear Regression and weights in neural networks. Learning rate The size of these steps […]

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Customer Engagement Dashboard

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Sanity Check of Experiments & Propensity Score Matching

Here’s a few questions to ask yourself to decide whether you should run an A/B test: Do I have an important question? Will answering this question make an impact worth the effort of running a test? What else could I be doing with my energy? Running experiments and being creative and visionary are two completely different brain […]

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ANOVA, ANCOVA, MANOVA, & MANCOVA

Comparison Chart BASIS FOR COMPARISON ANOVA ANCOVA Meaning ANOVA is a process of examining the difference among the means of multiple groups of data for homogeneity. ANCOVA is a technique that remove the impact of one or more metric-scaled undesirable variable from dependent variable before undertaking research. Uses Both linear and non-linear model are used. […]

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Data Visualization – How to Pick the Right Chart Type?

  There are four basic presentation types that you can use to present your data: Comparison Composition Distribution Relationship you are most likely using only the two, most commonly used types of data analysis: Comparison or Composition. choosing-a-good-chart To determine which chart is best suited for each of those presentation types, first you must answer a few […]

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