1598516160

The strength of a linear relationship between two quantitative variables can be measured using Correlation. It is a statistical method that is very easy in order to calculate and to interpret. It is generally represented by ‘r’ known as the coefficient of correlation.

This is the reason why it is highly misused by professionals because correlation cannot be termed for causation. It is not necessary that if two variables have a correlation then one is dependent on the other and similarly if there is no correlation between two variables it is possible that they might have some relation. This is where PPS(Predictive Power Score) comes into the role.

Predictive Power Score works similar to the coefficient of correlation but has some additional functionalities like:

- It works on both Linear and Non-Linear Relationships
- Can be applied to both Numeric and Categorical columns
- It finds more patterns in the data.

In this article, we will explore how we can use the Predictive Power Score to replace correlation.

PPS is an open-source python library so we will install it like any other python library using **pip install ppscore.**

**Importing required libraries**

We will import ppscore along with pandas to load a dataset that we will work on.

`import ppscore as pps`

`import pandas as pd`

**Loading the Dataset**

We will be using different datasets to explore different functionalities of PPS. We will first import an advertising dataset of an MNC which contains the target variable as ‘Sales’ and features like ‘TV’, ‘Radio’, etc.

`df = pd.read_csv(‘advertising.csv’)`

`df.head()`

**Finding Relation using PPScore**

We will use some basic functions defined in ppscore.

**Finding the Relationship score**

PP Score lies between 0(No Predictive Power) to 1(perfect predictive power), in this step we will find PPScore/Relationship between the target variable and the featured variable in the given dataset.

`pps.score(df, "Sales", "TV")`

#developers corner #coefficient of correlation #correlation analysis #dependency #heatmap #linear regression #replace correlation #visualization

1598516160

The strength of a linear relationship between two quantitative variables can be measured using Correlation. It is a statistical method that is very easy in order to calculate and to interpret. It is generally represented by ‘r’ known as the coefficient of correlation.

This is the reason why it is highly misused by professionals because correlation cannot be termed for causation. It is not necessary that if two variables have a correlation then one is dependent on the other and similarly if there is no correlation between two variables it is possible that they might have some relation. This is where PPS(Predictive Power Score) comes into the role.

Predictive Power Score works similar to the coefficient of correlation but has some additional functionalities like:

- It works on both Linear and Non-Linear Relationships
- Can be applied to both Numeric and Categorical columns
- It finds more patterns in the data.

In this article, we will explore how we can use the Predictive Power Score to replace correlation.

PPS is an open-source python library so we will install it like any other python library using **pip install ppscore.**

**Importing required libraries**

We will import ppscore along with pandas to load a dataset that we will work on.

`import ppscore as pps`

`import pandas as pd`

**Loading the Dataset**

We will be using different datasets to explore different functionalities of PPS. We will first import an advertising dataset of an MNC which contains the target variable as ‘Sales’ and features like ‘TV’, ‘Radio’, etc.

`df = pd.read_csv(‘advertising.csv’)`

`df.head()`

**Finding Relation using PPScore**

We will use some basic functions defined in ppscore.

**Finding the Relationship score**

PP Score lies between 0(No Predictive Power) to 1(perfect predictive power), in this step we will find PPScore/Relationship between the target variable and the featured variable in the given dataset.

`pps.score(df, "Sales", "TV")`

#developers corner #coefficient of correlation #correlation analysis #dependency #heatmap #linear regression #replace correlation #visualization

1598500594

The strength of a linear relationship between two quantitative variables can be measured using Correlation. It is a statistical method that is very easy in order to calculate and to interpret. It is generally represented by ‘r’ known as the coefficient of correlation.

#predictivepowerscore #machine-learning #regression #decisiontree #python #pps

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1598420793

The short answer, for most of you, is no. However, the complexity and capability of the products could be beneficial depending on what type of position or organization you work in.

In my effort to answer this common question about Power BI I researched the following:

– Power BI Desktop Gateway

– Syncing on-prem SQL server data

– Syncing SharePoint Online list data

– Syncing data from an Excel workbook

– Building, and sharing a dashboard

– Inserting a Power BI visualization into PowerPoint

To get in-Depth knowledge on Power BI you can enroll for a live demo on **Power BI online training**

The feature spread above gave me the opportunity to explore the main features of Power BI which break down as:

– Ingesting data, building a data set

– Creating dashboard or reports with visualizations based on that data

In a nutshell Power BI is a simple concept. You take a data set, and build visualizations that answer questions about that data. For example, how many products have we sold in Category A in the last month? Quarter? Year? Power BI is especially powerful when drilling up or down in time scale.

And there are some interesting ways to visualize that data:

However, there are a number of drawbacks to the current product that prevented me from being able to fold these visualizations into our existing business processes.

- Integration with PowerPoint is not free. This shocked me.

The most inspiring Power BI demo I saw at a Microsoft event showed a beautiful globe visualization within a PowerPoint presentation. It rendered flawlessly within PowerPoint and was a beautiful, interactive way to explore a geographically disparate data set. I was able to derive conclusions about the sales data displayed without having to look at an old, boring chart.

During the demo, nothing was mentioned about the technology required to make this embedded chart a reality. After looking into the PowerPoint integration I learned that not only was the add-in built by a third party, it was not free, and when I signed up for a free trial the add-in could barely render my Power BI visualization. The data drill up/down functionality was non-existent and not all of the visualizations were supported. Learn more from **Power bi online course**

- Only Dashboards can be shared with other users, and cannot be embedded in our organization’s community on SharePoint.

Folks in our organization spent 50% of their time in Outlook, and the rest in SharePoint, OneNote, Excel, Word, and the other applications needed for producing documents, and other work. Adding yet another destination to that list to check on how something is doing was impossible for us. Habits are extremely hard to change, and I see that consistently in our client’s organizations as well.

Because I was not able to fold in the visualizations with the PowerPoint decks we use during meetings, I had to stop presentations in the middle, navigate to Internet Explorer (because the visualizations only render well in that browser), and then go back to PowerPoint once we were done looking at the dashboard.

This broke up the flow of our meetings, and led to more distractions. I also followed up with coworkers after meetings to see if they ever visited the dashboard themselves at their desk. None of them had ever navigated to a dashboard outside of a meeting.

- The visualizations aren’t actually that great.

Creating visualizations that cover such a wide variety of data sets is difficult. But, the Excel team has been working on this problem for over 15 years. When I import my SharePoint or SQL data to Excel I’m able to create extremely customized Pivot Tables and Charts that show precisely the data I need to see.

I was never able to replicate visualizations from Excel in Power BI, to produce the types of visualizations I actually needed. Excel has the ability to do conditional formatting, and other customizations in charts and tables that is simply not possible with Power BI. Because of how generic the charts are, and the limited customization it looks “cool” without being functional.

In conclusion, if you have spare time and want to explore Power BI for your organization you should. However, if you are seriously thinking about how you can fold this product into your work processes, challenge yourself to build a dashboard and look at it once a week. See if you can keep that up for a month, and then think about how that change affected your work habits and whether the data analysis actually contributed value each time. At least half of you will realize that this gimmicky product is fancy, but not actually useful.

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