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beyondvalence.blogspot.com

Valence Analytics

Exploring and using data analytics through R, Python, SQL, and PowerPivot. Covers social media analysis, statistical analysis, and machine learning.

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Valence Analytics | beyondvalence.blogspot.com Reviews
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Exploring and using data analytics through R, Python, SQL, and PowerPivot. Covers social media analysis, statistical analysis, and machine learning.
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Valence Analytics | beyondvalence.blogspot.com Reviews

https://beyondvalence.blogspot.com

Exploring and using data analytics through R, Python, SQL, and PowerPivot. Covers social media analysis, statistical analysis, and machine learning.

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beyondvalence.blogspot.com beyondvalence.blogspot.com
1

Valence Analytics: R: Classifying Handwritten Digits (MNIST) using Random Forests

http://beyondvalence.blogspot.com/2014/01/r-classifying-handwritten-digits-mnist.html

Dedicated to Analytics and Machine Learning with R and Python. Thursday, January 16, 2014. R: Classifying Handwritten Digits (MNIST) using Random Forests. The last time we used random forests was to predict iris species. From their various characteristics. Here we will revisit random forests and train the data with the famous MNIST handwritten digits. Data set provided by Yann LeCun. The data can also be found on Kaggle. We will require the training and test data sets along with the. The data set itself ...

2

Valence Analytics: Text Mining: 5. Hierarchical Clustering for Frequent Terms in R

http://beyondvalence.blogspot.com/2014/01/text-mining-5-hierarchical-clustering.html

Dedicated to Analytics and Machine Learning with R and Python. Friday, January 10, 2014. Text Mining: 5. Hierarchical Clustering for Frequent Terms in R. Today we will discuss clustering the terms with methods we utilized from the previous posts in the Text Mining Series. To analyze recent tweets from @TheEconomist. Therefore, I shall post the code for retrieving. The code can be found on my GitHub! Check out Text Mining: 6 for K-Medoids clustering. The Economist Twitter Page. Covered in link above.*.

3

Valence Analytics: Links

http://beyondvalence.blogspot.com/p/links.html

Dedicated to Analytics and Machine Learning with R and Python. For a blog on R). Data Mining with R book. Microsoft Excel 2013: Building Data Models with PowerPivot. By Alberto Ferrari and Marco Russo. For companion material, click here. For downloading or installation information, click here. 6 Bold Predictions for Big Data in 2014. Top 10 Big Data Stories of 2013. Facebook, Yann LeCun, and Artificial Intelligence. Big Data TED Talk:. Subscribe to: Posts (Atom). View my complete profile. Predicting Frau...

4

Valence Analytics: Text Mining: 3. Stemming Text and Building a Term Document Matrix in R

http://beyondvalence.blogspot.com/2014/01/text-mining-3-stemming-text-and.html

Dedicated to Analytics and Machine Learning with R and Python. Sunday, January 5, 2014. Text Mining: 3. Stemming Text and Building a Term Document Matrix in R. In our last post. In the Text Mining Series, we talked about converting a Titter tweet list object into a text corpus. A collection of text documents, and we transformed the tweet text to prepare it for analysis. Read Text Mining 4 here. Start R and let us begin! In R, load the. Package, along with. Package will allow us to stem our text of tweets...

5

Valence Analytics: Predicting Fraudulent Transactions in R: Part 4. Model Criterion, Precision & Recall

http://beyondvalence.blogspot.com/2015/03/predicting-fraudulent-transactions-in-r.html

Dedicated to Analytics and Machine Learning with R and Python. Saturday, March 28, 2015. Predicting Fraudulent Transactions in R: Part 4. Model Criterion, Precision and Recall. To do this, we will use two measures, precision and recall. Beginning where we left off at the end of Part 3. Start R and let us get started. This is a series from Luis Torgo's. Data Mining with R book. Within those k transactions, the proportion of frauds among them; also known as positive predictive value. We show this smoothing...

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Marketing Mix Modeling Explained – With R | Gabriel Mohanna's Blog

https://analyticsartist.wordpress.com/2014/08/17/marketing-mix-modeling-explained-with-r

Gabriel Mohanna's Blog. Marketing Mix Modeling Explained – With R. Let’s assume there is only one advertising variable that affects sales. This simple model is usually defined as:. Sales = Base b. A sample R code can be:. This model has an R. Of 0184 so there is much work to be done. Complexity 1: The Adstock Case. The model above assumes that advertising in week. Which I covered in a previous blog post. Our model now becomes:. Sales = Base b. Where f() is a the adstock transformation function for the Ad...

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Dedicated to Analytics and Machine Learning with R, Python, and SQL. Saturday, April 18, 2015. Predicting Fraudulent Transactions in R: Part 5. Normalized Distance to Typical Price. That is where we use our predictive model to determine the classification of those unlabeled reports based on the the labeled reports. This is a series from Luis Torgo's Data Mining with R book. Normalized Distance to Typical Price. To calculate the normalized distance to typical price (NDTP) for a specific product of a trans...

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