In the last posts, I have explained the main concepts behind the Timeseries (Post 1) and in the second one a simple forecasting approach name as “Exponential Smoothing” has been proposed Post 2. In this post I am going to show how to do see the error of forecasting and also how to forecast when Read more about New Series of Time Series: Part 3 (Holt’s Exponential Smoothing)[…]
In the last post , I have explained the main concepts behind the timeseries. In this post, I am going to show how we can forecast for some periods. In the last post, I have mentioned that there is a possibility to have “seasonality” “Trend” and errors (residual) in one dataset: Seasonality+Trend+Residual we call it as Read more about New Series of Time Series: Part 2 (Exponential Smoothing)[…]
In the last posts, I have explained the main process of creating R custom visuals. In this post I am going to show how to: 1- Have more custom visuals 2- Different charts that we can have in Power BI 3- Explain some issues Have more custom visuals to have more R visuals, it is Read more about Interactive Charts using R and Power BI: Create Custom Visual Part 2[…]
In the last post, I have explained the main concepts behind the neural network, In this post I will show how to apply neural network in a scenario in R and how to see the results and hidden layers in a plot. For this post I got some great example from [1]. Scenario: Concert has Read more about Neural Network R codes in Power BI Part2[…]
In this and next one, I will share my understanding on Neural Network and how to write the related R code inside the Power BI. First, in this post I am going to explain what is main concept behind the Neural Network and How it works .The video https://www.youtube.com/watch?v=DG5-UyRBQD4&spfreload=10 helped me a lot to get better Read more about Neural Network Concepts Part 1[…]
Microsoft Data insight Summit 2017 has been held in Seattle from 12 to 13 Jun. It is the main conference for Microsoft Data Analysis with Power BI. All interesting and new topics in Power BI has been presented. Most of the speakers are from Microsoft product team or experienced speakers in this fields. there are Read more about Speaking Files in Microsoft Data Insight Summit 2017[…]
In the previous posts (from Part 1 to Part7), I have explained the whole process of doing machine learning inside the Azure ML, from import data, data cleaning, feature selection, training models, testing models, and evaluating. In the last post, I have explained one of the main ways of improving the algorithms performance name as “Tune Read more about Azure ML Part 9- Cross Validation: Machine Learning Prediction (6)[…]
In the previous posts from Part 1 to 7, I have explained how to do machine learning with Azure ML. I have explained some of the main components in Azure ML that helps us to do data wrangling, train the model, feature selection and evaluating the result. The data cleaning such as SQL transformation, select Read more about Azure ML Part 8- Tune Parameters: Machine Learning Prediction (5)[…]
In the previous posts from Part 1 to 6, I have explained how to do machine learning process. The data cleaning such as SQL transformation, select specific columns, remove missing values, Edit meta data, and normalize data. Also, I have explained how to find relevant attributes using Feature Selection Feature to identify which feature are more Read more about Azure ML Part 7: A Machine Learning Prediction scenario (4)[…]
In previous posts (Part 4 and Part 5), I have explained some of the main components of Azure ML via a prediction scenario. In post one the process of data cleaning (using SQL Transformation, Cleaning Missing Value, Select specific Columns, and Edit Meta Data) has been explained. and in the second Post, I have explained how to apply Read more about Azure ML Part 6: A Machine Learning Prediction scenario (3)[…]