India is the world’s fourth-largest petroleum consumer. There was a strong volume growth in Petroleum Consumption of India which is now slowed down in the recent two years. This will soon will be reflected in global oil consumption growth. According to “The Economics Times of India”- Domestic consumption data released by the Petroleum Planning and Analysis Cell shows the growth in consumption of petroleum products, which was 5% in FY12 and 4.9% in FY13, slumped to 1.6% in the April-June 2013 quarter. The data shows that only decontrolled products such as petrol, aviation fuel contributed to volume growth. “Excluding minor decontrolled products (Petcoke & others representing 11.1% of total in quantity terms), which are insignificant in value terms, the growth in consumption fell 3.1%,” according to the analysis cell.
There are number of factors which are responsible for this slow down including some economic reasons. By prediction the petroleum product usage we can predict the habit of using of Indian people and hence help in raise the Indian Economy. Azure Machine Learning can help us in this.
To solve this real world problem I have used the data which is made available by the Indian Government for research and analysis on their site https://data.gov.in/.
This dataset has a huge data with parameters like Light Distillates – LPG, Light Distillates – Petrol, Light Distillates – Naphtha, Middle Distillates – Kerosene that helps us to evaluate water quality.
I have used Machine Learning in Azure and processed this data that will help us to predict the Indian habit of using the petroleum products with quantity.
In Azure I have selected ->Data Analytics and Machine Learning. Then created a ML workspace. Then in ML studio I have created a new experiment. The technical architecture is-
1. Uploaded data.
2. Build and validate a model.
3. Created a web service that uses your trained models to make fast, live predictions
After creating new experiment in ML Studio
1) I have uploaded the dataset from https://data.gov.in/ .
2) Then I begin by identifying columns that add little-to-no value for predictive modeling.
3) I define values which are non-continuous by casting them as categorical.
4) Cleaned data, we must make sure our dataset contains no missing, “null”, or “NA” values.
5) Model Building.
6) Training the Model.
7) Model Evaluation.
8) Published to gallery.
9) Set up the web service.
The prediction can help to predict the Indian habit of using the petroleum products with quantity.
Relevant screenshots of services used from the Azure portal
Azure Machine learning helps up to solve the real world problem. With Predictive Analysis we can predict or recommend solutions .We can also publish this models as Web Services and to Azure ML Gallery.