Project descriptionIT Svit had a customer from the United Kingdom, a stock-breeding concern specializing at breeding pigs. The customer had a centralized reporting system in place, which allowed the farmers to report the farm operational parameters like:
- The quantity of livestock,
- The numbers of animals gotten ill,
- Symptoms of the disease
- Types of medication administered
- How many animals recuperated or perished after treatment
Project requirementsThe customer wanted us to provide the following services:
- Development of a Big Data solution for processing the data inputted by the farmers and delivering actionable business insights based on it
- Improvement of the efficiency of treatment by decreasing the time needed by the vets to assess the situation when the stock becomes ill
- A configurable and simple dashboard enabling the users to visualize the data in the form of linear graphs and compare multiple graphs with each other.
Project resultsAfter we trained the Machine Learning (ML) algorithm based on the historical data, the system delivered the following benefits:
- Diagnosys of the disease based on the symptoms,
- Recommendations for treatment at once,
- Prescriptive analytics helped the customer to reduce the time between identifying the disease and applying the treatment,
- Significantlyhigher percentages of recuperated livestock (approx 40% more)
- Increased business profitability.
The managing director of the cattle-breeding concern
We wanted IT Svit to improve our existing system and allow the users to identify the diseases easier and help the vets administer the meds faster. We got a Big Data solution powered by a Machine Learning algorithm, which is often able to identify the disease at once, based on the symptoms, and advise the doctors on the course of actions, based on the historical records. I am more than satisfied with the results of our partnership with IT Svit
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The main goal was to increase the business profitability and reduce the losses due to livestock diseases. It was composed of several parts:
- Normalization of the existing data set (some users wrote the medication titles in capital letters and some in plain text, etc.)
- Creating and training a Machine Learning model to track the effectiveness of the meds
- Developing a dashboard for data visualization and turning the data analysis results into clearly understandable graphs
Implementation and challenges resolved
IT Svit team applied our experience with developing Big Data solutions and solved all the challenges:
- We wrote a data normalization tool in Pythonto ensure the uniformity of data
- We used Python and JupyteR Notebook to make statistical analysis and highlight patterns
- The underlying cloud infrastructure was deployed in AWS and we used the AWS Machine Learning service in the process of training the model
- The data was stored in the cloud using the PostgreSQL databaseand Amazon RDS
- We used Flask and Chart.js to enable the data visualization and created a flexible dashboard for viewing the graphs. The users can compare any pair of parameters as needed.