Today, in the age of high technology, machine learning is at the peak of popularity. More and more companies are resorting to this tool to solve their business problems. Machine learning is of particular value for companies working with large amounts of data. By implementing machine learning, you can achieve a better understanding of customer behavior, their preferences, and the degree of satisfaction with the service or product provided.
A special advantage of machine learning is the ability to use special algorithms and models to predict results. Data analysis using machine learning allows you to find various connections and patterns, predict the most effective steps, and even make new discoveries.
General tasks that data processing and machine learning handle:
Create a schedule of expected expenses associated with high risks (insurance event occurrence);
Identify deviations in data and reduce the likelihood of human error;
Provide product recommendations to customers based on their interests and past purchases, displaying purchases of interest to the customer at the top of the page;
Implementing machine learning and setting up data processing is a large-scale undertaking that includes several stages.
1. Formulation the problem.
Machine learning technology must meet specific business requirements and solve specific business process problems. The more precisely the goal is formulated, the more predictable the result and effect of technology implementation will be. To successfully use machine learning, it is important to determine which performance indicators of the organization need to be improved, as well as to determine what parameters will be used to evaluate the result.
2.Drawing up technical specifications, which describe in detail the work plan, tasks, deadlines, technical tools and other information necessary for the implementation of the project.
3.Data collection, storage and pre-processing.
This stage is the longest and most energy-consuming. According to the designated tasks, it is necessary to manually create a training sample. If the company has not previously used data processing, the process is delayed even more and work on creating a sample starts from scratch. In addition to the fact that the data must be collected, it must also be filtered and the features that affect the final result must be determined.
4. Training the algorithm.
Development and configuration of the algorithmic part of machine learning. The most exciting and shortest stage.
5. Integration.
This stage requires a lot of time due to numerous approvals and additional communications between the customer and the contractor. This stage also includes training the organization's employees to work with machine learning.
6.Analysis of work, adjustment of the model.
Since the modern world does not stand still, and the implementation of machine learning takes a long period of time, it is not always possible to predict all the features and functionality of machine learning at the stages of setting the task and writing the technical specifications. Analysis of the work of machine learning allows you to retrain the algorithms of the system in a timely manner if necessary.
Machine learning has a wide range of functionality and is being implemented in a wide variety of areas:
In the food industry:
Ability to improve recipes based on customer feedback.
Forecasting demand for food products using neural networks.
Booking and forecasting the number of visits.
Assessing the competitiveness of an establishment.
Receiving recommendations on a more advantageous location for a restaurant.
Forecasting the cost of food products and the cost price of dishes.
Creating automatic reports.
In the hotel industry:
Assessing the competitiveness of hotels.
Calculating dynamic pricing in a hotel.
Analyzing guest reviews.
Predicting room cancellations.
Recognizing reviews (SPAM, fake reviews from competitors).
In the accounting field:
Using data to automatically assign an account name to each transaction.
Identifying anomalies (deviations) in accounting.
Identifying anomalies before filing documents with the tax office.
Predicting the useful life of assets using sensor observations and feature development.
Tracking sales, commissions and other metrics.
Identifying debtors.
Extracting the necessary data from PDF documents.
Creating an Excel file from PDF data.
In the field of agriculture:
Forecasting prices of agricultural products.
Analysis of crop yields.
Strategic use of land in agriculture taking into account ecosystem restoration.
Segmentation of agricultural fields.
Forecasting the depth of groundwater.
In the banking sector:
Consumer credit decision making based on time series classification and analysis.
Credit repayment forecasting using automated design.
Credit default forecasting.
Mortgage loan analytics.
Customer solvency analysis and forecasting.
Customer lifetime value (CLV) assessment.
Customer profit forecasting.
Transaction amount and days until next transaction forecasting.
Customer churn forecasting.
Urban real estate price forecasting.
Car price forecasting (new and used).
Fraud detection.
In the insurance industry
Vehicle damage assessment.
Health insurance claims forecasting.
Insurance claim denial forecasting.
Vehicle fraudulent claims forecasting.
Insurance claim data anomaly detection.
Bankruptcy forecasting.
Stress testing functions and results analysis.
Bank quality assessment.
In the real estate sector:
Forecasting real estate prices.
Forecasting the purchasing power of the population.
Forecasting the seasonality of real estate acquisition.
Identifying and analyzing the most popular areas for purchasing real estate.
Identifying buildings in emergency condition.
Classifying the type of property.
In the healthcare sector:
Search for drugs by specified parameters.
Forecasting and analyzing epidemics.
Forecasting birth and mortality rates.
Tracking diseases in a specified category of people.
Identifying anomalies in patient prescriptions.
Identifying anomalies in test results.
Segmenting patients by specified criteria.
Tracking patient conditions.
Determining and making a diagnosis.
In the marketing field:
Predicting product popularity.
Analysis and case studies.
Analysis of social media and the results obtained.
Lead qualification.
Analysis of content and identification of words that influence customer engagement.
Identifying marketing solutions that help prevent customer churn.
In the field of logistics:
Calculation and forecasting of spatial-temporal data of vehicle traffic flows.
Forecasting demand for logistics services.
Analysis of transport systems.
Time series analysis of transport data.
Identification and analysis of vulnerabilities for transport networks.
Optimization of transport schedules.
Forecasting traffic on roads.
Optimization of supply chain.
Forecasting fuel prices.
In the field of trade:
Wholesale and retail customer analysis.
Clustering of product cost data.
Forecasting product demand.
Identifying information on which products are most often purchased together.
Analyzing online transactions.
In the manufacturing sector:
Production line data analysis.
Product life forecasting.
Prediction and analysis of equipment failures and breakdowns.
Identification of errors and defects in the production process.
Identification of failures in product quality control.
Product quality forecasting.
Identification of optimal equipment operating modes.
Identification of critical factors in the production process that affect the final result.
Optimization of technological maintenance and repair of equipment.
Optimization and forecasting of purchasing, delivery, storage, demand and supply processes.
In the government sphere:
Modeling risks and forecasting political decisions and social problems.
Forecasting the poverty level of the population.
Analysis of appeals to government agencies.
Collection of relevant information on the work of government bodies.
Analysis of elections and forecasting models.
Identification of cause-and-effect relationships in the data obtained.
Segmentation of the population by income level.
Analysis of social networks during elections and other government campaigns.
Analysis of the media for their political conviction.
Comparison of different parties.
Analysis of political debates.
Identification of political and anti-political articles and messages on the Internet.
Implementing machine learning in an organization allows you to increase the efficiency of its work. But to get a high-quality result, it is not enough to just implement machine learning. Algorithm training, proper data preparation, and integration of the solution with the company's internal systems also play an important role. Our experience and professional skills allow us to develop flexible and multifunctional solutions based on machine learning individually for your organization.