Machine Learning in Data Science

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Machine Learning in Data Science

Machine Learning
in Data Science

By handling large volumes of data and recognizing patterns that might not be immediately apparent to humans, Machine Learning algorithms streamline decision-making and reduce the scope for error.

Machine Learning Services

Businesses streamline their operations and gain a competitive edge by better understanding their customers and market dynamics.

Our Machine Learning Solutions

Tailored solutions analyze specific data to provide insights and fit machine learning for business’ unique goals.

01 Predictive Analytics

Data science machine learning uses past data to guess what might happen. This is handy for things like figuring out when your customers are most likely to buy stuff or knowing when a machine in your factory might need repairs before it breaks down.

02 Customer Segmentation

It groups customers into different categories based on their behavior or preferences. Then, it personalizes the experience for each group. The solution then crafts targeted strategies or products to enhance customer satisfaction and boost business growth.

03 Fraud Detection

Fraud detection systems are trained to spot fishy activities, like unusual credit card transactions that might indicate someone’s stolen your card to go on a shopping spree. These ML systems continuously learn, becoming more adept at identifying subtle signs of fraud over time.

04 Natural Language Processing (NLP)

NLP is all about helping computers understand and respond to human language. It’s like teaching a parrot not just to mimic words but to actually understand what it’s saying. This tech powers things like chatbots that can answer your customer service questions.

05 Image and Speech Recognition

This service gives a computer the ability to see and hear. Image recognition identifies objects in photos (like spotting a cat in a sea of dogs), while speech recognition is all about understanding spoken words. It helps unlock your phone with your face or dictating texts instead of typing them.

06 Supply Chain Optimization

It looks at the entire supply chain process—from getting raw materials to delivering the final product—and finds the most efficient way to do it all. It leads to reduced costs, faster deliveries, and a more responsive and agile supply chain.

Applications Of Machine Learning in Industries

Healthcare

Medical image analysis for diagnosis and detection of diseases.
Predictive models for patient outcomes and disease progression.
Personalized medicine and treatment recommendation systems.
Analyzing data from remote sensors to monitor patients’ health conditions.

Finance

Stock market prediction and algorithmic trading.
Customer sentiment analysis for personalized financial advice.
Expanding access to credit for underserved populations.
Facial and voice recognition for convenient user authentication in transactions.

Retail

Recommender systems for personalized product recommendations.
Demand forecasting for inventory management.
Customer segmentation for targeted marketing campaigns.
Price optimization and dynamic pricing strategies.

Manufacturing

Predictive maintenance to identify equipment failures.
Quality control and defect detection in production processes.
Supply chain optimization and demand forecasting.
Process optimization for improved efficiency and productivity.

Transportation

Route optimization and fleet management for logistics companies.
Predictive maintenance for vehicles and infrastructure.
Traffic prediction and congestion management.
Autonomous vehicle technologies for self-driving cars and drones.

Energy

Energy demand forecasting and load management
Optimization of energy generation and distribution.
Predictive maintenance for energy infrastructure.
Innovative grid systems for efficient energy utilization.

Travel and Hospitality

Personalized travel recommendations and trip planning.
Revenue management and dynamic pricing.
Sentiment analysis for customer feedback.
Virtual and augmented reality offer virtual tours and real-time information.

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ML in Action: Data Science Success Stories

Check out a few case studies that show why VOLTERA will meet your business needs.

Healthcare

Data Science

Data Insights & Forecasting

Stock relocation solution

The client was faced with the challenge of creating an optimal assortment list for more than 2,000 drugstores located in 30 different regions. They turned to us for a solution. We used a mathematical model and AI algorithms that considered location, housing density and proximity to key locations to determine an optimal assortment list for each store. By integrating with POS terminals, we were able to improve sales and help the client to streamline its product offerings.

10% productivity boost 7% increase in sales

Mark S.

Partner Pharmacy network,

View case study →

Data Science

Sales automation

Data Insights & Forecasting

Client Identification

The client wanted to provide the highest quality service to its customers. To achieve this, they needed to find the best way to collect information about customer preferences and build an optimal tracking system for customer behavior. To solve this challenge, we built a recommendation and customer behavior tracking system using advanced analytics, Face Recognition, Computer Vision, and AI technologies. This system helped the club staff to build customer loyalty and create a top-notch experience for their customers.

5% customer retention boost 25% profit growth

Christopher Loss

CEO Dayrize Co, Restaurant chain,

View case study →

Retail

Sales automation

Data Engineering

Performance Measurement

The Retail company struggled with controlling sales and monitoring employees’ performance. We implemented a software solution that tracks sales, customer service, and employee performance in real-time. The system also provides recommendations for improvements, helping the company increase profits and improve customer service.

17% increase in sales 15% revenue boost

Amir R.

CEO, CEO Fashion Retailer

View case study →

Retail

Supply-chain automation

Web and mobile development

Supply chain dashboard

The client needed to optimize the work of employees by building a data source integration and reporting system to use at different management levels. Ultimately, we developed a system that unifies relevant data from all sources and stores them in a structured form, which saves more than 900 hours of manual work monthly.

900h+ manual work reduced 100+ system integrations

Michelle Nguyen

Senior Supply Chain Transformation Manager Unilever, World’s Largest Consumer Goods Company

View case study →

Would you like to explore more of our cases?

Machine Learning Technologies

Pandas

SciPy

TensorFlow

Numpy

ADTK

DBscan

G. AutoML

Keras

MLFlow

Natural L. AI

NLTK

OpenCV

Pillow

PyOD

PyTorch

FB Prophet

SageMaker

Scikit-learn

SpaCy

XGBoost

YOLO

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ML Implementing

Following these steps provides an integrating machine learning process into business.

Define Objectives

Start by defining clear goals. What do you want to achieve with ML?

01 Collect Data

Gather the raw materials—high-quality data from which the ML models will learn.

02 Clean Data

Tidy up the collected data to make it usable for ML algorithms.

03 Select Algorithm

Depending on the problem you’re solving, choose the most suitable ML algorithm.

04 Build Model

You build the ML model using the selected algorithm and prepared data.