Data Science Archives -

Predictive Analytics: develop strategies for your future based on data

Mon, 19 Aug 2024 12:09:05 +0000

Discovering what the future holds has never been as possible as it is today! With predictive analytics, your organization can not only understand the present but also predict future trends and behaviors. This tool, which combines data, statistics, and machine learning, helps guide strategic decisions: from optimizing operations to personalizing service offerings.

The truth is that digital transformation has expanded the accessibility and application of predictive analytics. As a result, what once seemed unpredictable and impossible has become more feasible and attainable. Continue reading to understand what predictive analytics is, why it’s so powerful, and how and where to use it!

What is Predictive Analytics and its Purpose?

The beginning of this article already gave a spoiler, but we explain: performing predictive analytics, as the name suggests, is nothing more than making predictions. Basically, when we can clearly read past events, it becomes possible to make predictions about the future.

Diving deeper into the subject, predictive analytics combines historical data, statistical algorithms, and machine learning techniques to predict future events. This way, it’s possible to obtain a solid foundation for making strategic decisions, enabling organizations to anticipate scenarios, assess probabilities, and respond promptly to market dynamics – which, as we know, is constantly evolving and changing.

Given that we generate large amounts of data, human efforts alone are not enough to analyze this information: it’s necessary to rely on technological assistance. This is why predictive analytics involves associating the vast amount of information we generate daily with tools such as data mining and Artificial Intelligence.

Through this strategy, companies across various sectors can gain powerful insights into indicators such as customer behavior, market trends, and emerging risks – we’ll discuss more applications in different economic sectors later in this article!

In short, predictive analytics not only optimizes processes and personalizes services but also strengthens strategic planning with more accurate forecasts. We could say that this type of analysis is almost like the crystal ball your business has been missing.

However, let’s be clear: obviously, predicting absolutely every future action is impossible. But when actions are repetitive and follow certain patterns, it’s possible to predict other potential actions. Technological advancements have made this practice increasingly accurate and reliable.

How Does Predictive Analytics Work?

There are two types of predictive models: supervised and unsupervised. Each of them works according to specific methodologies to process and interpret data:

However, for predictive analytics to be effective, it’s essential to rely on high-quality data. They must be complete, accurate, and error-free to provide reliable and useful predictions.

Learn more about supervised and unsupervised models:

Supervised Models

In this process, patterns and pre-existing relationships in the data are identified. These are then used to predict future behaviors or outcomes. Supervised models require a high accuracy rate for validation, after which they are applied to other data to make predictions.

A good example of the use of supervised models is identifying customers with a high likelihood of canceling services or purchases. By recognizing these patterns, more targeted and effective Customer Success strategies can be developed, helping to reduce churn.

Unsupervised Models

This type of model is used to explore data without a specific objective, aiming to identify hidden structures or correlations that may indicate trends or recurring behaviors.

How to Apply Predictive Analytics to Your Business

It’s already clear that predictive analytics allows you to identify trends, predict behaviors, and promote data-driven decision-making. In other words, there is enormous potential within this type of analysis. It’s up to you to decide where it would be best utilized.

Here are some ideas on how you can leverage the full power of predictive analytics in certain areas of your organization:

Predictive Analytics in Human Resources

Predict employee absenteeism;
Measure future turnover;
Track skills at risk of being lost;
Anticipate resignations and expedite replacements.

Analytics in the Marketing Sector

Identify the target audience for a new product;
Track optimal moments to send your best emails to your best audiences;
Identify user actions on your site and direct them accordingly.

Applications of Predictive Analytics in Sales and Retail

Forecast demand for a product or service;
Plan timely promotional events for potential customers;
Determine which products should or should not be stocked;
Develop loyalty strategies;
Identify opportunities to increase sales.

Predictive Analytics in Industry

Predict machine failures;
Anticipate equipment maintenance needs;
Reduce safety risks for workers;
Identify opportunities to improve productivity.

Forecasting in the Logistics Sector

Predict stock shortages;
Identify opportunities to improve inventory management;
Identify opportunities to optimize operations;
Anticipate and optimize demand operations.

Predictive Analytics in the Financial Market

- Identify the best moments for investment;
- Identify timely moments to cut costs;
- Greater control over the company’s capital management;
- Identify idle or underutilized resources.

Adopt Predictive Analytics in Your Business with BIX Tech and Gain a Competitive Advantage!

Predictive analytics focuses on events that may occur in the future. That’s why many organizations are adopting this strategy to define their next steps. Therefore, if you want to avoid future risks, identify opportunities, and make the best decisions for your business, this is a strategy you need to have! And BIX Tech is your ideal partner for adopting this technology.

So, are you ready to implement data in your strategies? Click the banner below to contact us and learn more about how we can help you!

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Fine-Tuning an OCR Model: what it is, why it’s important, and how to do it

Tue, 06 Aug 2024 11:33:28 +0000

Optical Character Recognition (OCR) is a technology that transforms images of text into editable and searchable text. With the increasing digitalization of documents—from contracts to receipts and reports—OCR has become an essential tool for automating the organization and analysis of information. However, standard OCR models often face limitations when dealing with unusual text formats, visual noise, and other context-specific variations.

In this article, we will explore how to perform fine-tuning of an OCR model, adapting it to overcome these limitations and meet the specific needs of your application. You will learn how to prepare a custom dataset by manually annotating the characters present in your images and how to train a tuned OCR model to provide more accurate and efficient results.

What is OCR and why is it important?

OCR stands for Optical Character Recognition. This technology is capable of converting images containing text into a readable digital format, facilitating the organization and storage of information.

With OCR, you can process and analyze long documents, contracts, and reports automatically, making what was once a manual and slow task more efficient.

What are the steps for implementing OCR?

OCR involves four main steps:

1. 1. Image Extraction: This involves acquiring image data and converting it into binary files. Input data can include photos of documents, scanned PDFs, or images of signs. However, machine learning models require data in a specific format for training. Therefore, after collecting the images, they should be converted into binary format.
  2. Pre-processing: Pre-processing applies a set of computer vision techniques aimed at optimizing the AI’s performance. Techniques include linearizing text (if the text is rotated relative to the horizontal), highlighting text contours and smoothing background noise, and changing the image’s color scale.
  3. Character Recognition: The OCR algorithm reads each character in the image, recognizes morphological features, and compares the result with a list of possible characters, returning the one that shows the highest similarity.
  4. Post-processing: After character recognition, the algorithm performs post-processing. This phase starts with error checking and correction, adjusting poorly recognized characters using linguistic contexts and natural language models. Then, the recognized characters are organized into a structured format, segmenting the text into lines, words, and paragraphs to preserve the original document’s structure.
    Additionally, post-processing includes normalizing data, adjusting text format to specific standards, such as removing extra spaces and correcting punctuation. Finally, the processed text is converted into a readable and usable format, ready to be integrated into automated systems or databases. Thus, post-processing transforms raw data into valuable information, ensuring maximum utility and accuracy of OCR results.

What are the limitations of standard OCR models?

Although OCR improves process efficiency, with the wide range of document formatting available today, processing has become challenging. Other limitations include:

Limited support for certain languages and orthographies.
Dependence on the quality of the extracted image.
Lack of contextual knowledge of the image.
Background noise in the image.

One way to overcome these limitations is to develop a custom model from the standard model for the specific application. The technique of using a pre-trained model and adapting it for a specific purpose is called fine-tuning. We will discuss how to fine-tune an OCR model below.

How to perform fine-tuning of an OCR model?

Fine-tuning an OCR model involves two main steps: preparing the dataset and training the model. Let’s delve into each of them now.

First step of fine-tuning an OCR model: Dataset Preparation

Preparing the dataset aims to transform the images into a format that the algorithm can process. This step starts with cropping the region of interest in the images, i.e., removing areas where no characters are present.

To do this, we developed the following set of functions:

				
					def detect_text_bounding_box(img, output_folder:str=''):
 """
    Detects text in the image using EasyOCR.
    Args:
        img: The image in which to detect text.
        output_folder (str, optional): The directory to save intermediate images. Default is an empty string.
    Returns:
        list: A list of polygon points representing the detected text.
    """
    cimg = img.copy()
    bbox_list, polygon_list = reader.detect(img)
    polygon_list = polygon_list[0]
    bbox_list = bbox_list[0]
    for bbox in bbox_list:
        x1, x2, y1, y2 = bbox
        polygon = [[x1, y1], [x2, y1], [x2, y2], [x1, y2]]
        polygon_list.append(polygon)          
    return [np.rint(polygon).astype(int) for polygon in polygon_list]

def rearrange_src_pts(box, w_rect, h_rect):
    bl, tl, tr, br = box
    if w_rect < h_rect:
        aux = w_rect
        w_rect = h_rect
        h_rect = aux
        box = [br, bl, tl, tr]   
    src_pts = np.int0(box).astype("float32")
    return src_pts, w_rect, h_rect

def simple_warp_rectangle(img, points, output_folder:str=''):
    cimg = img.copy()
    rect = cv2.minAreaRect(points)
    box = cv2.boxPoints(rect)
    box = np.int0(box)
    width = int(rect[1][0])
    height = int(rect[1][1])
    src_pts, width, height = rearrange_src_pts(box, width, height)
    dst_pts = np.array([[0, height-1],
                        [0, 0],
                        [width-1, 0],
                        [width-1, height-1]], dtype="float32")
    M = cv2.getPerspectiveTransform(src_pts, dst_pts)
    warped_img = cv2.warpPerspective(cimg, M, (width, height))
    return warped_img

Basically, the detect_text_bounding_box function takes an image as input and returns the coordinates of the polygon surrounding the region containing characters. From these coordinates, we use the simple_warp_rectangle function to crop the image only to the region of interest. By the end of this step, you will have cropped sections of images to be used for training the model.

With the cropped images, we can start annotating the data. This is the process of manually writing out which characters are present in each image. For this, we use the IPython library and the display_data function.

The display_data function creates a prompt where you can view the image and write the respective set of characters present in it.

				
					def display_data(data):
    label_dict = {}
    for i in data.iterrows():
        img_path = i[1]["path"]
        label = i[1]["label"]
        ipd.display(Image(filename=img_path))
        word_input = widgets.Text(value=label, placeholder='Type something', description='Word:', disabled=False)
        ipd.display(word_input)   
        label_dict[f"{img_path}"] = word_input # Store the object, so it can be changed after we run the cell.
    return label_dict

Finally, you should split the annotated dataset into training and testing sets.

Training the OCR Model

To train the OCR model, it is highly recommended to use a GPU processing environment due to the computational intensity involved. Google Colab is an excellent free option that offers this capability.

The first step is to clone the EasyOCR library repository using the command git clone. After cloning the repository, you need to change the working directory to where the repository was cloned. This ensures that we are in the correct context to run the training scripts.

				
					!git clone https://github.com/JaidedAI/EasyOCR.git {path/to/save}
%cd {path/to/save}/trainer

				
					import os
# Get the current working directory
current_working_directory = os.getcwd()
print(current_working_directory)

The next step is to import essential libraries for model training. These libraries include functions for data manipulation, training configuration, and running the training process itself.

				
					import os
import torch.backends.cudnn as cudnn
import yaml
from train import train
from utils import AttrDict
import pandas as pd

To configure the training process, use the get_config function. It reads a YAML file containing all the necessary configurations for training, including model parameters, data paths, and other specific settings. The function also prepares the set of characters the model should recognize based on the provided training data.

				
					def get_config(file_path):
    with open(file_path, 'r', encoding="utf8") as stream:
        opt = yaml.safe_load(stream)
    opt = AttrDict(opt)
    if opt.lang_char == 'None':
        characters = ''
        for data in opt['select_data'].split('-'):
            csv_path = os.path.join(opt['train_data'], data, 'labels.csv')
            df = pd.read_csv(csv_path, sep='^([^,]+),', engine='python', usecols=['filename', 'words'], keep_default_na=False)
            all_char = ''.join(df['words'])
            characters += ''.join(set(all_char))
        characters = sorted(set(characters))
        opt.character= ''.join(characters)
    else:
        opt.character = opt.number + opt.symbol + opt.lang_char
    os.makedirs(f'./saved_models/{opt.experiment_name}', exist_ok=True)
    return opt

At this stage, you need to create the training parameters configuration file. This YAML file contains all the necessary configuration variables for model training. Here is an example of how it should be configured:

				
					%%writefile config_files/custom_model.yaml
number: '0123456789'
symbol: "!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~ €"
lang_char: 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'
experiment_name: 'en_filtered'
train_data: 'all_data'
valid_data: 'all_data/en_val'
manualSeed: 1111
workers: 2
batch_size: 16 # 32
num_iter: 3000
valInterval: 1000
saved_model: '' #'saved_models/en_filtered/iter_300000.pth'
FT: False
optim: False # default is Adadelta
lr: 1.
beta1: 0.9
rho: 0.95
eps: 0.00000001
grad_clip: 5
#Data processing
select_data: 'en_train_filtered' # this is dataset folder in train_data
batch_ratio: '1'
total_data_usage_ratio: 1.0
batch_max_length: 34
imgH: 64
imgW: 600
rgb: False
contrast_adjust: False
sensitive: True
PAD: True
contrast_adjust: 0.0
data_filtering_off: False
# Model Architecture
Transformation: 'None'
FeatureExtraction: 'VGG'
SequenceModeling: 'BiLSTM'
Prediction: 'CTC'
num_fiducial: 20
input_channel: 1
output_channel: 256
hidden_size: 256
decode: 'greedy'
new_prediction: False
freeze_FeatureFxtraction: False
freeze_SequenceModeling: False

The YAML file defines several important parameters: the characters the model should recognize (number, symbol, lang_char), the experiment name (experiment_name), paths for training and validation data (train_data, valid_data), optimization and training settings, as well as details about the model architecture.

To fine-tune a pre-trained OCR model, you should specify the path to the model in the saved_model variable. On this page, you can find pre-trained models for different languages. With the configuration file ready, we can start training the model. To do this, load the configuration and call the train function:

				
					config_filename = 'custom_model'
path_config_file = f"{path/to/save}/trainer/config_files/{config_filename}.yaml"
opt = get_config(path_config_file)
train(opt, amp=False)

Model Usage

After training, you need to download the support files and configure them with the same values used during the training setup. These support files include a YAML file and a customized Python script, which should be copied to the correct EasyOCR directories:

				
					!cp /support_files/custom_example.yaml /root/.EasyOCR/user_network/{custom_model_name}.yaml
!cp /support_files/custom_example.py /root/.EasyOCR/user_network/{custom_model_name}.py
!cp {path/to/save}/trainer/saved_models/{experiment_name}/best_accuracy.pth /root/.EasyOCR/model/{custom_model_name}.pth

Finally, to use the trained model, initialize an EasyOCR reader with the customized model and recognize text in new images:

				
					custom_reader = easyocr.Reader(['en'], gpu=True, recog_network='custom_model')
custom_results = custom_reader.recognize(img)

Transform your organization’s efficiency with an OCR model!

If your organization aims to improve document processing efficiency, an OCR model might be the ideal solution. With OCR, you can convert images containing text into a readable digital format, simplifying information organization and storage.

BIX offers customized OCR solutions to meet your specific needs. Click the banner below and contact us to find out how we can help increase your organization’s efficiency and productivity!

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Databases: frequently asked questions answered

Thu, 01 Aug 2024 10:30:32 +0000

Databases are a fundamental part of modern digital systems, but for those new to the topic, it can be hard to visualize. In fact, there are many tools we can consider as examples of databases because they serve the purpose of storing a large volume of information without necessarily having this specific objective. This is the case with Instagram or Gmail. However, in the business context, it involves a broad technical understanding.

Therefore, we will answer the main questions of professionals from different sectors with simple and direct explanations. After reading this article, we hope you understand the definitive concept of a database, the advantages of its use, and the main types used by companies. Additionally, we will talk about cloud hosting and other tips.

What is a database?

A database is a collection of information that can have various formats, such as images, videos, and documents. This term usually refers to files that are stored electronically, but not always. A database is a collection of information that can have various formats, such as images, videos, and documents. This term usually refers to files that are stored electronically, but not always. For instance, in the United States, databases containing personal information are subject to various federal and state laws, such as HIPAA for health information, GLBA for financial information, and CCPA in California, which define and regulate how personal data should be handled.

Generally, it is also considered that this data can be structured or unstructured. This changes the type of database.

Characteristics of Structured Data vs. Unstructured Data

Structured data is usually quantitative, whereas unstructured data is typically qualitative. The model for structured data is predefined and difficult to alter, unlike unstructured data, which has a very flexible model. In terms of format, structured data has a limited number of data formats, while unstructured data exhibits a wide variety of formats.

For storage, SQL-based databases are used for structured data, while NoSQL databases are employed for unstructured data. Searching structured data is easy and fast due to its predefined structure, making it straightforward to locate and query the data. Conversely, unstructured data lacks a defined structure, making it very difficult to search. Lastly, the analysis of structured data is straightforward, while the analysis of unstructured data is more challenging.

You might know another way to refer to this concept. The English term “database,” or “base de dados” in Portuguese, can also be translated as “banco de dados.” Both are used synonymously, although “base” is more common in Portugal.

Database Management Systems (DBMS), known in Portuguese as Sistemas de Gerenciamento de Bancos de Dados (SGBD), refer to software like MySQL and SQL-Server that manage different databases. Despite these distinctions, it is understood that databases are fundamental for storing all types of information. This is extremely valuable in a world with such a large volume of data.

What is the difference between using spreadsheets and databases?

Tools like Microsoft Excel and Google Sheets are widely used for data analysis using spreadsheets. However, spreadsheets have a limit of approximately one million records, making it difficult to manage large volumes of data and synchronize all information.

Databases, on the other hand, have a much greater storage capacity. They are scalable, meaning they can grow with the business while maintaining the same efficiency. They can integrate data from different systems and enable more robust analyses.

Additionally, databases offer other advantages. DBMS are developed to keep track of all stored data and ensure its security, preventing losses and inconsistencies.

What are the main types of databases used in companies?

There are many types of databases: relational, distributed, blockchain, and others. However, two types are most commonly used and relevant for most business contexts.

SQL

SQL databases are widely used in systems with strong relationships between data. They are also common in financial systems, ERP (Enterprise Resource Planning), and e-commerce platforms. In these databases, SQL (Structured Query Language) is the primary tool for interacting with the data. With SQL, it is possible to perform complex queries, insertions, updates, and deletions of data. Thus, developers can efficiently manage large volumes of information.

One of the major advantages of SQL databases is adherence to ACID properties:

Atomicity: Ensures that transactions are either fully completed or not executed at all, avoiding intermediate states that could corrupt data.
Consistency: Guarantees that the rules defined by the database, such as constraints and primary or foreign keys, are always maintained.
Isolation: Ensures that transactions are performed independently, avoiding conflicts between simultaneous operations.
Durability: Ensures that transactions are permanently recorded, even in case of system failures.

These characteristics make SQL databases more precise and stable. They also help maintain user trust in other systems that rely on this information. Therefore, choosing an SQL database is a strategic decision for many companies.

NoSQL

NoSQL databases are used in specific contexts where flexibility is more important, such as in Internet of Things (IoT) applications and social networks. In these cases, they need to handle large volumes of rapidly evolving data, requiring less rigid structures than traditional SQL databases. The NoSQL type allows the storage of unstructured or semi-structured data, such as documents, graphs, key-value pairs, and wide columns.

This feature is ideal for dynamic applications with many real-time operations. For example, in social networks where users continuously produce content in the form of posts, comments, and likes, NoSQL databases scale horizontally. Similarly, in IoT applications where various devices collect data simultaneously, they allow for rapid ingestion with high processing capacity.

Additionally, NoSQL databases are designed to integrate easily with other technologies and advanced analytics. Therefore, companies that adopt NoSQL databases can keep pace with market changes at a faster rate.

What is the difference between on-premises and cloud databases?

Databases can be hosted either locally (on-premises) or in a cloud service. When hosting is local, the company has full control over the hardware and software and can customize it according to its specific needs. However, this approach requires a robust IT infrastructure and a dedicated maintenance team, which can represent a significant cost.

On the other hand, in the cloud, the company delegates the management of the infrastructure and focuses on other competencies. This way, it is possible to scale databases quickly, with upgrades and capacity adjustments. Cloud service providers also guarantee the quality of their services through Service Level Agreements (SLAs), which establish expected performance levels.

It is possible to migrate an on-premises database to the cloud with the help of tools like Google’s Database Migration Service. Additionally, companies can control their budget and optimize the use of cloud services with a cost calculator. This way, they can balance the costs of operation with its benefits.

LMs Practical Guide and Business Applications

Wed, 22 May 2024 16:02:41 +0000

Language Models (LMs) have emerged as powerful tools in the era of artificial intelligence, reshaping how machines comprehend and generate human language. In this practical guide, we’ll delve into what LMs are and how they are shaping the business landscape.

What Are Language Models (LMs)?

In simple terms, a Language Model is a computational system trained to understand and generate human language naturally. These models are based on machine learning algorithms, particularly Deep Learning techniques.

Over time, they capture nuances, context, and language patterns, becoming increasingly proficient.

How Do LMs Work?

LMs operate by processing sequences of words, learning the probability of a word occurring based on the context of preceding words. This contextual understanding enables LMs to generate coherent text and respond to queries more accurately.

Practical Business Applications

Content Generation: LMs can be employed in automated content creation, from simple essays to more complex texts, streamlining production processes and maintaining consistency in tone and style.
Customer Support: Integrating LMs into chatbots and virtual assistants significantly enhances the ability to understand customer queries and provide contextual, real-time responses.
Sentiment Analysis: When evaluating vast datasets, LMs can identify sentiments in customer reviews, feedback, and social media. This analysis is valuable for adjusting business strategies and improving customer satisfaction.
Automatic Translation: Global businesses benefit from LMs by utilizing automatic translation to break language barriers, facilitating efficient communication in international markets.
Research and Summarization: LMs can optimize information retrieval by analyzing large volumes of data to extract valuable insights. Moreover, they are efficient in summarizing extensive documents.

Practical Implementation

Model Selection: Choosing the right language model tailored to business needs, considering factors such as dataset size, desired text complexity, and available computational resources.
Personalized Training: In some cases, training LMs with specific business data can enhance accuracy and relevance in practical applications.
Integration into Systems: Incorporating LMs into existing systems, such as websites, applications, or customer service platforms, ensuring a smooth and efficient transition.

Ready to Elevate Your Business with Language Models?

Language Models offer a plethora of possibilities to transform business operations. From content generation to optimizing customer support, strategically implementing LMs can propel efficiency, innovation, and competitiveness in business. This practical guide aims to empower companies to explore and seamlessly integrate this technology into their operations effectively.

Schedule a consultation with our seasoned experts to discover how LMs can revolutionize your business strategies and propel you toward sustained success.

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Data Revolution: is your business ready to thrive?

Wed, 08 May 2024 13:20:07 +0000

In today’s business landscape, data isn’t merely a collection of facts and figures; it’s the very foundation upon which successful enterprises are built.

If your organization finds itself navigating the complexities of modern markets, where every decision requires a strategic and analytical approach, it’s high time to consider the seamless integration of Data Science into your operations.

Deciphering complexity

Are you drowning in a sea of data? Fear not, for Data Science is the secret ingredient that can transform this overwhelming influx of information into a strategic asset. By leveraging advanced analytics and cutting-edge technologies, Data Science empowers businesses to extract valuable insights from massive datasets.

Every piece of data becomes a catalyst for informed decision-making, and as the stakes grow higher, Data Science steps in with precision, providing not just predictions, but finely-tuned strategies optimized for success.

Foresight over intuition

In the fiercely competitive arena of modern business, relying solely on intuition is no longer sufficient. Enter Data Science, the strategic ally that empowers organizations to stay ahead of the curve. By decoding vast datasets and unveiling hidden patterns, Data Science enables businesses to identify innovative opportunities long before they reach the mainstream. Complex problems that once seemed insurmountable are now met with data-driven solutions, offering not just understanding, but profound insights that drive strategic decision-making.

For those who prioritize personalization, Data Science serves as a guiding light, illuminating the path to enhanced customer experiences. By analyzing vast troves of customer data, Data Science enables businesses to craft personalized recommendations that resonate with individual preferences, driving customer satisfaction and loyalty to new heights. Meanwhile, automation enthusiasts rejoice as Data Science’s machine learning algorithms streamline tasks and optimize processes, leading to unparalleled efficiency and resource savings.

From market insights to customer preferences, Data Science offers a holistic view of your business landscape, providing invaluable intelligence that informs every aspect of your strategy. When feedback is elusive, Data Science becomes your detective, analyzing customer data, online reviews, and social media interactions to offer a 360-degree perspective on your audience.

Ready to Transform Your Business with Data Science?

Are you ready to harness the transformative power of Data Science and propel your business toward sustained success? Schedule a consultation with our seasoned experts by clicking the banner bellow and unlock the full potential of data-driven insights.

Witness firsthand how Data Science can revolutionize your business strategies, empower your decision-makers, and drive unprecedented growth in your organization.

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How I wish someone would explain SHAP values to me

Thu, 04 Apr 2024 12:30:00 +0000

Have you ever struggled to interpret the decisions of an AI? SHAP was created to help you overcome these issues. The acronym stands for SHapley Additive exPlanations, a relatively recent method (less than 10 years old) that seeks to explain the decisions of artificial intelligence models in a more direct and intuitive way, avoiding “black box” solutions.

Its concept is based on game theory with robust mathematics. However, a complete understanding of the mathematical aspects is not necessary to use this methodology in our daily lives. For those who wish to delve deeper into the theory, I recommend reading this publication in English.

In this text, I will demonstrate practical interpretations of SHAP, as well as understanding its results. Without further ado, let’s get started! To do this, we’ll need a model to interpret, right?

I will use as a basis the model built in my notebook (indicated by the previous link). It is a tree-based model for binary prediction of Diabetes. In other words, the model predicts people who have this pathology. For the construction of this analysis, the shap library was used, initially maintained by the author of the article that originated the method, and now by a vast community.

First, let’s calculate the SHAP values following the package tutorials:

				
					# Library
import shap

# SHAP Calculation - Defining explainer with desired characteristics
explainer = shap.TreeExplainer(model=model)

# SHAP Calculation
shap_values_train = explainer.shap_values(x_train, y_train)

Note that I defined a TreeExplainer. This is because my model is based on a tree, so the library has a specific explainer for this family of models. In addition, up to this point, what we did was:

Define an explainer with the desired parameters (there are a variety of parameters for TreeExplainer, I recommend checking the options in the library).
Calculate the SHAP values for the training set.

What are SHAP values?

With the set of SHAP values already defined for our training set, we can evaluate how each value of each variable influenced the result achieved by the predictive model. In our case, we will be evaluating the results of the models in terms of probability, i.e., the X percentage that the model presented to say whether the correct class is 0 (no diabetes) or 1 (has diabetes).

It is worth noting that this may vary from model to model: If you use an XGBoost model, probably your default result will not be in terms of probability as it is for the random forest of the sklearn package.

To make the value in terms of probability, you can define it through the TreeExplainer, using parameters.

But the burning question is: How can I interpret SHAP values? To do this, let’s calculate the prediction probability result for the training set for any sample that predicted a positive value:

				
					# Prediction probability of the training set
y_pred_train_proba = model.predict_proba(x_train)

# Let's now select a result that predicted as positive
print('Probability of the model predicting negative -', 100*y_pred_train_proba[3][0].round(2), '%.')
print('Probability of the model predicting positive -', 100*y_pred_train_proba[3][1].round(2), '%.')

The above code generated the probability given by the model for the two classes. Let’s now visualize the SHAP values for that sample according to the possible classes:

				
					# SHAP values for this sample in the positive class
shap_values_train[1][3]
array([-0.01811709,  0.0807582 ,  0.01562981,  0.10591462, 0.11167778, 0.09126282,  0.05179034, -0.10822825])

# SHAP values for this sample in the negative class
shap_values_train[0][3]
array([ 0.01811709, -0.0807582 , -0.01562981, -0.10591462, -0.11167778, -0.09126282, -0.05179034,  0.10822825])

Simplified formula for SHAP, where i refers to the category that those values represent (in our case, category 0 or 1).

Let’s check this in code:

				
					# Sum of SHAP values for the negative class
print('Sum of SHAP values for the negative class in this sample:', 100*y_pred_train_proba[3][0].round(2) - 100*expected_value[0].round(2))
# Sum of SHAP values for the positive class
print('Sum of SHAP values for the positive class in this sample:', 100*y_pred_train_proba[3][1].round(2) - 100*expected_value[1].round(2))

"Sum of SHAP values for the negative class in this sample: -33.0
Sum of SHAP values for the positive class in this sample: 33.0"

And as a lesson from home office, here’s the following question: The sum of SHAP values for a class x added to the base value of that class will exactly give the probability value of the model found at the beginning of this section!

Note that the SHAP values match the result presented earlier. But what do the individual SHAP values represent? For this, let’s use more code, using the positive class as a reference:

				
					for col, vShap in zip(x_train.columns, shap_values_train[1][3]):
 print('###################', col)
 print('SHAP Value associated:', 100*vShap.round(2))

################### Pregnancies
SHAP Value associated: -2.0
################### Glucose
SHAP Value associated: 8.0
################### BloodPressure
SHAP Value associated: 2.0
################### SkinThickness
SHAP Value associated: 11.0
################### Insulin
SHAP Value associated: 11.0
################### BMI
SHAP Value associated: 9.0
################### DiabetesPedigreeFunction
SHAP Value associated: 5.0
################### Age
SHAP Value associated: -11.0

Here we evaluate the SHAP values for the positive class for sample 3. Positive SHAP values like Glucose, BloodPressure, SkinThickness, BMI, and DiabetesPedigreeFunction influenced the model in predicting the positive class correctly. In other words, positive values imply a tendency towards the reference category.

On the other hand, negative values like Age and Pregnancies aim to indicate that the true class is negative (the opposite). In this example, if both were also positive, our model would result in a 100% prediction for the positive class. However, since that did not happen, they represent the 17% that goes against the choice of the positive class.

In summary, you can think of SHAP as contributions to the model’s decision between classes:

In this case, the sum of SHAP values cannot exceed 50%.
Positive values considering a reference class indicate favorability towards that class in prediction.
Negative values indicate that the correct class is not the reference one but another class.

Additionally, we can quantify the contribution of each variable to the final response of that model in percentage terms by dividing by the maximum possible contribution, in this case, 50%:

				
					for col, vShap in zip(x_train.columns, shap_values_train[1][3]):
 print('###################', col)
 print('SHAP Value associated:', 100*(100*vShap.round(2)/50).round(2),'%')

################### Pregnancies
SHAP Value associated: -4.0 %
################### Glucose
SHAP Value associated: 16.0 %
################### BloodPressure
SHAP Value associated: 4.0 %
################### SkinThickness
SHAP Value associated: 22.0 %
################### Insulin
SHAP Value associated: 22.0 %
################### BMI
SHAP Value associated: 18.0 %
################### DiabetesPedigreeFunction
SHAP Value associated: 10.0 %
################### Age
SHAP Value associated: -22.0 %

Here, we can see that Insulin, SkinThickness, and BMI together had an influence of 62%. We can also notice that the variable Age can nullify the impact of SkinThickness or Insulin in this sample.

General Visualization

Now that we’ve seen many numbers, let’s move on to the visualizations. In my perception, one of the reasons why SHAP has been so widely adopted is the quality of its visualizations, which, in my opinion, surpass those of LIME.

Let’s make an overall assessment of the training set regarding our model’s prediction to understand what’s happening among all these trees:

				
					# Graph 1 - Variable Contributions
shap.summary_plot(shap_values_train[1], x_train, plot_type="dot", plot_size=(20,15));

Graph 1: Summary Plot for SHAP Values.

Evaluation of Graph 1

Before assessing what this graph is telling us about our problem, we need to understand each feature present in it:

The Y-axis represents the variables of our model in order of importance (SHAP orders this by default, but you can choose another order through parameters).
The X-axis represents the SHAP values. As our reference is the positive category, positive values indicate support for the reference category (contributes to the model predicting the positive category in the end), and negative values indicate support for the opposite category (in this case of binary classification, it would be the negative class).
Each point on the graph represents a sample. Each variable has 800 points distributed horizontally (since we have 800 samples, each sample has a value for that variable). Note that these point clouds expand vertically at some point. This occurs due to the density of values of that variable in relation to the SHAP values.
Finally, the colors represent the increase/decrease of the variable’s value. Deeper red tones are higher values, and bluish tones are lower values.

In general, we will look for variables that:

Have a clear color division, i.e., red and blue in opposite places. This information shows that they are good predictors because only by changing their value can the model more easily assess their contribution to a class.
Associated with this, the larger the range of SHAP values, the better that variable will be for the model. Let’s consider Glucose, which in some situations presents SHAP values around 0.3, meaning a 30% contribution to the model’s result (because the maximum any variable can reach is 50%).

The variables Glucose and Insulin exhibit these two mentioned characteristics. Now, note the variable BloodPressure: Overall, it is a confusing variable as its SHAP values are around 0 (weak contributions) and with a clear mix of colors. Moreover, you cannot see a trend of increase/decrease of this variable in the final response. It is also worth noting the variable Pregnancies, which does not have as large a range as Glucose but shows a clear color division.

Through this graph, you can get an overview of how your model arrives at its conclusions from the training set and variables. The following graph shows an average contribution from the previous plot:

				
					Graph 2 - Importance Contribution of Variables
shap.summary_plot(shap_values_train[1], x_train, plot_type="bar", plot_size=(20,15));

Graph 2: Variable Importance Plot based on SHAP Values.

Evaluation of Graph 2

Essentially, as the title of the X-axis suggests, each bar represents the mean absolute SHAP values. Thus, we evaluate the average contribution of the variables to the model’s responses. Considering Glucose, we see that its average contribution revolves around 12% for the positive category.

This graph can be created in relation to any of the categories (I chose the positive one) or even all of them. It serves as an excellent graph to replace the first one in explanations to managers or individuals more connected to the business area due to its simplicity.

Interpretation of Prediction for the Sample

In addition to general visualizations, SHAP provides more individual analyses per sample. Graphs like these are interesting to present specific results. For example, suppose you are working on a customer churn problem, and you want to show how your model understands the departure of the company’s largest customer.

Through the graphs presented here, you can effectively demonstrate in a presentation what happened through Machine Learning and discuss that specific case. The first graph is the Waterfall Plot built in relation to the positive category for the sample 3 we studied earlier.

				
					# Graph 3 - Impact of variables on a specific prediction of the model in Waterfall Plot version
shap.plots._waterfall.waterfall_legacy(expected_value=expected_value[1], shap_values=shap_values_train[1][3].reshape(-1), feature_names=x_train.columns, show=True)

Graph 3: Contribution of Variables to the Prediction of a Sample.

Evaluation of Graph 3

In this graph, you can see that your prediction starts at the bottom and rises to the probability result.

Each variable contributes positively (model predicting the positive category) and negatively (model predicting another class). In this example, we see, for instance, that the contribution of SkinThickness is offset by the contribution of Age.

Also, in this graph, the X-axis represents the SHAP values, and the arrow values indicate the contributions of these variables.

In the next graph, we have a new version of this visualization:

				
					# Graph 4 - Impact of variables on a specific prediction of the model in Line Plot version
shap.decision_plot(base_value=expected_value[1], shap_values=shap_values_train[1][3], features=x_train.iloc[3,:], highlight=0)

Graph 4: Contribution of Variables to the Prediction of a Sample through “Path”.

Evaluation of Graph 4

This graph is equivalent to the previous one. As our reference category is positive, the model’s result follows towards more reddish tones (on the right), indicating a prediction for the positive class, and towards the left, a prediction for the negative class. In this graph, values close to the arrow indicate the values of the variables (for the sample) and not the SHAP values.

Conclusion

SHAP emerges as a tool capable of explaining, in a graphical and intuitive way, how artificial intelligence models arrive at their results. Through the interpretation of the graphs, it is possible to understand the decision-making in Machine Learning in a simplified manner, allowing for explanations to be presented and knowledge to be conveyed to people who do not necessarily work in this area.

Throughout this text, we were able to assess the key concepts about SHAP values, as well as their visualizations. From SHAP values, we understand how the values of each variable influenced the model’s outcome. In this case, we evaluated the results in terms of probability. Analyzing the visualizations, it was possible to perceive that SHAP allows us to interpret specific and individual results, as well as understand what the scheme expresses about the problem.

Despite the robust mathematics, understanding this methodology is simpler than it seems. The SHAP technology does not stop here! There are many things that can be done with this technique, and that’s why I strongly recommend:

Do you want to discuss other applications of SHAP? Do you want to implement data science and make decision-making more accurate in your business? Get in touch with us! Let’s schedule a chat to discuss how technology can help your company!

Written by Kaike Reis.

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Mastering Retrieval-Augmented Generation (RAG) for Next-Level AI Solutions

Thu, 21 Mar 2024 10:00:00 +0000

Introducing the cutting-edge technique in generative AI: Retrieval-Augmented Generation (RAG). To grasp its essence, envision a scenario within a hospital room.

In the realm of medical practice, doctors leverage their extensive knowledge and expertise to diagnose and treat patients.

Yet, in the face of intricate medical conditions requiring specialized insights, doctors often consult academic literature or delegate research tasks to medical assistants. This ensures the compilation of relevant treatment protocols to augment their decision-making capabilities.

In the domain of generative AI, the role of the medical assistant is played by the process known as RAG.

So, what exactly is RAG?

RAG stands for Retrieval-Augmented Generation, a methodology designed to enhance the accuracy and reliability of Large Language Models (LLMs) by expanding their knowledge through external data sources. While LLMs, neural networks trained on massive datasets, possess the ability to generate prompts swiftly with billions of parameters, they falter when tasked with delving into specific topics or current facts.

This is where RAG comes into play, enabling LLMs to extend their robust expertise without necessitating the training of a new neural network for each specific task. It emerges as a compelling and efficient alternative to generate more dependable prompts.

Image developed with Artificial Intelligence.

Why is RAG pivotal?

In tasks that are both complex and knowledge-intensive, general LLMs may see a decline in performance, leading to the provision of false information, outdated guidance, or answers grounded in unreliable references. This degradation stems from intrinsic characteristics of LLMs, including reliance on past information, lack of updated knowledge, non-helpful model explainability, and a training regimen based on general data that overlooks specific business processes.

These challenges, coupled with the substantial computing power required for model development and training, make the utilization of a general model in certain applications, like chatbots, a potential detriment to user trust. A case in point is the recent incident involving the AirCanada chatbot, which furnished a customer with inaccurate information, ultimately misleading them into purchasing a full-price ticket.

RAG presents a viable solution to these issues by fine-tuning pre-trained LLMs with authoritative data sources. This approach offers organizations enhanced control and instills trust in the generated responses, mitigating the risks associated with the misuse of generative AI technology.

What are the practical applications of RAG?

RAG models have demonstrated reliability and versatility across various knowledge domains. In practical terms, any technical material, policy manual, or report can be leveraged to enhance Large Language Models (LLMs). This broad applicability positions RAG as a valuable asset for a diverse range of business markets. Some of its most conventional applications include:

Chatbots: Facilitating customer assistance by providing personalized and more accurate answers tailored to the specific business context.
Content generation: Offering capabilities such as text summarization, article generation, and personalized analysis of lengthy documents.
Information research: Improving the performance of search engines by efficiently retrieving relevant knowledge or documents based on user prompts.

Image developed with Artificial Intelligence.

How does RAG work?

RAG operates through the fine-tuning of a pre-trained model. Fine-tuning, a transfer learning approach, involves training the weights of a pre-trained model on new data. This process can be applied to the entire neural network or a subset of its layers, with the option to “freeze” layers that are not being fine-tuned.

The implementation of RAG is relatively straightforward, with the coauthors suggesting that it can be achieved with just five lines of code. The fine-tuning process typically involves four main steps:

Create external data and prepare the training dataset: Collect data from various sources, such as files, database records, or long-form text. Manipulate the data to fit the model’s data ingestion format.
Train a new fine-tuned model: After ensuring the data is appropriately formatted, proceed with the fine-tuning process. The duration of model training can vary from minutes to hours, depending on the dataset size and available computational power.
Model validation: Evaluate the training metrics, including training loss and accuracy, to validate the model. Generate samples from the baseline model and fine-tune it for comparison. If performance is suboptimal, iterate over data quality, quantity, and model hyperparameters.
Model deployment and utilization: Once validated, deploy the model for real-world tasks, ensuring integration with the system is reliable, safe, and scalable. Continuous monitoring is crucial to assess system performance and responsiveness.

To develop your own RAG model, you can follow a step-by-step tutorial on fine-tuning the GPT-3.5 provided by OpenAI.

Ready to explore the limitless possibilities of Retrieval-Augmented Generation (RAG) with our team of experts?

Let’s delve deeper into how RAG can transform your business and elevate your AI capabilities.

Connect with our specialists and unlock the full potential of generative AI tailored to your unique needs. Let innovation guide your journey – reach out to us now!

Article written by Murillo Stein, data scientist at BIX.

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Revolutionizing Manufacturing in 2024: A Dive into Cloud Computing Dynamics

Wed, 06 Mar 2024 12:55:15 +0000

In the rapidly evolving landscape of 2024, manufacturers find themselves at a pivotal juncture, actively seeking innovative solutions to overcome the multifaceted challenges that define the modern industrial terrain. Amid this transformative era, the adoption of cloud computing emerges not merely as a technological shift but as a powerful force, presenting manufacturers with unprecedented opportunities for operational efficiency, collaborative synergy, and groundbreaking innovation.

Unveiling the Potential: Cloud Solutions Reshaping Modern Manufacturing

Bid farewell to the constraints of on-premises operations. The realm of cloud computing is redefining manufacturing norms, offering not just convenience but a paradigm shift towards cost-effectiveness and operational excellence.

A Symphony of Solutions: Navigating Complexity for Manufacturing Excellence

In the pursuit of manufacturing excellence, navigating through complexities demands a versatile toolkit. Enterprise Resource Planning (ERP), Manufacturing Execution Systems (MES), Product Lifecycle Management (PLM), Supply Chain Management (SCM), Quality Management Systems (QMS), and Warehouse Management Systems (WMS) are not just solutions but strategic enablers. They enhance efficiency, slash costs, and craft exceptional customer experiences in a symphony of interconnected systems.

Embracing Cloud Computing: Elevating Manufacturers to New Heights

Agility and Scalability: The power of seamless scaling based on dynamic business requirements unveils a realm where manufacturers can adapt swiftly to changing demands without the constraints of traditional hardware investments.
Enhanced Data Accessibility: Granting real-time data access to empower departments, cloud solutions expedite decision-making, fostering an environment where information becomes a catalyst for agile, informed actions.
Fostering Improved Collaboration: Cloud services act as catalysts for seamless collaboration, transcending departmental boundaries and upgrading communication channels, creating a collaborative ecosystem.
Igniting Innovation: Cloud technology is more than a tool; it’s a gateway to innovation. It accelerates progress, enabling manufacturers to explore novel approaches and technologies that redefine their processes.
Efficiency at its Core: Cloud-enabled systems lay the foundation for centralized record systems, facilitating smarter decision-making through consolidated insights that drive operational efficiency.
Reduced Costs and Increased Predictability: Shifting from on-premises to cloud transforms costs into predictable, manageable expenses, liberating valuable resources for strategic investments.
Predictive Maintenance for Optimized Operations: Harnessing the potential of cloud technology, manufacturers can remotely monitor equipment, contributing not only to cost reduction but also to operational efficiency through proactive maintenance.
Real-time Supply Chain Management: Cloud-based solutions transcend traditional limitations, offering real-time tracking that empowers manufacturers to streamline inventory management and order fulfillment for enhanced customer satisfaction.
Fostering Greater Productivity: The strategic adoption of cloud solutions allows manufacturers to refocus on core aspects, leading to heightened productivity, reduced waste, and more effective resource allocation.
Attracting Talent and Embracing Innovation: Cloud-based applications align seamlessly with the preferences of the younger workforce, becoming an attractive feature for talent acquisition while fostering an environment of continuous innovation.

Photo credits by Freepik.

Navigating Manufacturing’s Future: The Imperative Role of Cloud Solutions

In an era where digitalization is not just a buzzword but a paramount necessity, cloud-based manufacturing solutions transcend from being choices to becoming strategic imperatives. They are architects of transformation, reshaping the very fabric of how manufacturing is approached, executed, and evolved.

Exploring Boundless Horizons: A Consultation with Experts

Embark on a journey of manufacturing transformation, where possibilities are as vast as the cloud itself. Connect with experts who comprehend the intricate interplay of cloud solutions in the manufacturing landscape. Together, let’s redefine the very essence of manufacturing excellence for a future that awaits.

The post Revolutionizing Manufacturing in 2024: A Dive into Cloud Computing Dynamics appeared first on .

2024 Clutch Badges Showcase BIX’s Excellence

Mon, 26 Feb 2024 12:45:40 +0000

Pioneering the frontier of cutting-edge technological solutions, BIX Tech proudly basks in the radiance of the prestigious 2024 Clutch Badges, solidifying its unrivaled leadership not only in Fort Lauderdale, Miami, and Florida but also extending its influence across the dynamic landscape of Latin America.

Garnering a stellar 5-star rating on Clutch, BIX transcends the mere realm of a technology company; it stands as a beacon of innovation, epitomizing an unwavering commitment to excellence.

Distinguished Achievements in Fort Lauderdale and Miami

In the vibrant tech landscape of Fort Lauderdale in 2024, BIX Tech seized the title of Top Chatbot Company, showcasing its mastery in crafting revolutionary chatbots that redefine user engagement. Simultaneously, in the dynamic city of Miami, BIX Tech claimed the coveted award for Best IT Services Company, a testament to its holistic approach and unparalleled expertise in delivering comprehensive technological services.

BIX’s prominence extends beyond these accolades, with notable distinctions in various categories such as Software Development, Angular Developers, Staff Augmentation, Artificial Intelligence, BI & Big Data, and Machine Learning, solidifying its authoritative position in the technological forefront of Miami.

Excellence Radiates in Florida and Beyond: 2024 Highlights

Within the diverse and thriving landscape of Florida, BIX Tech continues to captivate with a plethora of awards spanning specific service categories.

Its exceptional prowess in Manufacturing, Small Business, Retail, and more is celebrated through accolades in categories such as Software Development, Artificial Intelligence, BI & Big Data, Software Developers in Manufacturing, and Big Data Compliance, Fraud & Risk Management.

Global and Regional Recognition: United States and Latin America

Elevating its influence to a national scale, BIX proudly claims the title of the Top Machine Learning Company in the United States for 2024. Excelling not only in Software Development in Retail but also in Big Data Compliance, Fraud & Risk Management, BIX Tech reaffirms its dominance in shaping the technological landscape.

Adding a global touch to its acknowledgment, BIX Tech is recognized as the Top Qlik Company in 2024. Meanwhile, in the vibrant Latin American market, it stands out as the Top Machine Learning Company, Top Chatbot Company, and Top Artificial Intelligence Company, further solidifying its regional influence.

Unwavering Commitment to Excellence: Clutch Badges 2024

The 2024 Clutch Badges serve as tangible proof of BIX Tech’s steadfast commitment to excellence, innovation, and unwavering dedication to customer satisfaction. For BIX, these accolades are not just laurels; they signify the beginning of an exciting journey toward further heights of technological prowess.

Embark on Your Innovation Journey with Us

For those seeking more than ordinary technology, BIX Tech invites you to join the revolution. The 2024 Clutch Badges are emblematic of our commitment to propel your projects to unimaginable heights.

Connect with us now, and let’s innovate together, turning your ideas into reality.

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Crafting Tomorrow’s Strategies with Predictive Analytics

Tue, 20 Feb 2024 17:21:54 +0000

Predictive analytics, an intricate dance of data mastery, statistical finesse, and machine learning, serves as the compass for decoding the future. It goes beyond understanding the present; it’s the art of unveiling the unseen. At its core, predictive analytics is the magic that transforms data into insights, insights into strategies, and strategies into triumphs.

How Predictive Analytics Works

Predictive analytics is the wizardry of harnessing historical data, statistical algorithms, and machine learning to identify the likelihood of future outcomes. It’s the crystal ball that businesses use to gain insights into customer behavior, market trends, and potential risks.

Peering into Customer Minds: Personalizing the Experience

Embarking on the enchanting journey of predictive analytics, one of its captivating feats is unraveling the intricate tapestry of customer behavior. Delving into past interactions, purchase history, and preferences, businesses gain the ability to foresee what products or services a customer craves. This opens the gateway to personalized marketing, curated recommendations, and a customer experience that goes beyond mere expectations.

Beyond the realm of individual insights, predictive analytics transforms into a potent force for anticipating broader industry trends. By meticulously scrutinizing market data and spotting patterns, businesses not only keep pace but take the lead, making well-informed decisions regarding product development, strategic marketing maneuvers, and the overarching trajectory of their enterprise. It’s not merely a reaction; it’s a proactive stance, a strategic dance in the ever-evolving landscape of business.

Photo credits by Freepik.

Mitigating Risks: A Shield for the Unknown

Business is not without risks, but predictive analytics acts as a shield against the unknown. Whether identifying potential financial risks, supply chain disruptions, or other uncertainties, businesses can take pre-emptive actions to safeguard their operations.

Shaping Tomorrow: Partnering with Predictive Prowess

At BIX, we comprehend the strategic importance of predictive analytics. Our solutions are crafted to empower businesses with actionable insights, enabling them to make informed decisions and stay ahead in their respective industries. Partner with us to harness the full potential of predictive analytics for your business.

In a world where data holds the keys to success, predictive analytics emerges as a beacon of strategic clarity. We invite your businesses to embrace the magic of predictive analytics—unlocking tomorrow’s strategies based on today’s insights.

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Data Science Archives -

Predictive Analytics: develop strategies for your future based on data

What is Predictive Analytics and its Purpose?

How Does Predictive Analytics Work?

Supervised Models

Unsupervised Models

How to Apply Predictive Analytics to Your Business

Adopt Predictive Analytics in Your Business with BIX Tech and Gain a Competitive Advantage!

Fine-Tuning an OCR Model: what it is, why it’s important, and how to do it

What is OCR and why is it important?

What are the steps for implementing OCR?

What are the limitations of standard OCR models?

How to perform fine-tuning of an OCR model?

First step of fine-tuning an OCR model: Dataset Preparation

Training the OCR Model

Model Usage

Transform your organization’s efficiency with an OCR model!

Databases: frequently asked questions answered

What is a database?

Characteristics of Structured Data vs. Unstructured Data

What is the difference between using spreadsheets and databases?

What are the main types of databases used in companies?

SQL

NoSQL

What is the difference between on-premises and cloud databases?

More answers

LMs Practical Guide and Business Applications

What Are Language Models (LMs)?

How Do LMs Work?

Practical Business Applications

Practical Implementation

Ready to Elevate Your Business with Language Models?

Data Revolution: is your business ready to thrive?

Deciphering complexity

Foresight over intuition

Ready to Transform Your Business with Data Science?

How I wish someone would explain SHAP values to me

What are SHAP values?

General Visualization

Evaluation of Graph 1

Evaluation of Graph 2

Interpretation of Prediction for the Sample

Evaluation of Graph 3

Evaluation of Graph 4

Conclusion

Mastering Retrieval-Augmented Generation (RAG) for Next-Level AI Solutions

So, what exactly is RAG?

Why is RAG pivotal?

What are the practical applications of RAG?

How does RAG work?

Ready to explore the limitless possibilities of Retrieval-Augmented Generation (RAG) with our team of experts?

Revolutionizing Manufacturing in 2024: A Dive into Cloud Computing Dynamics

Unveiling the Potential: Cloud Solutions Reshaping Modern Manufacturing

A Symphony of Solutions: Navigating Complexity for Manufacturing Excellence

Embracing Cloud Computing: Elevating Manufacturers to New Heights

Navigating Manufacturing’s Future: The Imperative Role of Cloud Solutions

Exploring Boundless Horizons: A Consultation with Experts

2024 Clutch Badges Showcase BIX’s Excellence

Distinguished Achievements in Fort Lauderdale and Miami

Excellence Radiates in Florida and Beyond: 2024 Highlights

Global and Regional Recognition: United States and Latin America

Unwavering Commitment to Excellence: Clutch Badges 2024

Embark on Your Innovation Journey with Us

Crafting Tomorrow’s Strategies with Predictive Analytics

How Predictive Analytics Works

Peering into Customer Minds: Personalizing the Experience

Mitigating Risks: A Shield for the Unknown

Shaping Tomorrow: Partnering with Predictive Prowess