Dataconomy

APRIL 4, 2023

Some of the common types are: Linear Regression Deep Neural Networks Logistic Regression Decision Trees AI Linear Discriminant Analysis Naive Bayes Support Vector Machines Learning Vector Quantization K-nearest Neighbors Random Forest What do they mean? The information from previous decisions is analyzed via the decision tree.

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Dataconomy

APRIL 4, 2023

Some of the common types are: Linear Regression Deep Neural Networks Logistic Regression Decision Trees AI Linear Discriminant Analysis Naive Bayes Support Vector Machines Learning Vector Quantization K-nearest Neighbors Random Forest What do they mean? The information from previous decisions is analyzed via the decision tree.

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Pickl AI

SEPTEMBER 16, 2024

Summary: This blog highlights ten crucial Machine Learning algorithms to know in 2024, including linear regression, decision trees, and reinforcement learning. Disease Diagnosis: Predicting the presence or absence of a disease based on patient data. Each tree trained on the residual errors of the previous trees.

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IBM Journey to AI blog

DECEMBER 20, 2023

Supervised learning is commonly used for risk assessment, image recognition, predictive analytics and fraud detection, and comprises several types of algorithms. Regression algorithms —predict output values by identifying linear relationships between real or continuous values (e.g., temperature, salary).

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Dataconomy

JUNE 13, 2023

These algorithms are carefully selected based on the specific decision problem and are trained using the prepared data. Machine learning algorithms, such as neural networks or decision trees, learn from the data to make predictions or generate recommendations.

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Pickl AI

DECEMBER 14, 2023

Decision Trees Decision trees are a versatile statistical modelling technique used for decision-making in various industries. In marketing, a decision tree can help determine the most effective advertising channels based on customer demographics, improving campaign targeting and ROI.

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Pickl AI

JULY 1, 2024

Decision Trees These tree-like structures categorize data and predict demand based on a series of sequential decisions. Random Forests By combining predictions from multiple decision trees, random forests improve accuracy and reduce overfitting.

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IBM Journey to AI blog

FEBRUARY 23, 2024

Predictive analytics integrates with NLP, ML and DL to enhance decision-making capabilities, extract insights, and use historical data to forecast future behavior, preferences and trends. When a user sends a message, the system uses NLP to parse and understand the input, often by using DL models to grasp the nuances and intent.

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AWS Machine Learning Blog

MAY 8, 2024

This success story emphasizes the crucial role of predictive analytics in staying ahead in the competitive telecom industry, demonstrating the transformative impact of advanced AI models. million subscribers, which amounts to 57% of the Sri Lankan mobile market.

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Pickl AI

NOVEMBER 5, 2024

Key Takeaways Data-driven decisions enhance efficiency across various industries. Predictive analytics improves customer experiences in real-time. Together, Data Science and AI enable organisations to analyse vast amounts of data efficiently and make informed decisions based on predictive analytics.

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Pickl AI

NOVEMBER 18, 2024

Underfitting happens when a model is too simplistic and fails to capture the underlying patterns in the data, leading to poor predictions. Predictive analytics uses historical data to forecast future trends, such as stock market movements or customer churn. Decision trees are easy to interpret but prone to overfitting.

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Pickl AI

JULY 23, 2023

Using the right data analytics techniques can help in extracting meaningful insight, and using the same to formulate strategies. The analytics techniques like descriptive analytics, predictive analytics, diagnostic analytics and others find application in diverse industries, including retail, healthcare, finance, and marketing.

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Dataconomy

MAY 25, 2023

This enables them to extract valuable insights, identify patterns, and make informed decisions in real-time. AI algorithms can uncover hidden correlations within IoT data, enabling predictive analytics and proactive actions.

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Pickl AI

OCTOBER 15, 2024

They identify patterns in existing data and use them to predict unknown events. Predictive modeling is widely used in finance, healthcare, and marketing. Techniques like linear regression, time series analysis, and decision trees are examples of predictive models.

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Pickl AI

JULY 12, 2024

Algorithms in ML identify patterns and make decisions, which is crucial for applications like predictive analytics and recommendation systems. Supervised Learning Algorithms In supervised learning , algorithms learn from labelled data to predict outcomes for unseen data points.

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Pickl AI

FEBRUARY 4, 2024

Predictive Analytics: Leverage machine learning algorithms for accurate predictions. This makes Alteryx an indispensable tool for businesses aiming to glean insights and steer their decisions based on robust data. Additionally, it excels in automating workflows, saving valuable time and effort for users.

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Pickl AI

MARCH 20, 2024

Aspiring Data Scientists must equip themselves with a diverse skill set encompassing technical expertise, analytical prowess, and domain knowledge. Whether you’re venturing into machine learning, predictive analytics, or data visualization, honing the following top Data Science skills is essential for success.

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Pickl AI

SEPTEMBER 30, 2024

Common algorithms include decision trees, neural networks, and support vector machines. Enhancing Decision-Making with Data-Driven Insights Machine Learning empowers businesses to make smarter decisions by analysing vast amounts of data and uncovering hidden patterns. Data : Data serves as the foundation for ML.

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Pickl AI

AUGUST 23, 2023

By extracting insights from these datasets, professionals can make more informed investment decisions, reducing the risk associated with emotional biases. Predictive Analytics One of the most remarkable aspects of Data Science in stock market analysis is its predictive capabilities.

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Pickl AI

JANUARY 29, 2024

ML focuses on algorithms like decision trees, neural networks, and support vector machines for pattern recognition. ML opportunities are evident in predictive analytics, recommendation systems, and autonomous systems development. AI comprises Natural Language Processing, computer vision, and robotics.

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Pickl AI

OCTOBER 18, 2023

How it Works Random Forest creates a “forest” of decision trees and combines their outputs to achieve more stable and accurate predictions. – Predictive analytics to assess data quality issues before they become critical. – Predictive analytics to forecast data quality trends.

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IBM Journey to AI blog

JULY 6, 2023

One ride-hailing transportation company uses big data analytics to predict supply and demand, so they can have drivers at the most popular locations in real time. The company also uses data science in forecasting, global intelligence, mapping, pricing and other business decisions.

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phData

SEPTEMBER 10, 2024

Diagnostic Analytics : This type of analytics goes a step further to explain why churn is happening. Predictive Analytics : Predictive models use historical data to predict future churn. Techniques include machine learning algorithms such as logistic regression, decision trees, and neural networks.

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Pickl AI

NOVEMBER 2, 2023

By making data-driven decisions, organizations can increase efficiency, reduce costs, and identify growth opportunities. From predictive analytics to customer segmentation, Data Science empowers businesses to stay competitive.

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Defined.ai blog

AUGUST 10, 2023

From voice assistants like Siri and Alexa, which are now being trained with industry-specific vocabulary and localized dialogue data , to more complex technologies like predictive analytics and autonomous vehicles, AI is everywhere. It helps companies streamline operations, improve efficiency, and gain a competitive edge.

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Pickl AI

OCTOBER 24, 2024

ML systems are designed to improve their accuracy in performing specific tasks, such as image recognition, natural language processing, or predictive analytics. ML is particularly useful when vast amounts of data are involved, and the goal is to identify patterns or predict outcomes.

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Pickl AI

SEPTEMBER 23, 2024

Healthcare Data Science is revolutionising healthcare through predictive analytics, personalised medicine, and disease detection. For example, it helps predict patient outcomes, optimise hospital operations, and discover new drugs. Finance: AI-driven algorithms analyse historical data to detect fraud and predict market trends.

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IBM Journey to AI blog

JUNE 5, 2024

And most machine learning tools will automatically generate summaries of complex data, making it easier for executives and other decision-makers to understand reports without needing to review the raw data themselves. Predictive analytics. Predictive analytics are equally valuable for user insights.

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Pickl AI

DECEMBER 4, 2024

According to recent statistics, 56% of healthcare organisations have adopted predictive analytics to improve patient outcomes. For example: In finance, predictive analytics helps institutions assess risks and identify investment opportunities. In healthcare, patient outcome predictions enable proactive treatment plans.

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Pickl AI

JUNE 6, 2024

Model Building & Training Once the data is ready, data scientists choose appropriate algorithms like regression analysis, decision trees, or machine learning techniques. However, raw data is often messy and needs cleaning and transformation to be usable.

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