Practical AI/ML Examples

Explore our range of practical AI and Machine Learning examples using real-world datasets. Each example demonstrates how AI/ML can solve specific business problems.

Classification
Classification Example
Telco Customer Churn Dataset

The Telco Customer Churn Dataset is a widely used dataset in the analytics and machine learning community, particularly for studying customer behavior and retention strategies. It contains data on customers of a telecom company who either stayed with or left the company within a certain period.

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Clustering
Clustering Example
Mall Customer Segmentation Data

The Mall Customer Segmentation Dataset is commonly used in data science and machine learning for exploring clustering techniques and understanding customer demographics. It contains data on a shopping mall's customers, including attributes such as age, gender, annual income, and spending score. This dataset helps in segmenting customers into distinct groups based on their purchasing behaviour and income levels, providing valuable insights into customer profiles and marketing strategies.

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Regression
Regression Example
California Housing Prices Dataset

The California Housing Prices Dataset is widely used in machine learning and analytics for predicting housing prices based on various features. It contains data from the 1990 U.S. Census, including attributes such as median income, housing age, total rooms, population, and geographical location. The dataset is often employed in regression tasks to model and understand the relationships between these variables and housing prices, making it a valuable resource for real estate market analysis and price prediction studies.

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Sentiment Analysis
Sentiment Analysis Example
Amazon Fine Food Reviews Dataset

The Amazon Fine Food Reviews Dataset is frequently used in the fields of natural language processing and sentiment analysis. It contains over 500,000 customer reviews of fine foods on Amazon, including features such as review text, star ratings, helpfulness votes, and timestamps. This dataset is valuable for exploring consumer sentiment, product feedback, and recommendation systems, providing insights into customer opinions and behaviours related to food products on Amazon's platform.

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