These data record the level of atmospheric ozone concentration from eight daily meteorological measurements made in the Los Angeles basin in 1976. Although measurements were made every day that year, some observations were missing; here we have the 330 complete cases. The response, referred to as ozone, is actually the log of the daily maximum of the hourly-average ozone concentrations in Upland, California.

Detailed variable names:

*ozone* :

Upland Maximum Ozone

• vh : Vandenberg 500 mb Height

• wind : Wind Speed (mph)

• humidity : Humidity (%)

• temp : Sandburg AFB Temperature

• ibh : Inversion Base Height

• dpg : Daggot Pressure Gradient

• ibt : Inversion Base Temperature

• vis : Visibility (miles)

• doy : Day of the Year

Source: http://www-stat.stanford.edu/~tibs/ElemStatLearn/data.html

Data on flavor of 86 single malt scotch whiskies

Opinions about the company

This dataset contains 8.648 instances each on them with 31 descriptive variables from an industrial machine with some failures in it

Redfin house pricing dataset. Redfin is a real estate brokerage from USA.

This dataset contains sales and behavioral data for 10,000 customers, primarily designed for churn analysis and customer segmentation. It includes metrics tracked over several weeks (labeled W1 through W5) to capture trends in purchasing behavior.

Key Components of the Dataset: Customer Identification: CUSTOMER_ID uniquely identifies each record.

Sales Metrics: Includes total sales (Total_Sale), standard deviation of sales (STD_Sales), and average purchase value (APV). Note that many sales values appear to be log-transformed or normalized.

Temporal Tracking (Weekly Data): Detailed breakdown for weeks 1–5, including:

Visits: Number of visits per week (W1_Visits, etc.).

Sales Performance: Minimum, maximum, and standard deviation of sales per week (e.g., W3_Max_Sale, W4_Min_Sale).

Activity Flags: Binary indicators for whether a customer was active in specific weeks (week_1, week_2, etc.).

Recency: Days_since_last_visit tracks how long it has been since the customer's last interaction.

Customer Segmentation:

Value-based: Categorizes customers as High_value, Low_value, or Regular.

Engagement-based: Categorizes customers by visit frequency (e.g., Frequent_Visitors, Rare_Visitors).

Target Variable:

CHURN: A binary indicator (0 or 1) typically used to predict if a customer will stop using the service.

The market_basket.csv dataset is a transactional dataset typically used for Market Basket Analysis (identifying relationships between items purchased together).

Dataset Overview: Format: It contains 9,835 rows and a single column titled products.

Content: Each row represents an individual transaction (a "basket").

Data Structure: Each entry is a comma-separated list of items bought in that specific transaction.