Dataset: Product Sales Estimation Dataset

About this Dataset

Product Sales Estimation Dataset

Structure:

The dataset is structured as a table with 28 rows and 13 columns.
Each row represents a unique product sold by the restaurant(s).
Each column provides specific attributes or features of the products.

Columns:

Product_ID: Numeric identifier for each product.
Price: Numeric value representing the price of the product.
Discount: Numeric value representing the discount applied to the product (in decimal format).
Rating: Numeric value representing the rating of the product.
Number_of_Reviews: Numeric value representing the number of reviews received for the product.
Brand_Popularity: Numeric value representing the popularity score of the brand associated with the product.
Competition_Level: Categorical variable indicating the level of competition in the market (e.g., "competitive", "very competitive", "normal", "less", "easy").
Marketing_Spend: Numeric value representing the amount spent on marketing for the product.
Social_Media_Followers: Numeric value representing the number of social media followers for the product's brand.
Seasonality: Numeric value representing the degree of seasonality affecting the product's sales (in decimal format).
Past_60_Day_Sales: Numeric value representing the sales volume of the product in the past 60 days.
Percentage_Repeating_Customers: Numeric value representing the percentage of repeating customers for the product (in decimal format).
next_60_days_estimated_sales: Numeric value representing the estimated sales volume of the product for the next 60 days.

Data Quality

The dataset is assumed to be preprocessed and cleaned, with missing values handled appropriately.

Data types of each column:

Product_ID: Integer
Price: Numeric
Discount: Numeric
Rating: Numeric
Number_of_Reviews: Integer
Brand_Popularity: Numeric
Competition_Level: Categorical
Marketing_Spend: Numeric
Social_Media_Followers: Integer
Seasonality: Numeric
Past_60_Day_Sales: Numeric
Percentage_Repeating_Customers: Numeric
next_60_days_estimated_sales: Numeric

This metadata provides an overview of the dataset's purpose, structure, and content, making it easier for analysts and data scientists to understand and work with the data effectively.

Tables

Labelled Product Data

@kaggle.kartikeybartwal_next_60_days_product_sales_dataset.labelled_product_data

12.21 KB
160 rows
13 columns


CREATE TABLE labelled_product_data (
  "product_id" BIGINT,
  "price" BIGINT,
  "discount" DOUBLE,
  "rating" DOUBLE,
  "number_of_reviews" BIGINT,
  "brand_popularity" DOUBLE,
  "competition_level" VARCHAR,
  "marketing_spend" BIGINT,
  "social_media_followers" BIGINT,
  "seasonality" DOUBLE,
  "past_60_day_sales" BIGINT,
  "percentage_repeating_customers" DOUBLE,
  "next_60_days_estimated_sales" DOUBLE
);