Market basket analysis with Apriori algorithm
The retailer wants to target customers with suggestions on itemset that a customer is most likely to purchase .I was given dataset contains data of a retailer; the transaction data provides data around all the transactions that have happened over a period of time. Retailer will use result to grove in his industry and provide for customer suggestions on itemset, we be able increase customer engagement and improve customer experience and identify customer behavior. I will solve this problem with use Association Rules type of unsupervised learning technique that checks for the dependency of one data item on another data item.
Association Rule is most used when you are planning to build association in different objects in a set. It works when you are planning to find frequent patterns in a transaction database. It can tell you what items do customers frequently buy together and it allows retailer to identify relationships between the items.
Assume there are 100 customers, 10 of them bought Computer Mouth, 9 bought Mat for Mouse and 8 bought both of them. - bought Computer Mouth => bought Mat for Mouse - support = P(Mouth & Mat) = 8/100 = 0.08 - confidence = support/P(Mat for Mouse) = 0.08/0.09 = 0.89 - lift = confidence/P(Computer Mouth) = 0.89/0.10 = 8.9 This just simple example. In practice, a rule needs the support of several hundred transactions, before it can be considered statistically significant, and datasets often contain thousands or millions of transactions.
Number of Attributes: 7
https://user-images.githubusercontent.com/91852182/145270162-fc53e5a3-4ad1-4d06-b0e0-228aabcf6b70.png">
First, we need to load required libraries. Shortly I describe all libraries.
https://user-images.githubusercontent.com/91852182/145270210-49c8e1aa-9753-431b-a8d5-99601bc76cb5.png">
Next, we need to upload Assignment-1_Data. xlsx to R to read the dataset.Now we can see our data in R.
https://user-images.githubusercontent.com/91852182/145270229-514f0983-3bbb-4cd3-be64-980e92656a02.png">
https://user-images.githubusercontent.com/91852182/145270251-6f6f6472-8817-435c-a995-9bc4bfef10d1.png">
After we will clear our data frame, will remove missing values.
https://user-images.githubusercontent.com/91852182/145270286-05854e1a-2b6c-490e-ab30-9e99e731eacb.png">
To apply Association Rule mining, we need to convert dataframe into transaction data to make all items that are bought together in one invoice will be in ...
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
This is a realistic and structured pizza sales dataset covering the time span from **2024 to 2025. ** Whether you're a beginner in data science, a student working on a machine learning project, or an experienced analyst looking to test out time series forecasting and dashboard building, this dataset is for you.
π Whatβs Inside? The dataset contains rich details from a pizza business including:
β Order Dates & Times β Pizza Names & Categories (Veg, Non-Veg, Classic, Gourmet, etc.) β Sizes (Small, Medium, Large, XL) β Prices β Order Quantities β Customer Preferences & Trends
It is neatly organized in Excel format and easy to use with tools like Python (Pandas), Power BI, Excel, or Tableau.
π‘** Why Use This Dataset?** This dataset is ideal for:
π Sales Analysis & Reporting π§ Machine Learning Models (demand forecasting, recommendations) π Time Series Forecasting π Data Visualization Projects π½οΈ Customer Behavior Analysis π Market Basket Analysis π¦ Inventory Management Simulations
π§ Perfect For: Data Science Beginners & Learners BI Developers & Dashboard Designers MBA Students (Marketing, Retail, Operations) Hackathons & Case Study Competitions
pizza, sales data, excel dataset, retail analysis, data visualization, business intelligence, forecasting, time series, customer insights, machine learning, pandas, beginner friendly
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Market basket analysis with Apriori algorithm
The retailer wants to target customers with suggestions on itemset that a customer is most likely to purchase .I was given dataset contains data of a retailer; the transaction data provides data around all the transactions that have happened over a period of time. Retailer will use result to grove in his industry and provide for customer suggestions on itemset, we be able increase customer engagement and improve customer experience and identify customer behavior. I will solve this problem with use Association Rules type of unsupervised learning technique that checks for the dependency of one data item on another data item.
Association Rule is most used when you are planning to build association in different objects in a set. It works when you are planning to find frequent patterns in a transaction database. It can tell you what items do customers frequently buy together and it allows retailer to identify relationships between the items.
Assume there are 100 customers, 10 of them bought Computer Mouth, 9 bought Mat for Mouse and 8 bought both of them. - bought Computer Mouth => bought Mat for Mouse - support = P(Mouth & Mat) = 8/100 = 0.08 - confidence = support/P(Mat for Mouse) = 0.08/0.09 = 0.89 - lift = confidence/P(Computer Mouth) = 0.89/0.10 = 8.9 This just simple example. In practice, a rule needs the support of several hundred transactions, before it can be considered statistically significant, and datasets often contain thousands or millions of transactions.
Number of Attributes: 7
https://user-images.githubusercontent.com/91852182/145270162-fc53e5a3-4ad1-4d06-b0e0-228aabcf6b70.png">
First, we need to load required libraries. Shortly I describe all libraries.
https://user-images.githubusercontent.com/91852182/145270210-49c8e1aa-9753-431b-a8d5-99601bc76cb5.png">
Next, we need to upload Assignment-1_Data. xlsx to R to read the dataset.Now we can see our data in R.
https://user-images.githubusercontent.com/91852182/145270229-514f0983-3bbb-4cd3-be64-980e92656a02.png">
https://user-images.githubusercontent.com/91852182/145270251-6f6f6472-8817-435c-a995-9bc4bfef10d1.png">
After we will clear our data frame, will remove missing values.
https://user-images.githubusercontent.com/91852182/145270286-05854e1a-2b6c-490e-ab30-9e99e731eacb.png">
To apply Association Rule mining, we need to convert dataframe into transaction data to make all items that are bought together in one invoice will be in ...