Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/sayamalt/black-friday-sales-prediction
Successfully established a machine learning regression model which can estimate the gross Black Friday sales for a particular customer, based on a distinct set of related and meaningful features, to a fair level of accuracy.
https://github.com/sayamalt/black-friday-sales-prediction
artificial-neural-networks data-visualization deep-learning exploratory-data-analysis feature-engineering machine-learning model-deployment model-training-and-evaluation regression-analysis regression-modelling regression-testing supervised-learning
Last synced: about 2 months ago
JSON representation
Successfully established a machine learning regression model which can estimate the gross Black Friday sales for a particular customer, based on a distinct set of related and meaningful features, to a fair level of accuracy.
- Host: GitHub
- URL: https://github.com/sayamalt/black-friday-sales-prediction
- Owner: SayamAlt
- Created: 2022-05-15T20:19:40.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2023-04-28T22:40:32.000Z (almost 2 years ago)
- Last Synced: 2024-11-07T12:47:56.068Z (3 months ago)
- Topics: artificial-neural-networks, data-visualization, deep-learning, exploratory-data-analysis, feature-engineering, machine-learning, model-deployment, model-training-and-evaluation, regression-analysis, regression-modelling, regression-testing, supervised-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 23.7 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Black-Friday-Sales-Prediction
Successfully established a machine learning model which can accurately predict the net Black Friday sales for a specific customer, based on various characteristics pertaining to that particular customer.
## Deployed Web Application
Link: https://black-friday-sales-forecast.herokuapp.com/
## About Dataset
A retail company “ABC Private Limited” wants to understand the customer purchase behaviour (specifically, purchase amount) against various products of different categories. They have shared purchase summary of various customers for selected high volume products from last month.
The data set also contains customer demographics (age, gender, marital status, citytype, stayincurrentcity), product details (productid and product category) and Total purchaseamount from last month.
Now, they want to build a model to predict the purchase amount of customer against various products which will help them to create personalized offer for customers against different products.
## Data
Variable
Definition
User_ID
User ID
Product_ID
Product ID
Gender
Sex of User
Age
Age in bins
Occupation
Occupation(Masked)
City_Category
Category of the City (A,B,C)
StayInCurrentCityYears
Number of years of stay in the current city
Marital_Status
Marital Status
ProductCategory1
Product Category (Masked)
ProductCategory2
Product may belong to other category also (Masked)
ProductCategory3
Product may belong to other category as well (Masked)
Purchase
Purchase Amount (Target Variable)
The performances of all regression ML models have been evaluated on the basis of predictions of the purchase amount for the test data (test.csv), which contains similar data points as train except for their purchase amount.
Model evaluation has been done using the root mean squared error (RMSE). RMSE is very common and is a suitable general-purpose error metric. Compared to the Mean Absolute Error, RMSE punishes large errors:
Where y hat is the predicted value and y is the original value.