Product length prediction using Machine Learning (NLP)
Budget: ₹600 – ₹5,000 INR
PROBLEM STATEMENT:
Develop a machine learning model that can predict the length dimension of a product. Product length is crucial for packaging and storing products efficiently in the warehouse. Moreover, in many cases, it is an important attribute that customers use to assess the product size before purchasing. However, measuring the length of a product manually can be time-consuming and error-prone, especially for large catalogs with millions of products.
You will have access to the product title, description, bullet points, product type ID, and product length for 2.2 million products to train and test your submissions. Note that there is some noise in the data.
Task:
You are required to build a machine learning model that can predict product length from catalog metadata.
Dataset description:
The dataset folder contains the following files:
train.csv: 2249698 x 6
test.csv: 734736 x 5
sample_submission.csv: 734736 x 2
The columns provided in the dataset are as follows:
Column name Description
PRODUCT_ID Represents a unique identification of a product
TITLE Represents the title of the product
DESCRIPTION Represents the description of the product
BULLET_POINTS Represents the bullet points about the product
PRODUCT_TYPE_ID Represents the product type
PRODUCT_LENGTH Represents the length of the product
Evaluation metric:
score = max( 0 , 100*(1-metrics.mean_absolute_percentage_error(actual,predicted)))
Result submission guidelines:
The index is "PRODUCT_ID" and the target is the "PRODUCT_LENGTH" column.
The submission file must be submitted in .csv format only.
The size of this submission file must be 734736 x 2.
Note: Ensure that your submission file contains the following:
Correct index values as per the test file
Correct names of columns as provided in the sample_submission.csv file
Dataset can be downloaded from the link given below:
Develop a machine learning model that can predict the length dimension of a product. Product length is crucial for packaging and storing products efficiently in the warehouse. Moreover, in many cases, it is an important attribute that customers use to assess the product size before purchasing. However, measuring the length of a product manually can be time-consuming and error-prone, especially for large catalogs with millions of products.
You will have access to the product title, description, bullet points, product type ID, and product length for 2.2 million products to train and test your submissions. Note that there is some noise in the data.
Task:
You are required to build a machine learning model that can predict product length from catalog metadata.
Dataset description:
The dataset folder contains the following files:
train.csv: 2249698 x 6
test.csv: 734736 x 5
sample_submission.csv: 734736 x 2
The columns provided in the dataset are as follows:
Column name Description
PRODUCT_ID Represents a unique identification of a product
TITLE Represents the title of the product
DESCRIPTION Represents the description of the product
BULLET_POINTS Represents the bullet points about the product
PRODUCT_TYPE_ID Represents the product type
PRODUCT_LENGTH Represents the length of the product
Evaluation metric:
score = max( 0 , 100*(1-metrics.mean_absolute_percentage_error(actual,predicted)))
Result submission guidelines:
The index is "PRODUCT_ID" and the target is the "PRODUCT_LENGTH" column.
The submission file must be submitted in .csv format only.
The size of this submission file must be 734736 x 2.
Note: Ensure that your submission file contains the following:
Correct index values as per the test file
Correct names of columns as provided in the sample_submission.csv file
Dataset can be downloaded from the link given below: