Each project comes with 2-5 hours of micro-videos explaining the solution.
Code & Dataset
Get access to 50+ solved projects with iPython notebooks and datasets.
Add project experience to your Linkedin/Github profiles.
Understanding the problem statement
Importing the dataset from AWS
Importing important libraries and understanding its significance
Understanding CSR Matrix and hstack
Performing basic EDA and checking for null values
Creating function for handling null values
Performing slicing and making function for converting variables into categorical types
Merging two or more Dataset
Using TFIDF and Count Vectorizer for analyzing textual data
Applying LabelBinarizer for textual data
Sparse matrix its use and implementation
Selecting models Light GBM and Ridge as model
Defining parameters for the models
Training the model and using the model for making predictions
Saving the final predictions in CSV format
Mercari, Japan’s biggest community-powered shopping app, knows this problem deeply. They’d like to offer pricing suggestions to sellers, but this is tough because their sellers are enabled to put just about anything, or any bundle of things, on Mercari's marketplace.
In this machine learning project, we will build an algorithm that automatically suggests the right product prices. You’ll be provided user-inputted text descriptions of their products, including details like product category name, brand name, and item condition.