Ankit Kalauni's Projects
Full Stack Data Science Bootcamp by iNeuron
Data Structures practice code from GeeksforGeeks, Leetcode, HackerRank.
H&M Group is a family of brands and businesses with 53 online markets and approximately 4,850 stores. Our online store offers shoppers an extensive selection of products to browse through. But with too many choices, customers might not quickly find what interests them or what they are looking for, and ultimately, they might not make a purchase. To enhance the shopping experience, product recommendations are key. More importantly, helping customers make the right choices also has a positive implications for sustainability, as it reduces returns, and thereby minimizes emissions from transportation. In this competition, H&M Group invites you to develop product recommendations based on data from previous transactions, as well as from customer and product meta data. The available meta data spans from simple data, such as garment type and customer age, to text data from product descriptions, to image data from garment images. There are no preconceptions on what information that may be useful ā that is for you to find out. If you want to investigate a categorical data type algorithm, or dive into NLP and image processing deep learning, that is up to you.
HackerRank Artificial Intelligence practice code.
Hands on Machine Learning with Scikit-Learn and Tensorflow (Machine Learning Book) Source, Practice Code
The keyword extraction process helps us in identifying the important words. It also effective in topic modeling tasks. You can know a lot about your text data by only a few keywords. These keywords will help you to determine whether you want to read an article or not.
Coursera Introduction to Machine learning with Python Full Solved Assignment/Practice Code.
Machine learning Code for re-usability
Machine Learning Content-based recommendation system with python.
India Company Sales Data | Data Cleaning and Data Visualization Using PowerBi, Excel, SQL and Python