2012019099
Automated LinkedIn Bot
Python, Selenium, Web Mining
The aim of this data science project is to leverage selenium tool in Python and navigate LinkedIn platform. I have leveraged selenium library in Python and perfomed various operations such as accepting requests, sending requests, automated reply to message and search with filters. This will help to reduce the repetitive tasks in LinkedIn.
Please click the github link for details of the project.
Ecommerce reviews ranking
Machine Learning and NLP
Reviews are prevalent in many e-commerce websites such as Amazon or ebay. It provides an advantage to the customers to check the credibility of the product.
In this project, I have leveraged NLTK, Spacy and Sci-kit learn libraries to implement a model that provide ranking of reviews based on how relevant it is.
Please click the Github link for more details about the project.
Segmentation of customers using clustering
Unsupervised Machine Learning
This project involves segmenting the customers by analyzing the annual spending of customers on various products. This will give a better insight to cater to the needs of the customer.
Please click the Github link for more details about the project.
BigMart sales prediction
Gradient Boosting and XGB Regressor
The aim of this data science project is to build a predictive model and find out the sales of each product at a particular store. I have leveraged various models and found XGB and Gradient Boosting are top performers for this problem. This will help to understand the properties and store of BigMart which play a role in increasing the sales.
Please click the github link for more details about the project.
Optimizing Online Fraud Detection
XGBoost, Sklearn
In this project, we have implemented a machine learning model that will help to detect the fraud transactions online. Several classification algorithms have been implemented and XGBoost achieved an accuracy of around 85% with roc score around 90%.
Please click the link below to explore more!
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