Scripts, figures and working notes for the participation in SnakeCLEF-2022, part of the 13th CLEF Conference, 2022

Scripts, figures and working notes for the participation in FungiCLEF-2022, part of the 13th CLEF Conference, 2022

This code is going to be used to scrape the UCPD Daily Incident page at a pre-determined frequency and store the incidents on a generic JSON data-store.

Development of new ML library

This project investigates the performance of 5 machine learning techniques in classifying the Dry Beans Dataset

Using supervised learning on Lending Club loan data to predict default and / or bad loans

Movie Counsel helps you to get tailored Movie/Series recommendation with an inbuilt Sentiment Analyzer Tool for Movie Reviews

Forecasting the likelihood of a customer defaulting their auto loan using classification models

Deployment of classification algorithms for customer classification

Descriptive Statistical Analysis of the NBA Defensive Player of the Year race since 2013-14

To build a classification methodology to determine whether a person defaults the credit card payment for the next month.

The main part of this project is dedicated to conducting a binary classification task on the Amazon Kindle Store reviews data to predict whether a new review is helpful or not.

It is a web application which you aggregate Wildfire data, and make prediction of causes by the help of ML

Using machine learning (XG boost) to increase the annualized return of a personal loan portfolio.

Comparision of classifiers

Our project employs machine learning to pinpoint phishing URLs with 97.4% accuracy, leveraging HTTPS and website traffic as critical indicators. Insights into features like AnchorURL enhance cybersecurity strategies, showcasing the power of AI in combating online threats.

Model using XGBoost_Classifier

Salary Prediction API using Flask predicts salaries for freshers joining organizations based on factors like past experience, company switches, courses completed, and academic marks. This Flask-based API allows users to input their details and receive a salary prediction. With no user interface, it's designed for integration into other applications

TitanicClassification.py file contains project based on binary classification. The dataset comprises of data related to passengers and binary value of whether they survived or not.