I participated in this hackathon which provided data from thousands of restaurants in India regarding the time they take to deliver food for online order. Goal: predict the online order delivery time based on the given factors.
Analyzing Adidas Product Information Using Pandas, Matplotlib and Seaborn
Undergraduate Fellowship during the Spring 2021 Semester (Topic = Foundations of Machine Learning and Data Science)
Practical Machine learning with Python covering Topics like SVM, Regression Analysis and models, KNN(K-nearest neighbors), supervised learning and unsupervised learning etc.
Leveraging machine learning techniques to predict the likelihood of heart failure in patients based on a comprehensive dataset of patient information sourced from Kaggle.
Implementation of a linear, polynomial and Harmonic Classifier with Explanation of various Evaluation Methods in Machine Learning.
This repo will contain Machine Learning Related content
Master the art of data analysis with Python in MSIS 5192. Dive into Python programming, data manipulation, visualization, and statistical analysis. Unlock the potential of data-driven insights and gain valuable skills for the world of data science.
Predicting future VIX movements using forked repository from @maylathant. Investigation on VIX price action and predictive modeling in order to aid in risk management on SPX index fund.
PYTHON-DATA_SCIENCE_CASE_STUDY_PROJECT
Telecom Customer Churn Prediction This repository contains a machine learning project focused on predicting customer churn in the telecommunications industry. By leveraging a dataset of customer demographics and usage patterns, we develop and deploy a predictive model to identify customers at risk of leaving the service.
This project, developed during my data science internship at Eisystems Technologies, aims to predict insurance purchase likelihood using a logistic regression model. The project includes a fully functional Streamlit app that allows users to interact with the model and visualize predictions. Refer to readme file for more info.
Uses in game player statistics to determine what position a player may play or better suited to play.
The project revolves around the development of a user-friendly web interface system. This system aims to facilitate malware identification by allowing users to upload CSV files for analysis. The PE data within these files will be analyzed using a predefined data set, generating predictions to determine whether the file is malicious or not.
This is a web app where you can draw a letter in the russian alphabet and the ML algorithm will predict the letter that you drew.
A Multi Document Summarizer Using Bidirectional Encoder Representations from Transformers (BERT)
Sentiment Classification on Amazon reviews
A Lesson & implementation of Decision Tree Classifier in Numpy and Python
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