In this ML project i have used Natural language processing (NLP) techniques and other data preprocessing techniques to feed my Machine Learning Algorithm a good data, and deploy it using flask.

Embark on a transformative "100 Days of Machine Learning" journey. This curated repository guides enthusiasts through a hands-on approach, covering fundamental ML concepts, algorithms, and applications. Each day, engage in theoretical insights, practical coding exercises, and real-world projects. Balance theory with hands-on experience.

Create the Decision Tree classifier and visualize it graphically. The purpose is if we feed any new data to this classifier, it would be able to predict the right class accordingly.

Forecasted Airbnb 'Super host' status in Chicago with an 84% accuracy using Logistic Regression and assessed potential returns on investment employing the Herfindahl Index for strategic investment insights

Natural LangWiz is a repository for exploring Natural Language Processing (NLP) techniques through Jupyter notebooks. It covers everything from text preprocessing and sentiment analysis to advanced transformer models. Dive in to see how we turn raw text into actionable insights with a touch of NLP wizardry!

Data analysis and machine learning project focused on assessing water quality using Decision Tree and Random Forest models

The T20 Totalitarian project aims to leverage machine learning to predict the total score of a team in a T20 World Cup cricket match. By utilizing the powerful XGBoost algorithm, we aim to provide accurate predictions that can help in strategizing and understanding match dynamics better.

This repository contains an in-depth analysis of historical weather data from Szeged, Hungary. The project uses Python to clean and process data, generate insightful visualizations, and identify patterns and correlations in weather parameters such as temperature, humidity, and precipitation.

This repository demonstrates big data processing, visualization, and machine learning using tools such as Hadoop, Spark, Kafka, and Python.

This is an exciting project that aims to predict cryptocurrency prices using artificial intelligence (AI) and machine learning (ML) techniques. The project uses historical data of various cryptocurrencies and applies different algorithms to predict their prices in the future.

A collection of machine learning and AI projects implemented in Jupyter notebooks, covering regression, classification, and neural networks

This project analyzes and preprocess the resumes data (consist of 2K+ instances) applying Natural Language Processing (NLP). It also involves the classification applying a variety of Machine Learning (ML) techniques.