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.

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.

tracebloc data pipeline for training/test dataset setup

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.

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!

A project on visualizing and analyzing sales, revenue, and profit data across different markets from 2017 to 2020(India).

This is a simple ANN deep learning classification technique

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.

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

A simplification and extension of the Data Prep Kit project

Improving credit risk model using Machine learning techniques. We use a host of ml models and neural network to solve the issue.

Data Science Projects

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.

This project analyzes sales, profit, and quantity trends across different product categories in a superstore dataset (2011-2014) using Python, Pandas, Matplotlib, and Seaborn. It provides data-driven insights through visualizations to optimize business strategies.

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 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.

the kdd 99 anomaly detection application is a flask web app that predicts anomalies in the kdd 99 dataset using a decision tree classifier. it allows users to input features for prediction and offers a user-friendly interface with real-time predictions and low latency.

This project focuses on predicting the survival status of patients with cirrhosis using machine learning techniques.

The Power BI dashboard contains detailed information about each delivery (ball) bowled in an IPL match between RCB and Delhi Capitals (2024).The columns in the dataset provide information about various aspects of the delivery, the players involved, and the results of that delivery.