Cardiovascular disease (CVD), including heart disease, is a major cause of mortality worldwide,remains the leading cause of death globally, accounting for approximately 31% of all deaths worldwide in 2019. Early detection and intervention are crucial in reducing the burden of disease and improving patient outcomes. Machine learning (ML) has the potential to aid clinicians in identifying patients at risk of heart disease through the analysis of clinical and laboratory data. Several studies have been conducted to develop ML algorithms for predicting the risk of heart disease, achieving high levels of accuracy. The use of ML models has the potential to improve patient outcomes by enabling **earlier detection and intervention**, but further research is necessary to validate these models and ensure their effectiveness in real-world clinical settings. In this Project, we are continuing Heart Disease - Classification (Machine Learning vol1.0) added 2 machine learning models that is Decision Tree and Random Forest.

Sign Language is a means of communication that is predominantly used by those who are deaf or hard of hearing. This sort of gesture-based language allows users to effortlessly transmit ideas and thoughts while overcoming hurdles created by hearing impairments. The great majority of the world’s population lacks understanding of the language, which is a serious difficulty with this simple mode of communication. Learning Sign Language, like learning any other language, takes time and effort, which discourages the general populace from studying it. However, in the world of Machine Learning and Image Detection, there is a clear solution to this problem. Using predictive model technology to automatically categorize Sign Language signals can be utilized to provide real-time captioning for virtual conferences such as Zoom meetings and other such things. This would considerably enhance access to such services for persons with hearing impairments since it would work in tandem with voice-based captioning, establishing an online two-way communication system for those with hearing challenges.

With about 345 million monthly active users, Spotify has grown to become the most popular and extensively used music streaming network today. It provided a selection of music, genres, and performers from across the world for listeners to enjoy and access. Through this study, we will analyze and attempt to cluster songs on Spotify based on their audio properties.

This study aims to investigate the time series analysis of product demand in the supply chain context. Specifically, it focuses on exploring the patterns and trends in product demand over time, identifying the factors that influence demand, and developing forecasting models to predict future demand. The study utilizes historical sales data and applies various statistical techniques, such as time series predictive models depand on data set, to analyze and predict demand patterns. The findings of this study can provide insights for supply chain managers to optimize inventory management, production planning, and distribution strategies.

The sinking of the Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for everyone onboard, resulting in the death of 1502 out of 2224 passengers and crew. While there was some element of luck involved in surviving, it seems some groups of people were more likely to survive than others. In this challenge, we ask you to build a predictive model that answers the question: “what sorts of people were more likely to survive?” using passenger data (ie age, gender, socio-economic class, etc).

This dataset is a record of 7 common different fish species in fish market sales. With this dataset, a predictive model can be performed using machine friendly data and estimate the weight of fish can be predicted.

Cardiovascular disease (CVD), including heart disease, is a major cause of mortality worldwide, remains the leading cause of death globally, accounting for approximately 31% of all deaths worldwide in 2019. Early detection and intervention are crucial in reducing the burden of disease and improving patient outcomes. Machine learning (ML) has the potential to aid clinicians in identifying patients at risk of heart disease through the analysis of clinical and laboratory data. Several studies have been conducted to develop ML algorithms for predicting the risk of heart disease, achieving high levels of accuracy. The use of ML models has the potential to improve patient outcomes by enabling earlier detection and intervention, but further research is necessary to validate these models and ensure their effectiveness in real-world clinical settings.

DataCo SMART SUPPLY CHAIN, one of the enormous logistic company moving in 3PL, help many companies to fulfill their demands all world as DataCo's Client. In this repository, have served in simple analytical statistic and visualization.

Gap Earning between Male and Female