CoinMarketCap (CMC) Trending | CMC, Coingecko, Dexscreener, Dextools Trending services

Identify the software project, create business case, arrive at a problem statement. REQUIREMENT: Window XP, Internet, MS Office, etc. Problem Description: - 1. Introduction of AI and Machine Learning: - Artificial Intelligence applies machine learning, deep learning and other techniques to solve actual problems. Artificial intelligence (AI) brings the genuine human-to-machine interaction. Simply, Machine Learning is the algorithm that give computers the ability to learn from data and then make decisions and predictions, AI refers to idea where machines can execute tasks smartly. It is a faster process in learning the risk factors, and profitable opportunities. They have a feature of learning from their mistakes and experiences. When Machine learning is combined with Artificial Intelligence, it can be a large field to gather an immense amount of information and then rectify the errors and learn from further experiences, developing in a smarter, faster and accuracy handling technique. The main difference between Machine Learning and Artificial Intelligence is , If it is written in python then it is probably machine learning, If it is written in power point then it is artificial intelligence. As there are many existing projects that are implemented using AI and Machine Learning , And one of the project i.e., Bitcoin Price Prediction :- Bitcoin (₿ ) (founder - Satoshi Nakamoto , Ledger start: 3 January 2009 ) is a digital currency, a type of electronic money. It is decentralized advanced cash without a national bank or single chairman that can be sent from client to client on the shared Bitcoin arrange without middle people's requirement. Machine learning models can likely give us the insight we need to learn about the future of Cryptocurrency. It will not tell us the future but it might tell us the general trend and direction to expect the prices to move. These machine learning models predict the future of Bitcoin by coding them out in Python. Machine learning and AI-assisted trading have attracted growing interest for the past few years. this approach is to test the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. the application of machine learning algorithms to the cryptocurrency market has been limited so far to the analysis of Bitcoin prices, using random forests , Bayesian neural network , long short-term memory neural network , and other algorithms. 2. Applications/Scope of AI and Machine Learning :- a) Sentiment Analysis :- It is the classification of subjective opinions or emotions (positive, negative, and neutral) within text data using natural language processing. b) It is Characterized as a use of computerized reasoning where accessible data is utilized through calculations to process or help the handling of factual information. BITCOIN PRICE PREDICTION USING AI AND MACHINE LEARNING: - The main aim of this is to find the actual Bitcoin price in US dollars can be predicted. The chance to make a model equipped for anticipating digital currencies fundamentally Bitcoin. # It works the prediction by taking the coinMarkup cap. # CoinMarketCap provides with historical data for Bitcoin price changes, keep a record of all the transactions by recording the amount of coins in circulation and the volume of coins traded in the last 24-hours. # Quandl is used to filter the dataset by using the MAT Lab properties. 3. Problem statement: - Some AI and Machine Learning problem statements are: - a) Data Privacy and Security: Once a company has dug up the data, privacy and security is eye-catching aspect that needs to be taken care of. b) Data Scarcity: The data is a very important aspect of AI, and labeled data is used to train machines to learn and make predictions. c) Data acquisition: In the process of machine learning, a large amount of data is used in the process of training and learning. d) High error susceptibility: In the process of artificial intelligence and machine learning, the high amount of data is used. Some problem statements of Bitcoin Price Prediction using AI and Machine Learning: - a) Experimental Phase Risk: It is less experimental than other counterparts. In addition, relative to traditional assets, its level can be assessed as high because this asset is not intended for conservative investors. b) Technology Risks: There is a technological risk to other cryptocurrencies in the form of the potential appearance of a more advanced cryptocurrency. Investors may simply not notice the moment when their virtual assets lose their real value. c) Price Variability: The variability of the value of cryptocurrency are the large volumes of exchange trading, the integration of Bitcoin with various companies, legislative initiatives of regulatory bodies and many other, sometimes disregarded phenomena. d) Consumer Protection: The property of the irreversibility of transactions in itself has little effect on the risks of investing in Bitcoin as an asset. e) Price Fluctuation Prediction: Since many investors care more about whether the sudden rise or fall is worth following. Bitcoin price often fluctuates by more than 10% (or even more than 30%) at some times. f) Lacks Government Regulation: Regulators in traditional financial markets are basically missing in the field of cryptocurrencies. For instance, fake news frequently affects the decisions of individual investors. g) It is difficult to use large interval data (e.g., day-level, and month-level data) . h) The change time of mining difficulties is much longer. Moreover, do not consider the news information since it is hard to determine the authenticity of a news or predict the occurrence of emergencies.

:chart_with_upwards_trend: Python module for getting cryptocurrency data from Coinmarketcap :chart_with_downwards_trend:

This public repository performs web scrapping on the worlds largest "cryptocurrency-trend-analyser" website, www.coinmarketcap.com.

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Get trending crypto in coinmarketcap

Explore cryptocurrency price trends with this Python script. Fetches data from CoinMarketCap API, analyzes trends, and visualizes insights using Pandas and seaborn. Stay informed about cryptocurrency market dynamics!

📊 CryptoBase is a lightweight cryptocurrency market dashboard that displays real-time data such as 🪙 prices, 💹market caps, 📈volume, and 🔍trends — similar to platforms like CoinMarketCap.

A real-time Bitcoin ( or any other crypto ) market monitoring tool using the CoinMarketCap API that logs price data, detects trading patterns, forecasts market trends, evaluates prediction accuracy, and visualizesmovement live.

This project is a Python-based automated cryptocurrency data analysis tool. It connects to the CoinMarketCap API to retrieve real-time data, performs continuous data collection, and provides dynamic visualizations of cryptocurrency price trends over different time intervals.

Time Series Forecasting: Predicting Bitcoin Price The cryptocurrency market has seen its rise and fall in the past few years. With a variety of coins being exchanged for real money, it is important to know the trend in the coin price. In this article, we will build a fairly simple LSTM network to predict or forecast the prices of Bitcoin. Obtaining Bitcoin Data There are plenty of open sources available on the internet to extract historical data of Bitcoin prices. The one that I have used below is from https://coinmarketcap.com/.

coinmarketcap.com trending bot for sale.

A bot to trend a Token on CoinMarketCap to get listed on most visited and trending list.

A web application that fetches and displays real-time cryptocurrency prices and trends using the CoinMarketCap API.

python code that take crypto gainers and losers of the day from coinmarketcap.com , study their trend and output it in excel file

CryptoPulse is a Python-powered, fully automated data pipeline designed to monitor, analyze, and visualize real-time cryptocurrency market trends using the CoinMarketCap REST API.

Backtesting framework for trend-following crypto strategies using Binance data and CoinMarketCap snapshots. Includes BTC-specific trend logic and cross-market risk models (S&P 500 breadth + credit spreads) to dynamically reduce exposure during risk-off regimes.

Scripts written to track trends in Crypto currency prices utilizing the CoinMarketCap API to pull data along with the Twilio messaging package to push text message alerts for price changes

A Python-based web automation project that scrapes real-time cryptocurrency prices and market data from CoinMarketCap using Selenium and stores the data in a CSV file for analysis and trend tracking.

This repository hosts a Telegram Bot that tracks real-time cryptocurrency volume and price changes using CoinMarketCap and MEXC API. It provides buy/sell volume updates, market trends, and timely alerts in Telegram.

Python-based tool that fetches, processes, and visualizes historical Fear and Greed Index data from the CoinMarketCap API. This project enables you to analyze investor sentiment trends over time and derive meaningful insights.

https://coinmarketcap.com/ Crypto Tracker is a modern web application built with Angular that allows users to explore real-time cryptocurrency prices, market stats, and personal portfolio features. It provides a clean interface, fast performance, and user-friendly tools for tracking crypto trends.

Project Description: This project aims to analyze and visualize the dynamic cryptocurrency market using real-time data from the CoinMarketCap API. By leveraging data-driven insights, we can gain valuable knowledge about the behavior of different cryptocurrencies, identify potential investment opportunities, and assess market trends.

Effortlessly fetch real-time cryptocurrency market data using CoinMarketCap API. This Python project leverages pandas and seaborn for data processing and visualization. Track price trends, analyze percent changes, and visualize insights with dynamic line and point charts. Keep your crypto analysis up-to-date with scheduled API calls.

The Crypto Price Tracker project automates the extraction of cryptocurrency pricing data from the CoinMarketCap API. This application retrieves the latest cryptocurrency listings, processes the data, and generates visualizations to analyze price trends over time. It serves as a practical tool for tracking cryptocurrency price changes and understand

This cryptocurrency tracking app connects to the CoinMarketCap API to fetch real-time data on various cryptocurrencies, including prices, market cap, and percentage changes. The data is processed and displayed in a user-friendly dashboard with metrics and visualizations, allowing users to monitor and analyze crypto trends efficiently.

Show trend from coinmarketcap

Scrape coinmarketcap.com and analyse trends

Data analysis tool for trending cryptocurrencies, utilizing CoinMarketCap API

Track cryptocurrency prices and trends using CoinMarketCap API