This project involves web scraping data from crypto.com, performing data cleaning and preprocessing, building a predictive model using neural networks, and creating a Power BI report for visualization. The goal is to predict cryptocurrency prices based on various features and provide insightful visualizations.
##Tasks Performed
Scraped data for around 21,000 cryptocurrencies from crypto.com using Scrapy. Extracted key features including: Name Price 24-hour Change 24-hour Volume Market Cap
Handled missing values by filling them with the mean values of their respective columns. Converted data types of relevant columns for consistency: 'price' to float 'change_24h' to float 'volume_24h' to float 'market_cap' to float Ensured data quality by verifying and correcting inconsistencies.
Split the dataset into training and testing sets. Standardized the features using StandardScaler to ensure uniformity. Built a neural network model using TensorFlow and Keras: The model architecture includes multiple Dense layers with ReLU activation and Dropout layers for regularization. Compiled the model using the Adam optimizer and mean squared error loss function. Trained the model on the training data and evaluated it on the test data.
Created a Power BI report to visualize the scraped and processed data. Included various visualizations to provide insights into the cryptocurrency market, such as: Price trends Market cap distribution 24-hour volume and change analysis Integrated interactive elements to allow users to explore the data dynamically.