Sales prediction in Python using ARIMA (AutoRegressive Integrated Moving Average) involves time series analysis to forecast future sales based on historical data. By fitting an ARIMA model, you can capture trends and seasonality in the data, helping businesses make informed decisions and optimize inventory management.
Movie recommendation using linear regression involves building a model to predict user preferences for films based on features like genre, director, and actor popularity. By analyzing historical user ratings and film characteristics, linear regression helps suggest personalized movie recommendations, enhancing the user experience on streaming platforms.
Sales prediction in Python using ARIMA (AutoRegressive Integrated Moving Average) involves time series analysis to forecast future sales based on historical data. By fitting an ARIMA model, you can capture trends and seasonality in the data, helping businesses make informed decisions and optimize inventory management.
Movie recommendation using linear regression involves building a model to predict user preferences for films based on features like genre, director, and actor popularity. By analyzing historical user ratings and film characteristics, linear regression helps suggest personalized movie recommendations, enhancing the user experience on streaming platforms.