Predict sales for a given date and conditions
Predict cryptocurrency prices using historical data
Predict future stock prices based on historical data
Predict credit card approval using encrypted data
Analyze stock data with AI-generated insights
Predict stock prices based on input data
Predict house prices based on input details
Linear Thermal Transmittance Prediction
Optimize your bet sizes to grow a simulation account
Calculate FOB USD price based on inputs
Identify key support and resistance levels in stock prices
Visualize stock trading signals using moving averages and RSI
formatted json should be name,price,weight:0.0-1.0.
Streamlit Sales Prediction APP2 is a financial analysis tool designed to help businesses predict sales based on specific dates and conditions. Built using Streamlit, a powerful framework for building machine learning applications, this app provides an intuitive interface for users to input data, select models, and generate predictions. It is particularly useful for businesses looking to forecast revenue, plan inventory, and optimize resources effectively.
• Data Upload: Easily upload historical sales data in CSV format for analysis.
• Model Selection: Choose from multiple machine learning models optimized for sales prediction.
• Date and Condition Input: Specify the date and conditions for which you want to predict sales.
• Real-Time Prediction: Generate instant predictions based on the input data and selected model.
• Visualizations: View predictions alongside historical data for better context.
• Export Results: Download prediction results for further analysis or reporting.
• User-Friendly Interface: Navigate effortlessly through the app's clean and intuitive design.
streamlit run app.py
to launch the application.What file formats are supported for data upload?
The app supports CSV files. Ensure your data is formatted correctly before uploading.
Can I use my own machine learning model?
Yes, you can integrate custom models by modifying the app's codebase to include your model.
How accurate are the predictions?
Accuracy depends on the quality of your data and the selected model. Use historical data to validate and improve predictions.