Pa2 session 3
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This document discusses connecting Python to AI models using HTTP requests. It covers converting Pandas dataframes to dictionaries, retrieving rows from datasets, and using the Requests module to send payload data to APIs. Examples demonstrate accessing a mood classification AI by copying code from its integration page to make predictions on sentences and calculate accuracy on a dataset.
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Intellectual property refers to creations of the. mind, inventions in artistic, literary, scientific and industrial field. It is important application. for Protection of invention of inventor and maintaining the quality as well as standard of. work of inventor.Intellectual property refers to creations of the. mind, inventions in artistic, literary, scientific and industrial field. It is important application. for Protection of invention of inventor and maintaining the quality as well as standard of. work of inventor.Intellectual property refers to creations of the. mind, inventions in artistic, literary, scientific and industrial field. It is important application. for Protection of invention of inventor and maintaining the quality as well as standard of. work of inventor.Intellectual property refers to creations of the. mind, inventions in artistic, literary, scientific and industrial field. It is important application. for Protection of invention of inventor and maintaining the quality as well as standard of. work of inventor.Intellectual property refers to creations of the. mind, inventions in artistic, literary, scientific and industrial field. It is important application. for Protection of invention of inventor and maintaining the quality as well as standard of. work of inventor.Intellectual property refers to creations of the. mind, inventions in artistic, literary, scientific and industrial field. It is important application. for Protection of invention of inventor and maintaining the quality as well as standard of. work of inventor.Intellectual property refers to creations of the. mind, inventions in artistic, literary, scientific and industrial field. It is important application. for Protection of invention of inventor and maintaining the quality as well as standard of. work of inventor.Intellectual property refers to creations of the. mind, inventions in artistic, literary, scientific and industrial field. It is important application. for Protection of invention of inventor and maintaining the quality as well as standard of. work of inventor.Intellectual property refers to creations of the. mind, inventions in artistic, literary, scientific and industrial field. It is important application. for Protection of invention of inventor and maintaining the quality as well as standard of. work of inventor.Intellectual property refers to creations of the. mind, inventions in artistic, literary, scientific and industrial field. It is important application. for Protection of invention of inventor and maintaining the quality as well as standard of. work of inventor.Intellectual property refers to creations of the. mind, inventions in artistic, literary, scientific and industrial field. It is important application. for Protection of invention of inventor and maintaining the quality as well as standard of. work of inventor.Intellectual property refers to creations of the. mind, inventions in artistic, literary, scientific and industrial field. It is
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Chapter 2
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Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
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Chapter 6
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Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
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- 3. Statistics of columns/features – how does it help? • Describes the range of different features • Understand the range of feature the machine learning algorithm trained with • Variation within features • Standard deviation indicates the variation of data within the range
- 4. Statistics of columns/features - visualize
- 6. Statistics of categorical values
- 7. Statistics of categorical values
- 8. Statistics of categorical values
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- 10. Pandas dataframe to dictionary conversion • Upload the mood dataset file to your repl program. Link
- 11. Pandas dataframe to dictionary conversion • Import a csv file and execute the code below
- 12. Pandas dataframe to dictionary conversion • What is the datatype of records_array variable?
- 13. Pandas dataframe to dictionary conversion • What is the datatype of each entry of the records_array variable?
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- 16. Requests module in python • This module helps you to send HTTP requests in python • How to use HTTP requests to interact with an AI?
- 17. Requests module in python • This module helps you to send HTTP requests in python • How to use HTTP requests to interact with an AI? url: The http url to send request to data: String containing the payload (features in case of AI)
- 18. Requests module in python • This module helps you to send HTTP requests in python • How to use HTTP requests to interact with an AI? Example: data: {“Sentence”: “I am sad”}
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- 20. Lets connect your AI with python • Login to your Navigator accounts and select the mood AI • Click on the integration tab in the monitor tab
- 21. Lets connect your AI with python • Login to your Navigator accounts and select the mood AI • Click on the integration tab in the monitor tab • Select python from the drop down for connectors
- 22. Lets connect your AI with python • Login to your Navigator accounts and select the mood AI • Click on the integration tab in the monitor tab • Select python from the drop down for connectors • Copy paste the code into your repl account
- 23. Lets connect your AI with python • Login to your Navigator accounts and select the mood AI • Click on the integration tab in the monitor tab • Select python from the drop down for connectors • Copy paste the code into your repl account
- 24. Lets connect your AI with python • Copy paste the code into your repl account • Note that the code is a function, you have to call this function to get the prediction json.dumps() converts dictionary to striing Convert the output you receive into readable format
- 25. Lets connect your AI with python • Login to your Navigator accounts and select the mood AI • Click on the integration tab in the monitor tab • Select python from the drop down for connectors • Copy paste the code into your repl account • Upload the mood dataset into repl
- 26. Exercise • Convert the mood dataset rows into dictionaries • Trigger the AI service • Calculate accuracy across all samples of the dataset • Calculate the confusion matrix