Page 448 - AI Ver 3.0 Class 11
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#Experiential Learning

                    1.  Create a ‘Thing Translator’ of your own. Develop different classes using Google’s Teachable Machine to upload
                      images of various items such as clocks, bottles, cell phones, spectacles, fans, etc. Train your model and conduct
                      testing.
                    2.  Develop a chatbot on a platform of your choice (Python, Google Dialogflow, botsify.com) to provide advice to
                      students on handling mental health issues like stress and anxiety. Relevant data can be sourced from the
                      following links:

                      https://www.indiatoday.in/education-today/featurephilia/story/two-biggest-mental-health-
                      problems-in-students-and-how-to-deal-with-them-anxiety-depression-2283562-2022-10-10

                        https://observervoice.com/the-silent-struggle-understanding-mental-health-challenges-
                      among-indian-students-17713/
                    3.  Develop  a  mood-detecting  AI model. Follow the  instructions  provided  in the  video  on  the
                      website https://corp.aiclub.world/build-mood-ai. Then, proceed to https://aiclub.world/try-navigator
                      to develop and test your AI model. Identify the type of Machine Learning used.

                    4.  Explore MIT App Inventor with AI at  https://appinventor.mit.edu/explore/ai-with-mit-app-inventor.
                      Explore various AI-related projects such as Fake Voices: The Ethics of Deepfakes and Introduction
                      to Machine Learning: Image Classification. Choose a project to experiment with and consider
                      adding enhancements.

                    5.  Consider the following scenario:
                       MTechnicals Ltd. is a major insurance company offering a range of products. They have substantial data on their
                      current customer base and wish to launch a marketing campaign to boost sales of their life insurance product.
                      They have extracted a sample of historical data from their customer database and tasked you with building a
                      model to predict which customers are likely to purchase the life insurance product. The dataset includes:

                       Purchased, Gender, Highest level of education, House value, Age, If the purchase was made online, Marital
                      status, If the customer has any children, Occupation of the purchaser, Mortgage bracket of the customer, If the
                      customer is a homeowner, Region of the customer, A grading of family income.

                       The target variable ‘Purchased’ indicates whether the customer has purchased life insurance. Your task is to do
                      the following using Python

                         •  Clean the dataset as there are missing values.
                         •  Split the dataset into a training and test set.
                         •  Select any two supervised machine learning algorithms to train the models.
                         •  Train the models to predict whether a customer will buy the product.
                         •  Evaluate and compare the predictive accuracy of the models using appropriate performance metrics.

                       Download the dataset through the following link:
                      https://drive.google.com/file/d/1jvEsZX1QjMPJRiR2fegfqTVKPgwe0wuo/view?usp=sharing




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