AI Project cycle.pptx , Subtitle: "Predicting House Sale Prices”

"Unlocking the Power of AI: Understanding the Project Cycle through Predicting House Prices

Join us on a journey to demystify the AI project cycle, as we delve into the world of predictive modeling and explore the intricacies of forecasting house prices. From data collection to deployment, we'll navigate the entire cycle, highlighting key considerations and challenges along the way.

1. Title: " The Artificial Intelligence Project Cycle“ Subtitle : " Predicting House Sale Prices “ "Purpose: - Main goal is to understand the AI project cycle. This is the step-by - step process that developers follow to create and use AI models. But before diving into this topic, let's understand the background of AI."

2. What is AI? 1 Artificial Intelligence Artificial Intelligence (AI) is the technology that refers to development of computer system that can perform tasks that typically require human intelligence such as learning, problem-solving, and decision-making. 2 Recommendation Systems AI-powered recommendation systems, like the ones used by Netflix, analyze user data to suggest products, services, or content that users may find interesting or useful.

3. For instance , when you ask Siri "What's the weather like today?", AI algorithms work behind the scenes to : 1 . Recognize your voice and words 2 . Determine your location 3 . Fetch current weather data 4 . Respond with a clear and concise answer

4. "We can easily search for the weather on Google, so why use AI-powered virtual assistants like Siri or Alexa?"

5. Convenience+ Hands-free: Use voice commands while driving, cooking, or multitasking.+ Time-saving: Get quick answers without typing or searching. 2. Personalization + Virtual assistants : learn your preferences and habits over time.+ Tailored responses and recommendations based on your interests. 3.Contextual Understanding+ AI assistants : can understand natural language and context.+ Ask follow-up questions or clarify ambiguous requests. 4. Home Automation:+ Control : and integrate various smart devices with voice commands.+ Create custom routines and scenes for a more connected home. 5. Accessibility :+ Assistive technology for people with disabilities or language barriers.+ Enhanced user experience for seniors or those with mobility issues. While Google searches provide information, AI-powered virtual assistants offer a more personalized, interactive, and hands-free experience. They can simplify your daily life, making it easier to manage tasks, access information, and control your environment.

6. The Artificial Intelligence Project Cycle Artificial Intelligence (AI) has transformed the way we live and work, revolutionizing industries and redefining the boundaries of what's possible. At the heart of this technological revolution lies the AI project cycle, a systematic approach to developing AI-powered solutions that can tackle complex problems and unlock new opportunities. Let’s see Real-Life Example: Virtual Assistants Virtual assistants like Siri, Alexa, and Google Assistant are great examples of AI in action.

7. The AI project cycle is a structured approach to developing and implementing artificial intelligence solutions. It involves several stages that help ensure successful project outcomes. The AI project cycle has several stages. Let’s see - What is Ai Project cycle

8. Learning Through House Price Prediction The Challenge Predicting house prices is a complex task that requires analyzing a variety of factors, such as location, size, amenities, and market conditions. This challenge serves as an excellent case study for understanding the AI project cycle. The Approach By following the AI project cycle, we can develop a machine learning model that can accurately predict house prices based on historical data. This process involves defining the problem, collecting and preparing the data, training the model, and evaluating its performance. The Benefits Mastering the AI project cycle through a house price prediction task can provide valuable insights and skills that can be applied to a wide range of AI-powered solutions, from financial forecasting to customer segmentation and beyond. Example Overview: - "To make this easier to understand, we'll use a practical example: predicting house prices. Imagine we have data on 100 houses, including details like the number of rooms and the size of each house. We'll see how we can use this data to train an AI model to predict the selling prices of these houses."

9. Problem Scoping Definition Clearly Define the Objective The first step in the AI project cycle is to clearly define the objective of the project. Identify the problem you want to solve with AI. Understand the requirements and constraints. Example: Predicting house prices based on various factors like location, size, and age. Understand the Stakeholders Identifying the key stakeholders, such as homebuyers, real estate agents, and investors, and understanding their needs and pain points is essential for ensuring the project's relevance and impact. Identify the Constraints Recognizing the constraints and limitations of the project, such as data availability, computational resources, or regulatory requirements, can help guide the development of a realistic and feasible solution. Outline the Success Criteria Defining clear success criteria, such as a target accuracy level or a specific response time, allows the project team to measure the performance of the AI model and ensure it meets the desired objectives.

10. Data Collection(Acquisition and Exploration) Identify Data Sources The next step in the AI project cycle is to identify the relevant data sources that can provide the necessary information for training the house price prediction model. This may include real estate listings, property records, census data, and economic indicators. Gather and Curate Data Once the data sources have been identified, the project team must gather the data and curate it, ensuring that it is complete, accurate, and representative of the problem at hand. Explore and Understand the Data Analyzing the collected data, understanding its structure, and Gather relevant data that needed to solve the problem. Example: Collecting data on houses, including their size, location, number of rooms, age, and selling prices. "We have a dataset of 100 houses with information like the number of rooms and the size of each house. We use 90 of these houses' data to train the AI, and we use the remaining 10 houses' data to test how well the AI has learned.“

11. Data Preparation ( Development, Deployment) Data Cleaning The data collected may contain errors, missing values, or irrelevant information. Data cleaning involves identifying and addressing these issues to ensure the data is of high quality and suitable for model training. The data must be split into training and testing sets to evaluate the model's performance. The training set is used to train the model, while the testing set is used to assess its accuracy and generalization . Clean and preprocess the data to make it suitable for training the AI model. This involves handling missing values, normalizing data, and splitting the dataset . AI Model Development Definition: Developing an AI model is like teaching a computer how to solve a problem by showing it examples and letting it learn from those examples Another Example : Imagine you’re teaching a computer to recognize pictures of cats and dogs. You have many photos of each: - Training: You show the computer these photos, telling it which ones are cats and which are dogs. The computer looks at the pictures and starts to notice patterns (like cats usually have pointy ears and dogs have different types of ears). - Learning: The computer adjusts its settings based on what it sees, trying to get better at recognizing cats and dogs from the photos . Model Deployment : Putting the trained AI into a real-world application (e.g., a pet recognition app ).

12. Model Training and Evaluation Model Selection Choose the appropriate machine learning algorithm(s) for the problem at hand, such as decision trees, or neural networks . Example: Using historical house data to train a machine learning model to predict house prices. Hyperparameter Tuning Optimize the model's hyperparameters, such as learning rate or the number of hidden layers, to improve its performance. Model Training Train the selected model on the prepared dataset, monitoring the training process and making adjustments as needed. Model Evaluation Evaluate the trained model's performance on the testing dataset, using metrics such as R-squared, mean squared error, or root mean squared error.

13. Vocabulary