From the course: Complete Guide to Generative AI for Data Analysis and Data Science
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Challenge: Simulating forest fires
From the course: Complete Guide to Generative AI for Data Analysis and Data Science
Challenge: Simulating forest fires
- [Narrator] Let's work on a challenge related to simulations. In this challenge, we're going to create an agent-based model for forest fires. Now it's relatively simple. We're going to use a 1,000 by 1,000 grid. Each grid will have one tree. Now trees can be in one of three different states. They can be unburnt, burning, or burned. A burning tree becomes burned after one time unit. A burning tree sets its neighboring unburned trees on fire. Now we also want to run this simulation for at most 200 time units. And then after doing that initial simulation, I want you to add a rule to allow a burning tree to cause another tree up to three cells away to start burning with a particular probability. Let's start with 0.3 as our probability.
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Contents
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Distributions of data7m 27s
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Visualizing a normal distribution in a spreadsheet3m 29s
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Jupyter Notebook and Colab3m 51s
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Generating a normal distribution6m 23s
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Visualizing a normal distribution in Python4m 56s
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Visualizing a uniform distribution in Python3m
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Visualizing a bimodal distribution in Python5m 54s
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Challenge: Distributions of data40s
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Solution: Distribution of data4m 7s
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Inferential statistics4m 25s
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Hypothesis testing methodology4m 17s
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Analyzing customer preferences11m 20s
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Type I and type II errors1m 30s
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ANOVA tests for comparing means1m 55s
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Generating Python scripts for ANOVA3m 45s
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Testing independence of categorical variables1m 53s
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Generating Python Scripts for Chi-squared tests3m 33s
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Correlation analysis7m 12s
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Testing for normality2m 25s
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Generating Python for testing normality3m 46s
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Generating Python for correlation analysis2m 12s
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Challenge: Making inferences from data24s
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Solution: Making inferences from data3m 17s
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Linear regression7m 44s
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Evaluating linear regression models2m 37s
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Visualizing sales data1m 56s
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Building a linear regression model4m 16s
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Evaluating a sales linear regression model2m 46s
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Challenge: Building a regression model48s
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Solution: Building a regression model4m 32s
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Data files4m 9s
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Using spreadsheets with CSV files2m 43s
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Reviewing an example JSON file4m 29s
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Using jq with JSON files6m 23s
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Generating jq commands using AI6m 1s
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Dataframes in Python8m 20s
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Loading CSV data into dataframes3m 44s
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Loading JSON into dataframes6m 17s
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Inspecting dataframes4m 12s
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Data quality and data cleansing6m 28s
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Using AI for data quality and data cleansing5m 6s
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Challenge: Missing data35s
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Solution: Missing data4m
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Relational databases15m 15s
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NoSQL databases10m 21s
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Extraction, transformation, and loading data into databases5m 46s
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Introduction to SQL5m 45s
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Creating tables and inserting data8m 2s
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Querying data with SQL10m 28s
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Joining data with SQL6m 57s
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Descriptiive statistics in SQL4m 55s
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Generating synthetic data sets for a relational database7m 12s
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Generating a star schema, synthetic data, and queries3m 41s
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Challenge: Generate a relational data model1m 12s
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Solution: Generate a relational data model4m 32s
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Simple classification model8m 34s
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Handling missing data5m
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Comparing multiple algorithms6m 43s
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Classification with neural networks14m 22s
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Hyperparameter tuning6m 32s
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Evaluating feature importance2m 24s
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Challenge: Predicting consumer intent41s
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Solution: Predicting consumer intent7m 26s
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Introduction to graph theory5m 54s
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NetworkX4m 27s
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Analyzing a social network7m 15s
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Supply chains and network analysis3m 20s
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Generating a synthetic supply chain4m 5s
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Visualizing a complex supply chain3m 37s
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Finding highest betweenness scores4m 36s
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Advanced topics in supply chain analysis6m 26s
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Challenge: Analyzing a social network19s
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Solution: Analyzing a social network2m 35s
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Introduction to simulations2m 42s
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Types of simulations10m 3s
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Modeling inventory management7m 13s
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Agent-based modeling9m 43s
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Modeling the spread of infectious diseases4m 29s
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Agent-base infectious diseases modeling5m 21s
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Challenge: Simulating forest fires55s
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Solution: Simulating forest fires5m 49s
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