IE6400_HW7

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University Of Connecticut *

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6400

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Industrial Engineering

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Dec 6, 2023

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IE6400 Foundations Data Analytics Engineering Fall Semester 2023 Assignment 7 : Exploratory Data Analysis on Earthquake Dataset Dataset: Use the Earthquake dataset available at the following URL: Earthquake Dataset Objective: Perform Exploratory Data Analysis to uncover insights from the Earthquake dataset. Utilize Python and its libraries to analyze and visualize the data. Questions: Question 1: Load the Dataset Load the Earthquake dataset into a pandas DataFrame. Display the first five rows of the DataFrame and examine the general structure. Question 2: Summary and Info Provide a summary of the dataset. What are the data types of the different columns in the dataset? Question 3: Handling Missing Values Are there any missing values in the dataset? If yes, handle the missing values using an appropriate strategy. Question 4: Time Analysis Convert the time column to a datetime object and extract the year, month, and day. Analyze the distribution of earthquakes over years and months. Are there any patterns? Question 5: Magnitude Analysis Analyze the distribution of earthquake magnitudes. Are there any patterns or outliers? Visualize the distribution using an appropriate plot. Question 6: Depth Analysis Analyze the distribution of earthquake depths. Visualize the distribution and identify if there are any patterns or outliers. Question 7: Location Analysis Which locations are most prone to earthquakes? Visualize the top 10 locations with the highest number of earthquakes. Question 8: Correlation Analysis Is there any correlation between the magnitude and depth of earthquakes? Use a scatter plot and correlation coefficient to analyze the relationship.
Question 9: Advanced Visualization Create a heatmap or a geographical plot to visualize earthquakes' locations and magnitudes. Question 10: Insights and Observations Summarize the insights and observations gathered from the analysis. What are the key takeaways from the earthquake dataset? Note: Make sure to use appropriate visualizations to support your analysis and insights. Comment on your code to explain the logic and your observations. Ensure your code is clean and organized. Submission: Submit your Python notebook with the analysis, visualizations, and insights. Ensure all the code cells are executed and the outputs are visible.
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