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Machine Learning · Major Project

Earthquake Magnitude Prediction

Regression on the USGS catalogue, with honest limits stated up front

2-3 weeks build

Overview

Pulls historical events from the public USGS earthquake catalogue and models event magnitude from location, depth and recent regional seismic activity. The report states plainly that predicting when and where an earthquake will strike remains unsolved; this kit studies statistical patterns in past events, including the Gutenberg-Richter relationship, and maps them.

What makes it stand up

  • 01Catalogue data fetched from the official USGS API
  • 02Rolling regional activity features built from past events
  • 03Gutenberg-Richter analysis of magnitude frequency
  • 04States clearly that true earthquake prediction is an unsolved problem
  • 05Interactive Folium map of events, residuals and hotspots

Module breakdown

  1. 01

    Data fetch

    Queries the USGS catalogue by region and date range.

  2. 02

    Features

    Depth, location and rolling regional activity windows.

  3. 03

    Models

    Random forest and a small neural network regressor.

  4. 04

    Analysis

    Error by region and magnitude-frequency plots.

  5. 05

    Map

    Streamlit app with a Folium seismic map.

After this, you will be able to

  • Explain why earthquake forecasting is still an open problem
  • Defend what your model can and cannot claim
  • Demo the map and discuss patterns in the catalogue

Technology stack

  • Python
  • Pandas
  • scikit-learn
  • TensorFlow
  • Folium
  • Streamlit

Complete kit

₹6,299₹10,699
-41%
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Delivered in under 48 hours

Level
Major Project
Domain
Machine Learning
Build time
2-3 weeks
Difficulty

You receive

  • Complete, commented source code
  • 60-90 page project report (IEEE format)
  • Editable presentation deck
  • Architecture, ER and UML diagrams
  • Local setup and deployment guide
  • Sample dataset or seed data
  • Viva question bank with answers
  • One 45-minute walkthrough call
  • 30 days of doubt-clearing support
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