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Dependencies needed. Use
pip install <dependency>
- Machine Learning Libraries: Numpy, Pandas, Sklearn
- Natural Language Process Libraries: NLTK
- SQLlite Database Libraries: SQLalchemy
- Web App and Data Visualization: Flask, Plotly
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Run the following commands in the project's root directory to set up your database and model.
- To run ETL pipeline that cleans data and stores in database
python data/process_data.py data/disaster_messages.csv data/disaster_categories.csv data/DisasterResponse.db
- To run ML pipeline that trains classifier and saves
python models/train_classifier.py data/DisasterResponse.db models/classifier.pkl
- To run ETL pipeline that cleans data and stores in database
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Run the following command in the app's directory to run your web app.
python run.py
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Go to http://0.0.0.0:3001/
This project is about analyzing message data for disaster response. The data gotten from Figure Eight is used to build a model that classifies disaster messages and web app where an respondent can input a new message and get classification results in several categories
disaster_response_pipeline
|-- app
|-- templates
|-- master.html # Main page of the web app
|-- go.html # Classification result page of the web app
|-- run.py # Script for the app
|-- data
|-- disaster_message.csv # Mesage Data
|-- disaster_categories.csv # categories Data
|-- DisasterResponse.db # Clean Data
|-- process_data.py # script for building an ETL pipeline and data cleaning
|-- models
|-- classifier.pkl # saved model -> Random forest
|-- train_classifier.py # script for building a ML pipeline (there is also AdaBoost model setup)
|-- README