Disaster Response Pipeline Project

Instructions:

1. 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/model.pkl

2. Run the following command in the app's directory to run your web app. python run.py

3. Go to http://0.0.0.0:3001/

Files:

1. process_data.py

   • Load the CSV files.
   • Merge the messages & categories.
   • Process the categories to a format which is better suitable for processing.
   • Clean the data ( Remove Duplicates ).
   • Save the DataFrame into SQLite db.

2. train_classifier.py

   • Load and split the data from the SQLite DB into test and train sets.
   • The script uses a custom tokenize function using nltk to case normalize, lemmatize, and tokenize text. This function is used in the machine learning pipeline to vectorize and then apply TF-IDF to the text.
   • Use GridSearch to find the best parameters of a RandomForestClassifier.
   • Use the best parameters found above to train the model.
   • Measure & display the performance of the trained model on the test set.
   • Save the model as a Pickle file.