1. Introduction
2. File Descriptions
3. Installation
4. Screenshots

This Project is in collaboration with Figure Eight. The data contains pre-labelled tweet and messages from real-life disaster. The aim of the project is to build a Natural Language Processing tool that categorise messages.

The project contains the following parts: 


1. ETL Pipeline: process_data.py: reads in the data, cleans and stores it in a SQL database. The script merges the messages and categories datasets, splits the categories column into separate, clearly named columns, converts values to binary, and drops duplicates.
2. Dataset: disaster_categories.csv and disaster_messages.csv
3. DisasterResponse.db: created database from transformed and cleaned data.
4. ML Model: train_classifier.py: includes the code necessary to load data, transform it using natural language processing, run a machine learning model using GridSearchCV, RandomForest and train it.
5. Web App: run.py: Flask app and the user interface used to predict results and display them.

--> open folder in VS-Code

--> cd app

--> python run.py

--> Go to http://0.0.0.0:3001/

Dependencies

• Python 3.5+ (I used Python 3.7)
• Machine Learning Libraries: NumPy, SciPy, Pandas, Sciki-Learn
• Natural Language Process Libraries: NLTK
• SQLlite Database Libraqries: SQLalchemy
• Web App and Data Visualization: Flask, Plotly

Installing Clone this GIT repository: git clone https://github.com/singh728om/Twitter-Disaster-response

Executing Program:

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/classifier.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/


Below are a few screenshots of the web app.

After clicking Classify Message, you can see the categories which the message belongs to highlighted in green