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LSTM-Donors-Choose

DonorsChoose.org has funded over 1.1 million classroom requests through the support of 3 million donors, the majority of whom were making their first-ever donation to a public school. If DonorsChoose.org can motivate even a fraction of those donors to make another donation, that could have a huge impact on the number of classroom requests fulfilled.

Data Source: https://www.kaggle.com/donorschoose/io

Attribute Information:

  • project_id: A unique identifier for the proposed project.
  • project_titlei: Title of the project.
  • project_grade_category: Grade level of students for which the project is targeted.
  • project_subject_categories: One or more (comma-separated) subject categories for the project.
  • school_state: State where school is located (Two-letter U.S. postal code)
  • project_subject_subcategories: One or more (comma-separated) subject subcategories for the project.
  • project_resource_summary: An explanation of the resources needed for the project.
  • project_essay_1/2/3/4: Application essay.
  • teacher_id: A unique identifier for the teacher of the proposed project.
  • teacher_prefix: nan, Dr./Mr./Mrs./Ms./Teacher
  • teacher_number_of_previously_posted_projects: Number of project applications previously submitted by the same teacher.
  • project_is_approved: A binary flag indicating whether DonorsChoose approved the project.A value of 0 indicates the project was not approved, and a value of 1 indicates the project was approved.

Objective:

The goal is to predict whether or not a DonorsChoose.org project proposal submitted by a teacher will be approved, using the text of project descriptions as well as additional metadata about the project, teacher, and school.

Prerequisites

You need to have installed following softwares and libraries before running this project.

  1. Python 3: https://www.python.org/downloads/
  2. Anaconda: It will install ipython notebook and most of the libraries which are needed like sklearn, pandas, seaborn, matplotlib, numpy and scipy: https://www.anaconda.com/download/

Getting Started

Start by downloading the project and run "LSTM_DC_Models.ipynb" file in ipython-notebook.

Libraries

  • Gensim: Gensim is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.

    • pip install gensim
    • conda install -c conda-forge gensim

  • Keras: Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.

    • pip install Keras
    • conda install -c conda-forge keras

  • scikit-learn: scikit-learn is a Python module for machine learning built on top of SciPy.

    • pip install scikit-learn
    • conda install -c anaconda scikit-learn

  • nltk: The Natural Language Toolkit (NLTK) is a Python package for natural language processing.

    • pip install nltk
    • conda install -c anaconda nltk

Authors

• Manish Vishwakarma - Complete work