DonorsChoose.org receives hundreds of thousands of project proposals each year for classroom projects in need of funding. Right now, a large number of volunteers is needed to manually screen each submission before it's approved to be posted on the DonorsChoose.org website.Next year, DonorsChoose.org expects to receive close to 500,000 project proposals. As a result, there are three main problems they need to solve: How to scale current manual processes and resources to screen 500,000 projects so that they can be posted as quickly and as efficiently as possible. How to increase the consistency of project vetting across different volunteers to improve the experience for teachers. How to focus volunteer time on the applications that need the most assistance.

The goal of the project 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. DonorsChoose.org can then use this information to identify projects most likely to need further review before approval.

The train.csv data set provided by DonorsChoose contains the following features: Feature Description project_id A unique identifier for the proposed project. Example: p036502 project_title Title of the project. Examples:

Art Will Make You Happy!
First Grade Fun

project_grade_category Grade level of students for which the project is targeted. One of the following enumerated values:

Grades PreK-2
Grades 3-5
Grades 6-8
Grades 9-12

project_subject_categories One or more (comma-separated) subject categories for the project from the following enumerated list of values:

Applied Learning
Care & Hunger
Health & Sports
History & Civics
Literacy & Language
Math & Science
Music & The Arts
Special Needs
Warmth

Examples:

Music & The Arts
Literacy & Language, Math & Science

school_state State where school is located (Two-letter U.S. postal code). Example: WY project_subject_subcategories One or more (comma-separated) subject subcategories for the project. Examples:

Literacy
Literature & Writing, Social Sciences

project_resource_summary An explanation of the resources needed for the project. Example:

My students need hands on literacy materials to manage sensory needs!

project_essay_1 First application essay* project_essay_2 Second application essay* project_essay_3 Third application essay* project_essay_4 Fourth application essay* project_submitted_datetime Datetime when project application was submitted. Example: 2016-04-28 12:43:56.245 teacher_id A unique identifier for the teacher of the proposed project. Example: bdf8baa8fedef6bfeec7ae4ff1c15c56 teacher_prefix Teacher's title. One of the following enumerated values:

nan
Dr.
Mr.
Mrs.
Ms.
Teacher.

teacher_number_of_previously_posted_projects Number of project applications previously submitted by the same teacher. Example: 2

• See the section Notes on the Essay Data for more details about these features.

Additionally, the resources.csv data set provides more data about the resources required for each project. Each line in this file represents a resource required by a project: Feature Description id A project_id value from the train.csv file. Example: p036502 description Desciption of the resource. Example: Tenor Saxophone Reeds, Box of 25 quantity Quantity of the resource required. Example: 3 price Price of the resource required. Example: 9.95

Note: Many projects require multiple resources. The id value corresponds to a project_id in train.csv, so you use it as a key to retrieve all resources needed for a project:

The data set contains the following label (the value you will attempt to predict): Label Description 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. Notes on the Essay Data

Prior to May 17, 2016, the prompts for the essays were as follows:
__project_essay_1:__ "Introduce us to your classroom"
__project_essay_2:__ "Tell us more about your students"
__project_essay_3:__ "Describe how your students will use the materials you're requesting"
__project_essay_3:__ "Close by sharing why your project will make a difference"

Starting on May 17, 2016, the number of essays was reduced from 4 to 2, and the prompts for the first 2 essays were changed to the following:
__project_essay_1:__ "Describe your students: What makes your students special? Specific details about their background, your neighborhood, and your school are all helpful."
__project_essay_2:__ "About your project: How will these materials make a difference in your students' learning and improve their school lives?"

For all projects with project_submitted_datetime of 2016-05-17 and later, the values of project_essay_3 and project_essay_4 will be NaN. 

I have Created this Project on different-different Machine Learning Algorithms such as K-NN, Naive Bayes, Logistics Regression, SVM(Support Vector Machine) ,RF(Random Forest),XGBOOST,KMeans,Agglomerative,DBSCAN with hyperparameter Tunning(using Grid Search CV or Random Search CV).For text I used BOW,TFIDF,Word2Vector,TFIDF-weightage-word2vector.Some NLP concepts such as Glove,Spacy,Regx. Here I use KPI as AUC Score.

1. Applied AI Course
2. https://www.donorschoose.org/