Federal grants and opportunities Analysis

Federal Grants And Funding Opportunities Analysis/Dataset/README.md

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+# Federal Grants and Funding Opportunities Dataset
+
+## Overview
+This dataset provides comprehensive information on federal grants and funding opportunities spanning the years 2004 to 2024. With a total of 75,640 opportunities, it serves as a valuable resource for understanding the dynamics of federal funding movements.
+
+## Features
+The dataset includes the following key features:
+
+- `opportunity_id`: Primary key of the opportunity.
+- `opportunity_title`: The opportunity title.
+- `opportunity_number`: The opportunity number.
+- `opportunity_category`: The opportunity category.
+- `funding_instrument_type`: The funding instrument type.
+- `category_of_funding_activity`: The category of funding activity.
+- `cfda_numbers`: The Catalog of Federal Domestic Assistance number.
+- `eligible_applicants`: The eligible applicants.
+- `eligible_applicants_type`: The eligible applicants type.
+- `agency_code`: The agency code.
+- `agency_name`: The agency name (grant provider).
+- `post_date`: The post date of the opportunity.
+- `close_date`: The close date of the opportunity.
+- `last_updated_date`: The last updated date.
+- `archive_date`: The archive date of the opportunity.
+- `award_ceiling`: The maximum amount of grant per award.
+- `award_floor`: The minimum amount of grant per award.
+- `estimated_total_program_funding`: The total grant amount of the program.
+- `expected_number_of_awards`: The number of awards who can get the grant.
+- `cost_sharing_or_matching_requirement`: Defines if a portion of the project’s cost is not paid by federal funds.
+- `additional_information_url`: The additional information.
+
+## Usage
+Explore and analyze federal grants and funding opportunities using this dataset. It can be used for various tasks, including:
+
+- Exploratory Data Analysis (EDA)
+- Machine learning model development and training
+- Predictive analytics
+- Trend analysis and visualization
+
+## Acknowledgements
+This dataset is available on Kaggle and was provided by the user [webdevbadger](https://www.kaggle.com/webdevbadger). 
+
+## License
+The dataset is provided under the terms of the license specified by Kaggle and the dataset owner.
+
+

Federal Grants And Funding Opportunities Analysis/Model/README.md

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+# Federal Grants and Funding Analysis
+
+## Overview
+This comprehensive project involves the collection, analysis, and prediction of federal grants and funding opportunities from 2004 to 2024. The project covers a wide range of tasks, including data exploration, machine learning model development, and extensive visualization. The machine learning models implemented in the project have reported accuracies ranging from 71% to 93% for various tasks.
+
+## Contents
+1. [Dataset](#dataset)
+2. [Features](#features)
+3. [Use Case and Analysis Reference](#use-case-and-analysis-reference)
+4. [Feature Description](#feature-description)
+5. [Exploratory Data Analysis (EDA)](#exploratory-data-analysis-eda)
+6. [Machine Learning Models](#machine-learning-models)
+7. [Model Evaluation](#model-evaluation)
+8. [Visualization](#visualization)
+
+## Dataset
+The dataset includes a collection of federal grants and funding opportunities from 2004 to 2024, containing a total of 75,640 opportunities.
+
+## Features
+- `opportunity_id`: Primary key of the opportunity.
+- `opportunity_title`: The opportunity title.
+- `opportunity_number`: The opportunity number.
+- `opportunity_category`: The opportunity category.
+- `funding_instrument_type`: The funding instrument type.
+- `category_of_funding_activity`: The category of funding activity.
+- `cfda_numbers`: The Catalog of Federal Domestic Assistance number.
+- `eligible_applicants`: The eligible applicants.
+- `eligible_applicants_type`: The eligible applicants type.
+- `agency_code`: The agency code.
+- `agency_name`: The agency name (grant provider).
+- `post_date`: The post date of the opportunity.
+- `close_date`: The close date of the opportunity.
+- `last_updated_date`: The last updated date.
+- `archive_date`: The archive date of the opportunity.
+- `award_ceiling`: The maximum amount of grant per award.
+- `award_floor`: The minimum amount of grant per award.
+- `estimated_total_program_funding`: The total grant amount of the program.
+- `expected_number_of_awards`: The number of awards who can get the grant.
+- `cost_sharing_or_matching_requirement`: Defines if a portion of the project’s cost is not paid by federal funds.
+- `additional_information_url`: The additional information.
+
+## Use Case and Analysis Reference
+Refer to the notebook "Federal Grants and Funding Analysis" for a use case and analysis reference.
+
+## Feature Description
+For detailed descriptions of each feature, refer to the data dictionary provided.
+
+## Exploratory Data Analysis (EDA)
+The project involves an extensive Exploratory Data Analysis (EDA) to understand the dynamics of federal grants and funding opportunities. The analysis includes the use of various plots, heatmaps, and statistical measures.
+
+## Machine Learning Models
+The project includes the development and evaluation of the following machine learning models:
+- Linear Regression (75%)
+- Logistic Regression (Accuracy 76%)
+- Decision Tree (Accuracy: 75%)
+- Random Forest (Accuracy: 76%)
+- Support Vector Machine (SVM) (Accuracy: 77%)
+- Gradient Boosting (Accuracy: 78%)
+- XGBoost (Accuracy: 79%)
+- K-Nearest Neighbors (KNN) (Accuracy: 77%)
+
+Each model is trained and evaluated for specific tasks related to federal grants and funding analysis.
+
+![Graph for Accuracies](https://github.com/adi271001/ML-Crate/blob/Federal-Grants/Federal%20Grants%20And%20Funding%20Opportunities%20Analysis/Images/__results___45_0.png)
+
+## Visualization
+Visualization plays a crucial role in understanding the trends and patterns in the data. The project includes the creation of various plots, including violin plots, heatmaps, and network analysis plots.
+
+1. Correlation : ![correlation map](https://github.com/adi271001/ML-Crate/blob/Federal-Grants/Federal%20Grants%20And%20Funding%20Opportunities%20Analysis/Images/__results___10_0.png)
+2. Word Cloud : ![word cloud](https://github.com/adi271001/ML-Crate/blob/Federal-Grants/Federal%20Grants%20And%20Funding%20Opportunities%20Analysis/Images/__results___12_0.png)
+3. Missing Data : ![Missing Data](https://github.com/adi271001/ML-Crate/blob/Federal-Grants/Federal%20Grants%20And%20Funding%20Opportunities%20Analysis/Images/__results___8_1.png)
+
+## Conclusion
+In conclusion, the Federal Grants and Funding Analysis project has provided valuable insights into the dynamics of federal grants and funding opportunities spanning from 2004 to 2024. Through a combination of exploratory data analysis (EDA), machine learning modeling, and extensive visualization, we have gained a comprehensive understanding of various aspects related to federal funding movements.
+
+## Key Findings:
+# Dataset Overview: 
+The dataset, comprising 75,640 opportunities, proved to be a rich source of information, offering a detailed view of federal funding programs.
+
+# Exploratory Data Analysis (EDA): 
+EDA revealed patterns, trends, and relationships within the data, aiding in the identification of factors influencing the distribution of grants and funding.
+
+# Machine Learning Models: 
+Implemented machine learning models, including Decision Trees, Random Forest, Support Vector Machine (SVM), and XGBoost, demonstrated promising predictive capabilities with accuracies ranging from 71% to 93%.
+
+# Visualization: 
+Various visualizations, including heatmaps, violin plots, and network analyses, enhanced our ability to communicate complex patterns and relationships within the data.
+
+# Future Directions:
+Refinement of Models: 
+Fine-tune machine learning models to improve predictive performance and explore ensemble methods for enhanced accuracy.
+
+# Feature Engineering: 
+Investigate additional features or perform feature engineering to uncover latent patterns and boost model interpretability.
+
+# Dynamic Analysis:
+Extend the analysis to include temporal trends and dynamic shifts in federal funding priorities over the years.
+
+# External Data Integration: 
+Consider integrating external datasets to provide a more comprehensive understanding of the factors influencing federal funding decisions.
+
+# 0User-Friendly Tools: 
+Develop user-friendly tools or dashboards to enable stakeholders to interactively explore and interpret federal funding insights.
+
+# Acknowledgments:
+I extend my gratitude to Kaggle and the dataset provider webdevbadger for making this dataset available for analysis and also Project Maintainer and JWOC Team for the opportunity.
+
+This project serves as a foundation for ongoing research in the realm of federal grants and funding dynamics, with the potential to contribute to informed decision-making in various sectors.
+

Federal Grants And Funding Opportunities Analysis/README.md

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+# Federal Grants and Funding Analysis
+
+## Overview
+This project centers around the analysis and prediction of federal grants and funding opportunities spanning from 2004 to 2024. With a dataset comprising 75,640 opportunities, the project aims to provide insights into various aspects of federal funding dynamics.
+
+## Key Features
+- Diverse dataset including information such as opportunity details, funding types, applicant eligibility, and more.
+- Exploration through Exploratory Data Analysis (EDA) techniques.
+- Implementation and evaluation of machine learning models for predictive analytics.
+- Visualization of trends and patterns using various plots and heatmaps.
+
+## Machine Learning Models
+The project includes the development and evaluation of machine learning models, with reported accuracies ranging from 71% to 93%. Models include:
+- Linear Regression
+- Logistic Regression
+- Decision Tree
+- Random Forest
+- Support Vector Machine (SVM)
+- Gradient Boosting
+- XGBoost
+- K-Nearest Neighbors (KNN)
+
+## Usage
+To explore the project:
+1. Refer to the provided dataset for comprehensive federal grants information.
+2. Explore the "Federal Grants and Funding Analysis" notebook for use case scenarios and analyses.
+3. Use machine learning models for predictive tasks, following model-specific instructions.
+4. Visualize trends and patterns using various plots and heatmaps.

Federal Grants And Funding Opportunities Analysis/requirements.txt

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+matplotlib==3.4.3
+numpy==1.21.1
+pandas==1.3.1
+missingno==0.5.0
+scipy==1.7.0
+seaborn==0.11.2
+plotly==5.3.1
+wordcloud==1.8.1
+scikit-learn==0.24.2
+xgboost==1.4.2
+networkx==2.6.2
+tensorflow==2.6.0