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Federal-Grants-and-Funding-Opportunities-Analysis/README.md
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# Federal Grants and Funding Analysis | ||
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## 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. | ||
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## 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. | ||
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## 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) | ||
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## 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. |