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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.