Recession Analysis and Prediction Project 📉🔮

Table of Contents

• Introduction
• Current Economic Data
• Future Scope
• Vision
• Deployed Link
• Video Demo
• Model Accuracy
• Requirements
• Installations
• Usage
• License

Introduction 🌟

This project aims to analyze historical economic data to predict recessions using machine learning techniques. By leveraging various algorithms and statistical methods, we've developed a model that can forecast economic downturns with reasonable accuracy. Additionally, we've deployed this model for easy access and visualization.

Current Economic Data

Current Ecocnomic Data of India , Q1 2024

Indicator	Value (Latest)
GDP	$4.112 trillion
Unemployment Rate	3.1%
Inflation Rate	5.1%
GDP Growth	6.1%
Exchange Rate	1 USD = 82.75 INR
Forex Reserves	$619.07 billion

Description

• GDP (Gross Domestic Product): The total monetary value of all goods and services produced within a country's borders in a specific time period, usually annually or quarterly. [Source: IMF]

• Unemployment Rate: The percentage of the labor force that is unemployed and actively seeking employment. It's a key indicator of economic health, reflecting the balance between job supply and demand. [Source: Economic Times]

• Inflation Rate: The rate at which the general level of prices for goods and services is rising, leading to a decrease in purchasing power over time. High inflation can erode the value of money. [Source: Forbes]

• GDP Growth: The percentage increase in GDP from one period to another. It indicates the pace at which the economy is expanding or contracting. [Source: IMF]

• Exchange Rate: The value of one currency in relation to another. It determines the purchasing power of a currency in international trade. [Source: Economic Times]

• Forex Reserves (Foreign Exchange Reserves): The foreign currency deposits and bonds held by central banks and monetary authorities. They serve as a buffer to stabilize the domestic currency and ensure liquidity in the foreign exchange market. [Source: IMF]

Sources

• IMF (International Monetary Fund)
• Forbes
• Economic Times

Future Scope 🔍

The project has immense potential for expansion and improvement. Some future enhancements could include:

• Incorporating more sophisticated machine learning models for better predictions.
• Integrating real-time economic indicators for more accurate forecasts.
• Building a user-friendly web interface for easier interaction with the model.
• Expanding analysis to include regional or sector-specific recessions.

Vision 🚀

Vision is to provide policymakers, economists, and businesses with a reliable tool for anticipating recessions and making informed decisions to mitigate their impact. By harnessing the power of machine learning and data analysis, we strive to contribute to economic stability and resilience.

Deployed Link 🌐

The project is deployed on Render for easy access. You can access the deployed model through the following link: Recession Analysis and Prediction

Video Demo ▶️

To get a glimpse of how the project works and its capabilities, check out our video demo:

Usability Terms 📊

Field	Range	Instructions
Select year	1950 to 2050	📅 Input a year within the specified range (1950-2050).
Quarter	First to Fourth Quarter	🕒 Select a quarter from First to Fourth Quarter.
GDP Growth	0 to 10	💰 Input GDP growth rate within the range of 0 to 10.
Inflation Rate	0 to 20	💹 Input inflation rate within the range of 0 to 20.
Industrial Production	-5 to 5	🏭 Input industrial production within the range of -5 to 5.
Jobs in Market	10000 to 100000	👥 Input number of jobs within the range of 10000 to 100000.

Model Accuracy

Serial	Models	Description	Accuracy
0	LogisticRegression()	Logistic Regression model	88.44
1	RandomForestClassifier()	RandomForest Classifier model	100.00
2	DecisionTreeClassifier()	Decision Tree Classifier model	100.00
3	KNN()	K-Nearest Neighbors model	86.56
4	SVC()	Support Vector Classifier model	88.44

Requirements 📋

To run this project locally, ensure you have the following dependencies installed:

• Flask==2.0.1
• numpy==1.21.5
• joblib==1.1.1
• scikit-learn==0.24.2

Installations 💻

1. Clone this repository to your local machine.

git clone https://github.com/neerajcodes888/Recession-Analysis-With-Prediction.git

Navigate to the project directory.

cd Recession-Analysis-With-Prediction

Install the required dependencies.

pip install -r requirements.txt

Usage 🚀

• Explore the Jupyter Notebooks for data analysis, model training, and evaluation.
• Use the deployed link to access the model online for real-time predictions.

Feel free to contribute to this project by forking the repository and submitting pull requests with your enhancements!

License 📄

This Project is under CCO 1.0