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https://github.com/thenewtoncode/econvisor---industry-forecasting-tool

EconVisor: A web app using Flask, Python, and ML models for predicting Indian economic indicators. Democratizing financial forecasting, it offers a user-friendly interface and continuous improvements for accurate insights.
https://github.com/thenewtoncode/econvisor---industry-forecasting-tool

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EconVisor: A web app using Flask, Python, and ML models for predicting Indian economic indicators. Democratizing financial forecasting, it offers a user-friendly interface and continuous improvements for accurate insights.

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README 

        
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# EconVisor - Technical README



## Abstract



EconVisor is an advanced time series analysis application designed to empower users to predict Indian economic indicators using machine learning models. This technical README provides insights into the technical aspects, methodologies, and technologies employed in the development of EconVisor.



## Table of Contents



1. [Introduction](#introduction)

2. [Problem Statement](#problem-statement)

3. [Methodology](#methodology)

    - [Approach](#approach)

    - [Data Collection](#data-collection)

    - [Data Preprocessing](#data-preprocessing)

    - [Model Development](#model-development)

    - [User Interface Development](#user-interface-development)

    - [Model Evaluation](#model-evaluation)

4. [How the Web App Works](#how-the-web-app-works)

5. [Conclusion](#conclusion)



## Introduction



EconVisor is an application that leverages advanced machine learning models, including RandomForest, ARIMA, LSTM, and Simple RNN, to predict key Indian economic indicators. This README delves into the technical intricacies of the application, covering data collection, preprocessing, model development, user interface design, and the underlying technologies.



## Problem Statement



The primary challenge addressed by EconVisor is the development of an interactive and user-friendly application capable of predicting diverse economic indicators based on user-defined inputs. The goal is to provide actionable insights into the economic impact of different industries, aiding decision-makers in policy formulation, investment strategies, and resource allocation.



## Methodology



### Approach



EconVisor's approach involves sector-specific forecasts, comprehensive data collection from diverse sources, user customization options, GDP contribution analysis, and a commitment to accessibility and relevance. The methodology ensures that the application caters to the unique characteristics and impact of different industries on the Indian economy.



### Data Collection



The application relies on a diverse set of datasets covering various sectors of the Indian economy. These datasets, sourced from reputable organizations, contribute to a comprehensive understanding of economic trends and form the foundation for accurate forecasting models.



### Data Preprocessing



Datasets undergo rigorous cleaning processes, including anomaly removal, handling missing values, and outlier elimination. The data is then transformed into a time series format, ensuring its suitability for time series analysis. Feature extraction is performed selectively, focusing on relevant indicators for each subsector.



### Model Development



EconVisor employs advanced time series forecasting models, each chosen for its unique strengths:



- **RandomForest:** Versatile and powerful ensemble learning algorithm suitable for handling complex relationships in economic data.

- **ARIMA:** Classical time series forecasting model effective for capturing linear relationships and trends in the data.

- **LSTM (Long Short-Term Memory):** Recurrent neural network designed to handle long-range dependencies in time series data.

- **Simple RNN:** Basic form of recurrent neural network suitable for capturing sequential patterns in data.



Users have the flexibility to choose the most suitable model for their specific forecasting needs.



### User Interface Development



The user interface prioritizes efficiency, simplicity, and accessibility. Users can input their preferred future time frame, select the sector or subsector of interest, and choose the desired forecasting model. Clear visualizations, including line charts, aid users in interpreting predictions effectively.



### Model Evaluation



Rigorous model evaluation is performed using metrics such as Mean Squared Error (MSE), Percentage Error, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). These metrics provide insights into the relative accuracy of different models, enabling users to make informed decisions.



## How the Web App Works



EconVisor is implemented as a web application using a technology stack that includes Flask, Python, HTML, CSS, JavaScript, and various data science libraries for time series analysis.



### Technologies Used



- **Flask:** The web framework for building the application, handling routing, and managing backend functionalities.

- **Python:** The core programming language for implementing the application logic, data processing, and model development.

- **HTML/CSS/JS:** The trio of technologies for creating a responsive and user-friendly front-end interface.

- **Data Science Libraries:** Various libraries such as Pandas, NumPy, Scikit-Learn, and TensorFlow are employed for data manipulation, statistical analysis, and machine learning model development.



## Conclusion



In conclusion, EconVisor is not just an application; it's a comprehensive solution for predicting economic indicators. The technology stack, methodologies, and user-centric design contribute to its effectiveness in supporting decision-makers across various domains in the ever-evolving Indian economy.



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