InvestWise Predictor Project

Welcome to the InvestWise Predictor project repository! This project aims to develop an intelligent online tool that utilizes neural networks to predict investment opportunities and provide advice on suitable business ventures in different regions of Kenya. By leveraging a diverse set of economic indicators, trade data, and financial metrics, the tool aims to empower investors with data-driven insights for making informed investment decisions.

Project Goals

The primary goals of the InvestWise Predictor project are as follows:

• Predict potential investment opportunities in various regions of Kenya.
• Provide tailored advice on the types of businesses that could flourish based on data-driven insights.
• Create a user-friendly web interface that allows investors to interact with the tool and receive predictions.

Technology Stack

The project is built using a combination of programming languages, libraries, and frameworks:

• Programming Language: Python
• Neural Network Libraries: TensorFlow or PyTorch
• Back-End Web Framework: Flask
• Front-End Framework: React.js
• Version Control: Git and GitHub

Project Setup

To set up the project on your local machine for development and testing purposes, follow these steps:

1. Clone the repository:

   https://github.com/MadScie254/InvestSmartAI.git
   

2. Install Python Dependencies for the Back End:

   pip install -r backend/requirements.txt

3. Install Node.js Dependencies for the Front End:

   cd frontend
   npm install

4. Start the Back End Server:

   python backend/app.py

5. Start the Front End Development Server:

   cd frontend
   npm start

6. Access the tool in your web browser at http://localhost:3000.

Data Sources

The project utilizes the following data sets related to economic indicators and trade data in Kenya:

1. Annual GDP.csv
2. Inflation Rates.csv
3. Diaspora Remittances.csv
4. Value Direct Imports Per Commodities (Ksh Million).csv
5. Principal Exports Volume, Value, and Unit Prices (Ksh Million).csv
6. Foreign Trade Summary (Ksh Million).csv

Technology Stack

• Programming Language: Python
• Neural Network Libraries: TensorFlow
• Back-End Framework: Flask
• Front-End Framework: React.js
• Version Control: Git and GitHub

Project Structure

The project is structured as follows:

• backend/: Contains the back-end code developed using Flask.
• frontend/: Contains the front-end code developed using React.js.
• data/: Stores the data sets used for analysis and model training.

Planned Steps

The project will be developed in a series of well-defined steps:

1. Data Preprocessing and Feature Engineering
2. Model Development and Training
3. Web Application Development (Front End and Back End)
4. Deployment and Testing
5. Continuous Improvement and Maintenance

Next Steps

With the initial project setup completed, the next step is to proceed with Step 2: Data Preprocessing and Feature Engineering. In this step, we'll clean and preprocess the provided data sets, and engineer relevant features for the neural network models.

Stay tuned for further updates as we progress through the project!