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Local Chapter project

Artificial Intelligence as a Financial Planner

Machine LearningNLPData ScienceDeep LearningSentiment AnalysisChatbotWeb application
Start date
September 12, 2022
Finish date
November 12, 2022
Project status
completed
Artificial Intelligence as a Financial Planner

Challenge background

Financial planning is very important specially in uncertain times like this decade. We have seen how devastated the world could get by a blow of a pandemic and in event of a war. Price hikes and market crashes could leave thousands of families in distress. However, things could've been much different if financial knowledge was accessible to the common people. In fact not just Canadian, most of the world's earning population does not have a proper retirement plan and does not know where to start when building a portfolio. This project aims to solve that problem by building an application that is user friendly and contains all the knowledge of where to start investing, how to build a portfolio, where to invest and how their investment could look like in the next 5-10 years including technical insights of whats affecting or could influence their investment. Furthermore this project provides suggestions of whether a person can retire in the next few years based on their current plan and what changes needs to be brought in if not. The builtin features of this app will enable the users to have an in-depth knowledge of the companies they would like to invest and what others are saying about it. Any further confusion the user may have, could be answered by the Chatbot which will be trained every now and then to provide as much accurate answers and solutions as possible.

Project timeline

  1. 1

    Week 1

    Data Collection

  2. 2

    Week 2

    Data Cleaning and Analysis

  3. 3

    Week 3

    Predictive Models and implementing future projections using APIs

  4. 4

    Week 4

    Using Twitter and News API for sentiment analysis using NLP

  5. 5

    Week 5

    Getting Hands on with the streamlit app

  6. 6

    Week 6

    Training and testing the ML models

  7. 7

    Week 7

    Training and Testing the ML models continued.

  8. 8

    Week 8

    Deploying the application.

What you'll learn

  1. Collection of Data.
  2. Data Cleaning.
  3. Data Analysis.
  4. Data Visualization.
  5. Use of API in finance.
  6. Algorithmic Trading.
  7. Deep Learning.
  8. AWS

Get involved

What to expect from a Local Chapter project

First project

  • Welcomes beginners and experienced practitioners.
  • Focuses on education and collaborative delivery.
  • Produces open-source project work.

Benefits

  • Address a significant real-world problem with your skills.
  • Build your project portfolio.
  • Demonstrate your work to organizations and project partners.

Requirements

  • Working English communication.
  • A learning mindset.
  • Commitment to collaborative project work.