jonnyvpc / Chatbot-Pre-Data-spike

Extract key financial data and lay the groundwork for AI-driven insights

Financial Data Analysis for AI-Powered Chatbot Project

This project focuses on developing an AI-powered chatbot to revolutionize financial data analysis for Global Finance Corp. (GFC). The chatbot will analyze and provide insights on corporate financial performance from 10-K and 10-Q financial documents.

Project Overview

The project involves the following steps:

1. Data Extraction: Extract key financial data from 10-K reports.
2. Data Preparation: Clean and format the extracted data.
3. Data Analysis: Analyze the financial data to identify trends and indicators.
4. Data Visualization: Create visualizations to represent the financial data.
5. Summary and Conclusions: Provide insights into the financial health of the analyzed companies.

How to Extract Data from EDGAR

Follow these steps to extract 10-K and 10-Q data from the SEC's EDGAR database:

Step 1: Navigate to the EDGAR Database

Go to the SEC EDGAR Database.

Step 2: Search for a Company

Enter the company name or ticker symbol in the search bar and click "Search".

Step 3: Select the Filing Type

Select the "10-K" or "10-Q" filing type from the filter options.

Step 4: Download the Report

To help you sort quicker, click the arrows near the File Type. This helps you sort the File Type names.

Click on the document link to download the 10-K or 10-Q report.

Repository Contents

• Task One.ipynb: Jupyter Notebook containing the data analysis and visualizations.
• Cleaned_Financial_Data.csv: CSV file containing the cleaned financial data.
• LICENSE: MIT License file.

Usage

To run the notebook:

1. Clone the repository.
2. Navigate to the repository directory.
3. Open the notebook using Jupyter Notebook.

git clone https://github.com/jonnyvpc/Chatbot-Pre-Data-spike.git
cd Chatbot-Pre-Data-spike
jupyter notebook Task\ One.ipynb