Stock Price Prediction

Overview

In this project, we build a Facebook Prophet machine learning model to forecast the price of Tesla stock for the next 30 days. The project involves data collection, visualization, model building, and performance evaluation using real-time data. We’ll also dive into stock analysis fundamentals and automate the forecasting process for any stock of your choice.

Key Concepts

• Facebook Prophet: A forecasting tool used to predict time series data.
• Data Visualization: Creating engaging and informative graphs to visualize stock performance.
• Stock Analysis: Analyzing key metrics like PE ratio, EPS, Beta, Market Cap, Volume, and Price Range.
• Automation: Building an automated pipeline to forecast stock prices with ease.

Steps Involved

1. Importing Libraries

We start by importing all necessary Python libraries such as Pandas, Plotly, and Facebook Prophet to work with the data and build the model.

2. Data Collection

• Download the latest Tesla stock data from Yahoo Finance.
• Prepare the dataset to be used in the forecasting model.

3. Data Visualization

• Using Plotly Express, we visualize the historical performance of Tesla’s stock.
• We create interactive graphs and charts that display trends in the stock price over time.

4. Building the Model

• Create a Facebook Prophet model to forecast the stock price of Tesla 30 days into the future.
• Train the model using historical stock price data.

5. Evaluating the Model

• Compare the model's forecast with real stock data using Google Finance in Google Sheets.
• Evaluate the accuracy of the model’s predictions.

6. Stock Analysis

• Analyze key metrics of Tesla’s stock, including:
  • PE Ratio
  • EPS (Earnings Per Share)
  • Beta
  • Market Cap
  • Volume
  • Price Range

7. Automation

• Automate the entire forecasting process so that you can upload stock data and get the forecast along with visualizations in just a few seconds.

Learning Objectives

By the end of this project, you will be able to:

• Build and deploy a Facebook Prophet model for stock price forecasting.
• Forecast stock prices 30 days into the future using historical data.
• Visualize data effectively using Plotly Express.
• Evaluate the performance of forecasting models with real data.
• Automate stock forecasting for any stock of your choice with a simple upload.

Technologies Used

• Python: Programming language used for model building.
• Facebook Prophet: A forecasting tool for time series data.
• Plotly Express: A Python library for creating interactive data visualizations.
• Yahoo Finance API: For downloading historical stock data.
• Google Finance in Google Sheets: For evaluating stock forecast accuracy.