Project Summary
This project provides a comprehensive look at Tesla’s stock performance over the past ten years, incorporating a variety of technical indicators to analyze its movements. The data includes the date of recording and several key metrics: the opening price, the highest and lowest prices during the trading day, the closing price, and the trading volume. Additionally, it features momentum indicators like the 7-day and 14-day Relative Strength Index (RSI), which help assess whether the stock is overbought or oversold.
Project Overview: Utilized Python to develop a machine learning model to predict Tesla’s stock prices using historical data. The goal is to forecast future stock prices to inform investment decisions and trading strategies.
- DELIVERABLES
- Utilized Python to develop an advanced stock price prediction model for Tesla, leveraging machine learning algorithms to enhance forecasting accuracy.
- Implemented feature engineering techniques to identify key indicators influencing Tesla’s stock price, resulting in a more precise model.
- Implemented rigorous cross-validation procedures to assess model performance and ensure reliability across different market conditions.
- Produced detailed reports and presentations on model performance and investment recommendations, supporting strategic decision-making processes.
- Visualized the correlation between Tesla Stock closing price and other variables, this would help a potential investor to know the right time to invest in the stock.
- Compared and optimized various models’ efficiency to obtain a mean absolute error of 3.9.
- ANALYSIS IMPACT
- The Tesla stock price prediction model uses historical data and machine learning techniques to forecast future stock prices. By developing and deploying this model, investors and traders can make more informed decisions and potentially enhance their trading strategies.