This project aims to predict the stock price of Netflix using historical stock price data and machine learning techniques. By leveraging Python libraries such as pandas, scikit-learn, and TensorFlow, we built a predictive model to forecast future stock prices, helping investors and analysts make informed decisions.
Methodology
Data Collection and Preprocessing:
Imported historical stock price data from Yahoo Finance.
Cleaned and processed the data using pandas.
Split the data into training and testing sets.
Feature Engineering:
Created additional features such as moving averages and trading volumes.
Model Building:
Implemented various machine learning models including Linear Regression and LSTM.
Tuned hyperparameters for optimal performance.
Model Evaluation:
Evaluated models using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).