Stock-Price-Prediction-using-Machine-Learning-in-Python

• The Stock Price Prediction project is an attempt to use machine learning to forecast stock prices. The goal of this project is to build a model that can predict stock prices based on historical data. The project involves several steps, including data collection, data cleaning, and feature engineering. After preparing the data, we apply various machine learning algorithms to build a predictive model.

• To build the predictive model, we first fetch historical stock price data for various companies in the S&P 500 index. We use the Pandas library to process and clean the data, and then perform feature engineering to create relevant features that can help us make accurate predictions. We then train several machine learning models on this data, including Support Vector Machines (SVMs), Random Forests, and Gradient Boosting Machines (GBMs).

• To evaluate the performance of these models, we use various metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). We also use cross-validation to ensure that our models generalize well to new data.

• In conclusion, this project demonstrates the use of machine learning algorithms to predict stock prices based on historical data. While the performance of these models is subject to various limitations and uncertainties, they offer a valuable tool for investors and traders seeking to make informed decisions based on data-driven insights.