Developed a model for stock price prediction, leveraging time series data to capture temporal dependencies and forecast future prices. The model utilizes historical stock prices to train a deep learning network, ensuring accurate trend predictions and informed investment strategies.
Import libraries
Import dataset
Split into Training and Test set
Feature Scaling
Create Dataset
Reshape X values in correct format
Build a model
Prediction
Accuracy
Final DataFrame
- Stock price prediction models are used in algorithmic trading to automate the buying and selling of stocks. By predicting short-term price movements, algorithms can execute trades at optimal times, maximizing profit and minimizing risk.
- Investors and fund managers use stock price prediction models to optimize their portfolios. By forecasting future stock prices, they can make informed decisions on asset allocation, balancing risk and return to achieve financial goals.
- Financial institutions use stock price prediction to assess and manage risk. By predicting potential price fluctuations, they can develop strategies to hedge against adverse market movements, protecting investments and ensuring financial stability.
I used a compiler GPU provided in google colab while running this project.
NFLX.csv--> https://www.kaggle.com/datasets/jainilcoder/netflix-stock-price-prediction
Pandas
Numpy
Matplotlib
sci-kit learn
Tensorflow
Python