Netflix Stock Price Prediction 🎥

This repository contains code for predicting Netflix (NFLX) stock prices using two different machine learning models: Long Short-Term Memory (LSTM) for time series forecasting and Logistic Regression. 🍿

Overview

The project aims to forecast the closing stock prices of Netflix using historical data. Two models are implemented and compared:

• LSTM Model: Utilizes deep learning techniques for time series prediction.
• Logistic Regression Model: A classical machine learning approach adapted for stock price prediction.

Dataset

The dataset used is NFLX.csv containing daily stock prices from Kaggle.

Data Exploration and Preprocessing

• The dataset is loaded and initial exploratory analysis is performed to understand its structure and check for null values.
• The 'Close' column is selected as the target variable for prediction.
• Data normalization is performed using MinMaxScaler to scale the data between 0 and 1.
• LSTM requires data to be reshaped into a format suitable for time series forecasting.

Evaluation Metrics

Both models are evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R2) score to assess their performance in predicting stock prices.

Usage

To run the models:

• Clone the repository:

  https://github.com/NishatTasnim01/Netflix-Stock-Price-Prediction.git
  

• Install the required dependencies:

  pip install tensorflow scikit-learn pandas matplotlib
  

Contribute

🚀 Contributions are welcome! Please fork the repository and create a pull request with your improvements.