Skip to content

Latest commit

 

History

History
144 lines (116 loc) · 3.94 KB

README.md

File metadata and controls

144 lines (116 loc) · 3.94 KB

Stock Price Prediction

Overview

This project aims to develop a machine learning model to predict stock prices using historical data from Yahoo Finance. The dataset contains the daily stock prices of a specific company over a certain period.

Key Features

  • Data Preprocessing: Fetching, cleaning, and scaling the stock price data.
  • Model Building: Training a Linear Regression model to predict future stock prices.
  • Model Evaluation: Assessing model performance using Mean Squared Error (MSE) and R-squared (R2) score.
  • Model Visualization: Visualizing actual vs predicted stock prices.

Installation

Clone the Repository

To get started, clone this repository to your local machine using the following command:

git clone https://github.com/ziishanahmad/stock-price-prediction.git
cd stock-price-prediction

Set Up a Virtual Environment

It is recommended to use a virtual environment to manage your dependencies. You can set up a virtual environment using venv:

python3 -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

Install Required Libraries

Install the necessary libraries using pip:

pip install -r requirements.txt

Usage

Run the Jupyter Notebook

Open the Jupyter notebook to run the project step-by-step:

  1. Launch Jupyter Notebook: 
    jupyter notebook
  2. Open the stock_price_prediction.ipynb notebook.
  3. Run the cells step-by-step to preprocess the data, train the model, evaluate its performance, and visualize the results.

Detailed Explanation of the Code

Import Libraries

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import yfinance as yf
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score

Fetch the Data

ticker = 'AAPL'
start_date = '2010-01-01'
end_date = '2023-01-01'

stock_data = yf.download(ticker, start=start_date, end=end_date)
print(stock_data.head())
print(stock_data.describe())

Exploratory Data Analysis (EDA)

plt.figure(figsize=(12, 6))
plt.plot(stock_data['Close'])
plt.title('Closing Price Over Time')
plt.xlabel('Date')
plt.ylabel('Close Price USD ($)')
plt.show()

plt.figure(figsize=(12, 10))
sns.heatmap(stock_data.corr(), annot=True, cmap='coolwarm')
plt.title('Correlation Matrix')
plt.show()

Data Preprocessing

data = stock_data[['Close']].copy()
data['Prediction'] = data['Close'].shift(-1)
data.dropna(inplace=True)

X = data[['Close']].values
y = data['Prediction'].values

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

scaler = MinMaxScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

Build the Model

model = LinearRegression()
model.fit(X_train, y_train)

Evaluate the Model

y_pred = model.predict(X_test)

mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
print(f'Mean Squared Error: {mse:.2f}')
print(f'R-squared: {r2:.2f}')

plt.figure(figsize=(8, 6))
plt.plot(y_test, label='Actual Prices')
plt.plot(y_pred, label='Predicted Prices')
plt.title('Actual vs Predicted Stock Prices')
plt.xlabel('Time')
plt.ylabel('Close Price USD ($)')
plt.legend()
plt.show()

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License.

Acknowledgements

  • The stock price data is fetched from Yahoo Finance using the yfinance library.
  • The developers of TensorFlow for their deep learning framework.

Contact

For any questions or feedback, please contact: