Share Market Price Prediction

Project Overview

This project aims to predict the closing prices of a company's stock based on historical market data using machine learning techniques. The prediction model uses a Linear Regression algorithm to forecast future prices based on features such as Open, High, Low, and Close prices from historical records.

Data Source

The data for this project comes from the CSV file containing historical stock prices for a company, including the following columns:

• Date: The date of the stock data entry
• Open: The opening price of the stock
• High: The highest price of the stock during the trading day
• Low: The lowest price of the stock during the trading day
• Close: The closing price of the stock
• Adj Close: The adjusted closing price (corrected for splits and dividends)
• Volume: The number of shares traded

Dependencies

The project requires the following libraries:

• Pandas: For data manipulation and analysis
• NumPy: For numerical computations
• Scikit-learn: For machine learning model implementation
  • train_test_split: For splitting the data into training and testing sets
  • LinearRegression: For applying linear regression to the data
  • mean_squared_error, mean_absolute_error: For evaluating model performance
• Matplotlib: For basic data visualization
• Plotly: For advanced, interactive visualizations

Installation

To install the required libraries, run the following command:

pip install pandas numpy scikit-learn matplotlib plotly

Steps

1. Load the Data: The dataset is loaded from a CSV file (3MINDIA.BO.csv), which contains historical stock data.
2. Data Exploration:
   • Basic information about the dataset is printed using data.info().
   • Descriptive statistics are generated using data.describe().
3. Data Preprocessing:
   • Data is split into training and testing sets using the train_test_split function from Scikit-learn.
4. Model Training:
   • A linear regression model is trained on the data.
   • The model predicts the stock's closing prices based on historical data.
5. Model Evaluation:
   • The model's performance is evaluated using mean squared error and mean absolute error metrics.
6. Visualization:
   • Matplotlib and Plotly are used to create visualizations of the stock price trends and the model's predictions.

Usage

To run the project, ensure that you have the required dependencies installed and execute the Jupyter Notebook.