TeslaStocks

Tesla Stock Price Prediction

This project analyzes Tesla's historical stock data and implements a machine learning model to predict the stock's adjusted closing price. The project uses exploratory data analysis (EDA) to uncover trends and relationships within the data, and a machine learning model (Random Forest Regressor) to forecast stock prices.

Project Overview

This project includes:

• Data Cleaning and Preparation: Loads, inspects, and preprocesses data to ensure accuracy.
• Exploratory Data Analysis (EDA): Visualizes trends and correlations within Tesla's stock data.
• Feature Engineering: Creates lagged features to assist in predicting future values.
• Machine Learning Model: A Random Forest Regressor is used to predict the adjusted closing price.
• Model Evaluation: Uses Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) as evaluation metrics.

Dataset

• Source: The dataset used is historical data for Tesla’s stock price.
• Columns:
  • Date: Trading date.
  • Open, High, Low, Close: Stock prices at open, highest, lowest, and close for each day.
  • Adj Close: Adjusted closing price.
  • Volume: Number of shares traded.

Project Structure

• Data Preparation: Converts the Date column to datetime format, checks for missing values, and drops any unnecessary columns.
• EDA:
  • Time series visualization of adjusted close prices.
  • Correlation heatmap for understanding relationships between variables.
• Modeling:
  • Splits data into training and testing sets.
  • Trains a Random Forest Regressor on historical stock data.
  • Evaluates performance using MAE, MSE, and RMSE.
• Results:
  • The model achieved an MAE of 2.25, MSE of 26.49, and RMSE of 5.15, which indicate reasonable accuracy depending on Tesla’s price range.

Key Files

• TSLA_stock_data.csv: Historical stock data for Tesla (uploaded by the user).
• analysis_and_prediction.ipynb: Jupyter Notebook containing all code for data cleaning, EDA, model training, and evaluation.

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/tesla-stock-prediction.git

2. Navigate to the project directory:

   cd tesla-stock-prediction

3. Install required libraries:

   pip install -r requirements.txt

Usage

1. Run EDA: Explore the dataset to visualize trends and understand key statistics.
2. Train Model: Use the prepared data to train the Random Forest Regressor.
3. Evaluate Model: Review the model's performance metrics (MAE, MSE, RMSE).
4. Make Predictions: Use the trained model to predict stock prices.

Dependencies

• pandas: Data manipulation and analysis.
• plotly: Interactive visualizations.
• matplotlib and seaborn: Static visualizations.
• sklearn: Machine learning and evaluation metrics.
• numpy: Numeric operations.

Install dependencies with:

pip install pandas plotly matplotlib seaborn scikit-learn numpy