Project Tasks:

• Data Cleaning and Preprocessing:

  • Clean and preprocess the historical stock price dataset to ensure data accuracy and reliability.

  • Handle missing or inconsistent data points.

• Exploratory Data Analysis (EDA):

  • Visualize the distribution of opening, high, low, and closing prices over time.

  • Identify any outliers, anomalies, or patterns in the stock price data.

• Visualization Storytelling:

  • Develop a series of visualizations to tell a compelling story about the historical stock prices.

  • Use line charts, candlestick charts, and other visual elements to convey trends and fluctuations.

• Stock Price Growth Comparison:

  • Compare the cumulative growth of stock prices among the 10 companies.

  • Identify companies with notable growth or decline trends.

• Stock Price Prediction:

  • Explore and implement machine learning models for stock price prediction, considering factors like moving averages, technical indicators, etc.

• Time Series Analysis:

  • Apply time series analysis techniques to decompose trends, seasonality, and cyclic patterns in stock prices.

  • Use statistical methods to make short-term and long-term forecasts.