BajFinance Stock Price Prediction Project: Time Series Analysis Using ARIMA🤨 🧐

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Business Understanding 😇

This project I am interested in exploring BajFinance stock price prediction using data from 2000 to 2020. As we know, stock price is very valuable information in the financial world 💰💼. As a data scientist, I want to help investors and market analysts to forecast BajFinance's future stock price behavior.

Using machine learning techniques, I will create a model to predict stock price movements. This will provide valuable insights to investors in their investment decision making. We will utilize BajFinance's historical stock price dataset to train our model.

The ultimate goal of this project is to provide accurate predictions of BajFinance's stock price in the future, so that investors can make better investment decisions. Whats good Letsgo! 💪📈'

Data Understanding 😁

'This dataset contains 5 thousand data about the trading of BAJFINANCE shares during the period 2000-2020. Each data records information about the opening price, closing price, highest price, and lowest price of BAJFINANCE stock on that day. In addition, the dataset also records the average share price by trading volume (VWAP), the number of shares traded, the total value of transactions in a particular currency, as well as information on the number of trades and the number of shares physically delivered. Using this dataset, market analysts can identify price trends, trading volumes, as well as develop predictive models to forecast the future market behavior of BAJFINANCE shares.'

The dataset features:

• Date: The date of the stock transaction.
• Symbol: Stock symbol.
• Series: The series type of the stock (for example, EQ for equity).
• Prev Close: The previous closing price of the stock.
• Open: The opening price of the stock on that day.
• High: The highest price of the stock on that day.
• Low: The lowest price of the stock on that day.
• Last: The last recorded price for the day.
• Close: The closing price of the stock on that day.
• VWAP (Volume Weighted Average Price): Average stock price based on trading volume. (Dependent)
• Volume: The number of shares traded.
• Turnover: The total value of transactions in a given currency.
• Trades: The number of trades that took place.
• Deliverable Volume: The number of shares physically delivered.
• %Deliverable: Percentage of physically delivered share volume.

Data Preparation:

• Cleaning the data
• Feature Engineering 😢
• Stationarity Checking
• Making Time Series Data --> Stationary

Modeling 😨 😰

• ARIMA
• Auto ARIMA

Evaluation 🧐 🤓 😎

• MSE
• MAE