Stock Prediction and Investment System with Demat Proposal and Registration Guidelines
DOI:
https://doi.org/10.32628/IJSRSET2310547Keywords:
LSTM, SVM, KNN, and Linear regressionAbstract
The volatility and complexity of the stock market make it a challenging domain for investors to make information decisions. assisting investors in making more accurate investment choices and minimizing the risk. It is a very complex task for the user to analyze stock prices then making a better portfolio. The dataset includes daily high, low, close prices, and trading volumes. Data preprocessing involves handling missing data by either removing or imputing them. Scaling and normalization techniques are applied to standardize numerical features. For stock prediction, time series data is sorted and resampled. Ensemble models are trained, including LSTM, SVM, KNN, and linear regression, on historical stock price and engineered features.
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