Stock-market-prediction-using-ml

This code loads stock market data for Tata Motors, pre-processes it by dropping NaN values and irrelevant columns, and then visualizes the data using matplotlib and seaborn to gain insights into the stock's behavior over time. It plots the volume, open and close prices over time, correlation between open and close prices, average close price by month, distribution of close prices, and correlation matrix. It then creates a target variable based on future price movement and splits the data into training and testing sets. Three classification models (SVM, KNN, MLP) are trained on the scaled data and evaluated using accuracy, precision, recall, and F1-score metrics. These models aim to predict whether the high price will be higher or lower than the low price on the next day, based on the given features.