Project Overview: This GitHub repository hosts the code and resources for predicting the closing prices of Yes Bank stock. Stock price prediction is a crucial task in the financial world, as accurate forecasts can assist investors and traders in making informed decisions. This project aims to develop a robust predictive model using historical stock data and advanced machine learning techniques.
Key Features: Data Collection: We gather historical stock data of Yes Bank from reliable sources, ensuring quality and accuracy. Data Preprocessing: The collected data undergoes thorough preprocessing, including handling missing values, normalization, and feature engineering.
Exploratory Data Analysis (EDA): We analyze the data to gain insights, identify trends, and select relevant features for modeling.
Model Selection: We explore and compare various machine learning algorithms, such as linear regression, support vector machines, and deep learning, to determine the best-fit model.
Model Training: The selected model is trained on historical data, optimizing its parameters to achieve the highest predictive accuracy.
Evaluation Metrics: We employ appropriate evaluation metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) to quantify the model's performance.
Future Scope: As an extension, we might develop a user-friendly web application that provides real-time stock price predictions and visualizations.