Overview Welcome to the repository for my capstone project titled "Yes Bank's Closing Price Prediction by Regression." This project focuses on predicting the closing price of Yes Bank's stock using regression techniques. By analyzing historical stock data and leveraging machine learning, we aim to develop a model that can provide accurate predictions, helping investors make informed decisions.
Table of Contents
Introduction The stock market is a dynamic environment where investors rely on accurate predictions to make decisions. This project aims to build a predictive model for Yes Bank's stock closing prices, helping investors and analysts gain insights into potential future price movements.
Dataset The dataset used for this project consists of historical stock data for Yes Bank, including features such as open price, high price, low price, trading volume, and, most importantly, closing price. This data is essential for training and evaluating regression models.
Project Goals The primary objectives of this capstone project are:
- To preprocess and prepare the stock data for regression analysis.
- To perform feature engineering to create relevant features for modeling.
- To implement and evaluate different regression models for price prediction.
- To select the best-performing model and provide predictions for Yes Bank's closing prices.
Methods Used
1. Data Preprocessing Data preprocessing involves handling missing values, removing outliers, and ensuring data quality. Additionally, we will split the data into training and testing sets for model development and evaluation.
2. Feature Engineering Feature engineering is a critical step where we create meaningful features that can influence the closing price prediction. It includes selecting relevant columns, transforming data, and creating new features when necessary.
3. Regression Models Several regression models will be implemented and evaluated, including Linear Regression, Linear Regression using Lasso Regularization, Linear Regression with Ridge Regularization, Linear Regression with Elastic Net Regularization. We will assess their performance and select the most accurate model.
4. Evaluation Model evaluation will be based on various regression metrics to determine the model's accuracy in predicting Yes Bank's closing prices. Common metrics include Mean Absolute Error (MAE), Root Mean Square Error (RMSE), R-squared (R2) and Adjusted R-squared (R2).
5. Results The results section will present the findings of the regression analysis, including the performance of different models and their predictions for Yes Bank's closing prices.
Conclusion This capstone project aims to provide a reliable predictive model for Yes Bank's closing prices, aiding investors and analysts in making informed decisions. The conclusion section will summarize the project's outcomes and potential future work.