Abstract:
This research paper aims to explore the development of a predictive analytics model for stock market forecasting. The objective is to leverage historical stock market data and apply advanced statistical and machine learning techniques to predict future stock prices. The study will focus on the construction of a robust and accurate model that can assist investors in making informed decisions and maximizing their returns. The research will involve data collection, preprocessing, feature engineering, model selection, and evaluation. The findings of this study will contribute to the field of financial analytics and provide valuable insights for investors and financial institutions.

Table of Contents:

Chapter 1: Introduction
1.1 Background
1.2 Problem Statement
1.3 Research Objectives
1.4 Scope and Limitations
1.5 Significance of the Study

Chapter 2: Literature Review
2.1 Overview of Predictive Analytics in Stock Market Forecasting
2.2 Traditional Approaches to Stock Market Forecasting
2.3 Machine Learning Techniques for Stock Market Prediction
2.4 Evaluation Metrics for Model Performance
2.5 Summary of Existing Research

Chapter 3: Data Collection and Preprocessing
3.1 Data Sources and Acquisition
3.2 Data Cleaning and Transformation
3.3 Feature Selection and Engineering
3.4 Handling Missing Data and Outliers
3.5 Data Normalization and Scaling

Chapter 4: Model Development
4.1 Model Selection and Architecture
4.2 Training and Validation
4.3 Hyperparameter Tuning
4.4 Model Evaluation and Performance Metrics
4.5 Interpretation of Model Results

Chapter 5: Results and Discussion
5.1 Analysis of Predictive Model Performance
5.2 Comparison with Traditional Approaches
5.3 Interpretation of Feature Importance
5.4 Limitations and Future Directions
5.5 Conclusion