This repository contains code and resources for predicting the performance of Initial Public Offerings (IPOs). Using machine learning techniques, it aims to predict the future price movements of newly listed stocks based on a variety of financial and non-financial indicators.
Project Execution Steps
- Data Collection Objective: Gather historical IPO data to serve as the foundation for analysis and modeling. Method: Scrape IPO data from (Indonesia Stock Exchange Website). Details: Use web scraping techniques to extract relevant financial and non-financial indicators from the site.
- Data Preprocessing Objective: Clean and prepare the collected data for analysis. Tasks: -Handle missing values -Convert data types as needed -Normalize or scale features -Encode categorical variables -Details: Implement preprocessing steps to ensure the data is in a suitable format for exploratory data analysis and modeling.
- Exploratory Data Analysis (EDA) Objective: Understand the data through visualization and statistical analysis. Tasks: -Analyze data distributions -Identify correlations between features -Visualize key patterns and outliers Details: Use Jupyter notebooks to perform EDA and generate insights that will guide feature engineering and modeling.
- Modeling and Training Objective: Build and train machine learning models to predict IPO performance. Tasks: -Select appropriate models (e.g., Linear Regression, Random Forest, XGBoost) -Apply Hyperparameter tuning -Split the data into training and testing sets -Train models on the training set Details: Implement the modeling process, including hyperparameter tuning and cross-validation to improve model performance.
- Evaluation Objective: Assess the performance of the trained models. -Tasks: -Evaluate models using metrics such as RMSE, MAE, MSE, and R² -Compare the performance of different models Details: Use evaluation metrics to determine the accuracy and reliability of your models, and make adjustments as necessary.
- Strategy and Decision Making Objective: Develop actionable strategies based on the model's predictions. -Tasks: -Analyze model predictions and performance -Formulate investment strategies or recommendations -Details: Use insights from the models to make informed decisions about IPO investments or further analysis.