Project overview
In this project, you’ll assume the role of a data scientist working for an investment firm that wants to invest in Initial Public Offerings (IPOs) in the Indian stock market. The challenge is predicting the listing gains – whether an IPO will be profitable on its first day of trading.
You’ll build a deep learning binary classification model using PyTorch to make these predictions. The project takes you through professional data science workflows including proper data splitting, advanced regularization techniques, and comprehensive business-focused evaluation. You’ll work with a real-world dataset of past Indian IPOs and apply the sophisticated PyTorch techniques you learned in the course.
Objective: Build a robust deep learning classification model with proper regularization to predict IPO success and understand the business implications of different types of prediction errors.
Key skill required
To complete this project, it's recommended to build these foundational skills in Python
- Building and training deep learning classification models using PyTorch's Sequential API with proper data pipeline setup
- Implementing advanced regularization techniques including batch normalization, dropout, and early stopping
- Performing three-way data splitting with stratification and proper scaling methodology to avoid data leakage
- Comprehensive model evaluation using confusion matrices, precision, recall, and business-focused interpretation
Projects steps
Step 1: Loading the Data
Step 2: Target Engineering & Business Understanding
Step 3: Exploratory Data Analysis
Step 4: Data Splitting and Scaling
Step 5: Data Pipeline & PyTorch Setup
Step 6: Model Architecture Design
Step 7: Basic Training Implementation
Step 8: Advanced Regularization & Early Stopping
Step 9: Model Evaluation
Step 10: Next Steps
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