Predicting Listing Gains in the Indian IPO Market Using TensorFlow

  • Guided Project
  • 0.6 hours
  • Intermediate
  • Python

Practice building a deep learning model in TensorFlow to predict IPO listing gains using real Indian stock market data.

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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 - the percentage increase in share price on the day of listing compared to the IPO issue price. You'll build a deep learning classification model using TensorFlow and the Keras API to make these predictions. The project will take you through key data science steps including data exploration, visualization, preprocessing, and modeling. You'll work with a real-world dataset of past Indian IPOs and gain practical experience in applying deep learning to a financial use case. Objective: Build a deep learning classification model to predict IPO listing gains and guide investment decisions in the Indian stock market.

What You'll Learn

  • ✓ Perform exploratory analysis, visualization, and preprocessing on the dataset
  • ✓ Build a hold-out validation approach to model evaluation
  • ✓ Build and train the multi-layer classification deep learning model using the Sequential API in TensorFlow

Before You Start

  • ✓ Building and training deep learning regression models using TensorFlow's Sequential API
  • ✓ Performing exploratory data analysis and visualization to prepare data for modeling
  • ✓ Preprocessing and preparing data for analysis using the pandas library
  • ✓ Converting between tensors and NumPy arrays in TensorFlow

Project Steps

10 steps

  1. 1 Loading the Data
  2. 2 Exploring the Data
  3. 3 Data Visualization
  4. 4 Outlier Treatment
  5. 5 Setting the Target and Predictor Variables
  6. 6 Creating the Holdout Validation Approach
  7. 7 Define the Deep Learning Classification Model
  8. 8 Compile and Train the model
  9. 9 Model Evaluation
  10. 10 Next Steps
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