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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 – 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.
Key skill required
To complete this project, it's recommended to build these foundational skills in Python
- 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
Projects steps
Step 1: Loading the Data
Step 2: Exploring the Data
Step 3: Data Visualization
Step 4: Outlier Treatment
Step 5: Setting the Target and Predictor Variables
Step 6: Creating the Holdout Validation Approach
Step 7: Define the Deep Learning Classification Model
Step 8: Compile and Train the model
Step 9: Model Evaluation
Step 10: Next Steps
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