Predicting IPO Performance Using Deep Learning

[ashis2004]

Deep Learning Simplified Repository (Proposing new issue)

:red_circle: Project Title : Predicting IPO Performance Using Deep Learning :red_circle: Aim :This project aims to predict the performance of Initial Public Offerings (IPOs) based on historical data and market conditions using deep learning techniques. The dataset includes features such as company information, market conditions, and financial metrics to predict the IPO performance. :red_circle: Dataset : The dataset ipo_performance_data.csv includes the following features:

• Date: Date of the IPO.
• Company_Name: Name of the company.
• Industry: Industry of the company.
• Shares_Offered: Number of shares offered during the IPO.
• Offer_Price: The price at which shares were offered.
• Opening_Price: The price of the shares at market open.
• Closing_Price: The price of the shares at market close.
• Market_Cap: Market capitalization of the company.
• Revenue: Revenue of the company prior to IPO.
• Profit: Profit of the company prior to IPO.
• Market_Condition_Index: An index indicating the market condition on the IPO date.
• IPO_Performance: Percentage change from offer price to closing price. :red_circle: Approach : 1. Data Preprocessing: Handle missing values, encode categorical features, and standardize numerical features.

2. Exploratory Data Analysis (EDA): Analyze and visualize the data to understand the distributions and relationships between features.
3. Deep Learning Models: Implement various deep learning models to predict IPO performance:
   • Basic Neural Network (NN)
   • Convolutional Neural Network (CNN)
   • Recurrent Neural Network (RNN) with LSTM
   • Deep Neural Network (DNN)
4. Model Evaluation: Evaluate the performance of each model using metrics like Mean Squared Error (MSE) and Mean Absolute Error (MAE).

Results

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📍 Follow the Guidelines to Contribute in the Project :

• You need to create a separate folder named as the Project Title.
• Inside that folder, there will be four main components.
  • Images - To store the required images.
  • Dataset - To store the dataset or, information/source about the dataset.
  • Model - To store the machine learning model you've created using the dataset.
  • requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
• Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.

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:red_circle::yellow_circle: Points to Note :

• The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
• "Issue Title" and "PR Title should be the same. Include issue number along with it.
• Follow Contributing Guidelines & Code of Conduct before start Contributing.

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:white_check_mark: To be Mentioned while taking the issue :

• Full name : Ashish Kumar Patel
• GitHub Profile Link :
• Email ID :[email protected]
• Participant ID (if applicable):
• Approach for this Project :
• What is your participant role? (Mention the Open Source program) Gssoc'24 contributor

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Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

[github-actions[bot]]

Thank you for creating this issue! We'll look into it as soon as possible. Your contributions are highly appreciated! 😊

[ojaswichopra]

Full Name: Ojaswi Chopra GitHub Profile Link: ojaswichopra Email ID: [email protected] Approach for this Project:

Data Preprocessing: Handle missing values, encode categorical features, and standardize numerical features. Exploratory Data Analysis (EDA): Analyze and visualize the data to understand the distributions and relationships between features.

Deep Learning Models: Implement various deep learning models to predict IPO performance: Basic Neural Network (NN) Convolutional Neural Network (CNN) Recurrent Neural Network (RNN) with LSTM Deep Neural Network (DNN)

Model Evaluation: Evaluate the performance of each model using metrics like Mean Squared Error (MSE) and Mean Absolute Error (MAE).

What is your participant role?: GSSoC'24 Contributor

[ojaswichopra]

@ashis2004 @abhisheks008 Please assign this issue to me

[ashis2004]

@ojaswichopra I've already done that using these algorithms I've to just make PR

[abhisheks008]

@ashis2004 please finish your previously assigned issue first.