Open ashis2004 opened 1 month ago
Thank you for creating this issue! We'll look into it as soon as possible. Your contributions are highly appreciated! š
Full Name: Ojaswi Chopra GitHub Profile Link: ojaswichopra Email ID: ojaswichopra06@gmail.com 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
@ashis2004 @abhisheks008 Please assign this issue to me
@ojaswichopra I've already done that using these algorithms I've to just make PR
@ashis2004 please finish your previously assigned issue first.
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:Results
š Follow the Guidelines to Contribute in the Project :
requirements.txt
- This file will contain the required packages/libraries to run the project in other machines.Model
folder, theREADME.md
file must be filled up properly, with proper visualizations and conclusions.:red_circle::yellow_circle: Points to Note :
:white_check_mark: To be Mentioned while taking the issue :
Happy Contributing š
All the best. Enjoy your open source journey ahead. š