Online Shoppers' Intention Prediction

[srii5477]

Deep Learning Simplified Repository (Proposing new issue)

🔴 Project Title : Online Shoppers' Intention Prediction Model

🔴 Aim : Helping e-commerce businesses tailor their marketing and advertising on their online platforms based on whether the visiting user is intending to purchase an item or is not fully convinced by the value and usefulness of the product.

🔴 Dataset : Online Shopper Intention Dataset from UCI's Machine Learning Library

🔴 Approach : Do necessary data preprocessing and feature engineering, use an ANN and decide the optimal number of layers, activation function and other parameters by trial.

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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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🔴🟡 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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✅ To be Mentioned while taking the issue :

• Full name : Sridevi S.
• GitHub Profile Link : https://github.com/srii5477
• Email ID :
• Participant ID (if applicable):
• Approach for this Project : ANN
• What is your participant role? (Mention the Open Source program) Contributor in GSSOC Extd '24

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Happy Contributing 🚀

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

[github-actions]

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

[Teja-m9]

hey @srii5477 please assign this task to me as i have some experirnece in designing and creating these type of tasks

[srii5477]

I am asking for this issue to be assigned to me.

[abhisheks008]

Hi @srii5477 can you clarify on the approach you are taking for solving this problem statement? Also can you share the dataset URL?

[srii5477]

Hi, I'm planning to use a feedforward neural network to tackle this problem. The dataset URL is: https://archive.ics.uci.edu/dataset/468/online+shoppers+purchasing+intention+dataset. I will be using regularization techniques like dropout/early stopping to improve generalization.

[abhisheks008]

Hi, I'm planning to use a feedforward neural network to tackle this problem. The dataset URL is: https://archive.ics.uci.edu/dataset/468/online+shoppers+purchasing+intention+dataset. I will be using regularization techniques like dropout/early stopping to improve generalization.

Hi @srii5477 you need to implement at least 3-4 models for this problem statement. Hence please update your approach as per the requirements.

[Abhiiesante]

As an experienced ml, dl practitioner. Can you please assign this issue to me under 𝗚𝗦𝗦𝗼𝗖 '𝟮𝟰 𝗘𝘅𝘁𝗲𝗻𝗱𝗲𝗱 and Hacktoberfest
use a deep learning model like a fully connected neural network (FCNN) or LSTM for sequential shopping session data.
train the model using historical shopping data, optimizing for accuracy using techniques like dropout, regularization, and learning rate scheduling.
validate the model's performance on a test set using metrics such as accuracy, precision, recall, and F1-score to predict shopper intentions effectively.

[srii5477]

@abhisheks008 I will use an LSTM, Random Forest classifier, and XGBoost classifier in addition to the MLP to approach the problem.

[abhisheks008]

@abhisheks008 I will use an LSTM, Random Forest classifier, and XGBoost classifier in addition to the MLP to approach the problem.

As this repository mainly focuses on deep learning models, hence please update your approach based on the requirements and revert back.