Pratzybha commented 3 weeks ago

Pull Request for DL-Simplified 💡

Issue Title : Online Food Delivery Preferences

Info about the related issue (Aim of the project) : The project focuses on two primary objectives:

Predict whether customers will place future orders using CNN, RNN, and a hybrid RNN+LSTM model based on demographic data such as age, occupation, monthly income, and family size. Perform sentiment analysis on customer reviews to better understand customer experiences, using DNN, LSTM, and GRU models to classify the reviews as positive or negative.

Name: Pratibha Gajanan Balgi
GitHub ID: Pratzybha
Email ID: pratibhagajananbalgi.201ee240@nitk.edu.in
Idenitfy yourself: (Mention in which program you are contributing in. Eg. For a JWOC 2022 participant it's, JWOC Participant) Gssoc Ext 24 Contributor

Closes: #793

Describe the add-ons or changes you've made 📃

1) Data Preprocessing: -Removed missing and irrelevant data (e.g., 'Nil' reviews). -Tokenized reviews and converted them into sequences suitable for deep learning models.

2) Exploratory Data Analysis (EDA): -Analyzed the distribution of customer demographics such as age, income, family size etc. -Created visualizations like bar charts and word clouds for reviews to understand sentiment polarity.

3) Model Implementation for Prediction: -Built CNN, RNN, and RNN+LSTM models to predict customer reordering behavior. -Experimented with different architectures to capture patterns in structured data.

4) Model Implementation for Sentiment Analysis: -Developed DNN, LSTM, and GRU models for customer review analysis. -These models were optimized to handle varying text lengths and interpret user sentiment effectively.

5) Evaluation and Comparison: -Compared models using accuracy, precision, recall, and F1-score. -Identified the most accurate models for each task.

Type of change ☑️

What sort of change have you made:

[ ] Bug fix (non-breaking change which fixes an issue)
[x] New feature (non-breaking change which adds functionality)
[ ] Code style update (formatting, local variables)
[ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
[ ] This change requires a documentation update

How Has This Been Tested? ⚙️

-Compared models using accuracy, precision, recall, and F1-score. -Identified the most accurate models for each task.

Checklist: ☑️

[x] My code follows the guidelines of this project.
[x] I have performed a self-review of my own code.
[x] I have commented my code, particularly wherever it was hard to understand.
[x] I have made corresponding changes to the documentation.
[x] My changes generate no new warnings.
[x] I have added things that prove my fix is effective or that my feature works.
[x] Any dependent changes have been merged and published in downstream modules.