https://github.com/dineshdhamodharan24/data_science_final_project
Customer Insights & Recommendation System: Harnessing Decision Tree, Logistic Regression, and Random Forest models for behavior analysis. Utilizing EasyOCR and Python Imaging Library for image information extraction. Employing NLTK for sentiment analysis on textual data
https://github.com/dineshdhamodharan24/data_science_final_project
classification final-project guvi-projects image-processing nltk-python numpy ocr pandas recommendation-system sentiment-analysis sklearn-library streamlit text-processing worldcloud
Last synced: about 1 month ago
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Customer Insights & Recommendation System: Harnessing Decision Tree, Logistic Regression, and Random Forest models for behavior analysis. Utilizing EasyOCR and Python Imaging Library for image information extraction. Employing NLTK for sentiment analysis on textual data
- Host: GitHub
- URL: https://github.com/dineshdhamodharan24/data_science_final_project
- Owner: DineshDhamodharan24
- Created: 2024-02-04T05:42:09.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-06-20T07:49:28.000Z (12 months ago)
- Last Synced: 2025-06-25T02:44:11.692Z (11 months ago)
- Topics: classification, final-project, guvi-projects, image-processing, nltk-python, numpy, ocr, pandas, recommendation-system, sentiment-analysis, sklearn-library, streamlit, text-processing, worldcloud
- Language: Jupyter Notebook
- Homepage: https://www.linkedin.com/in/dinesh-dhamodharan-2bbb9722b/
- Size: 21.1 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Customer Insights and Recommendation System.
# About Project
Develop a comprehensive system that combines classification prediction models, image processing techniques, and text processing for a holistic understanding of customer behavior. Utilize algorithms such as Decision Tree, Logistic Regression, and Random Forest for customer behavior analysis. Incorporate image processing techniques like EasyOCR and Python Imaging Library (PIL) to extract information and identify objects from images. Implement sentiment analysis using NLTK for text-based data. Finally, build a product recommendation system using NLTK techniques to enhance personalized product suggestions for users based on their behavior and preferences.
# Classification Prediction:
In the classification prediction model, we aim to analyze customer behavior using the following algorithms: Decision Tree, Logistic Regression, and Random Forest.
# Result: Algorithm Page
# Converted
# Not Converted
# Image Processing:
In this module, we process images using techniques such as EasyOCR (Optical Character Recognition) to extract text from images, and the Python Imaging Library (PIL) to identify and extract objects from images. Additionally, PIL can be used to modify images by changing formats, rotating, and manipulating pixel sizes.
# Result
# Text Image -OCR
# Text Processing:
In this module, we provide sentiment analysis for text based on user input, utilizing text processing techniques such as NLTK (Natural Language Toolkit).
# Output:
# Product Recommendation System:
Build a recommendation system for product selection using NLTK techniques.
# Output:
# Prerequisites
Before running the code, ensure that you have the following dependencies installed:
* Streamlit
* Sklearn
* pandas
* numpy
* ocr
* plotly
* NLTK
# Conclusion
The "Customer Insights and Recommendation System" is a comprehensive project that employs advanced techniques in classification prediction, image processing, and text analysis to gain a deep understanding of customer behavior. By integrating Decision Tree, Logistic Regression, and Random Forest models, along with image processing tools like EasyOCR and Python Imaging Library, and sentiment analysis using NLTK, the system provides a holistic approach to customer data analysis. The product recommendation system further enhances user experience by offering personalized suggestions based on individual behavior and preferences. Make sure to install the specified dependencies before running the code to ensure seamless functionality.