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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.\n\n# Classification Prediction:\n\nIn the classification prediction model, we aim to analyze customer behavior using the following algorithms: Decision Tree, Logistic Regression, and Random Forest.\n\n# Result: Algorithm Page\n\n![image](https://github.com/DineshDhamodharan24/Data_Science_Final_Project/assets/142207421/ddc89f62-35f9-4ae9-ab0b-13b3315ccf2e)\n\n# Converted \n![image](https://github.com/DineshDhamodharan24/Data_Science_Final_Project/assets/142207421/03863315-8e5e-4b11-9452-563b1766cf16)\n# Not Converted \n![image](https://github.com/DineshDhamodharan24/Data_Science_Final_Project/assets/142207421/2f4f714f-8a1e-40b2-ad21-8103c140f897)\n\n# Image Processing:\n\nIn 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.\n\n# Result\n![image](https://github.com/DineshDhamodharan24/Data_Science_Final_Project/assets/142207421/27ec3c73-c639-455d-8710-b788e7be2f54)\n\n# Text Image -OCR\n![image](https://github.com/DineshDhamodharan24/Data_Science_Final_Project/assets/142207421/910438b8-8ecb-48ed-b537-2cc2f943ade3)\n\n\n# Text Processing:\n\nIn this module, we provide sentiment analysis for text based on user input, utilizing text processing techniques such as NLTK (Natural Language Toolkit).\n\n# Output: \n![image](https://github.com/DineshDhamodharan24/Data_Science_Final_Project/assets/142207421/6c71f2a4-e537-47c0-8dbc-831b7de8c0c1)\n\n![image](https://github.com/DineshDhamodharan24/Data_Science_Final_Project/assets/142207421/3f480f6a-b5d6-4314-bf98-6a52c0c6f186)\n\n# Product Recommendation System:\n\nBuild a recommendation system for product selection using NLTK techniques.\n\n# Output:\n![image](https://github.com/DineshDhamodharan24/Data_Science_Final_Project/assets/142207421/4735a50b-3961-4ae6-bd07-7dcf01cd595a)\n\n\n# Prerequisites\n\nBefore running the code, ensure that you have the following dependencies installed:\n\n* Streamlit\n* Sklearn\n* pandas\n* numpy\n* ocr\n* plotly\n* NLTK\n\n# Conclusion \n\nThe \"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. 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