Food Analysis & Modeling

This project is trying to Process , Analyze and Create Predictive modeling on a large set Food Data by Using Text Mining and NLP processes On Different type of Machine Learning Algorithms and evaluate & compare the performances depending on different factors.

Description

In the course of this project, I am exploring innovative approaches to process, visualize, and model Food Data alongside Food product reviews. Our endeavors thus far have led us to meticulously process nearly half a million datasets of Food Reviews. Employing a diverse array of text mining techniques, we have extracted valuable insights from the reviews.

The focal point of our efforts has been the development of a Regression Model and a Classification Model. These models are designed to predict the scores of reviews, discerning nuances in sentiment and categorizing them as positive or negative. Our journey involved navigating through a plethora of reviews to build a robust understanding of the factors influencing the overall score.

At this juncture, our commitment to excellence propels us to delve deeper into the realm of analysis. We are actively engaged in expanding our dataset and incorporating different machine learning techniques to enhance the precision and scope of our models. This ongoing pursuit of knowledge and refinement is steering us towards uncovering even more profound insights from the intricate world of Food Reviews.

Machine Learning algorithms used so far:

Linear regression , Logistic Regression , Decision Tree , k-nearest-neighbours , PCA - Dimensionality reduction algorithm , k-means-clustering , Aggglomerative Clustering , DBSCAN clustering , CountVectorizer , Tfidfvectorizer , AVG-Word2Vector , AVG-TFIDF-Word2Vector

Tech Stack

Python, Jupyter Notebook, Scikit-Learn, Classifier , Regressor , Text Mining , NLP , SQL , SQLAlchemy.

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Authors

• @LordSomen

Run Locally

Clone the project

  git clone https://github.com/LordSomen/Traffic-Analysis my_project

Go to the project directory

  cd my-project

navigate to different notebooks.

Lessons Learned

For me this project offered me various learning opportunities across different domains. Here are some potential learning outcomes:

1. Data Processing Skills:

   • Gain proficiency in handling and processing large datasets, specifically in the context of Food Reviews.
   • Learn techniques to clean, preprocess, and structure textual data for analysis.

2. Text Mining Techniques:

   • Acquire knowledge in various text mining techniques, such as sentiment analysis, topic modeling, and feature extraction.
   • Understand how to extract meaningful information from unstructured text data.

3. Machine Learning Models:

   • Develop expertise in building both Regression and Classification models for predictive analysis.
   • Learn the intricacies of model training, evaluation, and fine-tuning.

4. Feature Engineering:

   • Understand the importance of feature selection and engineering in improving model performance.
   • Explore different features relevant to Food Reviews and their impact on predictions.

5. Data Visualization:

   • Gain skills in visually representing insights from data, enabling effective communication of findings.
   • Learn how to use visualization tools to convey complex information in an understandable manner.

6. Model Evaluation and Interpretation:

   • Learn techniques for evaluating model performance and understanding the implications of different evaluation metrics.
   • Gain insights into interpreting model results and understanding the factors influencing predictions.

7. Continuous Improvement:

   • Develop a mindset of continuous improvement by actively seeking ways to enhance model accuracy and efficiency.
   • Understand the importance of iterating on models as new data becomes available or as project goals evolve.

8. Domain-Specific Knowledge:

   • Deepen your understanding of the Food Industry and the factors influencing consumer reviews on food products.
   • Explore how domain-specific insights can be integrated into data analysis and modeling.

9. Project Management:

   • Enhance project management skills by navigating through the various stages of data processing, analysis, and model development.
   • Learn to adapt and iterate based on project requirements and emerging insights.

10. Communication Skills:

    • Develop effective communication skills to convey complex technical concepts to both technical and non-technical stakeholders.
    • Learn how to present findings in a clear and compelling manner.

These learning outcomes collectively contribute to a well-rounded skill set in data science, machine learning, and domain-specific knowledge, making the project a valuable learning experience.

🛠 Skills

Python, Machine Learning, NLP, Text Mining, research, analytics.

🚀 About Me

Software Developer with experience of almost 2 Years working in different software roles. Very much Enthusiast about DataScience | AI | ML | DL . Skilled in Python , Data Analysis, Software Engineering, Github, Linux, Algorithms, SQL, and Object-Oriented Programming (OOP)

🔗 Links

Support

For any queries feell free to contact me at soumyajit637@gmail.com .