Multi-Round Interpersonal Dialogues Text Data Analysis

[abhisheks008]

ML-Crate Repository (Proposing new issue)

:red_circle: Project Title : Multi-Round Interpersonal Dialogues Text Data Analysis :red_circle: Aim : The aim is to analyze the dataset using machine learning methods. :red_circle: Dataset : https://www.kaggle.com/datasets/nexdatafrank/multi-round-interpersonal-dialogues-text-data :red_circle: Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

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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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:red_circle::yellow_circle: 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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:white_check_mark: To be Mentioned while taking the issue :

• Full name :
• GitHub Profile Link :
• Participant ID (If not, then put NA) :
• Approach for this Project :
• What is your participant role? (Mention the Open Source Program name. Eg. HRSoC, GSSoC, GSOC etc.)

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Happy Contributing 🚀

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

[github-actions[bot]]

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

[VanshGupta-2404]

Full name : Vansh Gupta GitHub Profile Link : https://github.com/VanshGupta-2404 Participant ID (If not, then put NA) : NA Approach for this Project :

To achieve the aim of the project, the following approach will be taken:

1. Exploratory Data Analysis (EDA)

   • Data Overview: Inspect the structure, size, and type of the dataset.
   • Text Data Preprocessing: Clean the text data by removing unnecessary characters, handling missing values, and normalizing the text.
   • Data Visualization: Visualize the frequency of words, sentence lengths, and other textual features.
   • Statistical Analysis: Compute statistics like mean, median, and mode of textual features.

2. Data Preprocessing and Feature Engineering

   • Tokenization: Split the text into individual words or tokens.

3. Model Building

   • Naive Bayes Classifier: A simple probabilistic classifier based on Bayes' theorem.
   • Support Vector Machine (SVM): A powerful classifier that works well with text data.
   • Random Forest Classifier: An ensemble method that uses multiple decision trees.
   • Recurrent Neural Networks (RNN): Advanced deep learning models for sequential data, especially LSTM or GRU networks.

4. Model Evaluation

   • Accuracy Score: Measure the proportion of correct predictions.

What is your participant role? (Mention the Open Source Program name. Eg. HRSoC, GSSoC, GSOC etc.): VSOC

[abhisheks008]

One issue at a time @VanshGupta-2404