Update README.md

Author: SimranShaikh20

Research Topic Prediction using Deep Learning/Model/README.md

@@ -1,20 +1,47 @@
 # Research-topic-Prediction
 
-### Problem Statement
+## Overview
+
 Researchers have access to large online archives of scientific articles. As a consequence, finding relevant articles has become more difficult. Tagging or topic modelling provides a way to give token of identification to research articles which facilitates recommendation and search process.
 
-Given the abstract and title for a set of research articles, predict the topics for each article included in the test set. 
+## Dataset
+
+The dataset used in this challenge consists of research papers with their titles, abstracts, and corresponding categories. The categories include:
+
+- Computer Science
+- Physics
+- Mathematics
+- Statistics
+- Quantitative Biology
+- Quantitative Finance
+
+## Approach
+
+The approach used to solve this challenge is as follows:
+
+1. **Data Preprocessing**: The title and abstract of each research paper are combined and preprocessed by removing punctuation, converting to lowercase, and removing stop words.
+2. **Feature Extraction**: The preprocessed text data is then converted into numerical features using the CountVectorizer and TfidfTransformer from scikit-learn.
+3. **Model Training**: A MultiOutputClassifier with a LinearSVC estimator is trained on the feature data to predict the categories of the research papers.
+4. **Model Evaluation**: The performance of the model is evaluated using accuracy score, precision, recall, and F1-score.
+5. **Submission**: The predicted categories for the test data are submitted in a CSV file.
 
-Note that a research article can possibly have more than 1 topic. The research article abstracts and titles are sourced from the following 6 topics: 
+## Code Structure
 
-1. Computer Science
+The code is organized into the following sections:
 
-2. Physics
+1. **Importing Libraries**: The necessary libraries, including scikit-learn, pandas, and numpy, are imported.
+2. **Loading Data**: The training and test data are loaded from CSV files.
+3. **Data Preprocessing**: The title and abstract of each research paper are combined and preprocessed.
+4. **Feature Extraction**: The preprocessed text data is converted into numerical features.
+5. **Model Training**: The MultiOutputClassifier with a LinearSVC estimator is trained on the feature data.
+6. **Model Evaluation**: The performance of the model is evaluated using accuracy score, precision, recall, and F1-score.
+7. **Submission**: The predicted categories for the test data are submitted in a CSV file.
 
-3. Mathematics
+## Dependencies
 
-4. Statistics
+The following dependencies are required to run the code:
 
-5. Quantitative Biology
+- scikit-learn
+- pandas
+- numpy
 
-6. Quantitative Finance