Drug Recommendation System based on Sentiment Analysis of Drug Reviews

Demo Video
The objective of this project is to develop a Drug Recommendation System that leverages sentiment analysis of drug reviews to provide personalized recommendations. This system aims to help users find suitable drugs based on the experiences and feedback of other users with similar conditions. The project involves data collection, preprocessing, sentiment analysis, recommendation generation, and deployment using Flask.
This Project is based on sentiment analysis of the drug whether the drug should be given for patients, it is advisable or not to the patients. This project is implemented using Natural Language processing using a bag of words model and other techniques like vectorization to analyze the drug reviews
Data Collection
Objective
The first step involves gathering a comprehensive dataset containing drug reviews to analyze user sentiments and derive recommendations.
Sources
Online Platforms
: Websites such as Drugs.com and user forums and Kaggle.
Existing Datasets
: Publicly available datasets like the UCI Drug Review dataset.
Content
The dataset should include:
Review Text
: The content of the user's review.
Drug Name
: The name of the drug being reviewed.
Condition
: The medical condition the drug is intended to treat.
User Ratings
: Numerical ratings provided by the users.
Metadata
: Additional information such as review date, user demographics, etc.
Methods
Collaborative Filtering
Leverage user reviews and ratings to find similar users and recommend drugs.
Content-Based Filtering
Recommend drugs similar to those the user has positively reviewed based on drug features and user preferences.
Hybrid Approaches
Combine collaborative and content-based methods for more accurate recommendations.
Installation Steps :-
Install Python 3.7
Install all dependencies cmd -
python -m pip install --user -r requirements.txt
Finally run cmd -
python app.py
Download

Detailed Project Description

Drug Recommendation System based on Sentiment Analysis of Drug Reviews applies machine learning to a real dataset: it prepares the data, trains and evaluates a model, and exposes the trained model through a simple interface so predictions can be made on new inputs.

How It Works
1. The dataset is collected/loaded, cleaned and explored (missing values, encoding, scaling).
2. Features are engineered and the data split into training and test sets.
3. One or more models are trained and compared using accuracy, precision/recall, F1 or RMSE.
4. The best model is saved and served through a web/GUI form that returns predictions.

Technology Roles
- Python
- Flask

Implementation Steps
1. Collect the dataset (public repository or own data) and understand each attribute.
2. Set up the environment (Python, Flask) and load the data into a notebook.
3. Clean the data: handle missing values, outliers, encoding and scaling; visualise distributions.
4. Split into train/validation/test sets and engineer relevant features.
5. Train several candidate models, tune hyper-parameters and compare metrics.
6. Save the best model and wrap it in a simple web/GUI form for live predictions.
7. Document results with plots, confusion matrix / error charts and conclusions.

Key Features
- Data pre-processing pipeline
- Model comparison
- Evaluation metrics and plots
- Prediction interface

Expected Output
On completion the student will have evaluation results (metrics, confusion matrix or error plots) and a working prediction form.

Deliverables
- Notebook / source code with comments
- Cleaned dataset and trained model file
- Evaluation report with metrics and plots
- Prediction interface