💊ML-Project-Drug-Review-Dataset💊

• Introduction📌
• Technology Used🚀
• Dataset Used📊
• Getting Started💥
• Proposed Methodology⭐
• ML Models Used🚀
• Results Analysis Screenshots📈
• Further Works💫
• Contributing Guidelines📑
• Code Of Conduct📑
• Project Admin⚡
• Contributing is fun🧡

This is an innovative machine learning project that utilizes patient reviews with many other attributes to analyze and evaluate the effectiveness of different drugs in treating specific conditions. By training on a vast dataset of patient experiences, the model can provide insightful ratings for the available drugs, based on their real-world usage. The project demonstrates the power of advanced machine learning techniques to extract meaningful insights from unstructured data, ultimately enabling more informed decision-making in the healthcare industry.

• pandas: This is used for data manipulation and analysis.
• numPy: This is used for numerical computing with Python.
• beautifulSoup: This is a library used for web scraping purposes to pull data out of HTML and XML files.
• sklearn: This stands for scikit-learn which is a popular machine learning library in Python, which provides tools for data preprocessing, classification, regression, clustering, and more. It is widely used in industry and academia for building machine learning models.
• seaborn: This is a visualization library based on matplotlib used for making attractive and informative statistical graphics.
• matplotlib: This is a plotting library for creating static, animated, and interactive visualizations in Python.

The dataset used for this project is the famous Drug Review Dataset (Drugs.com) by UCI. The dataset can be found and downloaded from here. The data provided is split into a train (75%) a test (25%) partition and stored in two .tsv (tab-separated-values) files, respectively.

TARGET VARIABLE:- Rating

• Fork this Repository.
• Clone the forked repository in your local system.

git clone https://github.com/<your-github-username>/ML-Project-Drug-Review-Dataset.git

• Open the project folder in any local editor like Visual Studio Code.
• Run the file main.py.

• Raise an issue if you find a bug or add a feature.
• Wait for the issue to be assigned and proceed only after the issue is assigned to you.
• Navigate to the project directory.

cd ML-Project-Drug-Review-Dataset

• Create a new branch for your feature.

git checkout -b <your_branch_name>

• Perfom your desired changes to the code base.
• Track and stage your changes.

# Track the changes
git status

# Add changes to Index
git add .

• Commit your changes.

git commit -m "your_commit_message"

• Push your committed changes to the remote repo.

git push origin <your_branch_name>

• Go to your forked repository on GitHub and click on Compare & pull request.
• Add an appropriate title and description to your pull request explaining your changes and efforts done.
• Click on Create pull request.
• Congrats! 🥳 You've made your first pull request to this project repo.
• Wait for your pull request to be reviewed and if required suggestions would be provided to improve it.
• Celebrate 🥳 your success after your pull request is merged successfully.

flowchart TD
A[Step 0 : Datasets provided by the UCI] --> B[Step 1 : Importing the necessary Libraries/Modules in the workspace]
B[Step 1 : Importing Libraries/Modules in the workspace] --> C[Step 2 : Loading and reading both the train and test datasets into the workspace using pandas]
C[Step 2 : Loading and reading the dataset into the workspace using pandas] --> D[Step 3 : Data Preprocessing Starts]
D[Step 3 : Data Preprocessing Starts] --> E[Step 3.1 : Extracting day, month, and year into separate columns]
E[Step 3.1 : Extracting day, month, and year into separate columns] --> F[Step 3.2 : Handling missing values using SimpleImputer]
F[Step 3.2 : Handling missing values using SimpleImputer] --> G[Step 3.3 : Convertiung the text using TfidfVectorizer in NLP]
G[Step 3.3 : Converting the text using TfidfVectorizer of NLP] --> H[Step 3.4 : Encoding the categorical columns using LabelEncoder]
H[Step 3.4 : Encoding the categorical columns using LabelEncoder] --> I[Step 3.5 : Converting the data types of the columns to reduce the memory usage]
I[Step 3.5 : Converting the data types of the columns to reduce the memory usage] --> J[Step 4 : Applying 4 different ML models to find the best accuracy]
J[Step 4 : Applying 4 different ML models to find the best accuracy] --> K[Step 5 : Plotting the different types of plots of every model]

• Computing the performance metrics including mean squared error, and r2 score for both training and testing data.
• Plotting and visualizing the scatter plot of predicted vs actual values for training data, testing data, and both training & testing sets.
• Plotting the residual plot for the testing data.

• Computing the accuracy score of this model.
• Plotting and visualizing the accuracy plot and confusion matrix.

• Computing the accuracy score of this model.
• Plotting and visualizing the scatter plot of actual vs predicted values, step plot of accuracy, and the confusion matrix.

• Computing the accuracy score of this model from epoch range 1 to 10.
• Plotting and visualizing the accuracy vs epoch plot, scatter plot of actual vs predicted values, and the confusion matrix.

• LinearRegression
• LogisticRegression
• Perceptron
• DecisionTreeClassifier