Solves a kaggle problem to develop algorithms for classify genetic mutations based on clinical evidence (text),which will guide in providing personalized cancer diagnosis and treatment

ML Algorithms Approaches to solve Personalized-Cancer-Diagnosis kaggle problem
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• It explores variety of approaches and machine learning algorithms like Logistic Regression,Naive Bayes,Random Forest,SVM to predict the Genetic Mutation category
• The probability scores and prediction category helps in diagnosing the person better giving personalized cancer treatment

• Categorising the Genetic Mutation in one of 9 categories using the medical text,genes,and genetic variation features
• Predicting Genetic Mutation Category using ML algorithms along with their probability scores

• Python
• Jupyter Notebook

To get a local copy up and running follow these simple steps.

Install python 3.10

1. Clone the repository

   git clone https://github.com/Abhinav1004/Personalized-Cancer-Diagnosis.git

2. Create Virtual Environment in Working directory

   cd Personalized-Cancer-Diagnosis
   virtualenv venv 
   source venv/bin/activate

3. Install the required libraries

   pip install -r requirements.txt

4. Install the required libraries

   pip install -r requirements.txt

5. Download the data from competition website link and paste the data in data folder present in Working Directory

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Abhinav Kumar Jha - [email protected] Project Link: https://github.com/Abhinav1004/Personalized-Cancer-Diagnosis