Hey Devs! 👋 I'm excited to share my latest project where I've combined the power of Python, Streamlit, and Support Vector Machines (SVM) to build an interactive app for predicting breast tumor diagnoses. Here’s a glimpse into what I’ve created:
🔍 Project Overview:
Breast cancer is a significant health concern, and early detection is crucial. My project utilizes fine-needle aspiration test data to classify tumors as malignant or benign. This application aims to support healthcare professionals in making informed decisions.
📊 Features and Highlights:
Data Upload and Exploration: Users can upload CSV or Excel files to explore data distributions and summary statistics instantly.
Exploratory Data Analysis (EDA): Visualize data with histograms, density plots, and correlation matrices to uncover insights before model training.
Data Preprocessing: Automate preprocessing steps like encoding categorical data and handling missing values to prepare data for machine learning.
Model Training with SVM: Build and optimize SVM models using Grid Search to achieve the best performance in classifying tumors.
Evaluation and Visualization: Assess model accuracy with classification reports, confusion matrices, and ROC curves. Visualize decision boundaries to understand how SVM classifies data points.
🔧 Tech Stack:
Python: For data processing, modeling, and visualization.
Streamlit: Interactive web app development.
Scikit-learn: Machine learning models and pipelines.
Matplotlib and Seaborn: Data visualization.
📈 Why It Matters:
This project showcases how machine learning can aid in healthcare diagnostics, emphasizing the importance of data-driven decisions in medical practices. It's a testament to the power of AI in making a real impact on people's lives.
👩💻 Join Me!:
Explore the app, dive into the code, and let's discuss how we can leverage technology for healthcare innovation. Your feedback and contributions are invaluable!
🔗 [https://analysis-of-breast-tumor-diagnosis-bxvsw5lwbt4hbgnhrfxeae.streamlit.app/]
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