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Cover image for πŸš€ Exploring Predictive Analysis of Breast Tumor Diagnosis with Streamlit and SVM! πŸš€
Amna Akram
Amna Akram

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πŸš€ Exploring Predictive Analysis of Breast Tumor Diagnosis with Streamlit and SVM! πŸš€

Hey Devs! πŸ‘‹ 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:

πŸ” 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.

πŸ“Š 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.

πŸ”§ 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.
πŸ“ˆ 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.

πŸ‘©β€πŸ’» 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!

πŸ”— [https://analysis-of-breast-tumor-diagnosis-bxvsw5lwbt4hbgnhrfxeae.streamlit.app/]

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