https://github.com/mominurr/tumors-classification-predictor
https://github.com/mominurr/tumors-classification-predictor
Last synced: 3 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/mominurr/tumors-classification-predictor
- Owner: mominurr
- Created: 2023-10-07T23:46:21.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-10-08T00:03:14.000Z (over 1 year ago)
- Last Synced: 2023-10-09T00:26:00.176Z (over 1 year ago)
- Language: Python
- Size: 1.01 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Tumor Classification Predictor
## Project Overview
This project focuses on building a predictive model for classifying tumors into different types based on their characteristics. It aims to develop a robust machine learning model that can accurately classify tumors into categories such as "Malignant" and "Benign."
## Functions and Features
### tumor_classifier
The "tumor_classifier" function is a comprehensive utility for performing tumor classification. It takes multiple input parameters representing tumor characteristics and provides two key outputs: model accuracy and predicted tumor type.
### Function Workflow
The function performs the following steps in its workflow:
1. **Data Loading and Cleaning:**
- Load the tumor classification dataset using the Pandas library.
- Perform data cleaning to handle missing values, duplicates, or any other data quality issues.
2. **Data Visualization:**
- Utilize Matplotlib and Seaborn to create data visualizations, including scatter plots and histograms. These visualizations help users understand the distribution of tumor characteristics.
3. **Model Training:**
- Train a machine learning classifier on the cleaned dataset. The classifier's goal is to predict the tumor type (e.g., "Malignant" or "Benign") based on tumor characteristics.
4. **Making Predictions:**
- Use the trained classifier to make predictions on new data. The input parameters for prediction are characteristics of the tumor.
5. **Model Evaluation:**
- Evaluate the performance of the trained model using accuracy and other relevant metrics.
## Video Presentation
To see a demonstration of our Tumor Classification Predictor project in action, please watch the following video:
[Demo Video](https://youtu.be/DRGinTnXzN4)
In this video, we provide a step-by-step walkthrough of how to use our project's features, objectives, and results. Feel free to watch the video to get a better understanding of our project.
## Dataset
### Dataset Overview
This project utilizes a dataset containing information about tumor characteristics. The dataset includes the following features:
- **radius_mean:** Mean of the radius of the tumor.
- **perimeter_mean:** Mean of the perimeter of the tumor.
- **area_mean:** Mean of the area of the tumor.
- **smoothness_mean:** Mean of the smoothness of the tumor.
- **compactness_mean:** Mean of the compactness of the tumor.
- **concavity_mean:** Mean of the concavity of the tumor.
- **symmetry_mean:** Mean of the symmetry of the tumor.
### Target Variable
The target variable in this dataset is:
- **diagnosis:** This variable indicates whether the tumor is "Malignant" or not, possibly denoting "Benign."
This dataset serves as the foundation for building a predictive model to classify tumors into categories based on their characteristics.
## Dependencies
This project requires the following Python libraries:
- NumPy
- Pandas
- Scikit-learn
- Matplotlib
- Seaborn (for visualization)
You can install these dependencies using pip:
pip install numpy pandas scikit-learn matplotlib seaborn
## Usage
To use this project, follow these steps:
1. Ensure you have Python installed on your machine.
2. Clone the project repository to your local machine:
```bash
git clone https://github.com/mominurr/Tumors-Classification-Predictor.git
cd Iris-Flower-Classification-Predictor
python flower_classifications_predictor.py
##Author:
[Mominur Rahman](https://github.com/mominurr)