diff --git a/Breast-Cancer-Classifier-Web-App-using-Streamlit/Benign tumour.png b/Breast-Cancer-Classifier-Web-App-using-Streamlit/Benign tumour.png new file mode 100644 index 0000000..2271585 Binary files /dev/null and b/Breast-Cancer-Classifier-Web-App-using-Streamlit/Benign tumour.png differ diff --git a/Breast-Cancer-Classifier-Web-App-using-Streamlit/Breast_Cancer_Classifier.ipynb b/Breast-Cancer-Classifier-Web-App-using-Streamlit/Breast_Cancer_Classifier.ipynb new file mode 100644 index 0000000..b3642ac --- /dev/null +++ b/Breast-Cancer-Classifier-Web-App-using-Streamlit/Breast_Cancer_Classifier.ipynb @@ -0,0 +1,337 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BREAST CANCER CLASSIFICATION" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "#importing the dependencies\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn import metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " mean_radius mean_texture mean_perimeter mean_area mean_smoothness \\\n", + "0 17.99 10.38 122.80 1001.0 0.11840 \n", + "1 20.57 17.77 132.90 1326.0 0.08474 \n", + "2 19.69 21.25 130.00 1203.0 0.10960 \n", + "3 11.42 20.38 77.58 386.1 0.14250 \n", + "4 20.29 14.34 135.10 1297.0 0.10030 \n", + "\n", + " diagnosis \n", + "0 0 \n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "4 0 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#importing the dataset\n", + "data = pd.read_csv('Breast_cancer_data.csv')\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(569, 6)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "mean_radius 0\n", + "mean_texture 0\n", + "mean_perimeter 0\n", + "mean_area 0\n", + "mean_smoothness 0\n", + "diagnosis 0\n", + "dtype: int64" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "X = data.drop(['diagnosis'],axis=1)\n", + "Y = data['diagnosis']" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size=0.2,stratify=Y,random_state=101)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(569, 5) (455, 5) (114, 5)\n" + ] + } + ], + "source": [ + "print(X.shape,X_train.shape,X_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "ran_classifier = RandomForestClassifier()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
RandomForestClassifier()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "RandomForestClassifier()" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ran_classifier.fit(X_train,Y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "testing_data_prediction1 = ran_classifier.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "score1 = metrics.accuracy_score(Y_test,testing_data_prediction1)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9122807017543859" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "score1" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "\n", + "filename = 'Breast_Cancer_Classifier.sav'\n", + "pickle.dump(ran_classifier,open(filename,'wb'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "486e0d5a79acdbfffd563ee7a67a93a5017bd2a4f66495483a69f0245c8a4a6c" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Breast-Cancer-Classifier-Web-App-using-Streamlit/Breast_Cancer_Classifier.sav b/Breast-Cancer-Classifier-Web-App-using-Streamlit/Breast_Cancer_Classifier.sav new file mode 100644 index 0000000..501a88e Binary files /dev/null and b/Breast-Cancer-Classifier-Web-App-using-Streamlit/Breast_Cancer_Classifier.sav differ diff --git a/Breast-Cancer-Classifier-Web-App-using-Streamlit/Breast_Cancer_Classifier_Web_App.py b/Breast-Cancer-Classifier-Web-App-using-Streamlit/Breast_Cancer_Classifier_Web_App.py new file mode 100644 index 0000000..406232e --- /dev/null +++ b/Breast-Cancer-Classifier-Web-App-using-Streamlit/Breast_Cancer_Classifier_Web_App.py @@ -0,0 +1,52 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed Feb 8 22:59:27 2023 + +@author: HP +""" + +import numpy as np +import pickle +import streamlit as st + +#load the model +loaded_model = pickle.load(open("Breast_Cancer_Classifier.sav",'rb')) + +def breast_cancer_classifer(input_data): + # changing the input_data to numpy array + input_data_as_numpy_array = np.asarray(input_data) + + # reshape the array as we are predicting for one instance + input_data_reshaped = input_data_as_numpy_array.reshape(1,-1) + + prediction = loaded_model.predict(input_data_reshaped) + print(prediction) + + if (prediction[0] == 0): + return 'This is a Benign Tumour(Non-Cancerous)' + else: + return 'This is a Malignent Tumour(Cancerous)' + +def main(): + + #giving a title + st.title('Breast Cancer Classifier Web App') + + #getting input from the user + mean_radius = st.text_input("Mean Radius") + mean_texture = st.text_input("Mean Texture") + mean_perimeter = st.text_input("Mean Perimeter") + mean_area = st.text_input("Mean Area") + mean_smoothness = st.text_input("Mean Smoothness") + + #code for prediction + diagnosis = '' + + # getting the input data from the user + if st.button('Heart Disease Test Result : '): + diagnosis = breast_cancer_classifer([mean_radius,mean_texture,mean_perimeter,mean_area,mean_smoothness]) + + st.success(diagnosis) + +if __name__ == "__main__": + main() diff --git a/Breast-Cancer-Classifier-Web-App-using-Streamlit/Breast_cancer_data.csv 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+7.76,24.54,47.92,181.0,0.05263,1 diff --git a/Breast-Cancer-Classifier-Web-App-using-Streamlit/Malignant Tumour.png b/Breast-Cancer-Classifier-Web-App-using-Streamlit/Malignant Tumour.png new file mode 100644 index 0000000..7fc2a82 Binary files /dev/null and b/Breast-Cancer-Classifier-Web-App-using-Streamlit/Malignant Tumour.png differ diff --git a/Breast-Cancer-Classifier-Web-App-using-Streamlit/README.md b/Breast-Cancer-Classifier-Web-App-using-Streamlit/README.md new file mode 100644 index 0000000..154844e --- /dev/null +++ b/Breast-Cancer-Classifier-Web-App-using-Streamlit/README.md @@ -0,0 +1,55 @@ +# Breast-Cancer-Classifier +This is a Breast Cancer Classifier using Machine Learning. + +![Type](https://img.shields.io/badge/Machine-Learning-red.svg) +![IDE](https://img.shields.io/badge/IDE-JupyterNotebook-orange.svg) +![Type](https://img.shields.io/badge/Type-Supervised-yellow.svg) +![Status](https://img.shields.io/badge/Status-Completed-cherryred.svg) + +Link to the web app : + + + + + + +### There are two types of tumours detected in human body : + +
+Benign tumour +

BENIGN TUMOUR

+ +
+
+
+Malignant tumour +

MALIGNANT TUMOUR

+ +
+ +

WORKFLOW OF THE PROJECT

+ +```mermaid +flowchart TD + +A[Step 0 : Collect Data] --> B[Step 1 : Import Libraries/Modules in the workspace] +B[Step 1 : Import Libraries/Modules in the workspace] --> C[Step 2 : Import the collected data into the workspace] +C[Step 2 : Import the collected data into the workspace] --> D[Step 3 : Data Preprocessing] +D[Step 3 : Data Preprocessing] --> E[Step 4 : Perform EDA by visualizing the data] +E[Step 4 : Perform EDA by visualizing the data] --> F[Step 5 : Train ML model using LOGISTIC REGRESSION] +F[Step 5 : Train ML model using LOGISTIC REGRESSION] --> G[Step 6 : Deploy ML model as a Web App] +``` diff --git a/Breast-Cancer-Classifier-Web-App-using-Streamlit/requirements.txt b/Breast-Cancer-Classifier-Web-App-using-Streamlit/requirements.txt new file mode 100644 index 0000000..4cf4b0d --- /dev/null +++ b/Breast-Cancer-Classifier-Web-App-using-Streamlit/requirements.txt @@ -0,0 +1,4 @@ +numpy==1.21.4 +pickle-mixin==1.0.2 +streamlit==1.2.0 +scikit-learn==1.0.1 \ No newline at end of file