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README.md

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----
-title: CancerPrediction
-emoji: 🐨
-colorFrom: yellow
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.42.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Cancer Prediction ML Classification
+emoji: 📉
+colorFrom: indigo
+colorTo: green
+sdk: streamlit
+sdk_version: 1.42.2
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import streamlit as st
+import pandas as pd
+import pickle
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import train_test_split
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import RandomForestClassifier
+from xgboost import XGBClassifier
+from scipy.stats import norm
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+## loading the model
+
+dt=pickle.load(open('dt.pkl','rb'))
+rf=pickle.load(open('rf.pkl','rb'))
+knn=pickle.load(open('kn.pkl','rb'))
+xgb=pickle.load(open('xg.pkl','rb'))
+log=pickle.load(open('log.pkl','rb'))
+
+
+# Title and description
+st.markdown('<h1 style="color:blue;text-align:center;">Cancer Prediction Tool</h1>', unsafe_allow_html=True)
+st.markdown("""
+Welcome to the Cancer Prediction Tool. This tool helps predict the likelihood of cancer based on various patient details. 
+Please enter the necessary details in the sidebar, and click 'Predict' to see the results.
+""")
+
+st.sidebar.header('Enter the details of the patient')
+
+Gender=st.sidebar.selectbox('Gender',('Male','Female'))
+
+Age=st.sidebar.number_input('Age',min_value=18,max_value=90)
+
+Tumor_Size=st.sidebar.number_input('Tumor Size',step=0.1,min_value=float(0.1),max_value=float(20))
+Tumor_Grade=st.sidebar.selectbox('Tumor Grade',('High','Medium','Low'))
+
+Symptoms_Severity=st.sidebar.selectbox('Symptoms Severity',('Mild','Moderate','Severe'))
+
+Family_History=st.sidebar.selectbox('Family History',('Yes','No'))
+
+Smoking_History=st.sidebar.selectbox('Smoking',('Former Smoker', 'Current Smoker', 'Non-Smoker'))
+
+Alcohol_Consumption=st.sidebar.selectbox('Alcohol Consumption',('High', 'Moderate','Low'))
+
+Exercise_Frequency=st.sidebar.selectbox('Exercise_frequency',('Regularly', 'Rarely', 'Occasionally', 'Never'))
+
+
+if st.sidebar.button('Predict'):
+    features=[[Age, Gender, Tumor_Size, Tumor_Grade, Symptoms_Severity,
+       Family_History, Smoking_History, Alcohol_Consumption,
+       Exercise_Frequency]]
+    features=pd.DataFrame(features,columns=['Age', 'Gender', 'Tumor_Size', 'Tumor_Grade', 'Symptoms_Severity',
+       'Family_History', 'Smoking_History', 'Alcohol_Consumption',
+       'Exercise_Frequency'])
+    
+    pred=log.predict(features)
+    if pred==1:
+        st.header("Patient has :red[Cancer]",divider='rainbow')
+    else:
+        st.header("Patient has :green[no Cancer]",divider='rainbow')
+    probability = norm.cdf(pred, loc=0, scale=1)
+    plt.figure(figsize=(2,2))
+    plt.pie([1-probability[0], probability[0]], labels=['No Cancer', 'Cancer'], autopct='%1.1f%%', shadow=True, startangle=140, explode=[0,0.05])
+    plt.title('Probability of Cancer', fontsize=12)
+    st.pyplot(plt)

dt.pkl

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+size 92183

kn.pkl

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log.pkl

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requirements.txt

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+streamlit
+pandas
+scikit-learn
+xgboost
+plotly
+seaborn
+matplotlib

rf.pkl

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svm.pkl

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xg.pkl

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