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akcigerKanseri.csv

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app.py

@@ -0,0 +1,71 @@
+# Author: Hanife Kaptan
+# Versions: python==3.11.2, xgboost==2.1.1, streamlit==1.36.0, scikit-learn==1.4.2, pandas==2.0.2
+
+import pandas as pd
+from sklearn.compose import ColumnTransformer
+from sklearn.preprocessing import OneHotEncoder, StandardScaler
+from sklearn.pipeline import Pipeline
+from xgboost import XGBClassifier 
+import streamlit as st
+
+df = pd.read_csv("akcigerKanseri.csv")
+
+X = df.drop("lung_cancer", axis=1)
+y = df["lung_cancer"]
+
+preprocess = ColumnTransformer(
+    transformers = [("cat", OneHotEncoder(), ["gender"]), ("num", (StandardScaler()), ["age"])]
+)
+
+my_model = XGBClassifier()
+
+pipe = Pipeline(steps=[("preprocessor", preprocess), ("model", my_model)])
+pipe.fit(X, y)
+
+def lung_cancer(gender, age, smoking, yellow_fingers, anxiety, peer_pressure,
+                chronic_disease, fatigue, allergy, wheezing, alcohol_consuming,
+                coughing, shortness_of_breath, swallowing_difficulty, chest_pain):
+    input_data = pd.DataFrame({"gender": [gender],
+                               "age": [age],
+                               "smoking": [smoking],
+                               "yellow_fingers": [yellow_fingers],
+                               "anxiety": [anxiety],
+                               "peer_pressure": [peer_pressure],
+                               "chronic_disease": [chronic_disease],
+                               "fatigue": [fatigue],
+                               "allergy": [allergy],
+                               "wheezing": [wheezing],
+                               "alcohol_consuming": [alcohol_consuming],
+                               "coughing": [coughing],
+                               "shortness_of_breath": [shortness_of_breath],
+                               "swallowing_difficulty": [swallowing_difficulty],
+                               "chest_pain": [chest_pain]})
+    prediction = pipe.predict(input_data)[0]
+    return prediction
+
+st.title("Akciğer Kanseri Tespiti :hospital:: @hanifekaptan")
+st.write("Kendinizle ilgili doğru seçenekleri seçiniz.")
+gender = st.radio("Gender", ["Male", "Female"]) # male ve female 1 ve 0 değerlerine dönüştürülecek
+age = st.number_input("Age", 0, 100)
+smoking = st.radio("Smoking", [True, False])
+yellow_fingers = st.radio("Yellow Fingers", [True, False])
+anxiety = st.radio("Anxiety", [True, False])
+peer_pressure = st.radio("peer_pressure", [True, False])
+chronic_disease = st.radio("chronic_disease", [True, False])
+fatigue = st.radio("fatigue", [True, False])
+allergy = st.radio("allergy", [True, False])
+wheezing = st.radio("wheezing", [True, False])
+alcohol_consuming = st.radio("alcohol_consuming", [True, False])
+coughing = st.radio("coughing", [True, False])
+shortness_of_breath = st.radio("shortness_of_breath", [True, False])
+swallowing_difficulty = st.radio("swallowing_difficulty", [True, False])
+chest_pain = st.radio("chest_pain", [True, False])
+
+if st.button("Predict"):
+    pred = lung_cancer(gender, age, smoking, yellow_fingers, anxiety, peer_pressure,
+                       chronic_disease, fatigue, allergy, wheezing, alcohol_consuming,
+                       coughing, shortness_of_breath, swallowing_difficulty, chest_pain)
+    if pred == 1:
+        st.write("Result: Positive")
+    elif pred == 0:
+        st.write("Result: Negative")

requirements.txt

@@ -0,0 +1,5 @@
+streamlit == 1.36.0
+python == 3.11.2
+xgboost == 2.1.1
+scikit-learn == 1.4.2
+pandas == 2.0.2