Update README.md

README.md

@@ -5,9 +5,108 @@ tags:
   - classification
   - healthcare
   - lung-cancer
+  - streamlit
 library_name: scikit-learn
 model_name: Datathon Lung Cancer Detector
 datasets:
   - custom
 language: en
 ---
+
+# 🫁 Datathon Lung Cancer Detector
+
+This model predicts whether a patient is likely to have lung cancer based on clinical and behavioral risk factors.  
+It was trained on a dataset of 309 entries with 15 input features and a binary diagnosis label.
+
+---
+
+## 📊 Input Features
+
+| Feature                 | Type     | Description                     |
+|------------------------|----------|---------------------------------|
+| `GENDER`               | 0 = Female, 1 = Male | Biological sex |
+| `AGE`                  | Integer  | Patient age                     |
+| `SMOKING`              | 0/1      | Smoking habit                   |
+| `YELLOW_FINGERS`       | 0/1      | Stained fingers from smoking    |
+| `ANXIETY`              | 0/1      | Anxiety symptoms                |
+| `PEER_PRESSURE`        | 0/1      | Influence from peers            |
+| `CHRONIC DISEASE`      | 0/1      | History of chronic illness      |
+| `FATIGUE`              | 0/1      | Feeling of tiredness            |
+| `ALLERGY`              | 0/1      | Known allergies                 |
+| `WHEEZING`             | 0/1      | Wheezing symptoms               |
+| `ALCOHOL CONSUMING`    | 0/1      | Alcohol consumption             |
+| `COUGHING`             | 0/1      | Persistent coughing             |
+| `SHORTNESS OF BREATH`  | 0/1      | Difficulty breathing            |
+| `SWALLOWING DIFFICULTY`| 0/1      | Trouble swallowing              |
+| `CHEST PAIN`           | 0/1      | Pain in chest area              |
+
+---
+
+## 🧠 Model Info
+
+- **Algorithm**: XG Boost Classifier(Highest Score)
+- **Framework**: Scikit-learn
+- **Target**: `DIAGNOSIS_LUNG_CANCER` (`YES` = Lung Cancer, `NO` = No Cancer)
+- **Dataset Size**: 309 samples
+- **Preprocessing**: Label encoding, binary encoding for yes/no inputs
+
+---
+
+## 🚀 Try It in Streamlit
+
+This model is also available as a web app built using [Streamlit]. Access on https://datathonlungcancer-fazneuznw5uwkemskdn9kn.streamlit.app/
+
+```python
+import streamlit as st
+import pandas as pd
+import joblib
+
+model = joblib.load('model.pkl')
+
+st.title('🫁 Lung Cancer Diagnosis')
+st.write("Please fill out the following information to assess the likelihood of lung cancer.")
+
+gender = st.selectbox('Gender', [0, 1], format_func=lambda x: "Female" if x == 0 else "Male")
+age = st.number_input('Age', max_value=120, value=0)
+smoking = st.selectbox('Smoking', ['Yes', 'No'])
+yellow_fingers = st.selectbox('Yellow Fingers', ['Yes', 'No'])
+anxiety = st.selectbox('Anxiety', ['Yes', 'No'])
+peer_pressure = st.selectbox('Peer Pressure', ['Yes', 'No'])
+chronic_disease = st.selectbox('Chronic Disease', ['Yes', 'No'])
+fatigue = st.selectbox('Fatigue', ['Yes', 'No'])
+allergy = st.selectbox('Allergy', ['Yes', 'No'])
+wheezing = st.selectbox('Wheezing', ['Yes', 'No'])
+alcohol = st.selectbox('Alcohol Consuming', ['Yes', 'No'])
+coughing = st.selectbox('Coughing', ['Yes', 'No'])
+shortness_of_breath = st.selectbox('Shortness of Breath', ['Yes', 'No'])
+swallowing_difficulty = st.selectbox('Swallowing Difficulty', ['Yes', 'No'])
+chest_pain = st.selectbox('Chest Pain', ['Yes', 'No'])
+
+def binary_encode(value):
+    return 1 if value == 'Yes' else 0
+
+data = pd.DataFrame([[gender, age,
+                      binary_encode(smoking),
+                      binary_encode(yellow_fingers),
+                      binary_encode(anxiety),
+                      binary_encode(peer_pressure),
+                      binary_encode(chronic_disease),
+                      binary_encode(fatigue),
+                      binary_encode(allergy),
+                      binary_encode(wheezing),
+                      binary_encode(alcohol),
+                      binary_encode(coughing),
+                      binary_encode(shortness_of_breath),
+                      binary_encode(swallowing_difficulty),
+                      binary_encode(chest_pain)]],
+                    columns=['GENDER', 'AGE', 'SMOKING', 'YELLOW_FINGERS', 'ANXIETY',
+                             'PEER_PRESSURE', 'CHRONIC DISEASE', 'FATIGUE', 'ALLERGY',
+                             'WHEEZING', 'ALCOHOL CONSUMING', 'COUGHING',
+                             'SHORTNESS OF BREATH', 'SWALLOWING DIFFICULTY', 'CHEST PAIN'])
+
+if st.button('Predict'):
+    prediction = model.predict(data)[0]
+    if prediction == 1:
+        st.error("⚠️ High risk of lung cancer. Please consult a doctor.")
+    else:
+        st.success("✅ No Lung Cancer.")