Upload app.py

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.tree import DecisionTreeClassifier
+# -----------------------------------------------------------
+
+df=pd.read_csv(r"C:\\Users\\Nandini Gupta\\Downloads\\ObesityDataSet_raw_and_data_sinthetic.csv")
+df_prep = df.copy()
+
+# create dummy variables
+df_prep = pd.get_dummies(df_prep,columns=["Gender","family_history_with_overweight","FAVC","CAEC","SMOKE","SCC","CALC","MTRANS"])
+
+# split dataset in features and target variable
+# Features
+X = df_prep.drop("NObeyesdad", axis = 1)
+
+# Target variable
+y = df_prep['NObeyesdad']
+
+# import sklearn packages for data treatments
+# Import train_test_split function
+
+# Split dataset into training set and test set
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)
+
+mm = MinMaxScaler()
+X_train_mm_scaled = mm.fit_transform(X_train)
+X_test_mm_scaled = mm.transform(X_test)
+model=DecisionTreeClassifier()
+clf_mm_scaled = model.fit(X_train_mm_scaled, y_train)
+clf_scaled = model.fit(X_train_mm_scaled,y_train)
+y_pred_mm_scaled = clf_scaled.predict(X_test_mm_scaled)
+# -----------------------------------------------------------
+
+st.title("Uncovering Hidden Relationships: Obesity, Lifestyle Expressions")
+st.markdown("FIND YOUR WAY TO HEALTH")
+st.header("LIFESTYLE CHOICES")
+col1, col2 = st.columns(2)
+with col1:
+    # st.text("Sepal characteristics")
+    gen = st.selectbox("Select your gender", options=["Male", "Female"])
+    age = st.slider("Age", 100, 10)
+    height = st.slider("Select your height", 1.0, 2.0, step=0.01, format="%0.2f")
+    weight = st.slider("Select  your height", 0.0, 300.0)
+    fm = st.selectbox("Family history of obesity", options=["Yes", "No"])
+    favc = st.selectbox("Frequent consumption of high caloric food ", options=["Yes", "No"])
+    fcvc = st.slider("Frequency of consumption of vegetables", 1.0, 4.0, step=0.1, format="%0.2f")
+    ncp = st.slider("Number of main meals", 1.0, 5.0, step=0.1)
+
+with col2:
+    # st.text("Pepal characteristics")
+    caec = st.selectbox("Consumption of food between meals", options=["Sometimes", "Frequently", "Always", "no"])
+    smoke = st.selectbox("do you smoke", options=["yes", "no"])
+    ch20 = st.slider("Consumption of water daily(L)", 1.0, 4.0, step=0.1)
+    scc = st.selectbox("Calories consumption monitoring", options=["no", "yes"])
+    faf = st.slider("Physical activity frequency per day", 1.0, 3.0, step=0.5)
+    tue = st.slider("Time using technology devices", 0.0, 12.0)
+    calc = st.selectbox("Consumption of alcohol", options=['no', 'Sometimes', 'Frequently', 'Always'])
+    mtrans = st.selectbox("Mode of transportation",
+                          options=['Public_Transportation', 'Walking', 'Automobile', 'Motorbike', 'Bike'])
+
+if st.button("Predict type of obesity"):
+    inp = [age, height, weight, fcvc, ncp, ch20, faf, tue]
+    if gen == 1:
+        inp.append(1)
+        inp.append(0)
+    elif gen == 0:
+        inp.append(0)
+        inp.append(1)
+    fm = int(input("Family history with obesity: yes(1), no(0)"))
+    if fm == 0:
+        inp.append(1)
+        inp.append(0)
+    elif fm == 1:
+        inp.append(0)
+        inp.append(1)
+    favc = int(input("Frequent consumption of high caloric food: yes(1), no(0)"))
+    if favc == 0:
+        inp.append(1)
+        inp.append(0)
+    elif favc == 1:
+        inp.append(0)
+        inp.append(1)
+    caec = int(input("Consumption of food between meals : Always(1),Frequently(2),Sometimes(3),No(4)"))
+    if caec == 1:
+        inp.append(1)
+        inp.append(0)
+        inp.append(0)
+        inp.append(0)
+    elif caec == 2:
+        inp.append(0)
+        inp.append(1)
+        inp.append(0)
+        inp.append(0)
+    elif caec == 3:
+        inp.append(0)
+        inp.append(0)
+        inp.append(1)
+        inp.append(0)
+    else:
+        inp.append(0)
+        inp.append(0)
+        inp.append(0)
+        inp.append(1)
+    smoke = int(input("Do you smoke: yes(1), no(0)"))
+    if smoke == 0:
+        inp.append(1)
+        inp.append(0)
+    elif smoke == 1:
+        inp.append(0)
+        inp.append(1)
+    scc = int(input("Do you monitor your calorie consumption: yes(1), no(0)"))
+    if scc == 0:
+        inp.append(1)
+        inp.append(0)
+    elif scc == 1:
+        inp.append(0)
+        inp.append(1)
+    calc = int(input("Consumption of alcohol: Always(1),Frequently(2),Sometimes(3),No(4)"))
+    if caec == 1:
+        inp.append(1)
+        inp.append(0)
+        inp.append(0)
+        inp.append(0)
+    elif caec == 2:
+        inp.append(0)
+        inp.append(1)
+        inp.append(0)
+        inp.append(0)
+    elif caec == 3:
+        inp.append(0)
+        inp.append(0)
+        inp.append(1)
+        inp.append(0)
+    else:
+        inp.append(0)
+        inp.append(0)
+        inp.append(0)
+        inp.append(1)
+    mtrans = int(input(
+        "What mode of transportation do you use: Automobile(1), Bike(2), Motorbike(3), Public Transport(4), Walking(5)"))
+    if mtrans == 1:
+        inp.append(1)
+        inp.append(0)
+        inp.append(0)
+        inp.append(0)
+        inp.append(0)
+    elif mtrans == 2:
+        inp.append(0)
+        inp.append(1)
+        inp.append(0)
+        inp.append(0)
+        inp.append(0)
+    elif mtrans == 3:
+        inp.append(0)
+        inp.append(0)
+        inp.append(1)
+        inp.append(0)
+        inp.append(0)
+    elif mtrans == 4:
+        inp.append(0)
+        inp.append(0)
+        inp.append(0)
+        inp.append(1)
+        inp.append(0)
+    else:
+        inp.append(0)
+        inp.append(0)
+        inp.append(0)
+        inp.append(0)
+        inp.append(1)
+
+    input_arr = np.array(input)
+    input_arr_scaled = mm.transform(input_arr)
+
+    # make prediction
+    result = model.predict(input_arr_scaled)[0]
+    st.success(f'The obesity type i{result}')