Rename MLProject.py to app.py

MLProject.py → app.py

@@ -1,93 +1,93 @@
-import numpy as np
-import streamlit as st
-import pandas as pd
-from sklearn.model_selection import train_test_split
-from sklearn.preprocessing import OneHotEncoder, StandardScaler
-from sklearn.model_selection import train_test_split
-from sklearn.tree import DecisionTreeClassifier
-from sklearn.neighbors import KNeighborsRegressor
-from sklearn.metrics import mean_squared_error
-
-
-st.title(":red[Welcome to My ML Project]")
-df=pd.read_csv(r"C:\Users\Administrator\Desktop\streamlit_336\tips.csv")
-
-y=df.pop("total_bill")
-x=df
-
-X_train, X_test, y_train, y_test=train_test_split(x,y,test_size=0.15,random_state=30)
-
-numerical_data=X_train.select_dtypes("number")
-cat_data=X_train.select_dtypes("object")
-
-
-encoder=OneHotEncoder(sparse=False)
-X_train_cat=pd.DataFrame(encoder.fit_transform(cat_data), columns=encoder.get_feature_names_out())
-scaler=StandardScaler()
-res=scaler.fit_transform(numerical_data)
-X_train_num=pd.DataFrame(res,columns=numerical_data.columns)
-Final_X_train_data=pd.concat([X_train_cat,X_train_num],axis=1)
-
-X_test_num=X_test.select_dtypes("number")
-X_test_cat=X_test.select_dtypes("object")
-
-X_test_num_trans=scaler.transform(X_test_num)
-res1=pd.DataFrame(X_test_num_trans, columns=X_test_num.columns)
-
-
-X_test_cat_trans=encoder.transform(X_test_cat)
-res2=pd.DataFrame(X_test_cat_trans, columns=encoder.get_feature_names_out())
-
-Final_X_test=pd.concat([res2,res1],axis=1)
-
-regression=KNeighborsRegressor()
-regression.fit(Final_X_train_data,y_train)
-y_pred=regression.predict(Final_X_test)
-
-mean_squared_error(y_test,y_pred)
-
-#Application
-tip = st.number_input("Enter Customer Tip")
-
-sex =["Female","Male"]
-select_sex=st.selectbox("Select Customer Gender",sex)
-
-smoker=["No","Yes"]
-select_smoker=st.selectbox("Select Customer Smoker or not",smoker)
-
-day=["Sun","Sat","Fri"]
-select_day=st.selectbox("Select Day",day)
-
-
-time_options = ["Dinner", "Lunch"]
-select_time = st.selectbox("Select Time", time_options)
-size=st.number_input("Enter Size")
-
-if st.button("Predict total bill"):
-    query_point=pd.DataFrame([
-        {
-            "tip":tip,
-            "sex":select_sex,
-            "smoker":select_smoker,
-            "day":select_day,
-            "time":select_time,
-            "size":size
-
-        }]
-    )
-    cat_query_point=query_point.select_dtypes("object")
-
-    num_query_point=query_point.select_dtypes("number")
-
-    cat_query_point_trans = pd.DataFrame(encoder.transform(cat_query_point),columns=encoder.get_feature_names_out())
-    num_query_point_trans=pd.DataFrame(scaler.transform(num_query_point),columns=X_test_num.columns)
-
-    final_query_point=pd.concat([cat_query_point_trans, num_query_point_trans], axis=1)
-
-    def fun(query_point):
-        res=regression.predict(query_point)[0]
-        return res
-
-    
-
-    st.write(fun(final_query_point))
+import numpy as np
+import streamlit as st
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import OneHotEncoder, StandardScaler
+from sklearn.model_selection import train_test_split
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.neighbors import KNeighborsRegressor
+from sklearn.metrics import mean_squared_error
+
+
+st.title(":red[Welcome to My ML Project]")
+df=pd.read_csv("tips.csv")
+
+y=df.pop("total_bill")
+x=df
+
+X_train, X_test, y_train, y_test=train_test_split(x,y,test_size=0.15,random_state=30)
+
+numerical_data=X_train.select_dtypes("number")
+cat_data=X_train.select_dtypes("object")
+
+
+encoder=OneHotEncoder(sparse=False)
+X_train_cat=pd.DataFrame(encoder.fit_transform(cat_data), columns=encoder.get_feature_names_out())
+scaler=StandardScaler()
+res=scaler.fit_transform(numerical_data)
+X_train_num=pd.DataFrame(res,columns=numerical_data.columns)
+Final_X_train_data=pd.concat([X_train_cat,X_train_num],axis=1)
+
+X_test_num=X_test.select_dtypes("number")
+X_test_cat=X_test.select_dtypes("object")
+
+X_test_num_trans=scaler.transform(X_test_num)
+res1=pd.DataFrame(X_test_num_trans, columns=X_test_num.columns)
+
+
+X_test_cat_trans=encoder.transform(X_test_cat)
+res2=pd.DataFrame(X_test_cat_trans, columns=encoder.get_feature_names_out())
+
+Final_X_test=pd.concat([res2,res1],axis=1)
+
+regression=KNeighborsRegressor()
+regression.fit(Final_X_train_data,y_train)
+y_pred=regression.predict(Final_X_test)
+
+mean_squared_error(y_test,y_pred)
+
+#Application
+tip = st.number_input("Enter Customer Tip")
+
+sex =["Female","Male"]
+select_sex=st.selectbox("Select Customer Gender",sex)
+
+smoker=["No","Yes"]
+select_smoker=st.selectbox("Select Customer Smoker or not",smoker)
+
+day=["Sun","Sat","Fri"]
+select_day=st.selectbox("Select Day",day)
+
+
+time_options = ["Dinner", "Lunch"]
+select_time = st.selectbox("Select Time", time_options)
+size=st.number_input("Enter Size")
+
+if st.button("Predict total bill"):
+    query_point=pd.DataFrame([
+        {
+            "tip":tip,
+            "sex":select_sex,
+            "smoker":select_smoker,
+            "day":select_day,
+            "time":select_time,
+            "size":size
+
+        }]
+    )
+    cat_query_point=query_point.select_dtypes("object")
+
+    num_query_point=query_point.select_dtypes("number")
+
+    cat_query_point_trans = pd.DataFrame(encoder.transform(cat_query_point),columns=encoder.get_feature_names_out())
+    num_query_point_trans=pd.DataFrame(scaler.transform(num_query_point),columns=X_test_num.columns)
+
+    final_query_point=pd.concat([cat_query_point_trans, num_query_point_trans], axis=1)
+
+    def fun(query_point):
+        res=regression.predict(query_point)[0]
+        return res
+
+    
+
+    st.write(fun(final_query_point))