add app files

ML_model.py

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+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split, GridSearchCV 
+from sklearn.preprocessing import StandardScaler
+from sklearn.pipeline import Pipeline
+from sklearn.linear_model import ElasticNet
+from sklearn.metrics import mean_absolute_error
+from sklearn.impute import KNNImputer
+import pickle
+
+def ml_model():
+    url = 'https://raw.githubusercontent.com/yxmauw/General_Assembly_Pub/main/project_2/cloud_app/streamlit_data.csv'
+    df = pd.read_csv(url, header=0) # load data
+    X = df.drop('SalePrice', axis=1)
+    y = df['SalePrice']
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
+
+    enet_ratio = [.5,.8,.9,.95]
+    alpha_l = [1.,10.,100.,500.,1000.] 
+
+    pipe_enet = Pipeline([
+                ('ss', StandardScaler()),
+                ('enet', ElasticNet())
+                ])
+
+    pipe_enet_params = {'enet__alpha': alpha_l,
+                        'enet__l1_ratio': enet_ratio
+                        }
+    cv_ct = 5
+    score = 'neg_mean_absolute_error'
+
+    pipe_enet_gs = GridSearchCV(pipe_enet,
+                                    pipe_enet_params,
+                                    cv=cv_ct,
+                                    scoring=score,
+                                    verbose=1
+                                    )
+
+    pipe_enet_gs.fit(X_train,y_train)
+
+    pickle.dump(pipe_enet_gs, open('final_model.sav','wb'))

app.py

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+# https://www.analyticsvidhya.com/blog/2021/07/streamlit-quickly-turn-your-ml-models-into-web-apps/
+import streamlit as st
+import pandas as pd
+import numpy as np
+from model_methods import predict
+
+# configuration of the page
+st.set_page_config(
+    layout="centered", 
+    page_icon="🏠", 
+    page_title="Are you planning to sell your house?", 
+    initial_sidebar_state='auto',
+)
+
+st.title("🏠Ames Housing Sale Price recommendation tool")
+st.markdown('''
+The algorithm driving this app is built on
+historical housing sale price data to generate
+recommended Sale Price! Please enter your house details 
+to get a Sale Price suggestion 🙂
+''')
+###########################################################
+st.info('Only Enter Numeric Values in the Following Fields')
+
+gr_liv_area = st.text_input('Enter house ground living area in square feet. Accept values 334 to 3395 inclusive', '')
+overall_qual = np.nan
+total_bsmt_sf = st.text_input('Enter house total basement area in square feet. Accept values 0 to 3206 inclusive', '')
+garage_area = st.text_input('Enter house garage area in square feet. Accept values 0 to 1356 inclusive', '')
+year_built = st.text_input('Enter the year your house was built. Accept values 1872 to 2010 inclusive', '')
+mas_vnr_area = st.text_input('Enter house masonry veneer area in square feet. Accept values 0 to 1129 inclusive', '')
+
+def predict_price():
+    data = list(map(float, [gr_liv_area,
+                            (float(gr_liv_area))**2,
+                            (float(gr_liv_area))**3,
+                            overall_qual, 
+                            total_bsmt_sf,
+                            garage_area,
+                            year_built,
+                            mas_vnr_area]))
+    result = np.format_float_positional((predict(data)[0]), unique=False, precision=0)
+    st.info(f'# Our SalePrice suggestion is ${result}')
+    st.write('with an estimated uncertainty of ± \$11K')
+
+if st.button('Recommend Saleprice'):
+    if gr_liv_area and overall_qual and total_bsmt_sf and garage_area and year_built and mas_vnr_area:
+        with st.sidebar:
+            try: 
+                predict_price()
+            except:
+                st.warning('''Oops, looks like you missed a spot. 
+                Please complete all fields to get a quote estimate 
+                for property Sale Price 🙏. 
+                \n\n Thank you. 🙂''')

model_methods.py

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+import numpy as np
+import pandas as pd
+from sklearn.linear_model import ElasticNet
+from sklearn.model_selection import GridSearchCV
+from sklearn.metrics import mean_absolute_error
+from sklearn.impute import KNNImputer
+import pickle
+    
+def predict(new_data):
+    # impute missing `Overall Qual` values
+    url = 'https://raw.githubusercontent.com/yxmauw/General_Assembly_Pub/main/project_2/cloud_app/streamlit_imp_data.csv'
+    imp_data = pd.read_csv(url, header=0)
+    imp = KNNImputer()
+    imp.fit(imp_data)
+    shaped_data = np.reshape(new_data, (1, -1))
+    input_data = imp.transform(shaped_data)
+    # load model
+    with open('project_2/cloud_app/final_model.sav','rb') as f:
+        model = pickle.load(f)
+    pred = model.predict([input_data][0])
+    return pred 

requirements.txt

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+streamlit==1.11.1
+scikit-learn==1.0.2
+pandas==1.4.2
+numpy==1.21.5