Create app.py

app.py

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+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import gradio as gr
+#import joblib
+from sklearn.linear_model import LinearRegression
+from sklearn.tree import DecisionTreeRegressor
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.model_selection import StratifiedShuffleSplit
+from sklearn.impute import SimpleImputer
+from sklearn.pipeline import Pipeline
+from sklearn.compose import ColumnTransformer
+from sklearn.preprocessing import StandardScaler
+from sklearn.preprocessing import OneHotEncoder
+from sklearn.metrics import mean_squared_error
+from sklearn.model_selection import cross_val_score
+from sklearn.model_selection import RandomizedSearchCV
+from sklearn.preprocessing import MinMaxScaler 
+from sklearn.model_selection import train_test_split
+
+df = pd.read_csv('Housing.csv')
+cat_columns = ['mainroad',
+       'guestroom', 'basement', 'hotwaterheating', 'airconditioning',
+        'prefarea']
+
+def binary_mapping(x):
+  return x.map({'yes': 1, "no": 0})
+
+df[cat_columns] = df[cat_columns].apply(binary_mapping)
+
+
+ohe = OneHotEncoder(sparse=False, handle_unknown='error', drop='first')
+ohe_df = pd.DataFrame(ohe.fit_transform(df[['furnishingstatus']]))
+
+ohe_df.columns = ohe.get_feature_names(['status'])
+
+df = pd.concat([df,ohe_df], axis=1)
+df.drop(['furnishingstatus'], axis = 1, inplace = True)
+df.head()
+
+
+
+df_new = df.copy(deep=True)
+num_columns = ['area', 'bedrooms', 'bathrooms', 'stories','parking']
+
+
+
+scaler = MinMaxScaler().fit(df_new[num_columns])
+df_new[num_columns] = scaler.transform(df_new[num_columns])
+y = df_new.pop('price')
+x = df_new
+x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42)
+model = RandomForestRegressor()
+model.fit(x_train, y_train)
+
+def prediction(properties):
+    print(properties)
+    df = pd.DataFrame(properties, columns=x_test.columns)
+    print(df)
+    df = df[x_test.columns].iloc[0].to_frame().T
+    df[num_columns] = scaler.transform(df[num_columns])
+    return model.predict(df)
+example = pd.DataFrame([7420, 4, 2, 3, 1, 0, 0, 0, 1, 2, 1, 0, 0]).T
+example.columns = x_test.columns
+
+demo = gr.Interface(
+    prediction,
+    [
+        gr.Dataframe(
+            headers=['area', 'bedrooms', 'bathrooms', 'stories', 'mainroad', 'guestroom',
+                      'basement', 'hotwaterheating', 'airconditioning', 'parking', 'prefarea',
+                      'status_semi-furnished', 'status_unfurnished'],
+            datatype=["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"],
+        )
+        
+    ],
+    "number",
+    description="Enter The Properties Of The Home",
+    title="California Housing Prices Prediction",
+    examples=[example],
+    
+
+)
+
+demo.launch()