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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() | |