Model.py

#Import Libraries
import numpy as np
import pandas as pd
import joblib
 
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
 
#load data
df = pd.read_csv("data/ohe_data_reduce_cat_class.csv")
 
# Split data
X= df.drop('price', axis=1)
y= df['price']
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.2, random_state=51)
 
# feature scaling
sc = StandardScaler()
sc.fit(X_train)
X_train = sc.transform(X_train)
X_test = sc.transform(X_test)
 
 
###### Load Model
 
model = joblib.load('bangalore_house_price_prediction_rfr_model.pkl')
 
 
# it help to get predicted value of house  by providing features value 
def predict_house_price(bath,balcony,total_sqft_int,bhk,area_type,location):
 
  x =np.zeros(len(X.columns)) # create zero numpy array, len = 107 as input value for model
 
  # adding feature's value accorind to their column index
  x[0]=bath
  x[1]=balcony
  x[2]=total_sqft_int
  x[3]=bhk
 

 
  if 'area_type'+area_type in X.columns:
    area_type_index = np.where(X.columns=="area_type"+area_type)[0][0]
    x[area_type_index] =1
 
  if 'location_'+location in X.columns:
    loc_index = np.where(X.columns=="location_"+location)[0][0]
    x[loc_index] =1
 
  # feature scaling
  x = sc.transform([x])[0] # give 2d np array for feature scaling and get 1d scaled np array
 
  return model.predict([x])[0] # return the predicted value by train XGBoost model