Skip to content

Commit

Permalink
Add files via upload
Browse files Browse the repository at this point in the history
  • Loading branch information
@ecubeproject
ecubeproject authored Mar 2, 2024
1 parent 1c66ed9 commit ceaaf75
Show file tree
Hide file tree
Showing 7 changed files with 292 additions and 0 deletions.
292 changes: 292 additions & 0 deletions models/Check_Models.ipynb
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,292 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "4d1c650b-01a0-4414-99ee-64f80bb561f1",
"metadata": {},
"outputs": [],
"source": [
"import pickle\n",
"import numpy as np\n",
"import pandas as pd\n",
"pd.set_option('display.max_rows', None)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "3c54e2be-71ea-4ace-b598-fa5bc2393f43",
"metadata": {},
"outputs": [],
"source": [
"# Load the pickled XGBoost model MUMBAI\n",
"with open('best_xgb_mumbai.pkl', 'rb') as f:\n",
" model = pickle.load(f)\n",
"\n",
"# Define the feature columns and their values\n",
"feature_columns = ['DiningTable', 'Refrigerator', 'JoggingTrack', 'BED', 'Area', 'AC',\n",
" 'RainWaterHarvesting', 'No. of Bedrooms', 'Location_encoded',\n",
" 'WashingMachine', 'Microwave', 'Intercom', 'Gymnasium', 'TV',\n",
" 'StaffQuarter', 'MultipurposeRoom', '24X7Security', 'IndoorGames',\n",
" 'Cafeteria', 'Gasconnection']\n",
"feature_values = [0, 0, 0, 0, 720, 0, 0, 1, 325, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "665e2222-c960-49b0-8f3d-5a8f5e560595",
"metadata": {},
"outputs": [],
"source": [
"# Convert feature_values to numpy array\n",
"X = np.array(feature_values).reshape(1, -1)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "1fe18a9f-b5c4-4800-b322-9ce3fab4a50c",
"metadata": {},
"outputs": [],
"source": [
"# Make prediction\n",
"predicted_price = model.predict(X)\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "6f7911ae-f634-4837-bca5-10b730835eb2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([4846619.5], dtype=float32)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"predicted_price\n",
"# ACTUAL PRICE WAS 4850000 SO THIS MODEL PREDICTS PROPERTY PRICES VERY WELL for a train observation"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "033d09f7-bbbe-4756-99ef-c87508a6a735",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted Price for Bangalore: 4579130.0\n"
]
}
],
"source": [
"# Load the pickled XGBoost model for Bangalore\n",
"with open('best_xgb_bangalore.pkl', 'rb') as f:\n",
" model_bangalore = pickle.load(f)\n",
"\n",
"# Define the feature columns and their values for Bangalore\n",
"feature_columns_bangalore = ['Area', 'MaintenanceStaff', 'Intercom', 'IndoorGames', 'School',\n",
" 'StaffQuarter', 'LiftAvailable', 'Gasconnection', 'LandscapedGardens',\n",
" 'VaastuCompliant', 'Hospital', 'ATM', 'ShoppingMall', 'GolfCourse',\n",
" 'Location_encoded', '24X7Security', 'CarParking', 'Cafeteria',\n",
" 'WashingMachine', 'RainWaterHarvesting']\n",
"feature_values_bangalore = [1304, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 561, 1, 1, 0, 0, 1]\n",
"\n",
"# Convert feature_values to numpy array\n",
"X_bangalore = np.array(feature_values_bangalore).reshape(1, -1)\n",
"\n",
"# Make prediction for Bangalore\n",
"predicted_price_bangalore = model_bangalore.predict(X_bangalore)\n",
"\n",
"print(\"Predicted Price for Bangalore:\", predicted_price_bangalore[0])\n",
"# Actual price was 4433000 SO THIS MODEL PREDICTS PROPERTY PRICES VERY WELL"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "8e20b150-29f3-4765-b62b-b33f331d36dc",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted Price for Chennai: 2740017.0\n"
]
}
],
"source": [
"# Load the pickled XGBoost model for Chennai\n",
"with open('best_xgb_chennai.pkl', 'rb') as f:\n",
" model_chennai = pickle.load(f)\n",
"\n",
"# Define the feature columns and their values for Chennai\n",
"feature_columns_chennai = ['Area', 'JoggingTrack', 'Cafeteria', 'RainWaterHarvesting', 'No. of Bedrooms',\n",
" 'MultipurposeRoom', 'Refrigerator', 'ShoppingMall', 'VaastuCompliant',\n",
" 'Location_encoded', 'Sofa', 'SwimmingPool', 'IndoorGames', 'LandscapedGardens',\n",
" 'BED', 'ClubHouse', 'SportsFacility', 'Resale', 'WashingMachine', 'AC']\n",
"feature_values_chennai = [563, 0, 0, 1, 1, 0, 0, 0, 0, 753, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0]\n",
"\n",
"# Convert feature_values to numpy array\n",
"X_chennai = np.array(feature_values_chennai).reshape(1, -1)\n",
"\n",
"# Make prediction for Chennai\n",
"predicted_price_chennai = model_chennai.predict(X_chennai)\n",
"\n",
"print(\"Predicted Price for Chennai:\", predicted_price_chennai[0])\n",
"# Actual Price is 2740000"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "f732f3a9-0edc-43c6-b08e-8c79d25866a6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted Price for Hyderabad: 8393575.0\n"
]
}
],
"source": [
"# Load the pickled XGBoost model for Hyderabad\n",
"with open('best_xgb_hyderabad.pkl', 'rb') as f:\n",
" model_hyderabad = pickle.load(f)\n",
"\n",
"# Define the feature columns and their values for Hyderabad\n",
"feature_columns_hyderabad = ['Area', 'Resale', 'BED', 'SwimmingPool', 'ClubHouse', 'Gymnasium',\n",
" 'CarParking', 'LiftAvailable', 'LandscapedGardens', 'IndoorGames',\n",
" 'MultipurposeRoom', 'ATM', 'Location_encoded', 'MaintenanceStaff',\n",
" 'School', 'Cafeteria', 'No. of Bedrooms', 'Gasconnection', 'AC', 'Intercom']\n",
"feature_values_hyderabad = [1951, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 47, 0, 0, 0, 3, 0, 0, 1]\n",
"\n",
"# Convert feature_values to numpy array\n",
"X_hyderabad = np.array(feature_values_hyderabad).reshape(1, -1)\n",
"\n",
"# Make prediction for Hyderabad\n",
"predicted_price_hyderabad = model_hyderabad.predict(X_hyderabad)\n",
"\n",
"print(\"Predicted Price for Hyderabad:\", predicted_price_hyderabad[0])\n",
"# Actual value was 8387000. "
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "6feaa1b5-8978-4859-b4ee-10eacd6c26a4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted Price for Delhi: 4365190.0\n"
]
}
],
"source": [
"# Load the pickled XGBoost model for Delhi\n",
"with open('best_xgb_delhi.pkl', 'rb') as f:\n",
" model_delhi = pickle.load(f)\n",
"\n",
"# Define the feature columns and their values for Delhi\n",
"feature_columns_delhi = ['Area', 'Resale', 'Location_encoded', 'IndoorGames', 'AC', 'DiningTable',\n",
" 'Intercom', 'BED', 'No. of Bedrooms', \"Children'splayarea\", 'Microwave',\n",
" 'CarParking', 'RainWaterHarvesting', 'LiftAvailable', 'Gasconnection',\n",
" 'PowerBackup', 'Refrigerator', 'MaintenanceStaff', 'SwimmingPool', 'SportsFacility']\n",
"feature_values_delhi = [800, 1, 836, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0]\n",
"\n",
"# Convert feature_values to numpy array\n",
"X_delhi = np.array(feature_values_delhi).reshape(1, -1)\n",
"\n",
"# Make prediction for Delhi\n",
"predicted_price_delhi = model_delhi.predict(X_delhi)\n",
"\n",
"print(\"Predicted Price for Delhi:\", predicted_price_delhi[0])\n",
"# Actual Price is 4200000"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "0e535a06-5019-40e7-ad5a-7d7a6f0c1839",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted Price for Kolkata: 7936000.5\n"
]
}
],
"source": [
"# Load the pickled XGBoost model for Kolkata\n",
"with open('best_xgb_kolkata.pkl', 'rb') as f:\n",
" model_kolkata = pickle.load(f)\n",
"\n",
"# Define the feature columns and their values for Kolkata\n",
"feature_columns_kolkata = ['Area', 'SwimmingPool', 'Resale', 'ClubHouse', 'RainWaterHarvesting',\n",
" 'Cafeteria', 'LiftAvailable', 'MaintenanceStaff', 'Location_encoded',\n",
" 'JoggingTrack', 'LandscapedGardens', 'No. of Bedrooms', 'VaastuCompliant',\n",
" 'MultipurposeRoom', 'PowerBackup', 'IndoorGames', 'WashingMachine',\n",
" 'CarParking', 'SportsFacility', 'Gymnasium']\n",
"feature_values_kolkata = [1310, 1, 0, 1, 1, 1, 1, 0, 174, 1, 1, 2, 1, 1, 1, 1, 0, 1, 1, 1]\n",
"\n",
"# Convert feature_values to numpy array\n",
"X_kolkata = np.array(feature_values_kolkata).reshape(1, -1)\n",
"\n",
"# Make prediction for Kolkata\n",
"predicted_price_kolkata = model_kolkata.predict(X_kolkata)\n",
"\n",
"print(\"Predicted Price for Kolkata:\", predicted_price_kolkata[0])\n",
"# Actual price is 7936000. So predicted price is very close"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "687d07e4-b3c1-405d-a7e2-dddb0dd6fd6a",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "metro_housing",
"language": "python",
"name": "metro_housing"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.18"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Binary file added models/best_xgb_bangalore.pkl
View file
Binary file not shown.
Binary file added models/best_xgb_chennai.pkl
View file
Binary file not shown.
Binary file added models/best_xgb_delhi.pkl
View file
Binary file not shown.
Binary file added models/best_xgb_hyderabad.pkl
View file
Binary file not shown.
Binary file added models/best_xgb_mumbai.pkl
View file
Binary file not shown.
Binary file added models/encoded location wise city (1).pdf
View file
Binary file not shown.

0 comments on commit ceaaf75

Please sign in to comment.