Machine_Learning_Model_Creation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d16754a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import chardet#for encoding\n",
    "import warnings# to avoid the warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "pd.pandas.set_option('display.max_columns',0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "aba5dee3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Lng</th>\n",
       "      <th>Lat</th>\n",
       "      <th>tradeTime</th>\n",
       "      <th>followers</th>\n",
       "      <th>totalPrice</th>\n",
       "      <th>square</th>\n",
       "      <th>livingRoom</th>\n",
       "      <th>drawingRoom</th>\n",
       "      <th>kitchen</th>\n",
       "      <th>bathRoom</th>\n",
       "      <th>constructionTime</th>\n",
       "      <th>ladderRatio</th>\n",
       "      <th>fiveYearsProperty</th>\n",
       "      <th>subway</th>\n",
       "      <th>communityAverage</th>\n",
       "      <th>floor_height</th>\n",
       "      <th>renovationCondition_2</th>\n",
       "      <th>renovationCondition_3</th>\n",
       "      <th>renovationCondition_4</th>\n",
       "      <th>buildingStructure_2</th>\n",
       "      <th>buildingStructure_3</th>\n",
       "      <th>buildingStructure_4</th>\n",
       "      <th>buildingStructure_5</th>\n",
       "      <th>buildingStructure_6</th>\n",
       "      <th>buildingType_2.0</th>\n",
       "      <th>buildingType_3.0</th>\n",
       "      <th>buildingType_4.0</th>\n",
       "      <th>district_2</th>\n",
       "      <th>district_3</th>\n",
       "      <th>district_4</th>\n",
       "      <th>district_5</th>\n",
       "      <th>district_6</th>\n",
       "      <th>district_7</th>\n",
       "      <th>district_8</th>\n",
       "      <th>district_9</th>\n",
       "      <th>district_10</th>\n",
       "      <th>district_11</th>\n",
       "      <th>district_12</th>\n",
       "      <th>district_13</th>\n",
       "      <th>elevator_1.0</th>\n",
       "      <th>floor_type_低</th>\n",
       "      <th>floor_type_底</th>\n",
       "      <th>floor_type_未知</th>\n",
       "      <th>floor_type_顶</th>\n",
       "      <th>floor_type_高</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.630809</td>\n",
       "      <td>0.627253</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.092738</td>\n",
       "      <td>415.0</td>\n",
       "      <td>0.272992</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>2.028094e-08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.262240</td>\n",
       "      <td>0.609756</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.597040</td>\n",
       "      <td>0.406733</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.110236</td>\n",
       "      <td>575.0</td>\n",
       "      <td>0.276039</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>6.523868e-08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.352324</td>\n",
       "      <td>0.512195</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.572140</td>\n",
       "      <td>0.717698</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.120735</td>\n",
       "      <td>297.5</td>\n",
       "      <td>0.279617</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2.587568e-08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.234474</td>\n",
       "      <td>0.487805</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.557084</td>\n",
       "      <td>0.414236</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.250219</td>\n",
       "      <td>392.0</td>\n",
       "      <td>0.162585</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>3.187004e-08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.300362</td>\n",
       "      <td>0.121951</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.616393</td>\n",
       "      <td>0.582255</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.049869</td>\n",
       "      <td>275.6</td>\n",
       "      <td>0.100757</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>3.187004e-08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.330259</td>\n",
       "      <td>0.170732</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Lng       Lat  tradeTime  ...  floor_type_未知  floor_type_顶  floor_type_高\n",
       "0  0.630809  0.627253       2016  ...              0             0             1\n",
       "1  0.597040  0.406733       2016  ...              0             0             1\n",
       "3  0.572140  0.717698       2016  ...              0             0             0\n",
       "4  0.557084  0.414236       2016  ...              0             0             0\n",
       "5  0.616393  0.582255       2016  ...              0             0             0\n",
       "\n",
       "[5 rows x 45 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(\"Data_For_Model.csv\",index_col=0)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88309f44",
   "metadata": {},
   "source": [
    "Choosing Best Machine Learning Model,top features for our model are\n",
    "\n",
    "1> tradeTime\n",
    "\n",
    "2> CommunityAverage\n",
    "\n",
    "3> square\n",
    "\n",
    "4> livingRoom\n",
    "\n",
    "5> bathRoom\n",
    "\n",
    "6> drawingRoom\n",
    "\n",
    "7> renovationCondition\n",
    "\n",
    "8> buildingStructure\n",
    "\n",
    "9> elevator\n",
    "\n",
    "10> constructionTime\n",
    "\n",
    "11> Followers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "22843ac6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Lng', 'Lat', 'tradeTime', 'followers', 'totalPrice', 'square',\n",
       "       'livingRoom', 'drawingRoom', 'kitchen', 'bathRoom', 'constructionTime',\n",
       "       'ladderRatio', 'fiveYearsProperty', 'subway', 'communityAverage',\n",
       "       'floor_height', 'renovationCondition_2', 'renovationCondition_3',\n",
       "       'renovationCondition_4', 'buildingStructure_2', 'buildingStructure_3',\n",
       "       'buildingStructure_4', 'buildingStructure_5', 'buildingStructure_6',\n",
       "       'buildingType_2.0', 'buildingType_3.0', 'buildingType_4.0',\n",
       "       'district_2', 'district_3', 'district_4', 'district_5', 'district_6',\n",
       "       'district_7', 'district_8', 'district_9', 'district_10', 'district_11',\n",
       "       'district_12', 'district_13', 'elevator_1.0', 'floor_type_低',\n",
       "       'floor_type_底', 'floor_type_未知', 'floor_type_顶', 'floor_type_高'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "88f39d95",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(columns=[\"Lng\",\"Lat\",\"ladderRatio\",\"fiveYearsProperty\",\"subway\",\"floor_height\",\n",
    "                  \"buildingType_2.0\",\"buildingType_3.0\",\"buildingType_4.0\",\"district_2\",\n",
    "                  \"district_3\",\"district_4\",\"district_5\",\"district_6\",\"district_7\",\n",
    "                  \"district_8\",\"district_9\",\"district_10\",\"district_11\",\"district_12\",\n",
    "                  \"district_13\",\"floor_type_低\",\"floor_type_底\",\"floor_type_未知\",\n",
    "                  \"floor_type_顶\",\"floor_type_高\"],inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b8df3ac3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(283901, 19)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "26f5ef94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tradeTime', 'followers', 'totalPrice', 'square', 'livingRoom',\n",
       "       'drawingRoom', 'kitchen', 'bathRoom', 'constructionTime',\n",
       "       'communityAverage', 'renovationCondition_2', 'renovationCondition_3',\n",
       "       'renovationCondition_4', 'buildingStructure_2', 'buildingStructure_3',\n",
       "       'buildingStructure_4', 'buildingStructure_5', 'buildingStructure_6',\n",
       "       'elevator_1.0'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f6bfddb",
   "metadata": {},
   "source": [
    "List of the model that we can use for our problem\n",
    "\n",
    "1> LinearRegression model\n",
    "\n",
    "2> KNN Model\n",
    "\n",
    "3> Decesion Tree\n",
    "\n",
    "4> Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "826088c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#input features for model\n",
    "X=data.drop(['totalPrice'],axis=1)\n",
    "#output features for model\n",
    "y=data['totalPrice']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "01e5f41c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.30,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e2f29407",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "By using Linear Regression we got:\n",
      "Training data accuracy 0.7574698746154127\n",
      "Testing data accuracy 0.7576318049777959\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lr_model = LinearRegression()\n",
    "lr_model.fit(X_train, y_train)\n",
    "print(\"By using Linear Regression we got:\")\n",
    "print(\"Training data accuracy\",lr_model.score(X_train, y_train))\n",
    "print(\"Testing data accuracy\",lr_model.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23a081e5",
   "metadata": {},
   "source": [
    "### RandomForest Model  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "795e2b98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rfm=RandomForestRegressor()\n",
    "rfm.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71a84367",
   "metadata": {},
   "source": [
    "Model Validation\n",
    "\n",
    "1> one way to verify the model is Mean Absolute Error (also called MAE).\n",
    "\n",
    "    i.e. error=actual−predicted\n",
    "    \n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "703a8ff5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9848032043888097\n",
      "0.895634280563376\n"
     ]
    }
   ],
   "source": [
    "print(rfm.score(X_train,y_train))\n",
    "print(rfm.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f061c6b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([369.544     , 525.074     , 600.42      , ..., 168.91547619,\n",
       "       209.733     , 405.251     ])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred=rfm.predict(X_test)\n",
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "32716c46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32.73071897974043\n",
      "32.73071897974043\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn import metrics\n",
    "print(mean_absolute_error(y_test,pred))\n",
    "print(metrics.mean_absolute_error(y_test,pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad58b687",
   "metadata": {},
   "source": [
    "#### Hyperparameter tuning \n",
    "1.GridSearchCV\n",
    "\n",
    "2.RandomizedSearchCV\n",
    "\n",
    "3.Bayesian Optimization-Automate Hyperparameter Tuning (Hyperopt)\n",
    "\n",
    "4.Sequential model based optimization\n",
    "\n",
    "5.Optuna-Automate Hyperparameter Tuning\n",
    "\n",
    "6.Genetic Algorithm\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79c5ad56",
   "metadata": {},
   "source": [
    "#### Main parameters used by RandomForest Algorithm are\n",
    "\n",
    "1> n_estimators --->    The number of trees in the forest.\n",
    "\n",
    "2> criterion--->{\"mse\", \"mae\"}-->The function to measure the quality of a split\n",
    "\n",
    "3> max_features--->{\"auto\", \"sqrt\", \"log2\"}-->    The number of features to consider when looking for the best split:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7afb6448",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': True,\n",
       " 'ccp_alpha': 0.0,\n",
       " 'criterion': 'mse',\n",
       " 'max_depth': None,\n",
       " 'max_features': 'auto',\n",
       " 'max_leaf_nodes': None,\n",
       " 'max_samples': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_impurity_split': None,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'n_estimators': 100,\n",
       " 'n_jobs': None,\n",
       " 'oob_score': False,\n",
       " 'random_state': None,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "72c80542",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.model_selection import RandomizedSearchCV\n",
    "# rf_grid= {'n_estimators': np.arange(80,120,10),\n",
    "#       'criterion':[\"mse\", \"mae\"],\n",
    "#       'max_features': [\"auto\", \"sqrt\", \"log2\"]\n",
    "#   }\n",
    "# print(rf_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "24865a62",
   "metadata": {},
   "outputs": [],
   "source": [
    "# rfm_randomcv=RandomizedSearchCV(estimator=rfm,param_distributions=rf_grid,cv=3,n_jobs=-1,\n",
    "#                                random_state=20,verbose=1)\n",
    "# rfm_randomcv.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cdd3586b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# rfm_randomcv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "318a2d94",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfm=RandomForestRegressor(max_depth=None,min_samples_leaf=5,min_samples_split=6,\n",
    "                          n_estimators=120,verbose=2,n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "458dd02f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 1 of 120\n",
      "building tree 2 of 120\n",
      "building tree 3 of 120building tree 4 of 120\n",
      "building tree 5 of 120\n",
      "\n",
      "building tree 6 of 120building tree 7 of 120\n",
      "\n",
      "building tree 8 of 120\n",
      "building tree 9 of 120\n",
      "building tree 10 of 120building tree 11 of 120\n",
      "building tree 12 of 120\n",
      "\n",
      "building tree 13 of 120\n",
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      "building tree 18 of 120\n",
      "building tree 19 of 120\n",
      "building tree 20 of 120building tree 21 of 120\n",
      "\n",
      "building tree 22 of 120\n",
      "building tree 23 of 120building tree 24 of 120\n",
      "\n",
      "building tree 25 of 120\n",
      "building tree 26 of 120\n",
      "building tree 27 of 120\n",
      "building tree 28 of 120\n",
      "building tree 29 of 120\n",
      "building tree 30 of 120\n",
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      "building tree 33 of 120\n",
      "building tree 34 of 120\n",
      "building tree 35 of 120\n",
      "building tree 36 of 120\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  17 tasks      | elapsed:    3.1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 37 of 120\n",
      "building tree 38 of 120\n",
      "building tree 39 of 120\n",
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      "building tree 45 of 120\n",
      "building tree 46 of 120building tree 47 of 120\n",
      "\n",
      "building tree 48 of 120\n",
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      "building tree 112 of 120\n",
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      "building tree 118 of 120\n",
      "building tree 119 of 120\n",
      "building tree 120 of 120\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 120 out of 120 | elapsed:   17.9s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(min_samples_leaf=5, min_samples_split=6, n_estimators=120,\n",
       "                      n_jobs=-1, verbose=2)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d11d9d22",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  17 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=12)]: Done 120 out of 120 | elapsed:    0.9s finished\n",
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  17 tasks      | elapsed:    0.1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.944283757229526\n",
      "0.8966624691850038\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Done 120 out of 120 | elapsed:    0.4s finished\n"
     ]
    }
   ],
   "source": [
    "print(rfm.score(X_train,y_train))\n",
    "print(rfm.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "68744d9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tradeTime', 'followers', 'square', 'livingRoom', 'drawingRoom',\n",
       "       'kitchen', 'bathRoom', 'constructionTime', 'communityAverage',\n",
       "       'renovationCondition_2', 'renovationCondition_3',\n",
       "       'renovationCondition_4', 'buildingStructure_2', 'buildingStructure_3',\n",
       "       'buildingStructure_4', 'buildingStructure_5', 'buildingStructure_6',\n",
       "       'elevator_1.0'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "24ea0f75",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data=[[2010,120,130,3,1,2,2,2005,45000,0,0,1,0,0,0,0,0,1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1c193ab1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[549.00384763]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  17 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 120 out of 120 | elapsed:    0.0s finished\n"
     ]
    }
   ],
   "source": [
    "print(rfm.predict(test_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6e3d9e7",
   "metadata": {},
   "source": [
    "Saving the model for future use..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "21adfc18",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e437d965",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('Housing_Model','wb') as f:\n",
    "    pickle.dump(rfm,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "10ba19db",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Testing the model\n",
    "with open('Housing_Model','rb') as f:\n",
    "    mod=pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "329e6f89",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done  17 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=12)]: Done 120 out of 120 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([549.00384763])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=mod.predict([[2010,120,130,3,1,2,2,2005,45000,0,0,1,0,0,0,0,0,1]])\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "23c26f77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "549.0038476271143\n"
     ]
    }
   ],
   "source": [
    "print(float(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a03b0c5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87455dab",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42b08835",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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