housing-price-prediction.ipynb

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     "text": [
      "/kaggle/input/simple-housing-price-prediction/sample_submission.csv\n",
      "/kaggle/input/simple-housing-price-prediction/train.csv\n",
      "/kaggle/input/simple-housing-price-prediction/test.csv\n"
     ]
    }
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   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
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   "source": [
    "train_data_path = \"../input/simple-housing-price-prediction/train.csv\"\n",
    "test_data_path = \"../input/simple-housing-price-prediction/test.csv\"\n",
    "\n",
    "train_data = pd.read_csv(train_data_path)\n",
    "test_data = pd.read_csv(test_data_path)\n"
   ]
  },
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    {
     "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>house_id</th>\n",
       "      <th>date</th>\n",
       "      <th>location</th>\n",
       "      <th>type</th>\n",
       "      <th>block</th>\n",
       "      <th>street</th>\n",
       "      <th>storey_range</th>\n",
       "      <th>area_sqm</th>\n",
       "      <th>flat_model</th>\n",
       "      <th>commence_date</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>199577</td>\n",
       "      <td>2006-09</td>\n",
       "      <td>Raleigh</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>107D</td>\n",
       "      <td>Agawan Court</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>110.0</td>\n",
       "      <td>D</td>\n",
       "      <td>2003</td>\n",
       "      <td>313000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>217021</td>\n",
       "      <td>2007-06</td>\n",
       "      <td>Fresno</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>678</td>\n",
       "      <td>Cleo St</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>64.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1988</td>\n",
       "      <td>167000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>308062</td>\n",
       "      <td>2010-09</td>\n",
       "      <td>Tucson</td>\n",
       "      <td>4 ROOM</td>\n",
       "      <td>5</td>\n",
       "      <td>E Pleasant View Way</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>92.0</td>\n",
       "      <td>K</td>\n",
       "      <td>1976</td>\n",
       "      <td>430000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>212465</td>\n",
       "      <td>2007-04</td>\n",
       "      <td>Austin</td>\n",
       "      <td>4 ROOM</td>\n",
       "      <td>326</td>\n",
       "      <td>Park Hollow Ln</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>92.0</td>\n",
       "      <td>K</td>\n",
       "      <td>1977</td>\n",
       "      <td>303800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60654</td>\n",
       "      <td>2001-10</td>\n",
       "      <td>Honolulu</td>\n",
       "      <td>4 ROOM</td>\n",
       "      <td>794</td>\n",
       "      <td>Ala Puawa Place</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>102.0</td>\n",
       "      <td>G</td>\n",
       "      <td>1998</td>\n",
       "      <td>212000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   house_id     date  location    type block               street  \\\n",
       "0    199577  2006-09   Raleigh  5 ROOM  107D       Agawan Court     \n",
       "1    217021  2007-06    Fresno  3 ROOM   678              Cleo St   \n",
       "2    308062  2010-09    Tucson  4 ROOM     5  E Pleasant View Way   \n",
       "3    212465  2007-04    Austin  4 ROOM   326       Park Hollow Ln   \n",
       "4     60654  2001-10  Honolulu  4 ROOM   794    Ala Puawa Place     \n",
       "\n",
       "  storey_range  area_sqm flat_model  commence_date     price  \n",
       "0     07 TO 09     110.0          D           2003  313000.0  \n",
       "1     07 TO 09      64.0          N           1988  167000.0  \n",
       "2     10 TO 12      92.0          K           1976  430000.0  \n",
       "3     10 TO 12      92.0          K           1977  303800.0  \n",
       "4     04 TO 06     102.0          G           1998  212000.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "train_data.head()\n",
    "train_data.dropna(axis=0)\n",
    "train_data.head()"
   ]
  },
  {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['house_id', 'date', 'location', 'type', 'block', 'street',\n",
       "       'storey_range', 'area_sqm', 'flat_model', 'commence_date', 'price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
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    }
   ],
   "source": [
    "train_data.columns"
   ]
  },
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   "cell_type": "code",
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   "id": "770715d2",
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   "outputs": [],
   "source": [
    "y_train = train_data.price\n",
    "\n",
    "features = ['house_id','area_sqm', 'commence_date']\n",
    "X_train = train_data[features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "db28d3f7",
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[140030. 163530. 169410. ... 462055. 452235. 468795.]\n"
     ]
    }
   ],
   "source": [
    "# from sklearn.tree import DecisionTreeRegressor\n",
    "# model_1 = DecisionTreeRegressor(random_state=1)\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "model_1 = RandomForestRegressor(random_state=1)\n",
    "model_1.fit(X_train,y_train)\n",
    "X_test = test_data[features]\n",
    "prediction_1 = model_1.predict(X_test)\n",
    "print(prediction_1)"
   ]
  },
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   "execution_count": 7,
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    "tags": []
   },
   "outputs": [],
   "source": [
    "output = pd.DataFrame({'house_id': test_data.house_id,\n",
    "                       'price': prediction_1})\n",
    "output.to_csv('submission.csv', index=False)"
   ]
  }
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