NMMH_TH1_21C29001 (4).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eWQmJ7-PXzE4"
   },
   "source": [
    "## Bài tập\n",
    "Cho dữ liệu gồm 100 hàng, gồm chiều rộng, chiều dài và giá của một ngôi nhà, hay xây dựng một mô hình dư đoán giá nhà dựa vào chiều dài và rộng.\n",
    "\n",
    "Hãy dùng mô hình học được: dự đoán xem căn nhà có thông số sau khả năng cao là giá bao nhiêu:\n",
    "- width = 5, length = 10\n",
    "- width = 20, length = 20\n",
    "- width = 10, length = 40\n",
    "\n",
    "## Nộp bài\n",
    "- Code và chạy kết quả lưu vào file notebook NMMH_TH1_MSSV.ipynb (notebook phải có kết quả chạy nếu ko xem như chưa làm)\n",
    "- Nộp file notebook có đuôi .ipynb và nộp về: huynhthanh98vn@gmail.com\n",
    "- Deadline: 23g59 thứ 6 ngày 15/04/2022. Nộp trễ bị chia đôi số điểm."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3BTrKYPhf4nK"
   },
   "source": [
    "# Bài làm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mDEPRECATION: Python 2.7 reached the end of its life on January 1st, 2020. Please upgrade your Python as Python 2.7 is no longer maintained. pip 21.0 will drop support for Python 2.7 in January 2021. More details about Python 2 support in pip can be found at https://pip.pypa.io/en/latest/development/release-process/#python-2-support pip 21.0 will remove support for this functionality.\u001b[0m\n",
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Collecting gdown\n",
      "  Downloading gdown-4.4.0.tar.gz (14 kB)\n",
      "  Installing build dependencies ... \u001b[?25ldone\n",
      "\u001b[?25h  Getting requirements to build wheel ... \u001b[?25ldone\n",
      "\u001b[?25h    Preparing wheel metadata ... \u001b[?25ldone\n",
      "\u001b[?25hCollecting tqdm\n",
      "  Downloading tqdm-4.64.0-py2.py3-none-any.whl (78 kB)\n",
      "\u001b[K     |████████████████████████████████| 78 kB 42 kB/s eta 0:00:01\n",
      "\u001b[?25hCollecting filelock\n",
      "  Downloading filelock-3.2.1-py2.py3-none-any.whl (9.3 kB)\n",
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      "  Downloading beautifulsoup4-4.9.3-py2-none-any.whl (115 kB)\n",
      "\u001b[K     |████████████████████████████████| 115 kB 37 kB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: six in /Users/lap010760/Library/Python/2.7/lib/python/site-packages (from gdown) (1.15.0)\n",
      "Requirement already satisfied: requests[socks] in /Library/Python/2.7/site-packages (from gdown) (2.22.0)\n",
      "Collecting importlib-resources; python_version < \"3.7\"\n",
      "  Downloading importlib_resources-3.3.1-py2.py3-none-any.whl (26 kB)\n",
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      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /Library/Python/2.7/site-packages (from requests[socks]->gdown) (3.0.4)\n",
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      "Requirement already satisfied: zipp>=0.4; python_version < \"3.8\" in /Users/lap010760/Library/Python/2.7/lib/python/site-packages (from importlib-resources; python_version < \"3.7\"->tqdm->gdown) (1.2.0)\n",
      "Requirement already satisfied: typing; python_version < \"3.5\" in /Users/lap010760/Library/Python/2.7/lib/python/site-packages (from importlib-resources; python_version < \"3.7\"->tqdm->gdown) (3.10.0.0)\n",
      "Requirement already satisfied: contextlib2; python_version < \"3\" in /Users/lap010760/Library/Python/2.7/lib/python/site-packages (from importlib-resources; python_version < \"3.7\"->tqdm->gdown) (0.6.0.post1)\n",
      "Requirement already satisfied: pathlib2; python_version < \"3\" in /Users/lap010760/Library/Python/2.7/lib/python/site-packages (from importlib-resources; python_version < \"3.7\"->tqdm->gdown) (2.3.7.post1)\n",
      "Requirement already satisfied: singledispatch; python_version < \"3.4\" in /Users/lap010760/Library/Python/2.7/lib/python/site-packages (from importlib-resources; python_version < \"3.7\"->tqdm->gdown) (3.7.0)\n",
      "Requirement already satisfied: backports.functools-lru-cache; python_version < \"3\" in /Users/lap010760/Library/Python/2.7/lib/python/site-packages (from soupsieve<2.0,>1.2; python_version < \"3.0\"->beautifulsoup4->gdown) (1.6.4)\n",
      "Requirement already satisfied: scandir; python_version < \"3.5\" in /Users/lap010760/Library/Python/2.7/lib/python/site-packages (from pathlib2; python_version < \"3\"->importlib-resources; python_version < \"3.7\"->tqdm->gdown) (1.10.0)\n",
      "Building wheels for collected packages: gdown\n",
      "  Building wheel for gdown (PEP 517) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for gdown: filename=gdown-4.4.0-py2-none-any.whl size=14762 sha256=e7796c29032947c33cb2cfeb28656657bdd677dd5f6bbab1aaced4dedbddeb59\n",
      "  Stored in directory: /Users/lap010760/Library/Caches/pip/wheels/1c/30/46/f1b4b354ed24376a779a40348470ca1ec9052cbffca7ff924c\n",
      "Successfully built gdown\n",
      "Installing collected packages: importlib-resources, tqdm, filelock, soupsieve, beautifulsoup4, gdown, PySocks\n",
      "\u001b[33m  WARNING: The script tqdm is installed in '/Users/lap010760/Library/Python/2.7/bin' which is not on PATH.\n",
      "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\n",
      "\u001b[33m  WARNING: The script gdown is installed in '/Users/lap010760/Library/Python/2.7/bin' which is not on PATH.\n",
      "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\n",
      "Successfully installed PySocks-1.7.1 beautifulsoup4-4.9.3 filelock-3.2.1 gdown-4.4.0 importlib-resources-3.3.1 soupsieve-1.9.6 tqdm-4.64.0\n"
     ]
    }
   ],
   "source": [
    "!pip install gdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "d8_65Glufld4",
    "outputId": "bf42ac56-41e3-47aa-ae18-2ed797a75bb4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/bin/sh: gdown: command not found\r\n"
     ]
    }
   ],
   "source": [
    "!gdown --id 1xmaX9QHTWnKe58oIMT1ohnhPLU15WQ2O"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "GUWos8eTfwIp"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "X9NEC4cRfsBM",
    "outputId": "8fc4b577-37fc-4d56-9cb7-e77df5bbd43c"
   },
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] File b'House_Price_2.csv' does not exist: b'House_Price_2.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-2a1a6a372782>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"House_Price_2.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtail\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    700\u001b[0m                     skip_blank_lines=skip_blank_lines)\n\u001b[1;32m    701\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 702\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    703\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    704\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    427\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    428\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 429\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    430\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    431\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    893\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    894\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 895\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    896\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    897\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1120\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'c'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1121\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'c'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1122\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1123\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1124\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'python'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   1851\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'usecols'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1852\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1853\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1854\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1855\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] File b'House_Price_2.csv' does not exist: b'House_Price_2.csv'"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"House_Price_2.csv\")\n",
    "df.tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Zgj_32ZYicx-"
   },
   "source": [
    "**Define functions to used to fit the close form model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "F196pTOmftiy"
   },
   "outputs": [],
   "source": [
    "def closed_form_params(X, y):\n",
    "  '''\n",
    "  input: X, y\n",
    "  output: W (parameters of linear function) with shape (1, number of parameters)\n",
    "  ''' \n",
    "  sample_size=X.shape[0] \n",
    "  Z = np.concatenate([X, np.ones((sample_size,1))], axis=-1)\n",
    "  W=np.matmul(np.matmul(\n",
    "                      np.linalg.inv(np.matmul(Z.T,Z)), Z.T\n",
    "                      ),\n",
    "               y).reshape(1, -1)\n",
    "  assert (W.shape == (1, X.shape[1]+1)) \n",
    "  return W"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "id": "3LWhbismg9th"
   },
   "outputs": [],
   "source": [
    "def predict(X, W):\n",
    "  '''\n",
    "  input: X\n",
    "  output: y_hat vector with shape (y_hat, ) \n",
    "  '''\n",
    "  Z = np.concatenate([X, np.ones((X.shape[0],1))], axis=-1)\n",
    "  Z = Z.astype(np.float32) \n",
    "  y_hat=np.dot(W, Z.T).T.reshape(-1, 1).flatten() \n",
    "  assert (y_hat.shape == (X.shape[0],))  \n",
    "  return y_hat "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "I4OI9Rm2iYS3"
   },
   "source": [
    "**Fit on the given House Price dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "ncUYqsjki0vg"
   },
   "outputs": [],
   "source": [
    "#define predictors and label variable\n",
    "train_variable=['width', 'length']\n",
    "target_variable='price'\n",
    "X=df[train_variable]\n",
    "y=df[target_variable]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "id": "oDgGv2hvjk9i"
   },
   "outputs": [],
   "source": [
    "X_test = pd.DataFrame(\n",
    "  {    \n",
    "    'width': [5, 20, 10], \n",
    "    'length': [10, 20, 40]\n",
    "   }, \n",
    "   dtype=np.float64 \n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
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    "colab": {
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     "height": 143
    },
    "id": "4LuXsfJnkGTb",
    "outputId": "4f14d80e-6669-48e8-ad3f-02c03b6b1b80"
   },
   "outputs": [
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       "                                                     [key], {});\n",
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       "\n",
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       "  </div>\n",
       "  "
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      "text/plain": [
       "   width  length\n",
       "0    5.0    10.0\n",
       "1   20.0    20.0\n",
       "2   10.0    40.0"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hQdapIb_mdj5"
   },
   "source": [
    "**Tính các hệ số hồi quy cho mô hình:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DvvJn389ldab",
    "outputId": "56be9c69-e69c-492a-d0f4-ca9000e20767"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4992438.83485118, 1997577.09722778,  844994.82587483]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params=closed_form_params(X, y)\n",
    "params "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "whWqSCFFmf9M"
   },
   "source": [
    "**Kiểm tra lại giá trị R^2 trên các hệ số hồi quy vừa tìm được**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yzcIzyfZmnWA",
    "outputId": "c12d4801-7653-47f6-b4f9-5e5bd6d18b3f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9978935462564359"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score \n",
    "prediction=predict(X, params) \n",
    "r2_score(prediction, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fuOIJzqonRJZ"
   },
   "source": [
    "**Result on prediction on the given testing set**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "id": "_M0HOKIbm6b5"
   },
   "outputs": [],
   "source": [
    "y_test=predict(X_test, params) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "colab": {
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    },
    "id": "HmmAk6eJo13s",
    "outputId": "daa999bb-7667-4146-e445-8b284716554b"
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       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-88cbccce-2dbd-4656-b071-870c98637045 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-88cbccce-2dbd-4656-b071-870c98637045');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "   width  length         price\n",
       "0    5.0    10.0  4.578296e+07\n",
       "1   20.0    20.0  1.406453e+08\n",
       "2   10.0    40.0  1.306725e+08"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test=predict(X_test, params) \n",
    " \n",
    "result = pd.concat((X_test, pd.DataFrame({\"price\": y_test})), axis=1)\n",
    "result "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "HzDCzdBPpUNS"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "NMMH_TH1_21C29001.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}