q5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "No module named 'scipy._lib'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-b63e96bf6ecc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpress\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmodules\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mirisClassifier\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0miris_knn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mKNN\u001b[0m \u001b[0;32mimport\u001b[0m  \u001b[0mKNN_classifier\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0moccupancy\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'datasets/occupancy.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/irisClassifier.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0moperator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_selection\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.5/site-packages/sklearn/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     74\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m     \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0m__check_build\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 76\u001b[0;31m     \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mbase\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mclone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     77\u001b[0m     \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_show_versions\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mshow_versions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     78\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.5/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0m__version__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0m_IS_32BIT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m _DEFAULT_TAGS = {\n",
      "\u001b[0;32m~/.local/lib/python3.5/site-packages/sklearn/utils/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msparse\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0missparse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mmurmurhash\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmurmurhash3_32\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.5/site-packages/scipy/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    108\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    109\u001b[0m     \u001b[0;32mfrom\u001b[0m \u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mversion\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mversion\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0m__version__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 110\u001b[0;31m     \u001b[0;32mfrom\u001b[0m \u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_version\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mNumpyVersion\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0m_NumpyVersion\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    111\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0m_NumpyVersion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__numpy_version__\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;34m'1.8.2'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    112\u001b[0m         \u001b[0;32mimport\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mImportError\u001b[0m: No module named 'scipy._lib'"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "from modules import *\n",
    "from irisClassifier import iris_knn\n",
    "from KNN import  KNN_classifier\n",
    "occupancy = pd.read_csv('datasets/occupancy.csv')\n",
    "weather = pd.read_csv('datasets/weather.csv', index_col=None)\n",
    "iris = px.data.iris()\n",
    "i = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl;line-height:300%;\"><font face=\"IRANSans\" size=5>\n",
    "<font color=red size=7>\n",
    "<p></p>\n",
    "<div align=center>بخش ۵ </div>\n",
    "</font></div><hr/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl;line-height:150%;\"><font face=\"IRANSans\" size=3>\n",
    "    دیتاست مربوط به وضعیت اشغال منازل مسکونی به شما داده شده است. با کمک دستور \n",
    "    <code>head</code>\n",
    "    چندتا از داده‌ها را مشاهده کنید. می‌خواهیم عوامل موثر بر وضعیت اشغال منازل را با استفاده از این داده‌ها شناسایی کنیم. نمودار سه بعدی نور، دما، میزان کربن دی اکسید را ترسیم کنید. خانه های پر و خالی را با رنگ‌های مختلف روی نمودار مشاهده کنید. به نظر شما با داشتن اطلاعات مربوط به یک خانه‌ی جدید(مثلا برای هر خانه همان نور و دما و کربن دی اکسید)، با توجه به کدام متغیر می‌توانیم پیش‌بینی کنیم که آن خانه پر یا خالی خواهد بود؟\n",
    "</font></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>date</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Light</th>\n",
       "      <th>CO2</th>\n",
       "      <th>HumidityRatio</th>\n",
       "      <th>Occupancy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>2015-02-02 14:19:00</td>\n",
       "      <td>23.7000</td>\n",
       "      <td>26.272</td>\n",
       "      <td>585.200000</td>\n",
       "      <td>749.200000</td>\n",
       "      <td>0.004764</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>2015-02-02 14:19:59</td>\n",
       "      <td>23.7180</td>\n",
       "      <td>26.290</td>\n",
       "      <td>578.400000</td>\n",
       "      <td>760.400000</td>\n",
       "      <td>0.004773</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>2015-02-02 14:21:00</td>\n",
       "      <td>23.7300</td>\n",
       "      <td>26.230</td>\n",
       "      <td>572.666667</td>\n",
       "      <td>769.666667</td>\n",
       "      <td>0.004765</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>2015-02-02 14:22:00</td>\n",
       "      <td>23.7225</td>\n",
       "      <td>26.125</td>\n",
       "      <td>493.750000</td>\n",
       "      <td>774.750000</td>\n",
       "      <td>0.004744</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>2015-02-02 14:23:00</td>\n",
       "      <td>23.7540</td>\n",
       "      <td>26.200</td>\n",
       "      <td>488.600000</td>\n",
       "      <td>779.000000</td>\n",
       "      <td>0.004767</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    date  Temperature  Humidity       Light         CO2  \\\n",
       "140  2015-02-02 14:19:00      23.7000    26.272  585.200000  749.200000   \n",
       "141  2015-02-02 14:19:59      23.7180    26.290  578.400000  760.400000   \n",
       "142  2015-02-02 14:21:00      23.7300    26.230  572.666667  769.666667   \n",
       "143  2015-02-02 14:22:00      23.7225    26.125  493.750000  774.750000   \n",
       "144  2015-02-02 14:23:00      23.7540    26.200  488.600000  779.000000   \n",
       "\n",
       "     HumidityRatio  Occupancy  \n",
       "140       0.004764          1  \n",
       "141       0.004773          1  \n",
       "142       0.004765          1  \n",
       "143       0.004744          1  \n",
       "144       0.004767          1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "head(occupancy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl;line-height:150%;\"><font face=\"IRANSans\" size=3>\n",
    "  ستون‌های موردنیاز شما در این دیتاست این‌ها هستند:\n",
    "<br/>\n",
    "  <lu>\n",
    "    Temperature:دما\n",
    "    </lu>\n",
    "    <br>\n",
    "    <lu>\n",
    "    Humidity: میزان شرجی بودن\n",
    "    </lu>\n",
    "    <br>\n",
    "    <lu>\n",
    "    Light: نور\n",
    "    </lu>\n",
    "    <br>\n",
    "    <lu>\n",
    "    CO2:کربن دی اکسید\n",
    "    </lu>\n",
    "    <br>\n",
    "    <lu>\n",
    "    Occupancy: وضعیت اشغال منازل\n",
    "    </lu>\n",
    "</font></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl;line-height:150%;\"><font face=\"IRANSans\" size=3>\n",
    "  دیتاست مربوط به انواع گل زنبق به شما داده شده است. با کمک دستور head می‌توانید چندتا از این داده‌ها را مشاهده کنید:\n",
    "</font></div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "      <th>species_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species  species_id\n",
       "0           5.1          3.5           1.4          0.2  setosa           1\n",
       "1           4.9          3.0           1.4          0.2  setosa           1\n",
       "2           4.7          3.2           1.3          0.2  setosa           1\n",
       "3           4.6          3.1           1.5          0.2  setosa           1\n",
       "4           5.0          3.6           1.4          0.2  setosa           1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "head(iris)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl;line-height:150%;\"><font face=\"IRANSans\" size=3>\n",
    "  ستون‌های موردنیاز شما در این دیتاست این‌ها هستند:\n",
    "<br/>\n",
    "  <lu>\n",
    "    sepal_length:طول کاسبرگ\n",
    "    </lu>\n",
    "    <br>\n",
    "    <lu>\n",
    "    sepal_width: عرض کاسبرگ\n",
    "    </lu>\n",
    "    <br>\n",
    "    <lu>\n",
    "    petal_length: طول گلبرگ\n",
    "    </lu>\n",
    "    <br>\n",
    "    <lu>\n",
    "    petal_width:عرض گلبرگ\n",
    "    </lu>\n",
    "    <br>\n",
    "    <lu>\n",
    "    species: گونه‌ی گل زنبق\n",
    "    </lu>\n",
    "<br>\n",
    "<br>\n",
    "  با کمک دستور \n",
    "    <code>unique</code>\n",
    "    گونه‌های مختلف گل زنبق را مشاهده کنید:\n",
    "</font></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl;line-height:150%;\"><font face=\"IRANSans\" size=3>\n",
    "    می‌خواهیم با استفاده از ویژگی‌های مختلف انواع گل زنبق، دسته‌بندی آن‌ها را شناسایی کنیم. ویژگی‌های مورد نظر ما طول و عرض کاسبرگ و عرض گلبرگ است. نمودار سه بعدی دسته بندی گل‌های زنبق را ترسیم کنید.\n",
    "</font></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "k = 3\n",
    "iris_plot = "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl;line-height:150%;\"><font face=\"IRANSans\" size=3>\n",
    " به نظر شما اگر اطلاعات مربوط به یک گل زنبق جدید به شما داده شود، چگونه می‌توانید دسته بندی آن را تشخیص دهید؟ راهنمایی: سعی کنید از نمودار کمک بگیرید!\n",
    "    <br><br>\n",
    "    حالا می‌خواهیم با استفاده از سه ویژگی گفته شده، دسته بندی داده‌ی جدید از گل زنبق را شناسایی کنیم. برای این کار از دستور \n",
    "    <code>iris_knn</code>\n",
    "    استفاده کنید و خروجی آن را در متغیر زیر ذخیره کنید.\n",
    "</font></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "result ="
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl;line-height:150%;\"><font face=\"IRANSans\" size=3>\n",
    "    بعد از اجرای دستور بالا، برای این‌که ببینید الگوریتم واقعا به چه صورت عمل کرده‌است، قطعه کد زیر را صرفا اجرا کنید:)\n",
    "  \n",
    "</font></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_neighbors():\n",
    "    neighbors = result[i]['neighbors']\n",
    "    test_item = result[i]['test']\n",
    "    iris_plot.add_scatter3d(x=[float(test_item['sepal_length'])], y=[float(test_item['sepal_width'])], z=[float(test_item['petal_width'])], name='Test')\n",
    "    print('predicted= '+ res[1]['actual'])\n",
    "    for n in neighbors:\n",
    "        if n['out'] == 'setosa':\n",
    "            color = 'blue'\n",
    "        elif n['out'] == 'versicolor':\n",
    "            color = 'red'\n",
    "        else:\n",
    "            color = 'green'\n",
    "        iris_plot.add_scatter3d(x=[float(n['sepal_length'])], y=[float(n['sepal_width'])], z=[float(n['petal_width'])], name=n['out'], marker={'color':color,'line':{'width':2,'color':'black'}})\n",
    "    if i > k:\n",
    "        i = 0\n",
    "    else:\n",
    "        i += 1\n",
    "    iris_plot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl;line-height:150%;\"><font face=\"IRANSans\" size=3>\n",
    "  دیتاست مربوط به پیش‌بینی بارندگی به شما داده شده است. با کمک دستور \n",
    "    <code>head</code>\n",
    "    می‌توانید چندتا از این داده‌ها را مشاهده کنید:\n",
    "</font></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Date</th>\n",
       "      <th>Location</th>\n",
       "      <th>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>Evaporation</th>\n",
       "      <th>Sunshine</th>\n",
       "      <th>WindGustDir</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindDir9am</th>\n",
       "      <th>...</th>\n",
       "      <th>Humidity3pm</th>\n",
       "      <th>Pressure9am</th>\n",
       "      <th>Pressure3pm</th>\n",
       "      <th>Cloud9am</th>\n",
       "      <th>Cloud3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Temp3pm</th>\n",
       "      <th>RainToday</th>\n",
       "      <th>RISK_MM</th>\n",
       "      <th>RainTomorrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-12-01</td>\n",
       "      <td>Albury</td>\n",
       "      <td>13.4</td>\n",
       "      <td>22.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>44.0</td>\n",
       "      <td>W</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1007.7</td>\n",
       "      <td>1007.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.9</td>\n",
       "      <td>21.8</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-12-02</td>\n",
       "      <td>Albury</td>\n",
       "      <td>7.4</td>\n",
       "      <td>25.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WNW</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1010.6</td>\n",
       "      <td>1007.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.2</td>\n",
       "      <td>24.3</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-12-03</td>\n",
       "      <td>Albury</td>\n",
       "      <td>12.9</td>\n",
       "      <td>25.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WSW</td>\n",
       "      <td>46.0</td>\n",
       "      <td>W</td>\n",
       "      <td>...</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1007.6</td>\n",
       "      <td>1008.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-12-04</td>\n",
       "      <td>Albury</td>\n",
       "      <td>9.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NE</td>\n",
       "      <td>24.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>...</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1017.6</td>\n",
       "      <td>1012.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.1</td>\n",
       "      <td>26.5</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-12-05</td>\n",
       "      <td>Albury</td>\n",
       "      <td>17.5</td>\n",
       "      <td>32.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>ENE</td>\n",
       "      <td>...</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1010.8</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>29.7</td>\n",
       "      <td>No</td>\n",
       "      <td>0.2</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date Location  MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine  \\\n",
       "0  2008-12-01   Albury     13.4     22.9       0.6          NaN       NaN   \n",
       "1  2008-12-02   Albury      7.4     25.1       0.0          NaN       NaN   \n",
       "2  2008-12-03   Albury     12.9     25.7       0.0          NaN       NaN   \n",
       "3  2008-12-04   Albury      9.2     28.0       0.0          NaN       NaN   \n",
       "4  2008-12-05   Albury     17.5     32.3       1.0          NaN       NaN   \n",
       "\n",
       "  WindGustDir  WindGustSpeed WindDir9am  ... Humidity3pm  Pressure9am  \\\n",
       "0           W           44.0          W  ...        22.0       1007.7   \n",
       "1         WNW           44.0        NNW  ...        25.0       1010.6   \n",
       "2         WSW           46.0          W  ...        30.0       1007.6   \n",
       "3          NE           24.0         SE  ...        16.0       1017.6   \n",
       "4           W           41.0        ENE  ...        33.0       1010.8   \n",
       "\n",
       "   Pressure3pm  Cloud9am  Cloud3pm  Temp9am  Temp3pm  RainToday  RISK_MM  \\\n",
       "0       1007.1       8.0       NaN     16.9     21.8         No      0.0   \n",
       "1       1007.8       NaN       NaN     17.2     24.3         No      0.0   \n",
       "2       1008.7       NaN       2.0     21.0     23.2         No      0.0   \n",
       "3       1012.8       NaN       NaN     18.1     26.5         No      1.0   \n",
       "4       1006.0       7.0       8.0     17.8     29.7         No      0.2   \n",
       "\n",
       "   RainTomorrow  \n",
       "0            No  \n",
       "1            No  \n",
       "2            No  \n",
       "3            No  \n",
       "4            No  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "head(weather)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl;line-height:150%;\"><font face=\"IRANSans\" size=3>\n",
    "همان‌طور که می‌بینید، پیچیدگی بیشتر مسئله‌ی مورد نظر با افزایش عوامل موثر بر آن همراه است. الگوریتمی که در مرحله قبل یافتید، با  کمک منتورها و با استفاده از دستور \n",
    "    <code>KNN_classifier</code>\n",
    "    روی داده‌ها پیاده کنید. \n",
    "<br><br>\n",
    "   فراموش نکنید که پیش از شروع کار، داده را تمیز کنید!\n",
    "</font></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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