runningclothes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3f9091d3",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'pandas'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[4], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mseaborn\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01msns\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'pandas'"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np \n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7295941d",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pd' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[5], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241m.\u001b[39mread_csv(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mRunning Weather - Log.csv\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(df\u001b[38;5;241m.\u001b[39mshape)\n\u001b[0;32m      3\u001b[0m df\u001b[38;5;241m.\u001b[39mhead()\n",
      "\u001b[1;31mNameError\u001b[0m: name 'pd' is not defined"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('Running Weather - Log.csv')\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f385f002",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Date', 'Start Time', 'Temp', 'Feels Like', 'Wind', 'Sky', 'Precip',\n",
       "       'Mi', 'Type', 'Felt', 'Top1', 'Top2', 'Bottom1', 'Bottom2',\n",
       "       'Head Warmth', 'Head Hat', 'Hands'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6d238a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 27 entries, 0 to 26\n",
      "Data columns (total 17 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   Date         27 non-null     object \n",
      " 1   Start Time   27 non-null     object \n",
      " 2   Temp         27 non-null     int64  \n",
      " 3   Feels Like   27 non-null     int64  \n",
      " 4   Wind         27 non-null     int64  \n",
      " 5   Sky          27 non-null     object \n",
      " 6   Precip       27 non-null     object \n",
      " 7   Mi           27 non-null     float64\n",
      " 8   Type         27 non-null     object \n",
      " 9   Felt         27 non-null     object \n",
      " 10  Top1         27 non-null     object \n",
      " 11  Top2         23 non-null     object \n",
      " 12  Bottom1      27 non-null     object \n",
      " 13  Bottom2      2 non-null      object \n",
      " 14  Head Warmth  19 non-null     object \n",
      " 15  Head Hat     12 non-null     object \n",
      " 16  Hands        17 non-null     object \n",
      "dtypes: float64(1), int64(3), object(13)\n",
      "memory usage: 3.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9266f7eb",
   "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>Temp</th>\n",
       "      <th>Feels Like</th>\n",
       "      <th>Wind</th>\n",
       "      <th>Mi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.333333</td>\n",
       "      <td>32.962963</td>\n",
       "      <td>7.777778</td>\n",
       "      <td>3.425926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.793662</td>\n",
       "      <td>8.798957</td>\n",
       "      <td>2.886751</td>\n",
       "      <td>0.689254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>35.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Temp  Feels Like       Wind         Mi\n",
       "count  27.000000   27.000000  27.000000  27.000000\n",
       "mean   38.333333   32.962963   7.777778   3.425926\n",
       "std     6.793662    8.798957   2.886751   0.689254\n",
       "min    20.000000   10.000000   5.000000   2.000000\n",
       "25%    35.000000   30.000000   5.000000   3.000000\n",
       "50%    40.000000   35.000000  10.000000   3.000000\n",
       "75%    40.000000   40.000000  10.000000   4.000000\n",
       "max    50.000000   45.000000  15.000000   5.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcc0a5c8",
   "metadata": {},
   "source": [
    "# Visualize Weather Conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93572812",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=df, x=\"Temp\");\n",
    "plt.title(\"Temperature\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84c5f2d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(data=df, y=\"Temp\");\n",
    "plt.title(\"Temperature\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36b93a58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=df, y=\"Temp\");\n",
    "plt.title(\"Temperature\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57267a81",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1470649",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=df, x=\"Feels Like\");\n",
    "plt.title(\"Feels Like Temperature\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2089cf4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(data=df, y=\"Feels Like\");\n",
    "plt.title(\"Feels Like Temperature\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96f40e89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PBrmqZ0mSq4E/bUbuJyQ5f9AOkrwY+CLw9uZmbx8HjgVW0vuL4LIp9luTZDzJ+Pbt2wftTpI0g0FO7n4K+CrwU83y3wBvH+Tgzc3dvgh8tqq+BFBV26pqd1XtoXfF0Csn27eqrqqqsaoaW7x48SDdSZIGMEjwH1FV1wB7AKpqF7B7pp2SBLga2FhVH+1rX9q32ZnA3bOqWJK0TwY5ufvDJIfz91f1vAr4wQD7vYbeffvvSnJ70/Y+4M1JVjbH2wxcMLuSJUn7YpDgfwdwHXBskv9F7yTvm2baqapuAzLJqhtmVaEkaU5Nd3fOo6vqgeY6/NcCP0svyL/bfBmLJGk/NN0c/1f6Xn++qu6pqrsNfUnav00X/P3TND/ddiGSpOGYLvhriteSpP3YdCd3X5HkcXoj/0XNa5rlqqpDWq9OkjTnpvsilgXDLESSNByDfIBLkvQ8YvBLUscY/JLUMQa/JHWMwS9JHWPwS1LHGPyS1DEGvyR1jMEvSR1j8EtSxxj8ktQxBr8kdYzBL0kd01rwJzkqyV8k2ZjkniRrm/bDkqxPcl/zfGhbNUiSnqvNEf8u4J1V9XPAq4C3JTkBeA9wS1UdB9zSLEuShqS14K+qrVX17eb1E8BGYBlwOrCu2WwdcEZbNUiSnmsoc/xJlgMnAt8AllTVVuj9cgCOHEYNkqSe1oM/yYuBLwJvr6rHZ9q+b781ScaTjG/fvr29AiWpY1oN/iQH0gv9z1bVl5rmbUmWNuuXAg9Ntm9VXVVVY1U1tnjx4jbLlKROafOqngBXAxur6qN9q64DVjevVwPXtlWDJOm5pvyy9TnwGuBc4K4ktzdt7wM+DFyT5HzgAeCsFmuQJE3QWvBX1W1Aplj9urb6lSRNz0/uSlLHGPyS1DEGvyR1jMEvSR1j8EtSxxj8ktQxBr8kdYzBL0kdY/BLUscY/JLUMQa/JHWMwS9JHWPwS1LHGPyS1DEGvyR1jMEvSR1j8EtSxxj8ktQxBr8kdYzBL0kd01rwJ/lkkoeS3N3XdkmSLUlubx6ntdW/JGlybY74PwWsmqT98qpa2TxuaLF/SdIkFrZ14Kr6epLlbR1fo3HllVeyadOmUZcxLzzzPqxdu3bElcwPK1as4KKLLhp1GRrAKOb4L0xyZzMVdOhUGyVZk2Q8yfj27duHWZ80kEWLFrFo0aJRlyHNWqqqvYP3RvzXV9XLm+UlwMNAAR8AllbVW2Y6ztjYWI2Pj7dWpyQ9HyXZUFVjE9uHOuKvqm1Vtbuq9gCfAF45zP4lSUMO/iRL+xbPBO6ealtJUjtaO7mb5HPAScARSb4PvB84KclKelM9m4EL2upfkjS5Nq/qefMkzVe31Z8kaTB+cleSOsbgl6SOMfglqWMMfknqGINfkjrG4JekjjH4JaljDH5J6hiDX5I6xuCXpI4x+CWpYwx+SeoYg1+SOsbgl6SOMfglqWMMfknqGINfkjrG4JekjjH4pb20Y8cOLr74Ynbs2DHqUqRZaS34k3wyyUNJ7u5rOyzJ+iT3Nc+HttW/1LZ169Zx11138elPf3rUpUiz0uaI/1PAqglt7wFuqarjgFuaZWm/s2PHDm666SaqiptuuslRv/YrrQV/VX0deGRC8+nAuub1OuCMtvqX2rRu3Tr27NkDwO7dux31a78y7Dn+JVW1FaB5PnKqDZOsSTKeZHz79u1DK1AaxM0338yuXbsA2LVrF+vXrx9xRdLg5u3J3aq6qqrGqmps8eLFoy5H+gmnnHIKCxcuBGDhwoWceuqpI65IGtywg39bkqUAzfNDQ+5fmhOrV6/mgAN6/3wWLFjAeeedN+KKpMENO/ivA1Y3r1cD1w65f2lOHH744axatYokrFq1isMPP3zUJUkDW9jWgZN8DjgJOCLJ94H3Ax8GrklyPvAAcFZb/UttW716NZs3b3a0r/1OqmrUNcxobGysxsfHR12GJO1XkmyoqrGJ7fP25K4kqR0GvyR1jMEvSR1j8EtSx+wXJ3eTbAfuH3Ud0iSOAB4edRHSFI6pqud8Ana/CH5pvkoyPtlVE9J85lSPJHWMwS9JHWPwS/vmqlEXIM2Wc/yS1DGO+CWpYwx+SeoYg1+SOsbgl6SOMfglqWP+P9IU6XX5Tv2TAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=df, y=\"Feels Like\");\n",
    "plt.title(\"Feels Like Temperature\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78da94c1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "82222066",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=df, x=\"Wind\");\n",
    "plt.title(\"Wind Speed\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb231fef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(data=df, y=\"Wind\");\n",
    "plt.title(\"Wind Speed\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6d816a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YfklqjOGXHp5NfQ8g7S2v8UtSYzzjl6TGGH5Jaozhl6TGGH5Jaozhl6TG/B+IBVy9VRxKxwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=df, y=\"Wind\");\n",
    "plt.title(\"Wind Speed\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "830d7352",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3b0e71b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=df, x=\"Precip\");\n",
    "plt.title(\"Precipitation\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9eff207e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=df, x=\"Sky\");\n",
    "plt.title(\"Cloud Conditions\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31b633e1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db4218d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_corr = df[['Temp', 'Feels Like', 'Wind']].copy()\n",
    "sns.heatmap(df_corr.corr(), annot=True);\n",
    "plt.title(\"Correlations\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3508be4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.plotting.scatter_matrix(df, alpha=0.2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64d2736e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=df, x=\"Temp\", y=\"Feels Like\"); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5685458f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "785f8a2f",
   "metadata": {},
   "source": [
    "# Clean Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7469e60f",
   "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>Start Time</th>\n",
       "      <th>Temp</th>\n",
       "      <th>Feels Like</th>\n",
       "      <th>Wind</th>\n",
       "      <th>Sky</th>\n",
       "      <th>Precip</th>\n",
       "      <th>Mi</th>\n",
       "      <th>Type</th>\n",
       "      <th>Felt</th>\n",
       "      <th>...</th>\n",
       "      <th>Top2</th>\n",
       "      <th>Bottom1</th>\n",
       "      <th>Bottom2</th>\n",
       "      <th>Head Warmth</th>\n",
       "      <th>Head Hat</th>\n",
       "      <th>Hands</th>\n",
       "      <th>Cloudy or Night</th>\n",
       "      <th>Sunny</th>\n",
       "      <th>Light</th>\n",
       "      <th>None</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10/18/22</td>\n",
       "      <td>1:00 PM</td>\n",
       "      <td>45</td>\n",
       "      <td>35</td>\n",
       "      <td>15</td>\n",
       "      <td>Cloudy or Night</td>\n",
       "      <td>None</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Easy</td>\n",
       "      <td>Perfect</td>\n",
       "      <td>...</td>\n",
       "      <td>Chicago Marathon quarter zip</td>\n",
       "      <td>Nike Calf Crops</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bondi Band</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Old Navy tech gloves (removed)</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10/20/22</td>\n",
       "      <td>9:00 AM</td>\n",
       "      <td>40</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "      <td>Sunny</td>\n",
       "      <td>None</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Easy</td>\n",
       "      <td>Perfect</td>\n",
       "      <td>...</td>\n",
       "      <td>Teal UA funnel neck</td>\n",
       "      <td>Nike unlined tights</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bondi Band</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Old Navy tech gloves (removed)</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11/13/22</td>\n",
       "      <td>3:15 PM</td>\n",
       "      <td>35</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>Cloudy or Night</td>\n",
       "      <td>None</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Easy</td>\n",
       "      <td>Cold</td>\n",
       "      <td>...</td>\n",
       "      <td>Run Mag Mile sunset half zip</td>\n",
       "      <td>Nike tights with mesh backs of knees</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wool single layer</td>\n",
       "      <td>baseball cap</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11/18/22</td>\n",
       "      <td>10:15 AM</td>\n",
       "      <td>30</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>Sunny</td>\n",
       "      <td>None</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Easy</td>\n",
       "      <td>Cold</td>\n",
       "      <td>...</td>\n",
       "      <td>Blue smartwool half zip</td>\n",
       "      <td>Black Athleta Rainier tights</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wool double layer</td>\n",
       "      <td>baseball cap</td>\n",
       "      <td>Sugoi gloves</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11/28/22</td>\n",
       "      <td>10:00 AM</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>5</td>\n",
       "      <td>Sunny</td>\n",
       "      <td>None</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Easy</td>\n",
       "      <td>Warm</td>\n",
       "      <td>...</td>\n",
       "      <td>black smartwool half zip</td>\n",
       "      <td>Black purple Athleta tights</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wool single layer</td>\n",
       "      <td>baseball cap</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Date Start Time  Temp  Feels Like  Wind              Sky Precip   Mi  \\\n",
       "0  10/18/22    1:00 PM    45          35    15  Cloudy or Night   None  3.0   \n",
       "1  10/20/22    9:00 AM    40          30    10            Sunny   None  3.0   \n",
       "2  11/13/22    3:15 PM    35          30     5  Cloudy or Night   None  3.0   \n",
       "3  11/18/22   10:15 AM    30          20    10            Sunny   None  3.0   \n",
       "4  11/28/22   10:00 AM    40          35     5            Sunny   None  3.0   \n",
       "\n",
       "   Type     Felt  ...                          Top2  \\\n",
       "0  Easy  Perfect  ...  Chicago Marathon quarter zip   \n",
       "1  Easy  Perfect  ...           Teal UA funnel neck   \n",
       "2  Easy     Cold  ...  Run Mag Mile sunset half zip   \n",
       "3  Easy     Cold  ...       Blue smartwool half zip   \n",
       "4  Easy    Warm   ...      black smartwool half zip   \n",
       "\n",
       "                                Bottom1 Bottom2        Head Warmth  \\\n",
       "0                       Nike Calf Crops     NaN         Bondi Band   \n",
       "1                   Nike unlined tights     NaN         Bondi Band   \n",
       "2  Nike tights with mesh backs of knees     NaN  wool single layer   \n",
       "3         Black Athleta Rainier tights      NaN  wool double layer   \n",
       "4           Black purple Athleta tights     NaN  wool single layer   \n",
       "\n",
       "       Head Hat                           Hands Cloudy or Night  Sunny  Light  \\\n",
       "0           NaN  Old Navy tech gloves (removed)               1      0      0   \n",
       "1           NaN  Old Navy tech gloves (removed)               0      1      0   \n",
       "2  baseball cap                             NaN               1      0      0   \n",
       "3  baseball cap                    Sugoi gloves               0      1      0   \n",
       "4  baseball cap                             NaN               0      1      0   \n",
       "\n",
       "   None  \n",
       "0     1  \n",
       "1     1  \n",
       "2     1  \n",
       "3     1  \n",
       "4     1  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create dummies \n",
    "\n",
    "sky_d = pd.get_dummies(df['Sky'])\n",
    "precip_d = pd.get_dummies(df['Precip'])\n",
    "\n",
    "df = pd.concat([df, sky_d, precip_d], axis=1)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e4fe3be",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2d43ddbb",
   "metadata": {},
   "source": [
    "# Predict Feels Like Temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6fcfb854",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import preprocessing\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d82de982",
   "metadata": {},
   "outputs": [],
   "source": [
    "# separate dependent variable from independent varibles \n",
    "y = np.array(df['Feels Like']).reshape(-1, 1)\n",
    "X = df[['Temp', 'Wind', 'Sunny','Light']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "724839de",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 5, test_size= 0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a704365",
   "metadata": {},
   "outputs": [],
   "source": [
    "ss = preprocessing.StandardScaler()\n",
    "X_train_ss = ss.fit_transform(X_train)\n",
    "X_test_ss = ss.transform(X_test)\n",
    "\n",
    "y_train_ss = ss.fit_transform(y_train)\n",
    "y_test_ss = ss.transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1769b0e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fit model \n",
    "lr = LinearRegression().fit(X_train_ss, y_train_ss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ffc3eff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE on train: 0.19203508820226903\n",
      "MAE on test: 0.3122583421991222\n"
     ]
    }
   ],
   "source": [
    "# check error \n",
    "pred = lr.predict(X_train_ss)\n",
    "print('MAE on train:', metrics.mean_absolute_error(y_train_ss, pred))\n",
    "pred = lr.predict(X_test_ss)\n",
    "print('MAE on test:', metrics.mean_absolute_error(y_test_ss, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0530bac6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check residuals \n",
    "\n",
    "residuals = y_test_ss - pred\n",
    "plt.scatter(residuals, pred)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d2e3c80",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[35.]\n",
      " [45.]\n",
      " [30.]\n",
      " [10.]\n",
      " [35.]\n",
      " [35.]\n",
      " [30.]]\n",
      "[[37.00302725]\n",
      " [47.2653885 ]\n",
      " [34.81331988]\n",
      " [11.49596367]\n",
      " [39.9445005 ]\n",
      " [34.73763875]\n",
      " [31.87184662]]\n"
     ]
    }
   ],
   "source": [
    "# unscale to check values \n",
    "\n",
    "y_test_inverse = ss.inverse_transform(y_test_ss)\n",
    "pred_inverse = ss.inverse_transform(pred)\n",
    "\n",
    "np.set_printoptions(suppress=True)\n",
    "\n",
    "print(y_test_inverse)\n",
    "print(pred_inverse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e916c4ef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e8de0d90",
   "metadata": {},
   "source": [
    "# Recommend Running Clothes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f15b9e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e253e95f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def what_to_wear(df, rec):\n",
    "    '''print recommendations of what to wear'''\n",
    "\n",
    "    print('You should wear:')\n",
    "    print('Top, base:   ', df.iloc[rec]['Top1'])\n",
    "    print('Top, mid:    ', df.iloc[rec]['Top2'])\n",
    "    print('Bottom, base:', df.iloc[rec]['Bottom1'])\n",
    "    print('Bottom, top: ', df.iloc[rec]['Bottom2'])\n",
    "    print('Head Warmth: ', df.iloc[rec]['Head Warmth'])\n",
    "    print('Head Hat:    ', df.iloc[rec]['Head Hat'])\n",
    "    print('Hands:       ', df.iloc[rec]['Hands'])\n",
    "\n",
    "    print('\\nConditions the last time you wore this:')\n",
    "    print('Temp:', df.iloc[rec]['Temp'], ', Feels Like:', df.iloc[rec]['Feels Like'], ', Wind:', df.iloc[rec]['Wind'], \n",
    "          ', Sunny:', df.iloc[rec]['Sunny'], ', Precip:', df.iloc[rec]['Light'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2623a671",
   "metadata": {},
   "outputs": [],
   "source": [
    "samples = df[['Temp', 'Feels Like', 'Wind', 'Sunny', 'Light']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d0302c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NearestNeighbors(n_neighbors=1)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neigh = NearestNeighbors(n_neighbors=1)\n",
    "neigh.fit(samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb9ed243",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You should wear:\n",
      "Top, base:    Blue Nathan base\n",
      "Top, mid:     New Balance half zip\n",
      "Bottom, base: Nike unlined tights\n",
      "Bottom, top:  nan\n",
      "Head Warmth:  nan\n",
      "Head Hat:     baseball cap\n",
      "Hands:        nan\n",
      "\n",
      "Conditions the last time you wore this:\n",
      "Temp: 45 , Feels Like: 40 , Wind: 5 , Sunny: 1 , Precip: 0\n"
     ]
    }
   ],
   "source": [
    "temp = 45\n",
    "feels_like = 40\n",
    "wind = 5\n",
    "sunny = 0\n",
    "precip = 1\n",
    "\n",
    "rec = neigh.kneighbors([[temp,feels_like,wind,sunny,precip]], return_distance=False)[0][0]\n",
    "\n",
    "what_to_wear(df, rec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a4782f9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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