EXPLORATION/EXPLORATION 5. 날씨 좋은 월요일 오후 세 시, 자전거 타는 사람은 몇 명?.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 프로젝트 1 : 손수 설계하는 선형회귀, 당뇨병 수치를 맞춰보자!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_diabetes\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DESCR',\n",
       " 'data',\n",
       " 'data_filename',\n",
       " 'feature_names',\n",
       " 'frame',\n",
       " 'target',\n",
       " 'target_filename']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes = load_diabetes()\n",
    "dir(diabetes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfX = pd.DataFrame(diabetes.data, columns=diabetes.feature_names)\n",
    "dfy = pd.DataFrame(diabetes.target, columns=['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>bp</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s5</th>\n",
       "      <th>s6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.038076</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.061696</td>\n",
       "      <td>0.021872</td>\n",
       "      <td>-0.044223</td>\n",
       "      <td>-0.034821</td>\n",
       "      <td>-0.043401</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.019908</td>\n",
       "      <td>-0.017646</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.001882</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.051474</td>\n",
       "      <td>-0.026328</td>\n",
       "      <td>-0.008449</td>\n",
       "      <td>-0.019163</td>\n",
       "      <td>0.074412</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.068330</td>\n",
       "      <td>-0.092204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.085299</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.044451</td>\n",
       "      <td>-0.005671</td>\n",
       "      <td>-0.045599</td>\n",
       "      <td>-0.034194</td>\n",
       "      <td>-0.032356</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.002864</td>\n",
       "      <td>-0.025930</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.089063</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.011595</td>\n",
       "      <td>-0.036656</td>\n",
       "      <td>0.012191</td>\n",
       "      <td>0.024991</td>\n",
       "      <td>-0.036038</td>\n",
       "      <td>0.034309</td>\n",
       "      <td>0.022692</td>\n",
       "      <td>-0.009362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.005383</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.036385</td>\n",
       "      <td>0.021872</td>\n",
       "      <td>0.003935</td>\n",
       "      <td>0.015596</td>\n",
       "      <td>0.008142</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>-0.031991</td>\n",
       "      <td>-0.046641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>0.041708</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.019662</td>\n",
       "      <td>0.059744</td>\n",
       "      <td>-0.005697</td>\n",
       "      <td>-0.002566</td>\n",
       "      <td>-0.028674</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.031193</td>\n",
       "      <td>0.007207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>-0.005515</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>-0.015906</td>\n",
       "      <td>-0.067642</td>\n",
       "      <td>0.049341</td>\n",
       "      <td>0.079165</td>\n",
       "      <td>-0.028674</td>\n",
       "      <td>0.034309</td>\n",
       "      <td>-0.018118</td>\n",
       "      <td>0.044485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>0.041708</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>-0.015906</td>\n",
       "      <td>0.017282</td>\n",
       "      <td>-0.037344</td>\n",
       "      <td>-0.013840</td>\n",
       "      <td>-0.024993</td>\n",
       "      <td>-0.011080</td>\n",
       "      <td>-0.046879</td>\n",
       "      <td>0.015491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>-0.045472</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>0.039062</td>\n",
       "      <td>0.001215</td>\n",
       "      <td>0.016318</td>\n",
       "      <td>0.015283</td>\n",
       "      <td>-0.028674</td>\n",
       "      <td>0.026560</td>\n",
       "      <td>0.044528</td>\n",
       "      <td>-0.025930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>-0.045472</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.073030</td>\n",
       "      <td>-0.081414</td>\n",
       "      <td>0.083740</td>\n",
       "      <td>0.027809</td>\n",
       "      <td>0.173816</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.004220</td>\n",
       "      <td>0.003064</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>442 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          age       sex       bmi        bp        s1        s2        s3  \\\n",
       "0    0.038076  0.050680  0.061696  0.021872 -0.044223 -0.034821 -0.043401   \n",
       "1   -0.001882 -0.044642 -0.051474 -0.026328 -0.008449 -0.019163  0.074412   \n",
       "2    0.085299  0.050680  0.044451 -0.005671 -0.045599 -0.034194 -0.032356   \n",
       "3   -0.089063 -0.044642 -0.011595 -0.036656  0.012191  0.024991 -0.036038   \n",
       "4    0.005383 -0.044642 -0.036385  0.021872  0.003935  0.015596  0.008142   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "437  0.041708  0.050680  0.019662  0.059744 -0.005697 -0.002566 -0.028674   \n",
       "438 -0.005515  0.050680 -0.015906 -0.067642  0.049341  0.079165 -0.028674   \n",
       "439  0.041708  0.050680 -0.015906  0.017282 -0.037344 -0.013840 -0.024993   \n",
       "440 -0.045472 -0.044642  0.039062  0.001215  0.016318  0.015283 -0.028674   \n",
       "441 -0.045472 -0.044642 -0.073030 -0.081414  0.083740  0.027809  0.173816   \n",
       "\n",
       "           s4        s5        s6  \n",
       "0   -0.002592  0.019908 -0.017646  \n",
       "1   -0.039493 -0.068330 -0.092204  \n",
       "2   -0.002592  0.002864 -0.025930  \n",
       "3    0.034309  0.022692 -0.009362  \n",
       "4   -0.002592 -0.031991 -0.046641  \n",
       "..        ...       ...       ...  \n",
       "437 -0.002592  0.031193  0.007207  \n",
       "438  0.034309 -0.018118  0.044485  \n",
       "439 -0.011080 -0.046879  0.015491  \n",
       "440  0.026560  0.044528 -0.025930  \n",
       "441 -0.039493 -0.004220  0.003064  \n",
       "\n",
       "[442 rows x 10 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>151.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>141.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>206.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>135.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>178.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>104.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>132.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>442 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     target\n",
       "0     151.0\n",
       "1      75.0\n",
       "2     141.0\n",
       "3     206.0\n",
       "4     135.0\n",
       "..      ...\n",
       "437   178.0\n",
       "438   104.0\n",
       "439   132.0\n",
       "440   220.0\n",
       "441    57.0\n",
       "\n",
       "[442 rows x 1 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dfX.values\n",
    "y = dfy['target'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "W = np.random.rand(10)\n",
    "b = np.random.rand()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.27006344, 0.74506016, 0.56395758, 0.8907705 , 0.08553655,\n",
       "       0.50063463, 0.77837307, 0.98421692, 0.84001236, 0.65093915])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "W"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5320504722847357"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model(X,W,b):\n",
    "    predictions = 0\n",
    "    for i in range(10):\n",
    "        predictions += X[:,i]*W[i]\n",
    "    predictions += b\n",
    "    return predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loss function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MSE(a,b):\n",
    "    mse = ((a-b)**2).mean()\n",
    "    return mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loss(X,W,b,y):\n",
    "    predictions = model(X,W,b)\n",
    "    Loss = MSE(predictions,y)\n",
    "    return Loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gradient(X,W,b,y):\n",
    "    N = len(W)\n",
    "    y_pred = model(X,W,b)\n",
    "    \n",
    "    dW = 1/N * 2 * (X.T).dot(y_pred-y)\n",
    "    db = 2 * (y_pred-y).mean()\n",
    "    return dW, db"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "learning_rate = 0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 10 : Loss 27959.7026\n",
      "Iteration 20 : Loss 27055.6696\n",
      "Iteration 30 : Loss 26186.5425\n",
      "Iteration 40 : Loss 25350.9550\n",
      "Iteration 50 : Loss 24547.5947\n",
      "Iteration 60 : Loss 23775.2007\n",
      "Iteration 70 : Loss 23032.5615\n",
      "Iteration 80 : Loss 22318.5133\n",
      "Iteration 90 : Loss 21631.9379\n",
      "Iteration 100 : Loss 20971.7609\n",
      "Iteration 110 : Loss 20336.9504\n",
      "Iteration 120 : Loss 19726.5147\n",
      "Iteration 130 : Loss 19139.5010\n",
      "Iteration 140 : Loss 18574.9942\n",
      "Iteration 150 : Loss 18032.1148\n",
      "Iteration 160 : Loss 17510.0181\n",
      "Iteration 170 : Loss 17007.8923\n",
      "Iteration 180 : Loss 16524.9576\n",
      "Iteration 190 : Loss 16060.4647\n",
      "Iteration 200 : Loss 15613.6939\n",
      "Iteration 210 : Loss 15183.9536\n",
      "Iteration 220 : Loss 14770.5792\n",
      "Iteration 230 : Loss 14372.9325\n",
      "Iteration 240 : Loss 13990.4001\n",
      "Iteration 250 : Loss 13622.3927\n",
      "Iteration 260 : Loss 13268.3442\n",
      "Iteration 270 : Loss 12927.7106\n",
      "Iteration 280 : Loss 12599.9692\n",
      "Iteration 290 : Loss 12284.6181\n",
      "Iteration 300 : Loss 11981.1748\n",
      "Iteration 310 : Loss 11689.1757\n",
      "Iteration 320 : Loss 11408.1755\n",
      "Iteration 330 : Loss 11137.7464\n",
      "Iteration 340 : Loss 10877.4773\n",
      "Iteration 350 : Loss 10626.9732\n",
      "Iteration 360 : Loss 10385.8546\n",
      "Iteration 370 : Loss 10153.7568\n",
      "Iteration 380 : Loss 9930.3297\n",
      "Iteration 390 : Loss 9715.2365\n",
      "Iteration 400 : Loss 9508.1538\n",
      "Iteration 410 : Loss 9308.7710\n",
      "Iteration 420 : Loss 9116.7895\n",
      "Iteration 430 : Loss 8931.9223\n",
      "Iteration 440 : Loss 8753.8939\n",
      "Iteration 450 : Loss 8582.4395\n",
      "Iteration 460 : Loss 8417.3045\n",
      "Iteration 470 : Loss 8258.2445\n",
      "Iteration 480 : Loss 8105.0245\n",
      "Iteration 490 : Loss 7957.4189\n",
      "Iteration 500 : Loss 7815.2107\n",
      "Iteration 510 : Loss 7678.1915\n",
      "Iteration 520 : Loss 7546.1611\n",
      "Iteration 530 : Loss 7418.9271\n",
      "Iteration 540 : Loss 7296.3045\n",
      "Iteration 550 : Loss 7178.1158\n",
      "Iteration 560 : Loss 7064.1902\n",
      "Iteration 570 : Loss 6954.3637\n",
      "Iteration 580 : Loss 6848.4787\n",
      "Iteration 590 : Loss 6746.3836\n",
      "Iteration 600 : Loss 6647.9331\n",
      "Iteration 610 : Loss 6552.9871\n",
      "Iteration 620 : Loss 6461.4114\n",
      "Iteration 630 : Loss 6373.0768\n",
      "Iteration 640 : Loss 6287.8592\n",
      "Iteration 650 : Loss 6205.6393\n",
      "Iteration 660 : Loss 6126.3026\n",
      "Iteration 670 : Loss 6049.7389\n",
      "Iteration 680 : Loss 5975.8424\n",
      "Iteration 690 : Loss 5904.5115\n",
      "Iteration 700 : Loss 5835.6483\n",
      "Iteration 710 : Loss 5769.1589\n",
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      "Iteration 2920 : Loss 3338.8058\n",
      "Iteration 2930 : Loss 3336.9044\n",
      "Iteration 2940 : Loss 3335.0145\n",
      "Iteration 2950 : Loss 3333.1360\n",
      "Iteration 2960 : Loss 3331.2689\n",
      "Iteration 2970 : Loss 3329.4130\n",
      "Iteration 2980 : Loss 3327.5682\n",
      "Iteration 2990 : Loss 3325.7344\n",
      "Iteration 3000 : Loss 3323.9115\n"
     ]
    }
   ],
   "source": [
    "losses = []\n",
    "\n",
    "for i in range(1, 3001):\n",
    "    dW, db = gradient(X_train, W, b, y_train)\n",
    "    W -= learning_rate*dW\n",
    "    b -= learning_rate*db\n",
    "    L = loss(X, W, b, y)\n",
    "    losses.append(L)\n",
    "    \n",
    "    if i % 10 == 0:\n",
    "        print('Iteration %d : Loss %0.4f' % (i, L))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3270.8161892345042"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = model(X_test, W, b)\n",
    "mse = loss(X_test, W, b, y_test)\n",
    "mse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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X+GZo7+uTvffT3t8xH250f9DdG929sa6ubnAVj7Cr5k9k/Z4j7P7geKZLEREZkiEFgrvvc/eEuyeB7wEXh027gIa0XeuBPaG9vo/2U44xsxhQycAvUWWNqxdMAuC5tXszXImIyNAMKRDCmECP3wZ6ZiA9AywLM4dmkho8XuXue4E2M1sSxgduAZ5OO+bWsH4j8JLn4HSdmbVlLJhSwb+vUSCISG6KnWkHM/sx8HGg1sx2AX8PfNzMFpG6tLMN+DyAu683s8eBDUAcuMPde77CezupGUslwHNhAXgI+IGZNZM6M1g2HB3LhOsumMw/P7+JnYeO0VBdmulyREQGxXLwwzgAjY2N3tTUlOkyTrHj4DEu//rL/PW157L88tmZLkdE5EPMbLW7N/a1Td9UHkbTakq5oL5Sl41EJCcpEIbZdR+ZzJpdrew4eCzTpYiIDIoCYZhdd0FqvP0/NNtIRHKMAmGY1VeVcuG08Tz91m7d20hEcooCYQT8zoVT2fh+m+5tJCI5RYEwAq5fOIXCaISf/HrXmXcWEckSCoQRML60kE/On8Azb+2hO5HMdDkiIgOiQBghv3tRPQfbu3hFf29ZRHKEAmGEXD6vjtryQp5YvfPMO4uIZAEFwggpiEZYumgqL23cz6H2rkyXIyJyRgqEEXRTYz3dCedJDS6LSA5QIIygcydVsHh6FT96YwfJpL6TICLZTYEwwm6+ZBpbD7Sz8r2DmS5FRKRfCoQRdu1HJlNVWsAPX9+e6VJERPqlQBhhxQVRbmps4Bcb9rHvSEemyxEROS0Fwij47MXTSCSdx97UFFQRyV4KhFEwo7aMy+bW8sPXt9MV1zeXRSQ7KRBGyW2/NZP9bZ387O09mS5FRKRPCoRRcsW8OuZNLOd7r72n22KLSFZSIIwSM+MPL5vFxvfb+GXzgUyXIyLyIQqEUbR00RTqxhXxvde2ZroUEZEPUSCMoqJYlFsvnc6r77bwzl798RwRyS4KhFH2+0umU1YY5dsvN2e6FBGRUygQRtn40kJu/egMnl27l3f3tWW6HBGRExQIGfCHl82ipCDKfS9uznQpIiInKBAyoLqskFsuncF/rN3LZp0liEiWUCBkyB9dNpPiWJRvvaSxBBHJDgqEDKkpL+KWj07nZ2v2sGGPZhyJSOYpEDLoT66YQ0VxAV97fmOmSxEROXMgmNn3zWy/ma1La6s2sxfMbHN4rErbdpeZNZvZJjO7Oq19sZmtDdvuMzML7UVm9lhof8PMZgxvF7NXZWkBf/qJObz6bguvbW7JdDkikucGcobwMHBNr7Y7gRfdfS7wYniOmc0HlgELwjH3m1k0HPMAsByYG5ae17wNOOzuc4B7gbuH2plc9LlLp1NfVcJXn92oP7MpIhl1xkBw91eBQ72alwIrwvoK4Ia09kfdvdPdtwLNwMVmNhmocPeVnrqz2yO9jul5rSeAK3vOHvJBUSzKX1x9Dhv2HuGp3+zOdDkikseGOoYw0d33AoTHCaF9KpD+V2B2hbapYb13+ynHuHscaAVqhlhXTrr+giksbBjP157fyJGO7kyXIyJ5argHlfv6ZO/9tPd3zIdf3Gy5mTWZWVNLy9i55h6JGP+0dAEHjnZy7wvvZrocEclTQw2EfeEyEOFxf2jfBTSk7VcP7Ant9X20n3KMmcWASj58iQoAd3/Q3RvdvbGurm6IpWenC+rHc/Ml01jxq22s39Oa6XJEJA8NNRCeAW4N67cCT6e1Lwszh2aSGjxeFS4rtZnZkjA+cEuvY3pe60bgJc/TvyDzF586l6rSQv7up+s0wCwio24g005/DKwEzjGzXWZ2G/A14Coz2wxcFZ7j7uuBx4ENwPPAHe6eCC91O/CvpAaatwDPhfaHgBozawb+jDBjKR9VlhZw56fP5dc7PuBHq3ZkuhwRyTOWqx/GGxsbvampKdNlDDt355bvr2L19sP8/EuX01BdmumSRGQMMbPV7t7Y1zZ9UznLmBlf+90LiJjxl0+s0aUjERk1CoQsNHV8CX973XmsfO8gP3xje6bLEZE8oUDIUv/jvzVw+bw6vvrsRpr36xbZIjLyFAhZysz4+o0XUFoY5Y4f/YaO7sSZDxIROQsKhCw2saKYb35mIZv2tfEPP9uQ6XJEZIxTIGS5j58zgT++YjY/XrWDZ97ec+YDRESGSIGQA/78U/NYPL2Kv3pijf6YjoiMGAVCDiiIRnjg5ouoLCngjx5p4sDRzkyXJCJjkAIhR0yoKObBWxZz4Ggnf/LDX9MVT2a6JBEZYxQIOeSC+vF846aFrNp2iDufXEOufstcRLJTLNMFyOBcv3AKWw+0c88L71JXXsRd156X6ZJEZIxQIOSgP/3EHA4c7eS7r75HdVkhn79idqZLEpExQIGQg8yMv79+AQfbu/jqcxsZV1zAZy+ZlumyRCTHKRByVDRi3POZhRzrjPPXT60l4c7nlkzPdFkiksM0qJzDimJR/uVzi7ny3An83U/XseJX2zJdkojkMAVCjiuKRXng9xdz1fyJ/P0z6/nOy82afSQiQ6JAGAMKYxHuv/kiblg0ha//fBN/+9N1xBP6noKIDI7GEMaIgmiEez6ziMnjS3jglS3sO9LBfb93IaWFeotFZGB0hjCGRCLGX11zLv+4dAEvbtzP79z/K7YfbM90WSKSIxQIY9Atl87g4T+4mL2tHVz/rV/y8qb9mS5JRHKAAmGMumJeHT/7wm8xtaqU//nwm3zzF5vo1riCiPRDgTCGTasp5cnbP8rvXlTPt15q5sZ/WcnWA7qEJCJ9UyCMcSWFUb5x00K+89mL2HagnWv/72v88PXtJJOamioip1Ig5InrLpjM81+6jIumj+dvf7qOz3x3Je/ua8t0WSKSRRQIeWRyZQk/vO0Svn7jBTS3HOW6+17jGz/fxLGueKZLE5EsoEDIM2bGTY0NvPhnV3D9wil8++Vm/vs3XuHxN3eS0GUkkbymQMhTNeVF3POZRTzxx5cyZXwJf/mTNVx332u8vHG/bn0hkqcUCHmucUY1T97+Ub7z2Ys41pXgDx5+k6Xf+X/8Yv37CgaRPGO5+kvf2NjoTU1NmS5jTOmKJ3ny17u4/5Ut7Dh0jHMnjeP2j8/m0+dPpjCmzw4iY4GZrXb3xj63KRCkt3giyc/W7OHbLzWzpaWdunFF3HzJND57yTQmjCvOdHkichZGLBDMbBvQBiSAuLs3mlk18BgwA9gGfMbdD4f97wJuC/v/L3f/eWhfDDwMlADPAl/0MxSmQBh5yaTzX5tbWPGrbbyyqYWCqPGpBZO48aJ6LptbSyyqswaRXDPSgdDo7gfS2v4ZOOTuXzOzO4Eqd/8rM5sP/Bi4GJgC/Ccwz90TZrYK+CLwOqlAuM/dn+vvZysQRte2A+08snI7T/1mF4ePdVNbXsQNi6bwOxfVc97kcZhZpksUkQEY7UDYBHzc3fea2WTgFXc/J5wd4O5fDfv9HPgyqbOIl9393ND+e+H4z/f3sxUImdEVT/Lypv38ZPUuXt60n+6EM6OmlKsXTOLq8yexqH48kYjCQSRb9RcIZ3uzfAd+YWYOfNfdHwQmuvtegBAKE8K+U0mdAfTYFdq6w3rvdslChbFI6h//BZM41N7Fc+v28vP1+3jol1v57qvvMWFcEVeeN5Er5tVy6exaKksKMl2yiAzQ2QbCx9x9T/hH/wUz29jPvn19bPR+2j/8AmbLgeUA06ZNG2ytMsyqywq5+ZLp3HzJdFqPd/Pyxv08v+59nnlrNz9etYOIwcKG8Vw2t46Pza5hYcN4iguimS5bRE7jrALB3feEx/1m9hSp8YF9ZjY57ZJRz834dwENaYfXA3tCe30f7X39vAeBByF1yehsapfhVVlSwA0XTuWGC6fSnUjy1s4PeO3dFl5rPsC3X9rMfS9upiBqnD+1ksbpVSyeXk3jjCpqy4syXbqIBEMeQzCzMiDi7m1h/QXgH4ErgYNpg8rV7v6XZrYA+DdODiq/CMwNg8pvAn8KvEFqUPlb7v5sfz9fYwi5o/VYN29uO0TT9sM0bTvEmt2tdMVTf5th6vgSFkypYMGUSs6fmnqcWFGkQWqRETJSYwgTgafCL24M+Dd3fz784/64md0G7ABuAnD39Wb2OLABiAN3uHsivNbtnJx2+lxYZIyoLC3gk/Mn8sn5EwHojCdYt7uV1dsPs3b3EdbvbuWFd/bR89mktryQcyaNY05dOXMmlDN7QuqxrlxBITKS9MU0yQpHO+Ns3HuEdbtbWb/nCO/ua6N5/1HauxIn9qkojjFnQjkza8tpqC5hWnUpDdWlNFSVMmFckWY3iQzASM4yEhkW5UUxGmdU0zij+kSbu/P+kQ6a9x89ZfnVlgO8/5sO0j/LFMYi1FelQmJyZQmTKoqZXFnMxMpiJlWkloqSmM4wRPqhQJCsZWZMrixhcmUJl82tO2VbZzzB7sPH2XHoGDsPHWPn4ePsOHiMnYePsWZXK4fauz70esUFESZVFDOxopgJFcXUlBVSU1ZIdXnqsaa8iOqyQmrLihQekpcUCJKTimJRZtWVM6uuvM/tnfEE+4908v6RDt5v7WBfeHz/SGp93e5WDhztpK2j7z8OFIsY1WWFVJcVMr60gMqSk0tFcQGVoa2id3tJgW4EKDlLgSBjUlEsmhpfqC7td7/OeILD7d0cbO/k4NEuDrV3ceBoJ4fae9a7aD3exdYD7bQe7+bI8TjHuxP9vmZJQZTy4hjlRTHKiqKUFcYYVxyjrCi1lBfFKCtMbSsvilFefGp7eVGMksIoJYVRimMR3TNKRo0CQfJaUSzKpMookyoHfhfXzniCI8fjtB7vDiHRfWK9Z2nvjHO0M057Z5z2zgR7PuigvSt+or2jOzngn1cYjVBcEEmFREGU4oIopSEwep6XFPR6XniyrbggSlEsEpYohWG9uCD1vCgWCW2pbVENzuctBYLIIBXFotSNi1I3buhfqosnkrR3JjiaFhLtnT3rCTq6ExzvSnC8Oyxdoa07wbGuk9s/ONbN8e4EHV0nt3XGBx42fSmIGoXRCEWnCZKiECSpfU5uL4hGKIilji04sRiFsV7Pe9ZjvZ5HIxTGLG3f1Pb0dY3rjCwFgkgGxKIRKksjVJYO/72ekkmnI54WKCEkOuNJuuJJOuN9PO/ua3uqvSuRDNtDe3eSI8fjJ553xZN0dCfoTjhdiSTdiSQjNZv91ICIUBi1ECwRYpHUtljUiEWMWOTkejSSCpRozz4RC9sioe3D+0QjPa9jxKKRD+0Ti5z6Oid+bq/X72ufaNoSi0SIGFkRdgoEkTEmEjFKC2OUFmbm19vdSSSd7oTTnUzSHU+m1hPJE4HRHT8ZHj1LV9xPfZ7wcGza80SSeCJ5MnxObHc640kSySTxpBNPpGroiCeIJzy0JVN1JZMkEk53sqfOVHtqvyTJDH0160RIWAiNaGq9J5jSn3/pk/O4fuGUYa9BgSAiw8osfCKOQgm5dzPDZE9ohFBLhDCJp4VGn+s9+ySTJ0Ooj2MTIYji4bFnPXmiLUkiyYlwS/TaL+HO+BE4swQFgojIKSIRoyiSe0E2HDSfTUREAAWCiIgECgQREQEUCCIiEigQREQEUCCIiEigQBAREUCBICIiQc7+CU0zawG2D/HwWuDAMJaTSepLdhorfRkr/QD1pcd0d6/ra0POBsLZMLOm0/1N0VyjvmSnsdKXsdIPUF8GQpeMREQEUCCIiEiQr4HwYKYLGEbqS3YaK30ZK/0A9eWM8nIMQUREPixfzxBERKSXvAsEM7vGzDaZWbOZ3Znpes7EzLaZ2Voze8vMmkJbtZm9YGabw2NV2v53hb5tMrOrM1c5mNn3zWy/ma1Laxt07Wa2OPw3aDaz+ywDf2vwNH35spntDu/NW2Z2bbb3xcwazOxlM3vHzNab2RdDe869L/30JRffl2IzW2Vmb4e+/ENoH933xd3zZgGiwBZgFlAIvA3Mz3RdZ6h5G1Dbq+2fgTvD+p3A3WF9fuhTETAz9DWawdovBy4C1p1N7cAq4FLAgOeAT2dJX74M/O8+9s3avgCTgYvC+jjg3VBvzr0v/fQlF98XA8rDegHwBrBktN+XfDtDuBhodvf33L0LeBRYmuGahmIpsCKsrwBuSNgBkI4AAAKDSURBVGt/1N073X0r0Eyqzxnh7q8Ch3o1D6p2M5sMVLj7Sk/93/5I2jGj5jR9OZ2s7Yu773X3X4f1NuAdYCo5+L7005fTyea+uLsfDU8LwuKM8vuSb4EwFdiZ9nwX/f8PlA0c+IWZrTaz5aFtorvvhdQvBTAhtOdC/wZb+9Sw3rs9W3zBzNaES0o9p/M50RczmwFcSOrTaE6/L736Ajn4vphZ1MzeAvYDL7j7qL8v+RYIfV1Ly/ZpVh9z94uATwN3mNnl/eybi/3rcbras7lPDwCzgUXAXuCboT3r+2Jm5cBPgC+5+5H+du2jLdv7kpPvi7sn3H0RUE/q0/75/ew+In3Jt0DYBTSkPa8H9mSolgFx9z3hcT/wFKlLQPvCqSHhcX/YPRf6N9jad4X13u0Z5+77wi9xEvgeJy/PZXVfzKyA1D+gP3L3J0NzTr4vffUlV9+XHu7+AfAKcA2j/L7kWyC8Ccw1s5lmVggsA57JcE2nZWZlZjauZx34FLCOVM23ht1uBZ4O688Ay8ysyMxmAnNJDTBlk0HVHk6T28xsSZgtcUvaMRnV84sa/Dap9wayuC/h5z4EvOPu96Rtyrn35XR9ydH3pc7Mxof1EuCTwEZG+30ZzZH0bFiAa0nNRtgC/E2m6zlDrbNIzSR4G1jfUy9QA7wIbA6P1WnH/E3o2yYyMBunV/0/JnXK3k3qk8ttQ6kdaCT1S70F+DbhC5VZ0JcfAGuBNeEXdHK29wX4LVKXENYAb4Xl2lx8X/rpSy6+LxcAvwk1rwP+T2gf1fdF31QWEREg/y4ZiYjIaSgQREQEUCCIiEigQBAREUCBICIigQJBREQABYKIiAQKBBERAeD/A/DSC4ntjXsXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(losses)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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uGjtW4yc9mIVvuBIkBjrp8dLVagUnIavbRnfpwix8w5UgMdBJjpdOihUcB0l40NroLl2YwjdcCZIUlORks2pWSHE+aP26adwKuvXXblQWU/iGK0EyO5OcBZoEKzgu4nrQBhkV1YrbYnje7UZlMR++4UmQGOikxkuPHtbgam0mwd0UlriyuoPMDXR7JG56tRuVxRS+kWPdUnh2Iextg2MaYfp8mDyr0lKFZu6Mk1j58B1cx/2Mll1s0xF8l9lMm3FVpUWLhEAPWp/fcZBR0RiPB+qYKnigViPm0jFyiuDRa2HvFkBzr49em2tPORfWrqKlbjGNNbuoEWis2UVL3WIurF1VadHKS4Dv2Kp4Vi+m8I2c1dfVx0rr6sy1p51nFzKoe39B06Du/dVxbUEI8B0nqornuqVw+yRYMCz3GpUREtdxE465dIzcED9Ie5qo5msLQoB+SEwVz95RSe+DqndUAuHcjXEdNwWYwjdy/ty9W9zb005Crq3iyV8B+yERk/D9jUrCKOa4jpsCBnTpiMhYEXlORN4UkddF5CtO+3Ei8rSI/Np5PTZvnxtFZJOIbBSRGXFeQNpJRFr69PlQ18c/W9eQa087Cbi2oMlfsdwTCeiHwMQ1OsvwqM+PD/8Q8E+q+lHgTOBqETkZmAc8q6oTgGed/3E+mw2cApwD3CEita5HzjiJyQKdPAv++ntwzFhAcq9//b3qsHYScG1Bkr9iuycS0A+B8RqFhR2dBTxuIoyyiBjQpaOq24Htzvv3ReRNYAxwAfCXzmZLgOeBG5z2+1X1APCWiGwCpgIvRC182klCLZTDTJ6V7B9/GCp8bUHCHGO9J9L2HU+fX+hrh2hGJQGOW221ggJF6YjIOOBU4EXgBOdh0PtQON7ZbAyQ7yxsc9r6HmuOiLSKSGt7e3twyauAas4CzQJ+Lb8gYY52T+QR16gkwHGrrTSH70lbETkS+Alwnaq+J96p024fFKXdqeoiYBHkljj0K0c1UVVZoFWSuOV3cjWI5RdkbdaquieiIK5Ric/jVtsD2JeFLyJ15JT9var6kNP8joiMcj4fBex02tuAsXm7NwLbohG3uqiapJUqSdxa9spWVj58Bw90fIHNQy7lgY4vsPLhO1wt9yCWX5BY9aq5J6qEJFeCLYUBLXzJmfJ3A2+q6m15Hz0CXA60OK/L89p/JCK3AaOBCcBLUQpdLcRVC6XsVEmY29rHF7FQFjFUDgLQKLtYqIu45fFBXHjqvxdsG9Ty8xvmmJh7okpGbGEJMjpLA35cOs3A3wCvichap+1fyCn6pSJyBfA74GIAVX1dRJYCb5CL8LlaVbuLD2tAQuKdw1IlYW5XHvxvhtYcLGgbKge58uB/A4UKP07XS8XviaQnJpXxYZSYB3BE+InSWYm7Xx5gusc+NwE3hZDLSBMJSW4Ky+ia3b7bq83yKyDJI7Z1Szm0/JoPymXs3ZL7H2JV+mlV8H2xWjphyWhNjgLSmNTjwv6GE323J3kNgNBENWKL4bfR8cR819pIHU+k616rFFZaIQxJH/qWi95rTbnPd+inFxZaj8Ch2nqGftq90Fokll8SfeVRjNhi+m3Ud+4I1J7I/q0goglYqKCpqUlbW1srLUZwbp/k/sNoOA4GH2E3WRopp4LoqxQhNzKqdAZsFHJ5/TaOGQtfXV+yaG3zP0xjza7i9p4RNC7cXNiY1P6NEBFZo6pNfrc3Cz8MXkPczndzf1Bo2UC6rI0sWkflzEZNqq88ihFblG6hPDlerG3iuJ4VhyOpADp0MIsHX8aCvvsmtX8riCn8MHgNffvS1QlP3ACHOtPj/onTXRXTgySSipTlfMglObrJ68Hnt39icgtdUNvOA91/wV/oK4yW3WzT4bkVzM6bU7x/kvu3QpjCD4NbTQ4vei3+fJJsbcRlHcX0IImk5km5H3JJjm5ykxf8908UdXBc7sFB3fv5zBHrOVvvGvjBnuT+pTIls82HH5a+P4yDf3BX7p4ILNgTm3gls+CYfj7bW/pxY/LtNres8FxbddW8s8orW997YsJfwas/KlZ+H7/Uvb3SPmYv3/egBvd726t/wo6WFgzDpSoLvn8zCfbh9zVQIBfWGzTSy3z45abv0DfwjyUZ1kYRUgtu+XIhK13r3jb3Ykse7X6JpOZJFC4At1FC6w8pUlxdnfDrp3LKJ2nzJB6jO+3q9PjutiC3Tyq+hrDzIWEt9ARHj1WqUq4p/KjxuskgnlKvceGVHB0yafodRnAixdVRc+2lE0nmaxQuADdl6Wqlkrs/kliyOKCPWwHp7bco3WBRuIWS2L/kDJHza1Zy/aCljJZdbNMR3HJoFo/umRbreS3xKg4mz8oNcRfsyb323nRpWoDimLHB2n1y88GL6dDBBW0dOpibD14c6riRFB2LIoEsiLJM6ujOQ653e44s+u561EWJeCyOHpi0/WYCcPmRL9FSt5jGml3UCDTW7KKlbjGXHxlv2TGz8MtJQq0NV2JafKL16LOZ9x6OZZOLsrjl0CzWHH12qONGUvMkqAsgyEQsQoGln+TRncd3/329knc7DhZ8d2OkOCYeiC4SJk2/mQBcX/cAQw8V1226vu4B+tZtihKbtDW8iSFEMarJqorjNVfjNRH78UtzPvswfVnupLA+51rW3czcB1+lq+cDnbFqyLXuSj/kJHzVE3ZCundrm7Q1IiMG66pqqg96ha3GNRFb7jIebt/9K1uLyih+u/sSbh1yd2F9mySPXpJChUJGTeGXEde429pV5Y0iSED2bFVUH/RKuNu7JR43RAKyRm99ciNd3YVW6cOHmjmmvo4Fx/wkcZEwiSau9XoHwBR+mXBLDFr58B3MrFtcUOo1VqvNir1FR0xhq54kIGvUK8R1yb6pLPjX+PzOVUmFQkZN4ZcJt7jb67i/qNRrrFZbAqzEqiGmsFVPEpA1auvtRkwFJqQtLLNMuFlHo+OOcPB7XI/2Za9spbllBePnPU5zywrXtV0zi5clH5eFn4A1BxK/3q6tTTEgZuGXCTfraJuOoNE1wiEmqy2AlRhJbZpqptwWfhQhoyGtyQtPHcOYLY8x9uVbOV7b2Skj2XLaXM449ZxQx40Ec1f6wiz8MuFmHX2X2RyqrS/cME6rLYCV2F/qt0FsiWn94pbQ50av8tu7BdAPlF9Yi3fdUs547eucSDs1AifSzhmvfT0ZlnR/7kq/ZGCEYAq/TLgtiTftM1cx6ILvly+TMEDmYiS1aaqZBLhYPIlC+ZXzuFEQdlI7rodkwjCXThlxD0cs88SNz4kim6AbgAQX5ootoqcSkULlqr8fVUBDAsKe+8MUvuHK3BknuWbEJmaCLgkkNe3fQ/l1NJzI2S0rSk94K3ekUBC/fNi49rgqpSZsHsFcOoYrbi6oipQ/yIBfNXJc3E2HauuZ/4fPsnVPJ8oHk/BBIq9Wf/gaOvsUT+vUwaz+8DVRSF1MEBdS2EJrXg+tsJVSk+LycjALPwYqsZJNHFQ8IzYFFlMshHULuLibvvmHz/Ljg1MLNgtaf/26NyZweteVxYXv3pjAqvP9i+eboFZ3mBFXFJmvCUiOGwiz8COmN5wxjCVlOKTAYoqcdUs5tPyagsnDQ8uvCT6y6RPRs2TfVNfNgkzCF26rHu0REoXV7ZcoSjGXU94SGVDhi8gPRWSniKzPa1sgIltFZK3zd27eZzeKyCYR2SgiM+ISPKlYOGOEpMBiipqOJ+YXZV8P6t5PxxPhon+8JtuDTMKXvYZ7uSOh/Ia9epHkyC0HPxb+PYBbZsXtqjrF+fspgIicDMwGTnH2uUMkrtTDZGLhjBGSAospauo7dwRq90sUWbLX1z3AUPGq4R4DaVsAJQXyDujDV9Wfi8g4n8e7ALhfVQ8Ab4nIJmAq8ELJEqYMC2eMkOnzObT8mgKL91BtPYMSZDFFzbae4TTWFGdfb+sZTpjHXBRlqYd6PHS82iMhqZFQXiRc3jA+/C+LyDrH5XOs0zYGyI/banPaihCROSLSKiKt7e3Fa5ymlcTXG0kRy7qbmdd1JW09I+hRoa1nBPO6rmRZd3OlRYuNF2ub6LsmkWquPSwXnjqGVfPO4q2W81g176zgE/IZHHFVG6VG6fwA+Aa5mZtvAN8B/p6i5REAjxWcVXURsAhyK16VKEfiqJoFPhLArU9uZOvBP+XH/GlB+wsBIkvSxqfrX0X6DBBFcu1BiCVSbPp8WHYV9HR90FZTlygfdV+qJWIuKkpS+Kr6Tu97EbkLeMz5tw3ILybSCGwrWToj02RxPmRo5/ZA7W7EW/iuZ4D/k4MVACymJJeOiIzK+/czQG8EzyPAbBEZIiLjgQlAvMuwhyGGpB4Ly4yOKCJLUkcEZZdjixR74gbo6VMNtKc7155ALGKuGD9hmfeRm3Q9SUTaROQK4BYReU1E1gGfBL4KoKqvA0uBN4CfAVerxlUvNiQxFUuymyw6MjkfEkHZ5dhGRp3vBmuvMFkcIQ6Enyidz7s0393P9jcBN4URqixEVCypr4/QLUIH0nmTBfJ/xlR/HVI4HxKmLxqOc1egDcf5PtfoYSPCR4q5XUPKsIi5YrJbWiGCpB43H6HgPkudtpsskP8zxhIIFS/vEJRyloPwONd3P/bv/O3qD5Ve+M7rGuqOgK4/FG9fd0TOJZqwCpFWALCY7JZWiCDEzM19oxSHKqXxJrv1yY2c3f0/rBx8Lb8ZcikrB1/L2d3/4+6aymIJBC/C9kXn7/23e5zrjM3fD1f4zusaBg3JReUUUJOL2klgHfnEFABMENm18CMoluTlplFyN1eq3BB9aHrvaW6uW3w4s7JRcmn0N74HcFbhxhksgeCF7m1zj032aC8iSAnifvo91MjI67idv4eLFhW6eg7+odgFVUod+ZhI3QgxZrKr8CNYwMLLRzhmWAOr5p3lskd6uHHwgwylOI3+xsEPAjcXblzuOukJZi9HMoz3Pdp9MH0+LL8auvP6vnawuyESV7/3c9xl3c3ceuB7bNvfyej6Blbuv8j9QZaUh30Uc0sJX9QkCNl16UDoYknVHEVyAi6Lq3u1p6BoVLno7nHPIfRqd8Ut1daNuPrd47irP3wNKx++gwc6vsDmIZfyQMcX+L0e6X6MJDzso4jEq7KlD7Ot8EMS2EeYosU8xOMH69qegqJR5eLYGpdJzX7ai3h2YWEmK+T+j2PRDy88jvv4um0slEUF1TKPoIODfR0FzkNn2StbaW5Zwfh5j9PcsqL8uShRzC1V2fxUdl06EeHbR5i2xTyCznEkvGhUueiqO5ohXXvd2/0coJyLfvSHy3Gv/PGHGVpT6OYbIt3s7jmS4cceW+DyWNbdXPks1yjmlqpsfsos/HKRNkvBrPaSGDLIPSPWq72IBBcoG12z27X92Jo/FLlGE5GAGEVfJvj7KAVT+OUijZZC2AUhskiQsEo3kjIf4uJ+3N9wouumbu2JyHKNoi+T8n1EhCn8clFlloLhgdf32XCsv/mboCOrOOaFPCYqh55yLodq6ws2PVRbz9BPF49SE1EHKYpRapWNdEW9IgDKSFNTk7a2tlZajHjp68OHnKWQ4pvHcMHte64dnIu0yZ+MjeK7j+ueun2SR1jm2Jxl6yNEsW+mNuQi2LKe+BQ1IrJGVX0vlmCTtuUigrh/IwW4fc9xJSdFVA+qiP7cjz4niQPXQaqiWPckYwq/nFgkSzbo+z0v8Ei5Cjt/E9e8UEQJXVUbwZZizIdvBCdF+QSJIK75m7iOW+6JyrRFsKUYU/gZI3QyTJyZh2l7kPiVt8wZsaGPW+6Jyn5GKhVP3qoybNI2Q0QykdbfhN5X1xe3+yVtk9pB5Y3LR10Nvm+Pe6qjYRSn7/uuTfz2Q9BJW1P4GaK5ZUX4Ym8LhuFe8V9y8fqlEteDJC7SJm+S8Xh4LtAvcs++qUWbV0NxwqgIqvDNpZMhIkmGictvnLbEtLTJm2Q8XEhLXJQ9pHP1uKRgUToZIpIl34LW2PHrckhIiWW/yzp2NJzI0M7t7u3lELTacIlgG/1T9xFpklePC7QsaAUwCz9DfHLiyEDtrgSZ0AsywZuAFPbeOY6tezpRPij45TZReEvXJXTo4IK2Dh3MLV2XRCKHTVSmr/x4kPunUpiFnyGe29AeqN0Tv/kEQTivEQgAABBqSURBVBKDEpCY1l/Br75W2pJ9U3m35iDXD1rKaNnNNh3OLYdm8eiBqSwIIUOgtYSrnLQtYh/k/qkUpvAzRNkLWiWl1K9PgvTP6GENPLJnGo8cnFbQPiakuyENSqOcpGmJwkQUjBsAc+lkiMAFrcLGxaesYFyQ/onL3ZAGpWG4k4iCcQNgCj9DzJ1xEp8b/AtWDr6W3wy5lJWDr+Vzg3/hrqSiSLAqt18+5AMqiBIPvNqZT9KgNAx30jDnMKBLR0R+CMwEdqrqJKftOOABYBzwNjBLVX/vfHYjcAXQDVyrqk/GInm5qYIElwtrVzGzbjGDuvcD0Ci7aKldzKDajwN9riWKwlxB/fJh+jiCeixBfcZe7oZAkRp9rvkrx/9frt8zsWizQBPrRkVIw5zDgIlXIvLnwD7gv/IU/i3Au6raIiLzgGNV9QYRORm4D5gKjAaeAT6iqt0ehwdCJF6VSwknJasyLEGSheJKsPIibKZtQhKhAmUzr1vKoeXXHH4AA3QymBsOXskjPcVzA4lINkrqvZ1RIi+PrKo/F5FxfZovAP7Seb8EeB64wWm/X1UPAG+JyCZyyv8FvwL5JgKLzrclFsTaTXLlvyCTqAHj4kPHHwcdUfRVPG6yQvBEqJAKzWvS9cXld3Lm8vs4XtvZKSPZctpcTnnzdobmKXuABnKRP30ngxPhw+/7gNq7Jfc/VP7eNnxRqg//BFXdDuC8Hu+0jwHyf3ltTlsRIjJHRFpFpLW9PWBYIISusBcoZjaIokxy5b8gk6gB/O+RxB8H6WO3+QXEff8gE8QRzFu4Kebza1byb3onJ9JOjcCJtDNpzb/S4JK4BTBaiteOjcSHH3KOo+OJ+QWjEYBB3fvpeCKdy/1lkagnbd1+da4+I1VdpKpNqto0cmQJ/smQqe2BFlkOoiiTnHIfZBK1nwSr1Y/8JzsW/DE9Xz+GHQv+mBeX3xl+weogfez2UEUpuv2CThBH8LB2U8zXD1rKUDlY0NYgB+lW95/fNh1euG0UE38RPMzqO3cEajeSR6kK/x0RGQXgvO502tuAsXnbNQLbShevH0KG/AUKfwuiKJMcihiw7O2y7maaD3yP8fvvpfnA91jW3czqR/6TSWv+tcBa/Te9k/NrVhbtH8gNMX0+1NQVttXUufex58NTw5X0jeBh7RapMVp2uW5bQ49rtu5/1FwaefRPFA+zbT3DA7UbyaPUxKtHgMuBFud1eV77j0TkNnKTthOAl8IK6UrQmi59GD2sgdPfe9rJlNzFNh3BLYdmsebos4s3DhJtMuGvoPVu9/Yk4DO5ySvj89naW2joY60OFXe/c2A3hEj///fiOb8QcoI2gno+bpEaO/aPYDTFSn+HjOC2ntlcp/cfztb9LrOZduGXuDnqyI4IHmaLB1/G9V13FIxWOnQwiwdfFiq7GJJfg6Za8BOWeR+5CdoRItIGfJ2col8qIlcAvwMuBlDV10VkKfAGcAi4eqAInZIJmYr/3ZN/zaQ1iw8rr0bZxbfqFrP+5HGASzSE3yzQXz8VrD2heLm8TqzZ5eq46+t3DuyGeHYhdBc+SOg+6D5pG/Jh70lEx+0brrn6kes5ds2/FjwoO3UwW0+/nmljZ3LJk9PjV3QRPMymnDeH+Q8fKn5AnTcnlGhJKSeRhYeOnyidz3t8NN1j+5uAm8II5ZsQqfhnbP4+uPhVz9j8feCLpcuUZB9+ADxdXjqCRhcXxQ4ZzphhDaX/WIL0W1x1dyI6brHimAnA2Jdv5XjdxU4ZwZbT53LG+V/kDMqk1AI+zLyV31WRP6CSUE4iKQ+duMluLZ2ELwBdabxKKf9HzaXM1ztdrdVV54eIEw/ab3HV3Ql5XC/FcfNFMznj/JwhcaLzV1YCPMwGUn5RK8AklJNIwkOnHGS3tEK1LAAdE15p4p+44EusP/2b7GAkPSrsYCTrT//mYWVWMlXSb4Giv8rN5Fm5OY4Fe3KvHg+2cl9DEspJJOGhUw6ya+HH5QdOQJnfKOg3TfzUL0LU1mqV9JuXgti6p5PmlhWp8A+XW/nNnXGSa3ZyOWvQRLI4UArIrsKPU8FUuMxvVAQavkeRcu/Wb4/9I6y5B7QbpBZO/zuYeVuw45YRL8UhcLg96f7hYUPr+H1Hl2t7HCShBk0SHjrlILsKH6pGMVecuMpJPPaPhSGu2v3B/wlV+m6KQyjOPkyyf9irvNYAZbdCUem690l46JSDbCt8IxqiqKzpxpp7vNsTqvDdFIebxQ/J9Q/v7Sy27vtrrxYq/dApB6bwjfDEFfHklcIRU2pHVPRVHM0t6VqMuz9/dhZi1auZ7EbpGNERV8ST1AZrTyhpWBgjHy95PzlxZOIX6Tb6xxS+EZ64QipP/zvP9mWvbKW5ZQXj5z1Oc8uKRCuduFbHigsveZ/b0J7ckFPDFwMugFIOSl4AxUgOcS2M4RKls2zMP/lfZMSIjPHzHvdaEoe3Ws4rtzgGMSyAkmTK6U803+UARBDx5NrHM28rmqC9tWVFJrIik0ZWYtWrmdS6dCJZdCOB58oqQfo4K1mRSSNtcxFGMalV+OVM/47qXGnyO5ebIH2chFT8LJK2uQijmNS6dMpp5UVxrqxU4yuVIH2clazIJJKFWPVqJrUKv5z+xCjOlZVqfKUSpI+zkhUZNVmch8riNfdHahV+Oa28KM6VVb+z3x9c0D42SzMYWRxhZvGaByK1Pvxy+hOjOFcW/c5BJmIvPHUMnz19DLXOsoa1Inz29Fz/2rxHeBJdtjkmsnjNA5FaCx/Ka+WFPVcW/c5B3FjLXtnKT9ZspdvJC+lW5YHVW3jgpS109eTazEIrnSyOMLN4zQORaoWfJqrJ7+zmpoHiawvyg3N7OHR1F6f52LxHaWQxhj6L1zwQpvDLSDX4nd38onMffBXkAwXda4kf01DHHpcKi24/uCBWl1f1ScObLI4ws3jNA2EK3wiEqyXe426J19fV0FBX6+sH118Z4b70+vkN/1TTCNMvWbzmgTCFbwQiiCW+p6OL2y+ZUnKUjhfdCaj/lBSChB1WwwgzKFm85v4whW8EIoglPnpYg+8fnJs19ocDh1xdQmMy7IPNx8IOjaCkNizTqAxu9VTqaoS62kI3SxS+0pkfH5XJ2i1+S3BY2KERlFAWvoi8DbwPdAOHVLVJRI4DHgDGAW8Ds1T19+HENJKCl1/UrS2Ilelmrf5kzVY+e/oYntvQXjYfbKUzM4NY7RZ2aAQlCpfOJ1V1V97/84BnVbVFROY5/98QwXmMhODlpgmjGL2s1ec2tLNq3lklHzcIQV0kcTwcguQuWNihEZQ4XDoXAEuc90uAC2M4h1FlJMFaDeIiiatkdtAicll0eRmlE1bhK/CUiKwRkTlO2wmquh3AeT3ebUcRmSMirSLS2t7eHlIMI+0kofRE2ESxKPznQfrByhUbQQnr0mlW1W0icjzwtIhs8Lujqi4CFkFuicOQchgpJwlJMkFcJHGNSKyInBEnoSx8Vd3mvO4EHgamAu+IyCgA53VnWCGN6icJ1moQF0lcI5Ik9INRvZS8iLmIHAHUqOr7zvungYXAdGB33qTtcap6fX/HskXMjaTgdyK27wQv2ELqRvkp5yLmJwAPSy7NfRDwI1X9mYisBpaKyBXA74CLQ5zDMMpKmESxrKftG8mnZAs/SszCNwzDCE5QC98ybQ3DMDKCKXzDMIyMYArfMAwjI5jCNwzDyAim8A3DMDJCIqJ0RKQd+G3Ehx0B7Bpwq/JjcgXD5ApGUuWC5MqWZrk+pKoj/R4wEQo/DkSkNUi4UrkwuYJhcgUjqXJBcmXLklzm0jEMw8gIpvANwzAyQjUr/EWVFsADkysYJlcwkioXJFe2zMhVtT58wzAMo5BqtvANwzCMPEzhG4ZhZITUKXwROU5EnhaRXzuvx3ps90MR2Ski6/3uLyI3isgmEdkoIjNikusc5/ibnPUCetsfEJG1zt/bIrLWaR8nIp15n91ZZrkWiMjWvPOfm/dZJfvrVhHZICLrRORhERnmtJfUX17nyftcROR7zufrROQ0HzL6usY45BKRsSLynIi8KSKvi8hX8vbx/E7jlsv57G0Rec05d2teeyX766S8/lgrIu+JyHXOZ+Xor4ki8oKIHBCRf/azb0n9paqp+gNuAeY57+cB3/LY7s+B04D1fvYHTgZeBYYA44HNQG2UcgG1znH/CBjsnO9kl+2+A8x33o/rew1R91d/cgELgH922aei/QX8FTDIef+tvO8xcH/5+V6Ac4EnAAHOBF70IaOvezUmuUYBpznvjwJ+NdB3Wg65nM/eBkaUck/EKVef4+wgl9RUrv46HjgDuCn/XFHfX6mz8IELgCXO+yXAhW4bqerPgXcD7H8BcL+qHlDVt4BN5JZsjFKuqcAmVf2Nqh4E7nf2O4yICDALuC/AuWOXy+O4FesvVX1KVQ852/0SaAxwbt/n6SPvf2mOXwLDJLeEZ3/7+rpX45BLVber6ssAqvo+8CYQ1eosYfqrPyrWX322mQ5sVtWosv8HlEtVd6rqaqArwL6B+yuNCv8EVd0O4LweH9H+Y4Atedu1EewH4kcuP+f4M+AdVf11Xtt4EXlFRP5HRP4sgExRyfVlZ/j7w7xhY1L6C+DvyVltvQTtLz/n8dqmv33D3qth5DqMiIwDTgVezGt2+07LJZcCT4nIGhGZk7dNIvoLmE2xwRV3f5Wyb+D+CrPEYWyIyDPAiS4ffS3O07q0FcSsRiDXgOcAPk/hzbYd+D+qultETgeWicgpqvpemeT6AfAN5/9vkHM3/f0A+5RDrt5zfA04BNzrNA3YX6Wcp59t/OxbKmHkyn0ociTwE+C6vD7w+k7LJVezqm4TkeOBp0VkgzMiD0sU/TUYOB+4Me/zcvRXHPsWkUiFr6qf8vpMRN7pHbI6Q7GdAQ/vtX8bMDZvu0ZgW8Ry9XsOERkEXAScnnfOA8AB5/0aEdkMfARozdsmNrlU9Z28Y90FPObnWuKWyznG5cBMYLo6jkw//RX0PANsM7iffcPeq2HkQkTqyCn7e1X1od4N+vlOyyKXqva+7hSRh8m5LX5OhfvL4dPAy/l9VKb+KmXfwP2VRpfOI8DlzvvLgeUR7f8IMFtEhojIeGAC8FLEcq0GJojIeMeSmO3s18ungA2q2tbbICIjRaTWef9Hjly/KZdcffybnwF6o54q2l8icg5wA3C+qnb07lBifw30vfTK+7eS40xgrzOM7m/fsPdqyXI5c0F3A2+q6m35O/TznZZDriNE5ChHjiPITb7n31MV6a+8z/uOsMvVX6XsG7y/BprVTdofMBx4Fvi183qc0z4a+GnedveRG953kXtKXtHf/s5nXyM3I74R+HRMcp1LLmJiM/C1Pse4B/hSn7bPAq+Tm51/GfjrcsoF/D/gNWCdc4ONSkJ/kZsk3gKsdf7uDNNfbucBvtT7fZAbWv+H8/lrQJMPGT3vtQD9VJJcwDRyQ/91eX107kDfaRnk+iPnu3nV+Z4S0V/OZ0OB3cAxfY5Zjv46kZyeeg/Y47w/Our7y0orGIZhZIQ0unQMwzCMEjCFbxiGkRFM4RuGYWQEU/iGYRgZwRS+YRhGRjCFbxiGkRFM4RuGYWSE/w83NfsIGwbrhwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_test[:, 0], y_test, label=\"true\")\n",
    "plt.scatter(X_test[:, 0], predictions, label=\"pred\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 프로젝트 2 : 날씨 좋은 월요일 오후 세 시, 자전거 타는 사람은 몇 명?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>datetime</th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-01 01:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-01 02:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-01 03:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-01 04:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              datetime  season  holiday  workingday  weather  temp   atemp  \\\n",
       "0  2011-01-01 00:00:00       1        0           0        1  9.84  14.395   \n",
       "1  2011-01-01 01:00:00       1        0           0        1  9.02  13.635   \n",
       "2  2011-01-01 02:00:00       1        0           0        1  9.02  13.635   \n",
       "3  2011-01-01 03:00:00       1        0           0        1  9.84  14.395   \n",
       "4  2011-01-01 04:00:00       1        0           0        1  9.84  14.395   \n",
       "\n",
       "   humidity  windspeed  casual  registered  count  \n",
       "0        81        0.0       3          13     16  \n",
       "1        80        0.0       8          32     40  \n",
       "2        80        0.0       5          27     32  \n",
       "3        75        0.0       3          10     13  \n",
       "4        75        0.0       0           1      1  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv(\"~/aiffel/Project/EXPLORATION/E5/train.csv\")\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['datetime'] = pd.to_datetime(train['datetime'], format='%Y-%m-%d %H:%M:%S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['year'] = pd.DatetimeIndex(train['datetime']).year\n",
    "train['month'] = pd.DatetimeIndex(train['datetime']).month\n",
    "train['day'] = pd.DatetimeIndex(train['datetime']).day\n",
    "train['hour'] = pd.DatetimeIndex(train['datetime']).hour\n",
    "train['minute'] = pd.DatetimeIndex(train['datetime']).minute\n",
    "train['second'] = pd.DatetimeIndex(train['datetime']).second"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>datetime</th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>count</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "      <th>second</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-01 01:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-01 02:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-01 03:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-01 04:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             datetime  season  holiday  workingday  weather  temp   atemp  \\\n",
       "0 2011-01-01 00:00:00       1        0           0        1  9.84  14.395   \n",
       "1 2011-01-01 01:00:00       1        0           0        1  9.02  13.635   \n",
       "2 2011-01-01 02:00:00       1        0           0        1  9.02  13.635   \n",
       "3 2011-01-01 03:00:00       1        0           0        1  9.84  14.395   \n",
       "4 2011-01-01 04:00:00       1        0           0        1  9.84  14.395   \n",
       "\n",
       "   humidity  windspeed  casual  registered  count  year  month  day  hour  \\\n",
       "0        81        0.0       3          13     16  2011      1    1     0   \n",
       "1        80        0.0       8          32     40  2011      1    1     1   \n",
       "2        80        0.0       5          27     32  2011      1    1     2   \n",
       "3        75        0.0       3          10     13  2011      1    1     3   \n",
       "4        75        0.0       0           1      1  2011      1    1     4   \n",
       "\n",
       "   minute  second  \n",
       "0       0       0  \n",
       "1       0       0  \n",
       "2       0       0  \n",
       "3       0       0  \n",
       "4       0       0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.subplot(2,3,1)\n",
    "sns.countplot(x='year', data=train)\n",
    "\n",
    "plt.subplot(2,3,2)\n",
    "sns.countplot(x='month', data=train)\n",
    "\n",
    "plt.subplot(2,3,3)\n",
    "sns.countplot(x='day', data=train)\n",
    "\n",
    "plt.subplot(2,3,4)\n",
    "sns.countplot(x='hour', data=train)\n",
    "\n",
    "plt.subplot(2,3,5)\n",
    "sns.countplot(x='minute', data=train)\n",
    "\n",
    "plt.subplot(2,3,6)\n",
    "sns.countplot(x='second', data=train)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train[['season', 'workingday', 'weather', 'temp', 'atemp', 'humidity', 'windspeed', 'year', 'month', 'day', 'hour',]].values\n",
    "y = train[['count']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-33.96697339],\n",
       "       [ 59.23679918],\n",
       "       [112.62548605],\n",
       "       ...,\n",
       "       [195.79183719],\n",
       "       [197.74006566],\n",
       "       [294.24680107]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = model.predict(X_test)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "140.47528542723452"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse = mean_squared_error(y_test, predictions, squared = False)\n",
    "rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "col = ['season', 'workingday', 'weather', 'temp', 'atemp', 'humidity', 'windspeed', 'year', 'month', 'day', 'hour']\n",
    "X_test_df = pd.DataFrame(X_test, columns=col)\n",
    "X_test_df['predictions'] = predictions\n",
    "X_test_df['y_test'] = y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>season</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>predictions</th>\n",
       "      <th>y_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.56</td>\n",
       "      <td>9.090</td>\n",
       "      <td>80.0</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-33.966973</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.76</td>\n",
       "      <td>16.665</td>\n",
       "      <td>71.0</td>\n",
       "      <td>19.0012</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>59.236799</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>24.60</td>\n",
       "      <td>27.275</td>\n",
       "      <td>88.0</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>112.625486</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.40</td>\n",
       "      <td>20.455</td>\n",
       "      <td>56.0</td>\n",
       "      <td>23.9994</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>44.829705</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22.14</td>\n",
       "      <td>25.760</td>\n",
       "      <td>49.0</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>303.039383</td>\n",
       "      <td>310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2173</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.30</td>\n",
       "      <td>13.635</td>\n",
       "      <td>61.0</td>\n",
       "      <td>19.9995</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76.753725</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2174</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.70</td>\n",
       "      <td>31.820</td>\n",
       "      <td>39.0</td>\n",
       "      <td>16.9979</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>356.228964</td>\n",
       "      <td>713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2175</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.86</td>\n",
       "      <td>22.725</td>\n",
       "      <td>72.0</td>\n",
       "      <td>15.0013</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>195.791837</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2176</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.30</td>\n",
       "      <td>13.635</td>\n",
       "      <td>42.0</td>\n",
       "      <td>31.0009</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>197.740066</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2177</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.24</td>\n",
       "      <td>30.305</td>\n",
       "      <td>65.0</td>\n",
       "      <td>16.9979</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>294.246801</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2178 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      season  workingday  weather   temp   atemp  humidity  windspeed    year  \\\n",
       "0        1.0         1.0      2.0   6.56   9.090      80.0     8.9981  2011.0   \n",
       "1        2.0         1.0      2.0  14.76  16.665      71.0    19.0012  2011.0   \n",
       "2        3.0         1.0      3.0  24.60  27.275      88.0     8.9981  2011.0   \n",
       "3        2.0         1.0      1.0  16.40  20.455      56.0    23.9994  2011.0   \n",
       "4        4.0         1.0      2.0  22.14  25.760      49.0     8.9981  2012.0   \n",
       "...      ...         ...      ...    ...     ...       ...        ...     ...   \n",
       "2173     1.0         1.0      1.0  12.30  13.635      61.0    19.9995  2012.0   \n",
       "2174     1.0         1.0      1.0  28.70  31.820      39.0    16.9979  2012.0   \n",
       "2175     2.0         1.0      1.0  18.86  22.725      72.0    15.0013  2011.0   \n",
       "2176     1.0         1.0      1.0  12.30  13.635      42.0    31.0009  2012.0   \n",
       "2177     3.0         1.0      1.0  26.24  30.305      65.0    16.9979  2011.0   \n",
       "\n",
       "      month   day  hour  predictions  y_test  \n",
       "0       2.0   4.0   9.0   -33.966973     127  \n",
       "1       4.0   8.0  10.0    59.236799      73  \n",
       "2       9.0   7.0   9.0   112.625486     190  \n",
       "3       4.0   4.0   1.0    44.829705      11  \n",
       "4      10.0  17.0  13.0   303.039383     310  \n",
       "...     ...   ...   ...          ...     ...  \n",
       "2173    3.0   7.0   1.0    76.753725       5  \n",
       "2174    3.0  15.0  17.0   356.228964     713  \n",
       "2175    5.0   6.0  22.0   195.791837     151  \n",
       "2176    1.0  18.0  13.0   197.740066     130  \n",
       "2177    9.0   1.0  23.0   294.246801     126  \n",
       "\n",
       "[2178 rows x 13 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.subplot(2,2,1)\n",
    "sns.lineplot(x=X_test_df['temp'], y=X_test_df['predictions'])\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "sns.lineplot(x=X_test_df['temp'], y=X_test_df['y_test'])\n",
    "\n",
    "plt.subplot(2,2,3)\n",
    "sns.lineplot(x=X_test_df['humidity'], y=X_test_df['predictions'])\n",
    "\n",
    "plt.subplot(2,2,4)\n",
    "sns.lineplot(x=X_test_df['humidity'], y=X_test_df['y_test'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "프로젝트 1  \n",
    "\n",
    "첫번째 프로젝트는 처음에 학습을 진행했을 때는 원하는 값이 나오지 않았습니다. 그래서, learning rate 와 트레이닝 횟수를 계속 조절하였고, loss값을 3000에 근접하도록 얻을 수 있었습니다.\n",
    "\n",
    "<br>\n",
    "\n",
    "프로젝트 2  \n",
    "\n",
    "두번째 프로젝트는 처음에 학습을 진행했을 때, loss 값이 거의 0에 가까운 값이 나왔습니다. 모델이 너무 잘 맞추는 것이 'casual', 'registered' 속성때문이라고 판단되어, 두개의 속성을 제거하였습니다. 추가적으로 'holiday', 'minute', 'second' 속성도 제거하여, 학습을 진행하였습니다.\n",
    "결과적으로 loss 값이 140.47 정도가 나왔습니다."
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "aiffel",
   "language": "python",
   "name": "aiffel"
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
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  "toc": {
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   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
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    "top": "54px",
    "width": "280.797px"
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   "toc_section_display": true,
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