Machine Learning with scikit-learn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit the dimonds datasets into a regression model to see if different properties can predict the price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.21</td>\n",
       "      <td>Premium</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>59.8</td>\n",
       "      <td>61.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS1</td>\n",
       "      <td>56.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>327</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.31</td>\n",
       "      <td>Good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>63.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   carat      cut color clarity  depth  table  price     x     y     z\n",
       "1   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "2   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
       "3   0.23     Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
       "4   0.29  Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
       "5   0.31     Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"datasets/diamonds.csv\", index_col = 0) #index_col = 0 so that we don't generate duplicate indexes\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "convert everything into numerical values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Ideal', 'Premium', 'Good', 'Very Good', 'Fair'], dtype=object)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cut'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "Length: 53940, dtype: int8"
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df['cut'].astype(\"category\").cat.codes #arbitary classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a dictionary\n",
    "cut_class_dict = {'Fair': 1, 'Good': 2, 'Very Good': 3, 'Premium': 4, 'Ideal': 5}\n",
    "clarity_dict = {\"I3\": 1, 'I2': 2, 'I1':3, 'SI2':4, 'SI1':5, 'VS2': 6, 'VS1': 7, 'VVS2': 8, \"VVS1\": 9, \"IF\": 10, \"FL\": 11}\n",
    "color_dict = {\"J\":1, \"I\":2, 'H':3, 'G':4, 'F':5, 'E':6, 'D':7}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['cut'] = df['cut'].map(cut_class_dict)\n",
    "df['clarity'] = df['clarity'].map(clarity_dict)\n",
    "df['color'] = df['color'].map(color_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.23</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>61.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.21</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>59.8</td>\n",
       "      <td>61.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.23</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>56.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>327</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.29</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.31</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>63.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   carat  cut  color  clarity  depth  table  price     x     y     z\n",
       "1   0.23    5      6        4   61.5   55.0    326  3.95  3.98  2.43\n",
       "2   0.21    4      6        5   59.8   61.0    326  3.89  3.84  2.31\n",
       "3   0.23    2      6        7   56.9   65.0    327  4.05  4.07  2.31\n",
       "4   0.29    4      2        6   62.4   58.0    334  4.20  4.23  2.63\n",
       "5   0.31    2      1        4   63.3   58.0    335  4.34  4.35  2.75"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "from sklearn import svm, preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First thing is to shuffle the datasets if they are sorted in any way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = sklearn.utils.shuffle(df)\n",
    "\n",
    "X = df.drop(\"price\", axis = 1).values\n",
    "# scale your data\n",
    "X = preprocessing.scale(X)\n",
    "y = df['price'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.25750736,  0.08588908, -0.8264134 , ...,  0.59624924,\n",
       "         0.53888544,  0.3135449 ],\n",
       "       [-0.20662095, -0.80969515, -1.41427211, ..., -0.05451945,\n",
       "        -0.00396275, -0.02654667],\n",
       "       [-1.02939387, -0.80969515, -0.8264134 , ..., -1.26691043,\n",
       "        -1.20348215, -1.1743557 ],\n",
       "       ...,\n",
       "       [-0.81842646, -0.80969515,  0.34930404, ..., -0.87466629,\n",
       "        -0.83574628, -0.7775822 ],\n",
       "       [ 0.21531388,  0.08588908, -2.00213083, ...,  0.37338325,\n",
       "         0.30248381,  0.39856779],\n",
       "       [-1.0926841 , -0.80969515,  0.93716275, ..., -1.38280074,\n",
       "        -1.38735009, -1.28771955]])"
      ]
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     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_size = 200\n",
    "X_train = X[:-test_size]\n",
    "y_train = y[:-test_size]\n",
    "\n",
    "X_test = X[-test_size:]\n",
    "y_test = y[-test_size:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,\n",
       "  gamma='auto_deprecated', kernel='linear', max_iter=-1, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#clf stands for classifier\n",
    "clf = svm.SVR(kernel = \"linear\")\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "clf.score(X_test, y_test) # R-squred"
   ]
  },
  {
   "cell_type": "code",
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      "Model: 3089.114062354586, Acutal: 2872\n",
      "Model: 6199.486289064636, Acutal: 8170\n",
      "Model: 1070.813171898421, Acutal: 761\n",
      "Model: 6006.7802027023945, Acutal: 5637\n",
      "Model: 4476.46817708455, Acutal: 4334\n",
      "Model: 2838.4970763435167, Acutal: 2159\n",
      "Model: 1274.676733556037, Acutal: 1052\n",
      "Model: 1044.9572525487415, Acutal: 838\n",
      "Model: 4186.529959286023, Acutal: 3899\n",
      "Model: 4604.747790092614, Acutal: 3749\n",
      "Model: 868.7822330343183, Acutal: 1107\n",
      "Model: 1631.3605440387992, Acutal: 1385\n",
      "Model: -17.673084796275816, Acutal: 541\n",
      "Model: -129.84314229188612, Acutal: 463\n",
      "Model: 197.00218722115414, Acutal: 765\n",
      "Model: 389.5580291331089, Acutal: 625\n",
      "Model: 7013.225363344466, Acutal: 8261\n",
      "Model: 626.1119461809999, Acutal: 758\n",
      "Model: 5091.466599191119, Acutal: 6445\n",
      "Model: 1987.7320446676, Acutal: 2030\n",
      "Model: 11776.94470636607, Acutal: 16737\n",
      "Model: 6805.57879650449, Acutal: 5174\n",
      "Model: 5339.241056663322, Acutal: 7569\n",
      "Model: -752.5905836789184, Acutal: 410\n",
      "Model: 4839.38514764039, Acutal: 4442\n",
      "Model: 2234.856214557735, Acutal: 1845\n",
      "Model: 1407.767447977707, Acutal: 1192\n",
      "Model: 6644.03964389607, Acutal: 8128\n",
      "Model: 653.5062910758161, Acutal: 738\n",
      "Model: 1906.0827623864914, Acutal: 1662\n",
      "Model: 4624.456236962291, Acutal: 4161\n",
      "Model: 3531.1222859638433, Acutal: 2851\n",
      "Model: 357.0945700317834, Acutal: 625\n",
      "Model: 4028.5129383659823, Acutal: 3975\n",
      "Model: 1162.8179688109221, Acutal: 921\n",
      "Model: -70.87479911431046, Acutal: 698\n",
      "Model: 466.4158014593172, Acutal: 684\n",
      "Model: 12934.34768093867, Acutal: 17473\n",
      "Model: 9151.923027508963, Acutal: 8540\n",
      "Model: 5775.088840988419, Acutal: 4863\n",
      "Model: -350.45346741785806, Acutal: 545\n",
      "Model: 5781.952621851746, Acutal: 4770\n",
      "Model: 1227.794604554354, Acutal: 1180\n",
      "Model: 309.02060258899337, Acutal: 710\n",
      "Model: 8024.303437422524, Acutal: 7730\n",
      "Model: 763.4557662474122, Acutal: 620\n",
      "Model: 5638.719608846346, Acutal: 7075\n",
      "Model: 5128.652722999013, Acutal: 5977\n",
      "Model: 359.7281422464953, Acutal: 737\n",
      "Model: 12908.223003339977, Acutal: 15254\n",
      "Model: 385.1357609296474, Acutal: 638\n",
      "Model: 1447.3069359879078, Acutal: 1350\n",
      "Model: 8473.789629367062, Acutal: 14236\n",
      "Model: 4103.060251296323, Acutal: 4039\n",
      "Model: 6322.880171870716, Acutal: 8346\n",
      "Model: 2442.406067298233, Acutal: 2245\n",
      "Model: 7200.704813789085, Acutal: 9331\n",
      "Model: 502.7448782996971, Acutal: 684\n",
      "Model: 5101.552359907895, Acutal: 6587\n",
      "Model: 5317.771584538631, Acutal: 7114\n",
      "Model: 1248.4794568798425, Acutal: 1244\n",
      "Model: 274.47007742293, Acutal: 720\n",
      "Model: 3231.9075055612134, Acutal: 3419\n",
      "Model: 1448.50915871088, Acutal: 1359\n",
      "Model: 1663.3064290775067, Acutal: 1063\n",
      "Model: 1281.5629469122928, Acutal: 1153\n",
      "Model: 6472.102608371947, Acutal: 7318\n",
      "Model: 2462.1685202994704, Acutal: 2208\n",
      "Model: -418.15017269289865, Acutal: 540\n",
      "Model: 3064.815520782283, Acutal: 2528\n",
      "Model: 10421.207055454102, Acutal: 7922\n",
      "Model: 1984.7087033907785, Acutal: 1966\n",
      "Model: 1236.1761077885249, Acutal: 886\n",
      "Model: 1419.1292206094863, Acutal: 1286\n",
      "Model: 4929.120448703882, Acutal: 5222\n",
      "Model: 5009.52644363675, Acutal: 5067\n",
      "Model: 5650.558672211165, Acutal: 7079\n",
      "Model: 4014.1642441050326, Acutal: 4232\n",
      "Model: 4917.588222577466, Acutal: 4480\n",
      "Model: 3966.679077298825, Acutal: 3537\n",
      "Model: -62.5270356677911, Acutal: 484\n",
      "Model: 2197.8877158460573, Acutal: 2069\n",
      "Model: 9036.943664286111, Acutal: 16783\n",
      "Model: 6378.700926732588, Acutal: 11387\n",
      "Model: -170.68535831316103, Acutal: 409\n",
      "Model: 2585.511678264711, Acutal: 2270\n",
      "Model: 2001.746411315867, Acutal: 1670\n",
      "Model: 1710.3609324159233, Acutal: 1666\n",
      "Model: 3253.718602452104, Acutal: 3304\n",
      "Model: 3472.6482136822056, Acutal: 2958\n",
      "Model: 590.0363321728091, Acutal: 796\n",
      "Model: 1242.7182633003508, Acutal: 1316\n",
      "Model: 4085.571205360651, Acutal: 4220\n",
      "Model: 2248.3510839253495, Acutal: 2176\n",
      "Model: 5698.02019586861, Acutal: 8401\n",
      "Model: 4147.838151466743, Acutal: 4092\n",
      "Model: 4025.1571338020676, Acutal: 4119\n",
      "Model: 684.5872231868502, Acutal: 984\n",
      "Model: 5357.26403539255, Acutal: 5758\n",
      "Model: 5503.8283008819835, Acutal: 5667\n",
      "Model: 2453.612861317656, Acutal: 2375\n",
      "Model: 6041.178120882182, Acutal: 4931\n",
      "Model: 704.8138977431558, Acutal: 1061\n",
      "Model: 2897.419544719404, Acutal: 2726\n",
      "Model: 1809.7610809066691, Acutal: 1787\n",
      "Model: 4979.3042445799965, Acutal: 6289\n",
      "Model: 1850.5759378431871, Acutal: 1132\n",
      "Model: 6075.080988282398, Acutal: 6566\n",
      "Model: 6121.27363619403, Acutal: 7815\n",
      "Model: -352.65652059663853, Acutal: 557\n",
      "Model: 322.6848134745369, Acutal: 658\n",
      "Model: 2997.53068131019, Acutal: 2456\n",
      "Model: 1529.922620435822, Acutal: 1043\n",
      "Model: 1653.5741612342895, Acutal: 1263\n",
      "Model: 1876.0401717115865, Acutal: 1248\n",
      "Model: 812.4553969154604, Acutal: 873\n",
      "Model: 3307.843001793036, Acutal: 2800\n",
      "Model: 4702.969516833394, Acutal: 5854\n",
      "Model: 1557.7861005655786, Acutal: 1186\n",
      "Model: 4270.982619114688, Acutal: 3459\n",
      "Model: 293.7723103296744, Acutal: 588\n"
     ]
    }
   ],
   "source": [
    "for X,y in zip(X_test, y_test):\n",
    "    print(f\"Model: {clf.predict([X])[0]}, Acutal: {y}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,\n",
       "  gamma='auto_deprecated', kernel='rbf', max_iter=-1, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf2 = svm.SVR(kernel = 'rbf')\n",
    "clf2.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: 4193.778233488596, Acutal: 5497\n",
      "Model: 966.567719541325, Acutal: 544\n",
      "Model: 959.0002078646398, Acutal: 1094\n",
      "Model: 1261.3734623381374, Acutal: 1746\n",
      "Model: 6572.504013660244, Acutal: 12981\n",
      "Model: 6528.014623295886, Acutal: 7811\n",
      "Model: 2749.612025914901, Acutal: 2343\n",
      "Model: 1221.9631460802466, Acutal: 642\n",
      "Model: 1449.6693852927326, Acutal: 1624\n",
      "Model: 691.1189060995453, Acutal: 906\n",
      "Model: 6825.251466576901, Acutal: 9494\n",
      "Model: 6195.554785244762, Acutal: 6180\n",
      "Model: 680.2716884376514, Acutal: 603\n",
      "Model: 2769.5547726707036, Acutal: 2818\n",
      "Model: 2793.2998687591244, Acutal: 2120\n",
      "Model: 677.3672638808052, Acutal: 928\n",
      "Model: 1224.1896531490893, Acutal: 845\n",
      "Model: 6290.473689423823, Acutal: 8858\n",
      "Model: 3930.6940833757867, Acutal: 4315\n",
      "Model: 5189.019110252065, Acutal: 16914\n",
      "Model: 2583.099278908936, Acutal: 2515\n",
      "Model: 1229.723210842576, Acutal: 810\n",
      "Model: 1094.2730320115847, Acutal: 591\n",
      "Model: 1381.5999753173728, Acutal: 1000\n",
      "Model: 996.7146336219162, Acutal: 1035\n",
      "Model: 2047.1631328828926, Acutal: 1861\n",
      "Model: 1637.1306031072465, Acutal: 1431\n",
      "Model: 5383.430460102144, Acutal: 5606\n",
      "Model: 3831.4133255407705, Acutal: 3780\n",
      "Model: 2851.637680601815, Acutal: 2292\n",
      "Model: 2076.3701468948707, Acutal: 826\n",
      "Model: 3844.9355657031206, Acutal: 4158\n",
      "Model: 847.4300185539546, Acutal: 638\n",
      "Model: 1382.7700031195095, Acutal: 1008\n",
      "Model: 1275.6289740137443, Acutal: 530\n",
      "Model: 983.8303768572746, Acutal: 1155\n",
      "Model: 1098.1947792306264, Acutal: 624\n",
      "Model: 1516.8855411206644, Acutal: 1213\n",
      "Model: 5778.860638920131, Acutal: 7465\n",
      "Model: 5546.0477248342595, Acutal: 6022\n",
      "Model: 5105.326972808925, Acutal: 9139\n",
      "Model: 1239.2843898075917, Acutal: 1235\n",
      "Model: 2592.970116317909, Acutal: 2670\n",
      "Model: 1081.086965795978, Acutal: 1176\n",
      "Model: 6100.401926054325, Acutal: 10556\n",
      "Model: 2419.4973991598986, Acutal: 2398\n",
      "Model: 6059.126114965609, Acutal: 15005\n",
      "Model: 3932.5998768810923, Acutal: 3648\n",
      "Model: 3051.3409815238283, Acutal: 2779\n",
      "Model: 4685.428035390523, Acutal: 5387\n",
      "Model: 5426.240679278595, Acutal: 8962\n",
      "Model: 5415.217429853657, Acutal: 6177\n",
      "Model: 2288.509026298628, Acutal: 1786\n",
      "Model: 2643.4065051075977, Acutal: 2429\n",
      "Model: 891.9832967745565, Acutal: 734\n",
      "Model: 3305.3897455104043, Acutal: 3642\n",
      "Model: 2340.8118233774508, Acutal: 2171\n",
      "Model: 657.1472747502844, Acutal: 872\n",
      "Model: 505.59250023694267, Acutal: 489\n",
      "Model: 785.1304386669494, Acutal: 464\n",
      "Model: 7234.912732744195, Acutal: 8176\n",
      "Model: 1825.4477255080926, Acutal: 1385\n",
      "Model: 6272.745630968955, Acutal: 12084\n",
      "Model: 6865.785341709998, Acutal: 10919\n",
      "Model: 1293.6691081364097, Acutal: 936\n",
      "Model: 3135.7837885607437, Acutal: 3270\n",
      "Model: 5953.955561606945, Acutal: 15760\n",
      "Model: 4807.3829114964055, Acutal: 16427\n",
      "Model: 978.5362305315639, Acutal: 814\n",
      "Model: 1231.9820884219853, Acutal: 1235\n",
      "Model: 2791.4698241018214, Acutal: 2709\n",
      "Model: 988.5502183761823, Acutal: 486\n",
      "Model: 4465.686613117908, Acutal: 4670\n",
      "Model: 703.0232605131059, Acutal: 710\n",
      "Model: 2655.4990034307198, Acutal: 2727\n",
      "Model: 1712.52726843261, Acutal: 1683\n",
      "Model: 1013.1777550602187, Acutal: 1102\n",
      "Model: 5255.732751852185, Acutal: 17534\n",
      "Model: 829.9246262910615, Acutal: 789\n",
      "Model: 2833.609016865638, Acutal: 2872\n",
      "Model: 5791.381608957692, Acutal: 8170\n",
      "Model: 1364.827197962217, Acutal: 761\n",
      "Model: 5037.743119670731, Acutal: 5637\n",
      "Model: 3552.7326640527062, Acutal: 4334\n",
      "Model: 3029.8755486738337, Acutal: 2159\n",
      "Model: 1267.3161927460865, Acutal: 1052\n",
      "Model: 1014.3790881120904, Acutal: 838\n",
      "Model: 3994.5309198920395, Acutal: 3899\n",
      "Model: 4524.960028092497, Acutal: 3749\n",
      "Model: 910.1830687306647, Acutal: 1107\n",
      "Model: 1477.2554328800402, Acutal: 1385\n",
      "Model: 467.85798635964966, Acutal: 541\n",
      "Model: 985.1833389282478, Acutal: 463\n",
      "Model: 1121.8165052769468, Acutal: 765\n",
      "Model: 913.1526930038094, Acutal: 625\n",
      "Model: 6604.448477279373, Acutal: 8261\n",
      "Model: 895.9627276120109, Acutal: 758\n",
      "Model: 3756.603210066144, Acutal: 6445\n",
      "Model: 1819.1082777324727, Acutal: 2030\n",
      "Model: 6361.111260358572, Acutal: 16737\n",
      "Model: 5818.074267733176, Acutal: 5174\n",
      "Model: 5395.9191138310525, Acutal: 7569\n",
      "Model: 1470.9201278462679, Acutal: 410\n",
      "Model: 4595.762717484029, Acutal: 4442\n",
      "Model: 1949.9356704418349, Acutal: 1845\n",
      "Model: 1002.6579212659985, Acutal: 1192\n",
      "Model: 7057.159510467649, Acutal: 8128\n",
      "Model: 618.3392359791319, Acutal: 738\n",
      "Model: 1728.3527900694353, Acutal: 1662\n",
      "Model: 3887.1310532423026, Acutal: 4161\n",
      "Model: 3372.114339369445, Acutal: 2851\n",
      "Model: 1013.2359377850075, Acutal: 625\n",
      "Model: 4100.942738352687, Acutal: 3975\n",
      "Model: 1543.9355896847117, Acutal: 921\n",
      "Model: 1325.9527244106596, Acutal: 698\n",
      "Model: 609.3688960792747, Acutal: 684\n",
      "Model: 5535.761655840268, Acutal: 17473\n",
      "Model: 6174.072556611891, Acutal: 8540\n",
      "Model: 6167.46774344281, Acutal: 4863\n",
      "Model: 2078.2998665124796, Acutal: 545\n",
      "Model: 4842.46982347012, Acutal: 4770\n",
      "Model: 973.325271067311, Acutal: 1180\n",
      "Model: 808.0664836249571, Acutal: 710\n",
      "Model: 6302.363457950996, Acutal: 7730\n",
      "Model: 880.4825763461045, Acutal: 620\n",
      "Model: 5885.009343555032, Acutal: 7075\n",
      "Model: 5126.000033960876, Acutal: 5977\n",
      "Model: 1026.4772128484974, Acutal: 737\n",
      "Model: 5863.806532710431, Acutal: 15254\n",
      "Model: 1663.8329521129708, Acutal: 638\n",
      "Model: 1359.0543929473274, Acutal: 1350\n",
      "Model: 6379.5194754991535, Acutal: 14236\n",
      "Model: 3742.6439862256884, Acutal: 4039\n",
      "Model: 6613.203802429864, Acutal: 8346\n",
      "Model: 2144.748061527811, Acutal: 2245\n",
      "Model: 6498.434151553684, Acutal: 9331\n",
      "Model: 958.8346703491961, Acutal: 684\n",
      "Model: 5349.088053760618, Acutal: 6587\n",
      "Model: 4850.057363454345, Acutal: 7114\n",
      "Model: 1407.0547185792977, Acutal: 1244\n",
      "Model: 570.4464969092896, Acutal: 720\n",
      "Model: 3031.663968164063, Acutal: 3419\n",
      "Model: 1559.6429438130915, Acutal: 1359\n",
      "Model: 1297.039027955564, Acutal: 1063\n",
      "Model: 861.4072898152776, Acutal: 1153\n",
      "Model: 6486.92614697544, Acutal: 7318\n",
      "Model: 2005.9389814424549, Acutal: 2208\n",
      "Model: 817.6255989506876, Acutal: 540\n",
      "Model: 2437.9332232667075, Acutal: 2528\n",
      "Model: 7060.524279553001, Acutal: 7922\n",
      "Model: 1991.1708281016192, Acutal: 1966\n",
      "Model: 1185.2394723647603, Acutal: 886\n",
      "Model: 2926.351103518537, Acutal: 1286\n",
      "Model: 4950.681483671307, Acutal: 5222\n",
      "Model: 4155.045043799463, Acutal: 5067\n",
      "Model: 5609.096300350813, Acutal: 7079\n",
      "Model: 3945.1349190041515, Acutal: 4232\n",
      "Model: 3785.1653027649395, Acutal: 4480\n",
      "Model: 3440.0691622524896, Acutal: 3537\n",
      "Model: 1035.5422014032797, Acutal: 484\n",
      "Model: 1971.403675892966, Acutal: 2069\n",
      "Model: 7361.495904823405, Acutal: 16783\n",
      "Model: 5172.748798840967, Acutal: 11387\n",
      "Model: 1210.4337878154047, Acutal: 409\n",
      "Model: 2434.4930208030564, Acutal: 2270\n",
      "Model: 1908.845377826997, Acutal: 1670\n",
      "Model: 1858.3595543297047, Acutal: 1666\n",
      "Model: 3088.627627765689, Acutal: 3304\n",
      "Model: 3301.4104667728884, Acutal: 2958\n",
      "Model: 825.6929683633034, Acutal: 796\n",
      "Model: 2099.3003719980634, Acutal: 1316\n",
      "Model: 3490.254827991209, Acutal: 4220\n",
      "Model: 2371.6194982756556, Acutal: 2176\n",
      "Model: 5624.131889051025, Acutal: 8401\n",
      "Model: 4030.29225778162, Acutal: 4092\n",
      "Model: 4065.4767046483294, Acutal: 4119\n",
      "Model: 786.73886327527, Acutal: 984\n",
      "Model: 5445.801139771784, Acutal: 5758\n",
      "Model: 5348.408012194228, Acutal: 5667\n",
      "Model: 2298.4212645096504, Acutal: 2375\n",
      "Model: 4945.1452840204665, Acutal: 4931\n",
      "Model: 574.6330565947637, Acutal: 1061\n",
      "Model: 2731.014577541831, Acutal: 2726\n",
      "Model: 1596.303469148704, Acutal: 1787\n",
      "Model: 4532.50460345306, Acutal: 6289\n",
      "Model: 1529.5098801628596, Acutal: 1132\n",
      "Model: 6060.601782031871, Acutal: 6566\n",
      "Model: 6158.876573263273, Acutal: 7815\n",
      "Model: 1019.0920253549589, Acutal: 557\n",
      "Model: 681.19603029154, Acutal: 658\n",
      "Model: 2834.6106038388775, Acutal: 2456\n",
      "Model: 1160.9941193103987, Acutal: 1043\n",
      "Model: 1907.7419340629015, Acutal: 1263\n",
      "Model: 1959.1191851816618, Acutal: 1248\n",
      "Model: 964.2595888961373, Acutal: 873\n",
      "Model: 3183.515252295338, Acutal: 2800\n",
      "Model: 4195.023448797704, Acutal: 5854\n",
      "Model: 1466.3633696031623, Acutal: 1186\n",
      "Model: 3671.0368307313774, Acutal: 3459\n",
      "Model: 809.7271086478504, Acutal: 588\n"
     ]
    }
   ],
   "source": [
    "for X, y in zip(X_test, y_test):\n",
    "    print(f\"Model: {clf2.predict([X])[0]}, Acutal: {y}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "no negatives this time, but the score is way off. That's why voting models gives way better scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5616380115387061"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf2.score(X_test, y_test)"
   ]
  }
 ],
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