Problem 2.3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 404,
   "id": "97686cc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "id": "1bfdc55f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"C:\\\\Users\\\\dhruv\\\\Downloads\\\\Housing.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "id": "a197adc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "dft = data.drop(columns=['furnishingstatus'])\n",
    "col = dft.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "id": "73161ba7",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "id": "65dd9704",
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "#CREATING BIN_MAP FUNCTION\n",
    "#\n",
    "def bin_map(var):\n",
    "    i = 0\n",
    "    while(i < 545):\n",
    "        if(var[i] == 'yes'):\n",
    "            var[i] = 1\n",
    "        if(var[i] == 'no'):\n",
    "            var[i] = 0\n",
    "        i = i + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "id": "9a28e591",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dhruv\\AppData\\Local\\Temp\\ipykernel_22080\\3636318678.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  var[i] = 1\n",
      "C:\\Users\\dhruv\\AppData\\Local\\Temp\\ipykernel_22080\\3636318678.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  var[i] = 0\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "#MAPPING 'YES' AND 'NO' TO '1'S' AND '0'S'\n",
    "#\n",
    "bin_map(dft.mainroad)\n",
    "bin_map(dft.guestroom)\n",
    "bin_map(dft.basement)\n",
    "bin_map(dft.hotwaterheating)\n",
    "bin_map(dft.airconditioning)\n",
    "bin_map(dft.prefarea)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "id": "375c62c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "dft = scaler.fit_transform(dft)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "id": "3308cba6",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = scaler.fit_transform(np.array(data.price).reshape(545,1))\n",
    "Wye = pd.DataFrame(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "id": "a36325ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>stories</th>\n",
       "      <th>mainroad</th>\n",
       "      <th>guestroom</th>\n",
       "      <th>basement</th>\n",
       "      <th>hotwaterheating</th>\n",
       "      <th>airconditioning</th>\n",
       "      <th>parking</th>\n",
       "      <th>prefarea</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.566365</td>\n",
       "      <td>1.046726</td>\n",
       "      <td>1.403419</td>\n",
       "      <td>1.421812</td>\n",
       "      <td>1.378217</td>\n",
       "      <td>0.405623</td>\n",
       "      <td>-0.465315</td>\n",
       "      <td>-0.734539</td>\n",
       "      <td>-0.219265</td>\n",
       "      <td>1.472618</td>\n",
       "      <td>1.517692</td>\n",
       "      <td>1.804941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.004484</td>\n",
       "      <td>1.757010</td>\n",
       "      <td>1.403419</td>\n",
       "      <td>5.405809</td>\n",
       "      <td>2.532024</td>\n",
       "      <td>0.405623</td>\n",
       "      <td>-0.465315</td>\n",
       "      <td>-0.734539</td>\n",
       "      <td>-0.219265</td>\n",
       "      <td>1.472618</td>\n",
       "      <td>2.679409</td>\n",
       "      <td>-0.554035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.004484</td>\n",
       "      <td>2.218232</td>\n",
       "      <td>0.047278</td>\n",
       "      <td>1.421812</td>\n",
       "      <td>0.224410</td>\n",
       "      <td>0.405623</td>\n",
       "      <td>-0.465315</td>\n",
       "      <td>1.361397</td>\n",
       "      <td>-0.219265</td>\n",
       "      <td>-0.679063</td>\n",
       "      <td>1.517692</td>\n",
       "      <td>1.804941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.985755</td>\n",
       "      <td>1.083624</td>\n",
       "      <td>1.403419</td>\n",
       "      <td>1.421812</td>\n",
       "      <td>0.224410</td>\n",
       "      <td>0.405623</td>\n",
       "      <td>-0.465315</td>\n",
       "      <td>1.361397</td>\n",
       "      <td>-0.219265</td>\n",
       "      <td>1.472618</td>\n",
       "      <td>2.679409</td>\n",
       "      <td>1.804941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.554979</td>\n",
       "      <td>1.046726</td>\n",
       "      <td>1.403419</td>\n",
       "      <td>-0.570187</td>\n",
       "      <td>0.224410</td>\n",
       "      <td>0.405623</td>\n",
       "      <td>2.149083</td>\n",
       "      <td>1.361397</td>\n",
       "      <td>-0.219265</td>\n",
       "      <td>1.472618</td>\n",
       "      <td>1.517692</td>\n",
       "      <td>-0.554035</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>-1.576868</td>\n",
       "      <td>-0.991879</td>\n",
       "      <td>-1.308863</td>\n",
       "      <td>-0.570187</td>\n",
       "      <td>-0.929397</td>\n",
       "      <td>0.405623</td>\n",
       "      <td>-0.465315</td>\n",
       "      <td>1.361397</td>\n",
       "      <td>-0.219265</td>\n",
       "      <td>-0.679063</td>\n",
       "      <td>1.517692</td>\n",
       "      <td>-0.554035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>-1.605149</td>\n",
       "      <td>-1.268613</td>\n",
       "      <td>0.047278</td>\n",
       "      <td>-0.570187</td>\n",
       "      <td>-0.929397</td>\n",
       "      <td>-2.465344</td>\n",
       "      <td>-0.465315</td>\n",
       "      <td>-0.734539</td>\n",
       "      <td>-0.219265</td>\n",
       "      <td>-0.679063</td>\n",
       "      <td>-0.805741</td>\n",
       "      <td>-0.554035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>-1.614327</td>\n",
       "      <td>-0.705921</td>\n",
       "      <td>-1.308863</td>\n",
       "      <td>-0.570187</td>\n",
       "      <td>-0.929397</td>\n",
       "      <td>0.405623</td>\n",
       "      <td>-0.465315</td>\n",
       "      <td>-0.734539</td>\n",
       "      <td>-0.219265</td>\n",
       "      <td>-0.679063</td>\n",
       "      <td>-0.805741</td>\n",
       "      <td>-0.554035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>-1.614327</td>\n",
       "      <td>-1.033389</td>\n",
       "      <td>0.047278</td>\n",
       "      <td>-0.570187</td>\n",
       "      <td>-0.929397</td>\n",
       "      <td>-2.465344</td>\n",
       "      <td>-0.465315</td>\n",
       "      <td>-0.734539</td>\n",
       "      <td>-0.219265</td>\n",
       "      <td>-0.679063</td>\n",
       "      <td>-0.805741</td>\n",
       "      <td>-0.554035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>-1.614327</td>\n",
       "      <td>-0.599839</td>\n",
       "      <td>0.047278</td>\n",
       "      <td>-0.570187</td>\n",
       "      <td>0.224410</td>\n",
       "      <td>0.405623</td>\n",
       "      <td>-0.465315</td>\n",
       "      <td>-0.734539</td>\n",
       "      <td>-0.219265</td>\n",
       "      <td>-0.679063</td>\n",
       "      <td>-0.805741</td>\n",
       "      <td>-0.554035</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>545 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        price      area  bedrooms  bathrooms   stories  mainroad  guestroom  \\\n",
       "0    4.566365  1.046726  1.403419   1.421812  1.378217  0.405623  -0.465315   \n",
       "1    4.004484  1.757010  1.403419   5.405809  2.532024  0.405623  -0.465315   \n",
       "2    4.004484  2.218232  0.047278   1.421812  0.224410  0.405623  -0.465315   \n",
       "3    3.985755  1.083624  1.403419   1.421812  0.224410  0.405623  -0.465315   \n",
       "4    3.554979  1.046726  1.403419  -0.570187  0.224410  0.405623   2.149083   \n",
       "..        ...       ...       ...        ...       ...       ...        ...   \n",
       "540 -1.576868 -0.991879 -1.308863  -0.570187 -0.929397  0.405623  -0.465315   \n",
       "541 -1.605149 -1.268613  0.047278  -0.570187 -0.929397 -2.465344  -0.465315   \n",
       "542 -1.614327 -0.705921 -1.308863  -0.570187 -0.929397  0.405623  -0.465315   \n",
       "543 -1.614327 -1.033389  0.047278  -0.570187 -0.929397 -2.465344  -0.465315   \n",
       "544 -1.614327 -0.599839  0.047278  -0.570187  0.224410  0.405623  -0.465315   \n",
       "\n",
       "     basement  hotwaterheating  airconditioning   parking  prefarea  \n",
       "0   -0.734539        -0.219265         1.472618  1.517692  1.804941  \n",
       "1   -0.734539        -0.219265         1.472618  2.679409 -0.554035  \n",
       "2    1.361397        -0.219265        -0.679063  1.517692  1.804941  \n",
       "3    1.361397        -0.219265         1.472618  2.679409  1.804941  \n",
       "4    1.361397        -0.219265         1.472618  1.517692 -0.554035  \n",
       "..        ...              ...              ...       ...       ...  \n",
       "540  1.361397        -0.219265        -0.679063  1.517692 -0.554035  \n",
       "541 -0.734539        -0.219265        -0.679063 -0.805741 -0.554035  \n",
       "542 -0.734539        -0.219265        -0.679063 -0.805741 -0.554035  \n",
       "543 -0.734539        -0.219265        -0.679063 -0.805741 -0.554035  \n",
       "544 -0.734539        -0.219265        -0.679063 -0.805741 -0.554035  \n",
       "\n",
       "[545 rows x 12 columns]"
      ]
     },
     "execution_count": 412,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame(dft,columns=col)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "id": "ec72f3cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "Wye = pd.DataFrame(np.array(data.price))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "id": "9bf6ae76",
   "metadata": {},
   "outputs": [],
   "source": [
    "Ex = data.drop(columns=['price'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "id": "ad77cc35",
   "metadata": {},
   "outputs": [],
   "source": [
    "#d = pd.DataFrame(np.hstack([Ex,Wye]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "id": "799b9278",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=2)\n",
    "principalComponents = pca.fit_transform(Ex)\n",
    "principalDf = pd.DataFrame(data = principalComponents\n",
    " , columns = ['principal component 1', 'principal component 2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "id": "ea42ea25",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame(np.hstack([principalDf,Wye]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "id": "428fafb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.264248</td>\n",
       "      <td>-1.129485</td>\n",
       "      <td>4.566365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.194952</td>\n",
       "      <td>-3.347516</td>\n",
       "      <td>4.004484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.460935</td>\n",
       "      <td>1.278579</td>\n",
       "      <td>4.004484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.625400</td>\n",
       "      <td>0.538743</td>\n",
       "      <td>3.985755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.502535</td>\n",
       "      <td>1.070341</td>\n",
       "      <td>3.554979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>-1.078671</td>\n",
       "      <td>1.444086</td>\n",
       "      <td>-1.576868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>-2.533313</td>\n",
       "      <td>-0.848245</td>\n",
       "      <td>-1.605149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>-2.063004</td>\n",
       "      <td>0.305558</td>\n",
       "      <td>-1.614327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>-2.441185</td>\n",
       "      <td>-0.810540</td>\n",
       "      <td>-1.614327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>-1.140103</td>\n",
       "      <td>-0.688090</td>\n",
       "      <td>-1.614327</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>545 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2\n",
       "0    3.264248 -1.129485  4.566365\n",
       "1    5.194952 -3.347516  4.004484\n",
       "2    2.460935  1.278579  4.004484\n",
       "3    3.625400  0.538743  3.985755\n",
       "4    2.502535  1.070341  3.554979\n",
       "..        ...       ...       ...\n",
       "540 -1.078671  1.444086 -1.576868\n",
       "541 -2.533313 -0.848245 -1.605149\n",
       "542 -2.063004  0.305558 -1.614327\n",
       "543 -2.441185 -0.810540 -1.614327\n",
       "544 -1.140103 -0.688090 -1.614327\n",
       "\n",
       "[545 rows x 3 columns]"
      ]
     },
     "execution_count": 418,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "id": "e7d595f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "train1=df1.sample(frac=0.8,random_state=1) \n",
    "test1=df1.drop(train1.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "id": "c6bcceb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR\n",
    "svr_rbf = SVR(kernel='rbf', C=1e3, gamma=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "id": "e076da39",
   "metadata": {},
   "outputs": [],
   "source": [
    "train1ex = np.array(train1.drop(columns=[2])).reshape(436,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "id": "64cd67cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "train1wye = np.array(train1.drop(columns=[0,1])).reshape(436,1).ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "id": "847266ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "test1ex = np.array(test1.drop(columns=[2])).reshape(109,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "id": "6a919707",
   "metadata": {},
   "outputs": [],
   "source": [
    "test1wye = np.array(test1.drop(columns=[0,1])).reshape(109,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 513,
   "id": "03d55bb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = svr_rbf.fit(train1ex,train1wye)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 514,
   "id": "9808518c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.19151924e+00],\n",
       "       [ 3.30102310e+00],\n",
       "       [ 1.39622186e+00],\n",
       "       [ 3.93459752e-01],\n",
       "       [ 3.36186921e-01],\n",
       "       [ 4.36029068e+00],\n",
       "       [ 2.58189216e+00],\n",
       "       [ 1.96121431e+00],\n",
       "       [ 1.38799113e+00],\n",
       "       [ 5.98075163e-01],\n",
       "       [ 1.53558191e+00],\n",
       "       [ 4.23350039e-01],\n",
       "       [ 1.23999285e+00],\n",
       "       [ 6.28406224e-01],\n",
       "       [-2.84284115e-01],\n",
       "       [-9.25794346e-02],\n",
       "       [ 1.13813357e+00],\n",
       "       [ 1.14881210e+00],\n",
       "       [ 9.57050121e-01],\n",
       "       [ 2.11094127e-01],\n",
       "       [-4.59462076e-01],\n",
       "       [ 4.89055118e-01],\n",
       "       [ 9.88438638e-01],\n",
       "       [ 2.26987599e-01],\n",
       "       [ 1.18487895e+00],\n",
       "       [ 1.17244703e+00],\n",
       "       [ 1.39474671e+00],\n",
       "       [ 2.28369243e-01],\n",
       "       [ 7.05032576e-01],\n",
       "       [ 4.05263991e-01],\n",
       "       [ 9.53412617e-01],\n",
       "       [ 2.67012193e-01],\n",
       "       [ 3.18465224e-01],\n",
       "       [ 3.63766240e-01],\n",
       "       [ 8.22664454e-01],\n",
       "       [-1.23963110e-01],\n",
       "       [ 8.78462158e-01],\n",
       "       [ 1.28957501e+00],\n",
       "       [-1.34580596e-01],\n",
       "       [-5.37043563e-02],\n",
       "       [-3.04081703e-01],\n",
       "       [-6.07783029e-01],\n",
       "       [-4.67106739e-01],\n",
       "       [-1.08885329e-01],\n",
       "       [ 1.47711129e-01],\n",
       "       [ 4.50823183e-01],\n",
       "       [-4.29157181e-01],\n",
       "       [-6.36425238e-01],\n",
       "       [-3.80772185e-01],\n",
       "       [-6.98067381e-01],\n",
       "       [ 4.16601081e-01],\n",
       "       [-2.96608270e-01],\n",
       "       [ 1.54450890e-01],\n",
       "       [-9.82117842e-01],\n",
       "       [-6.92335414e-01],\n",
       "       [-4.62576470e-01],\n",
       "       [-2.20182045e-01],\n",
       "       [-8.08757955e-01],\n",
       "       [-8.08917695e-01],\n",
       "       [-6.51788685e-01],\n",
       "       [-3.80032844e-01],\n",
       "       [-3.78707046e-01],\n",
       "       [-3.61730447e-03],\n",
       "       [-9.55285148e-02],\n",
       "       [-1.08031964e-01],\n",
       "       [-3.66645425e-01],\n",
       "       [-3.44994357e-01],\n",
       "       [ 1.10838364e-01],\n",
       "       [ 8.24190922e-01],\n",
       "       [-3.82576758e-02],\n",
       "       [ 1.45870343e-01],\n",
       "       [-6.46586193e-01],\n",
       "       [ 3.23557673e-01],\n",
       "       [-8.33166551e-01],\n",
       "       [ 2.31803642e-01],\n",
       "       [-3.97594414e-01],\n",
       "       [-7.21799292e-01],\n",
       "       [-8.90278452e-01],\n",
       "       [-5.73060951e-01],\n",
       "       [-5.96010997e-01],\n",
       "       [-1.00565473e+00],\n",
       "       [-2.64599980e-01],\n",
       "       [-9.03087824e-01],\n",
       "       [-6.07433305e-01],\n",
       "       [-7.46155877e-01],\n",
       "       [-4.95912815e-01],\n",
       "       [-7.74922359e-01],\n",
       "       [-5.00362464e-01],\n",
       "       [ 2.75375395e-01],\n",
       "       [-8.56080255e-01],\n",
       "       [ 1.19702367e-01],\n",
       "       [ 2.26249858e-01],\n",
       "       [-2.41687655e-01],\n",
       "       [-6.22696587e-01],\n",
       "       [-7.24296237e-01],\n",
       "       [-1.08497870e+00],\n",
       "       [-9.15844398e-01],\n",
       "       [-5.94345980e-01],\n",
       "       [-8.31366891e-01],\n",
       "       [-8.78676322e-01],\n",
       "       [-8.61433149e-01],\n",
       "       [-8.79978297e-01],\n",
       "       [-8.35936086e-01],\n",
       "       [-7.39650680e-01],\n",
       "       [-8.66176984e-01],\n",
       "       [-5.48547119e-01],\n",
       "       [-7.63062077e-01],\n",
       "       [-7.38190753e-01],\n",
       "       [-9.74750391e-01]])"
      ]
     },
     "execution_count": 514,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction1 = model.predict(test1ex)\n",
    "prediction1.reshape(109, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 515,
   "id": "eefea8d2",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x231b82396a0>]"
      ]
     },
     "execution_count": 515,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(test1wye)\n",
    "plt.plot(prediction1,'+')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 516,
   "id": "116aa700",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error = 0.47\n",
      "Mean squared error = 0.45\n",
      "Median absolute error = 0.33\n",
      "Explain variance score = 0.56\n",
      "R2 score = 0.56\n"
     ]
    }
   ],
   "source": [
    "import sklearn.metrics as sm\n",
    "print(\"Mean absolute error =\", round(sm.mean_absolute_error(test1wye, prediction1), 2)) \n",
    "print(\"Mean squared error =\", round(sm.mean_squared_error(test1wye, prediction1), 2)) \n",
    "print(\"Median absolute error =\", round(sm.median_absolute_error(test1wye, prediction1), 2)) \n",
    "print(\"Explain variance score =\", round(sm.explained_variance_score(test1wye, prediction1), 2)) \n",
    "print(\"R2 score =\", round(sm.r2_score(test1wye, prediction1), 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "id": "3ba5ed80",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=6)\n",
    "principalComponents = pca.fit_transform(Ex)\n",
    "principalDf = pd.DataFrame(data = principalComponents\n",
    " , columns = ['principal component 1', 'principal component 2','principal component 3', 'principal component 4','principal component 5', 'principal component 6'])\n",
    "\n",
    "df2 = pd.DataFrame(np.hstack([principalDf,Wye]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "id": "ce2d2ad1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.044159</td>\n",
       "      <td>0.059643</td>\n",
       "      <td>0.115610</td>\n",
       "      <td>4.398738</td>\n",
       "      <td>0.810404</td>\n",
       "      <td>-1.162575</td>\n",
       "      <td>2.131547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>-2.767009</td>\n",
       "      <td>0.582098</td>\n",
       "      <td>1.861415</td>\n",
       "      <td>-0.099819</td>\n",
       "      <td>-1.104261</td>\n",
       "      <td>0.482675</td>\n",
       "      <td>-1.333386</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>436 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3         4         5         6\n",
       "62   1.668434 -1.577144 -0.350531 -0.284510 -0.738116 -0.047705  1.232537\n",
       "247  2.043753 -1.676954 -1.799614  0.760626 -0.463055  0.400701 -0.115977\n",
       "142  1.691797 -1.073583 -0.967244  0.813824 -0.525831  0.919381  0.445904\n",
       "107  0.706713  0.801670  0.287002 -0.536019  1.818314  1.209268  0.726844\n",
       "483 -0.640626 -0.483671 -0.736872 -0.194905  0.549685 -0.005113 -0.977528\n",
       "..        ...       ...       ...       ...       ...       ...       ...\n",
       "359 -1.203946 -0.065272 -0.719406  0.272859 -0.190551  0.152505 -0.565482\n",
       "36   1.669630 -1.038315 -0.498018  3.720000  2.610809  0.056067  1.753214\n",
       "30   2.528118 -2.072387 -1.250339 -0.374623 -0.620325 -0.739087  1.944253\n",
       "20   0.044159  0.059643  0.115610  4.398738  0.810404 -1.162575  2.131547\n",
       "527 -2.767009  0.582098  1.861415 -0.099819 -1.104261  0.482675 -1.333386\n",
       "\n",
       "[436 rows x 7 columns]"
      ]
     },
     "execution_count": 430,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train2=df2.sample(frac=0.8,random_state=1) \n",
    "test2=df2.drop(train2.index)\n",
    "\n",
    "from sklearn.svm import SVR\n",
    "svr_rbf = SVR(kernel='rbf', C=1e3, gamma=0.1)\n",
    "train2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "id": "2e542f58",
   "metadata": {},
   "outputs": [],
   "source": [
    "train2ex = np.array(train2.drop(columns=[6])).reshape(436,6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "id": "58b80e97",
   "metadata": {},
   "outputs": [],
   "source": [
    "train2wye = np.array(train2.drop(columns=[0,1,2,3,4,5])).reshape(436,1).ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "id": "b158adbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "test2ex = np.array(test2.drop(columns=[6])).reshape(109,6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "id": "0d0ef0f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "test2wye = np.array(test2.drop(columns=[0,1,2,3,4,5])).reshape(109,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "id": "7a56772a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x231b816ec40>]"
      ]
     },
     "execution_count": 494,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = svr_rbf.fit(train2ex,train2wye)\n",
    "\n",
    "prediction2 = model.predict(test2ex)\n",
    "prediction2.reshape(109, 1)\n",
    "\n",
    "plt.plot(test2wye)\n",
    "plt.plot(prediction2,'+')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "id": "f17d848e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error = 0.63\n",
      "Mean squared error = 0.94\n",
      "Median absolute error = 0.44\n",
      "Explain variance score = 0.1\n",
      "R2 score = 0.09\n"
     ]
    }
   ],
   "source": [
    "import sklearn.metrics as sm\n",
    "print(\"Mean absolute error =\", round(sm.mean_absolute_error(test2wye, prediction), 2)) \n",
    "print(\"Mean squared error =\", round(sm.mean_squared_error(test2wye, prediction), 2)) \n",
    "print(\"Median absolute error =\", round(sm.median_absolute_error(test2wye, prediction), 2)) \n",
    "print(\"Explain variance score =\", round(sm.explained_variance_score(test2wye, prediction), 2)) \n",
    "print(\"R2 score =\", round(sm.r2_score(test2wye, prediction), 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3833a9b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "id": "cfeaa246",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=11)\n",
    "principalComponents = pca.fit_transform(Ex)\n",
    "principalDf = pd.DataFrame(data = principalComponents\n",
    " , columns = ['principal component 1', 'principal component 2','principal component 3',\n",
    "              'principal component 4','principal component 5', 'principal component 6',\n",
    "             'principal component 7','principal component 8','principal component 9',\n",
    "              'principal component 10','principal component 11'])\n",
    "#,'principal component 9','principal component 10','principal component 11'\n",
    "\n",
    "df3 = pd.DataFrame(np.hstack([principalDf,Wye]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "id": "dc317f1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>-0.005113</td>\n",
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       "      <td>0.720711</td>\n",
       "      <td>-0.405571</td>\n",
       "      <td>0.746588</td>\n",
       "      <td>0.217113</td>\n",
       "      <td>-0.977528</td>\n",
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       "      <td>0.903971</td>\n",
       "      <td>-0.430595</td>\n",
       "      <td>0.405776</td>\n",
       "      <td>1.753214</td>\n",
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       "      <td>-2.072387</td>\n",
       "      <td>-1.250339</td>\n",
       "      <td>-0.374623</td>\n",
       "      <td>-0.620325</td>\n",
       "      <td>-0.739087</td>\n",
       "      <td>0.054959</td>\n",
       "      <td>-0.117463</td>\n",
       "      <td>0.502399</td>\n",
       "      <td>-0.585040</td>\n",
       "      <td>1.183018</td>\n",
       "      <td>1.944253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.044159</td>\n",
       "      <td>0.059643</td>\n",
       "      <td>0.115610</td>\n",
       "      <td>4.398738</td>\n",
       "      <td>0.810404</td>\n",
       "      <td>-1.162575</td>\n",
       "      <td>1.328682</td>\n",
       "      <td>-1.589544</td>\n",
       "      <td>-0.499869</td>\n",
       "      <td>0.278410</td>\n",
       "      <td>0.796768</td>\n",
       "      <td>2.131547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>-2.767009</td>\n",
       "      <td>0.582098</td>\n",
       "      <td>1.861415</td>\n",
       "      <td>-0.099819</td>\n",
       "      <td>-1.104261</td>\n",
       "      <td>0.482675</td>\n",
       "      <td>0.698249</td>\n",
       "      <td>-0.361066</td>\n",
       "      <td>0.474880</td>\n",
       "      <td>-0.202054</td>\n",
       "      <td>1.022571</td>\n",
       "      <td>-1.333386</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>436 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1         2         3         4         5         6   \\\n",
       "62   1.668434 -1.577144 -0.350531 -0.284510 -0.738116 -0.047705 -0.121506   \n",
       "247  2.043753 -1.676954 -1.799614  0.760626 -0.463055  0.400701 -0.852142   \n",
       "142  1.691797 -1.073583 -0.967244  0.813824 -0.525831  0.919381 -1.288370   \n",
       "107  0.706713  0.801670  0.287002 -0.536019  1.818314  1.209268  0.212905   \n",
       "483 -0.640626 -0.483671 -0.736872 -0.194905  0.549685 -0.005113 -0.635587   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "359 -1.203946 -0.065272 -0.719406  0.272859 -0.190551  0.152505 -0.658521   \n",
       "36   1.669630 -1.038315 -0.498018  3.720000  2.610809  0.056067  1.965541   \n",
       "30   2.528118 -2.072387 -1.250339 -0.374623 -0.620325 -0.739087  0.054959   \n",
       "20   0.044159  0.059643  0.115610  4.398738  0.810404 -1.162575  1.328682   \n",
       "527 -2.767009  0.582098  1.861415 -0.099819 -1.104261  0.482675  0.698249   \n",
       "\n",
       "           7         8         9         10        11  \n",
       "62  -0.160084  0.522901  0.678605 -1.033756  1.232537  \n",
       "247 -0.194329 -2.276169 -0.701348  1.500006 -0.115977  \n",
       "142  1.552603  0.030389  1.088125 -0.051945  0.445904  \n",
       "107 -0.145298 -0.293530  0.394505  1.622250  0.726844  \n",
       "483  0.720711 -0.405571  0.746588  0.217113 -0.977528  \n",
       "..        ...       ...       ...       ...       ...  \n",
       "359 -0.470938 -0.347374 -0.178396 -0.807956 -0.565482  \n",
       "36   0.918476  0.903971 -0.430595  0.405776  1.753214  \n",
       "30  -0.117463  0.502399 -0.585040  1.183018  1.944253  \n",
       "20  -1.589544 -0.499869  0.278410  0.796768  2.131547  \n",
       "527 -0.361066  0.474880 -0.202054  1.022571 -1.333386  \n",
       "\n",
       "[436 rows x 12 columns]"
      ]
     },
     "execution_count": 438,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train3=df3.sample(frac=0.8,random_state=1) \n",
    "test3=df3.drop(train3.index)\n",
    "\n",
    "from sklearn.svm import SVR\n",
    "svr_rbf = SVR(kernel='linear', C=1e3, gamma=0.1)\n",
    "train3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "id": "0b5b07e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "train3ex = np.array(train3.drop(columns=[11])).reshape(436,11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "id": "0123978b",
   "metadata": {},
   "outputs": [],
   "source": [
    "train3wye = np.array(train3.drop(columns=[0,1,2,3,4,5,6,7,8,9,10])).reshape(436,1).ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "id": "bf950f7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "test3ex = np.array(test3.drop(columns=[11])).reshape(109,11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "id": "54a07c31",
   "metadata": {},
   "outputs": [],
   "source": [
    "test3wye = np.array(test3.drop(columns=[0,1,2,3,4,5,6,7,8,9,10])).reshape(109,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "id": "771e55ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x231b7e30e20>]"
      ]
     },
     "execution_count": 443,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = svr_rbf.fit(train3ex,train3wye)\n",
    "\n",
    "prediction3 = model.predict(test3ex)\n",
    "prediction3.reshape(109, 1)\n",
    "\n",
    "plt.plot(test3wye,'+')\n",
    "plt.plot(prediction3, '*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "id": "4695fae0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error = 0.41\n",
      "Mean squared error = 0.37\n",
      "Median absolute error = 0.28\n",
      "Explain variance score = 0.65\n",
      "R2 score = 0.64\n"
     ]
    }
   ],
   "source": [
    "import sklearn.metrics as sm\n",
    "print(\"Mean absolute error =\", round(sm.mean_absolute_error(test3wye, prediction3), 2)) \n",
    "print(\"Mean squared error =\", round(sm.mean_squared_error(test3wye, prediction3), 2)) \n",
    "print(\"Median absolute error =\", round(sm.median_absolute_error(test3wye, prediction3), 2)) \n",
    "print(\"Explain variance score =\", round(sm.explained_variance_score(test3wye, prediction3), 2)) \n",
    "print(\"R2 score =\", round(sm.r2_score(test3wye, prediction3), 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "id": "bc495929",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2.149083</td>\n",
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       "      <td>-0.554035</td>\n",
       "      <td>3.554979</td>\n",
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       "      <td>-0.734539</td>\n",
       "      <td>-0.219265</td>\n",
       "      <td>-0.679063</td>\n",
       "      <td>-0.805741</td>\n",
       "      <td>-0.554035</td>\n",
       "      <td>-1.614327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>-1.033389</td>\n",
       "      <td>0.047278</td>\n",
       "      <td>-0.570187</td>\n",
       "      <td>-0.929397</td>\n",
       "      <td>-2.465344</td>\n",
       "      <td>-0.465315</td>\n",
       "      <td>-0.734539</td>\n",
       "      <td>-0.219265</td>\n",
       "      <td>-0.679063</td>\n",
       "      <td>-0.805741</td>\n",
       "      <td>-0.554035</td>\n",
       "      <td>-1.614327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>-0.599839</td>\n",
       "      <td>0.047278</td>\n",
       "      <td>-0.570187</td>\n",
       "      <td>0.224410</td>\n",
       "      <td>0.405623</td>\n",
       "      <td>-0.465315</td>\n",
       "      <td>-0.734539</td>\n",
       "      <td>-0.219265</td>\n",
       "      <td>-0.679063</td>\n",
       "      <td>-0.805741</td>\n",
       "      <td>-0.554035</td>\n",
       "      <td>-1.614327</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>545 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1         2         3         4         5         6   \\\n",
       "0    1.046726  1.403419  1.421812  1.378217  0.405623 -0.465315 -0.734539   \n",
       "1    1.757010  1.403419  5.405809  2.532024  0.405623 -0.465315 -0.734539   \n",
       "2    2.218232  0.047278  1.421812  0.224410  0.405623 -0.465315  1.361397   \n",
       "3    1.083624  1.403419  1.421812  0.224410  0.405623 -0.465315  1.361397   \n",
       "4    1.046726  1.403419 -0.570187  0.224410  0.405623  2.149083  1.361397   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "540 -0.991879 -1.308863 -0.570187 -0.929397  0.405623 -0.465315  1.361397   \n",
       "541 -1.268613  0.047278 -0.570187 -0.929397 -2.465344 -0.465315 -0.734539   \n",
       "542 -0.705921 -1.308863 -0.570187 -0.929397  0.405623 -0.465315 -0.734539   \n",
       "543 -1.033389  0.047278 -0.570187 -0.929397 -2.465344 -0.465315 -0.734539   \n",
       "544 -0.599839  0.047278 -0.570187  0.224410  0.405623 -0.465315 -0.734539   \n",
       "\n",
       "           7         8         9         10        11  \n",
       "0   -0.219265  1.472618  1.517692  1.804941  4.566365  \n",
       "1   -0.219265  1.472618  2.679409 -0.554035  4.004484  \n",
       "2   -0.219265 -0.679063  1.517692  1.804941  4.004484  \n",
       "3   -0.219265  1.472618  2.679409  1.804941  3.985755  \n",
       "4   -0.219265  1.472618  1.517692 -0.554035  3.554979  \n",
       "..        ...       ...       ...       ...       ...  \n",
       "540 -0.219265 -0.679063  1.517692 -0.554035 -1.576868  \n",
       "541 -0.219265 -0.679063 -0.805741 -0.554035 -1.605149  \n",
       "542 -0.219265 -0.679063 -0.805741 -0.554035 -1.614327  \n",
       "543 -0.219265 -0.679063 -0.805741 -0.554035 -1.614327  \n",
       "544 -0.219265 -0.679063 -0.805741 -0.554035 -1.614327  \n",
       "\n",
       "[545 rows x 12 columns]"
      ]
     },
     "execution_count": 445,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lin Regression\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "model= LinearRegression()\n",
    "model\n",
    "\n",
    "datalin = pd.DataFrame(np.hstack([Ex,Wye]))\n",
    "datalin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "id": "0814734a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(109, 12)"
      ]
     },
     "execution_count": 446,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_lin=datalin.sample(frac=0.8,random_state=1) \n",
    "test_lin=datalin.drop(train_lin.index)\n",
    "test_lin.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "id": "30c5c4db",
   "metadata": {},
   "outputs": [],
   "source": [
    "testlinY = test_lin.drop(columns=[0,1,2,3,4,5,6,7,8,9,10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "id": "f624aa94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 448,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_lin.drop(columns=[11]),train_lin.drop(columns=[0,1,2,3,4,5,6,7,8,9,10]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "id": "3d190833",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model.predict(test_lin.drop(columns=[11]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "id": "3af0dc77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error = 0.44\n",
      "Mean squared error = 0.38\n",
      "Median absolute error = 0.33\n",
      "Explain variance score = 0.63\n",
      "R2 score = 0.63\n"
     ]
    }
   ],
   "source": [
    "import sklearn.metrics as sm\n",
    "print(\"Mean absolute error =\", round(sm.mean_absolute_error(testlinY, pred), 2)) \n",
    "print(\"Mean squared error =\", round(sm.mean_squared_error(testlinY, pred), 2)) \n",
    "print(\"Median absolute error =\", round(sm.median_absolute_error(testlinY, pred), 2)) \n",
    "print(\"Explain variance score =\", round(sm.explained_variance_score(testlinY, pred), 2)) \n",
    "print(\"R2 score =\", round(sm.r2_score(testlinY, pred), 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7cea19f3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1473f1a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee05fa22",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "367f2cb9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ad603e7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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