assignment_6b.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>id</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>...</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7129300520</td>\n",
       "      <td>20141013T000000</td>\n",
       "      <td>221900.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1180</td>\n",
       "      <td>5650</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1180</td>\n",
       "      <td>0</td>\n",
       "      <td>1955</td>\n",
       "      <td>0</td>\n",
       "      <td>98178</td>\n",
       "      <td>47.5112</td>\n",
       "      <td>-122.257</td>\n",
       "      <td>1340</td>\n",
       "      <td>5650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6414100192</td>\n",
       "      <td>20141209T000000</td>\n",
       "      <td>538000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2570</td>\n",
       "      <td>7242</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>2170</td>\n",
       "      <td>400</td>\n",
       "      <td>1951</td>\n",
       "      <td>1991</td>\n",
       "      <td>98125</td>\n",
       "      <td>47.7210</td>\n",
       "      <td>-122.319</td>\n",
       "      <td>1690</td>\n",
       "      <td>7639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5631500400</td>\n",
       "      <td>20150225T000000</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>770</td>\n",
       "      <td>10000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>770</td>\n",
       "      <td>0</td>\n",
       "      <td>1933</td>\n",
       "      <td>0</td>\n",
       "      <td>98028</td>\n",
       "      <td>47.7379</td>\n",
       "      <td>-122.233</td>\n",
       "      <td>2720</td>\n",
       "      <td>8062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2487200875</td>\n",
       "      <td>20141209T000000</td>\n",
       "      <td>604000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1960</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1050</td>\n",
       "      <td>910</td>\n",
       "      <td>1965</td>\n",
       "      <td>0</td>\n",
       "      <td>98136</td>\n",
       "      <td>47.5208</td>\n",
       "      <td>-122.393</td>\n",
       "      <td>1360</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1954400510</td>\n",
       "      <td>20150218T000000</td>\n",
       "      <td>510000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1680</td>\n",
       "      <td>8080</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1680</td>\n",
       "      <td>0</td>\n",
       "      <td>1987</td>\n",
       "      <td>0</td>\n",
       "      <td>98074</td>\n",
       "      <td>47.6168</td>\n",
       "      <td>-122.045</td>\n",
       "      <td>1800</td>\n",
       "      <td>7503</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id             date     price  bedrooms  bathrooms  sqft_living  \\\n",
       "0  7129300520  20141013T000000  221900.0         3       1.00         1180   \n",
       "1  6414100192  20141209T000000  538000.0         3       2.25         2570   \n",
       "2  5631500400  20150225T000000  180000.0         2       1.00          770   \n",
       "3  2487200875  20141209T000000  604000.0         4       3.00         1960   \n",
       "4  1954400510  20150218T000000  510000.0         3       2.00         1680   \n",
       "\n",
       "   sqft_lot  floors  waterfront  view  ...  grade  sqft_above  sqft_basement  \\\n",
       "0      5650     1.0           0     0  ...      7        1180              0   \n",
       "1      7242     2.0           0     0  ...      7        2170            400   \n",
       "2     10000     1.0           0     0  ...      6         770              0   \n",
       "3      5000     1.0           0     0  ...      7        1050            910   \n",
       "4      8080     1.0           0     0  ...      8        1680              0   \n",
       "\n",
       "   yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
       "0      1955             0    98178  47.5112 -122.257           1340   \n",
       "1      1951          1991    98125  47.7210 -122.319           1690   \n",
       "2      1933             0    98028  47.7379 -122.233           2720   \n",
       "3      1965             0    98136  47.5208 -122.393           1360   \n",
       "4      1987             0    98074  47.6168 -122.045           1800   \n",
       "\n",
       "   sqft_lot15  \n",
       "0        5650  \n",
       "1        7639  \n",
       "2        8062  \n",
       "3        5000  \n",
       "4        7503  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data= pd.read_csv(\"kc_house_data.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>price</th>\n",
       "      <th>sqft_living</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>221900.0</td>\n",
       "      <td>1180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>538000.0</td>\n",
       "      <td>2570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>180000.0</td>\n",
       "      <td>770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>604000.0</td>\n",
       "      <td>1960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>510000.0</td>\n",
       "      <td>1680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21608</th>\n",
       "      <td>360000.0</td>\n",
       "      <td>1530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21609</th>\n",
       "      <td>400000.0</td>\n",
       "      <td>2310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21610</th>\n",
       "      <td>402101.0</td>\n",
       "      <td>1020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21611</th>\n",
       "      <td>400000.0</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21612</th>\n",
       "      <td>325000.0</td>\n",
       "      <td>1020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21613 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          price  sqft_living\n",
       "0      221900.0         1180\n",
       "1      538000.0         2570\n",
       "2      180000.0          770\n",
       "3      604000.0         1960\n",
       "4      510000.0         1680\n",
       "...         ...          ...\n",
       "21608  360000.0         1530\n",
       "21609  400000.0         2310\n",
       "21610  402101.0         1020\n",
       "21611  400000.0         1600\n",
       "21612  325000.0         1020\n",
       "\n",
       "[21613 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data[['price', 'sqft_living']]\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "data = scaler.fit_transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>price</th>\n",
       "      <th>sqft_living</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.866717</td>\n",
       "      <td>-0.979835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.005688</td>\n",
       "      <td>0.533634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.980849</td>\n",
       "      <td>-1.426254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.174090</td>\n",
       "      <td>-0.130550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.081958</td>\n",
       "      <td>-0.435422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21608</th>\n",
       "      <td>-0.490545</td>\n",
       "      <td>-0.598746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21609</th>\n",
       "      <td>-0.381588</td>\n",
       "      <td>0.250539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21610</th>\n",
       "      <td>-0.375865</td>\n",
       "      <td>-1.154047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21611</th>\n",
       "      <td>-0.381588</td>\n",
       "      <td>-0.522528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21612</th>\n",
       "      <td>-0.585882</td>\n",
       "      <td>-1.154047</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21613 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          price  sqft_living\n",
       "0     -0.866717    -0.979835\n",
       "1     -0.005688     0.533634\n",
       "2     -0.980849    -1.426254\n",
       "3      0.174090    -0.130550\n",
       "4     -0.081958    -0.435422\n",
       "...         ...          ...\n",
       "21608 -0.490545    -0.598746\n",
       "21609 -0.381588     0.250539\n",
       "21610 -0.375865    -1.154047\n",
       "21611 -0.381588    -0.522528\n",
       "21612 -0.585882    -1.154047\n",
       "\n",
       "[21613 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data= pd.DataFrame(data=data, columns=[\"price\",\"sqft_living\"])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(data.iloc[:, 1], data.iloc[:, 0], test_size = 0.10, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(19451,) (19451,) (2162,) (2162,)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape, y_train.shape, x_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 32\n",
    "n_minibatches = x_train.shape[0] // batch_size\n",
    "\n",
    "def create_mini_batches(x_tr, y_tr, batch_size):\n",
    "    mini_batches = [] \n",
    "    global n_minibatches\n",
    "    for i in range(n_minibatches + 1): \n",
    "        X_mini = x_tr.iloc[i * batch_size : (i + 1) * batch_size]\n",
    "        Y_mini = y_tr.iloc[i * batch_size : (i + 1) * batch_size]\n",
    "        mini_batches.append((X_mini, Y_mini)) \n",
    "        if x_tr.shape[0] % batch_size != 0:\n",
    "            X_mini = x_tr.iloc[i * batch_size: x_tr.shape[0]]\n",
    "            Y_mini  = y_tr.iloc[i * batch_size: y_tr.shape[0]]\n",
    "        mini_batches.append((X_mini,Y_mini)) \n",
    "    return mini_batches \n",
    "\n",
    "mini_batches_all = create_mini_batches(x_train, y_train, batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration: 1\tValue of m: 0.7854519952272078, \tValue of c: -0.42623490300776085\n",
      "Iteration: 2\tValue of m: 0.6991958183454019, \tValue of c: -0.1998127151903294\n",
      "Iteration: 3\tValue of m: 0.664502091937808, \tValue of c: -0.1104670174729536\n",
      "Iteration: 4\tValue of m: 0.6505413559816704, \tValue of c: -0.07521396904228948\n",
      "Iteration: 5\tValue of m: 0.6449210012033875, \tValue of c: -0.06130526796593927\n",
      "Iteration: 6\tValue of m: 0.6426573033205328, \tValue of c: -0.05581818221857195\n",
      "Iteration: 7\tValue of m: 0.6417451379058001, \tValue of c: -0.05365366755820519\n",
      "Iteration: 8\tValue of m: 0.641377407070127, \tValue of c: -0.0527998970002293\n",
      "Iteration: 9\tValue of m: 0.6412290908908382, \tValue of c: -0.05246316698493894\n",
      "Iteration: 10\tValue of m: 0.6411692428991926, \tValue of c: -0.052330372341350184\n",
      "\n",
      "The local minima occurs at: 0.6411692428991926, -0.052330372341350184\n"
     ]
    }
   ],
   "source": [
    "m = 1 #Initial value of slope\n",
    "c = -1 #Initial value of intercept\n",
    "lr = 0.1 #Learning Rate\n",
    "delta_m = 1 #Initialising Δm\n",
    "delta_c = 1 #Initialising Δc\n",
    "max_iters = 10 #Maximum number of iterations  \n",
    "iters_count = 0 #Counting Iterations\n",
    "\n",
    "\n",
    "def deriv(m_f, c_f, datax, datay):\n",
    "    m_deriv = 0\n",
    "    c_deriv = 0\n",
    "    for i in range(datax.shape[0]):\n",
    "        x, y = datax.iloc[i], datay.iloc[i]\n",
    "        m_deriv += (y-m_f*x-c_f)*x\n",
    "        c_deriv += (y-m_f*x-c_f)\n",
    "        m_deriv = -m_deriv/len(datax)\n",
    "        c_deriv = -c_deriv/len(datay)\n",
    "    return m_deriv, c_deriv  \n",
    "\n",
    "\n",
    "while iters_count < max_iters:\n",
    "    for i in range(n_minibatches):\n",
    "        x_b, y_b = mini_batches_all[i]\n",
    "        delta_m, delta_c = deriv(m, c, x_b, y_b)\n",
    "        delta_m = -lr * delta_m\n",
    "        delta_c = -lr * delta_c\n",
    "        m += delta_m\n",
    "        c += delta_c\n",
    "    iters_count += 1\n",
    "    print(f\"Iteration: {iters_count}\\tValue of m: {m}, \\tValue of c: {c}\")\n",
    "\n",
    "print(f\"\\nThe local minima occurs at: {m}, {c}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "x_train = np.array(x_train)\n",
    "y_train = np.array(y_train)\n",
    "x_test = np.array(x_test)\n",
    "y_test = np.array(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_train = []\n",
    "for i in x_train:\n",
    "  y_p_tr = (m * i) - c\n",
    "  y_pred_train.append(y_p_tr)\n",
    "y_pred_train = np.array(y_pred_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_test = []\n",
    "for i in x_test:\n",
    "  y_p_te = (m * i) - c\n",
    "  y_pred_test.append(y_p_te)\n",
    "y_pred_test = np.array(y_pred_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_test = []\n",
    "for i in x_test:\n",
    "  y_p_te = (m * i) - c\n",
    "  y_pred_test.append(y_p_te)\n",
    "y_pred_test = np.array(y_pred_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root mean square error 0.7113934377666099\n",
      "Mean square error 0.5060806232973954\n",
      "Mean absolute error 0.4716670901963416\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "from sklearn.metrics import mean_squared_error \n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "\n",
    "#Training Accuracies\n",
    "mse = math.sqrt(mean_squared_error(y_train, y_pred_train)) \n",
    "print('Root mean square error', mse) \n",
    "mse = (mean_squared_error(y_train, y_pred_train)) \n",
    "print('Mean square error', mse) \n",
    "mae=mean_absolute_error(y_train, y_pred_train)\n",
    "print('Mean absolute error', mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root mean square error 0.762331323793402\n",
      "Mean square error 0.5811490472366007\n",
      "Mean absolute error 0.49207871795139424\n"
     ]
    }
   ],
   "source": [
    "mse = math.sqrt(mean_squared_error(y_test, y_pred_test)) \n",
    "print('Root mean square error', mse) \n",
    "mse = (mean_squared_error(y_test, y_pred_test)) \n",
    "print('Mean square error', mse) \n",
    "mae=mean_absolute_error(y_test, y_pred_test)\n",
    "print('Mean absolute error', mae)"
   ]
  },
  {
   "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": []
  }
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