AllTypesGradientDescentHousePrice.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d9ce46d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "0117b515",
   "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>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1056</td>\n",
       "      <td>2</td>\n",
       "      <td>39.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2600</td>\n",
       "      <td>4</td>\n",
       "      <td>120.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1440</td>\n",
       "      <td>3</td>\n",
       "      <td>62.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1521</td>\n",
       "      <td>3</td>\n",
       "      <td>75.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1200</td>\n",
       "      <td>2</td>\n",
       "      <td>51.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   area  bedrooms   price\n",
       "0  1056         2   39.07\n",
       "1  2600         4  120.00\n",
       "2  1440         3   62.00\n",
       "3  1521         3   75.00\n",
       "4  1200         2   51.00"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#loading Dataset\n",
    "\n",
    "df = pd.read_csv('/Users/admin/Documents/datasets/homeprices_banglore.csv')\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "27db7863",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.08827586, 0.25      ],\n",
       "       [0.62068966, 0.75      ],\n",
       "       [0.22068966, 0.5       ],\n",
       "       [0.24862069, 0.5       ],\n",
       "       [0.13793103, 0.25      ],\n",
       "       [0.12758621, 0.25      ],\n",
       "       [0.6662069 , 0.75      ],\n",
       "       [0.86206897, 0.75      ],\n",
       "       [0.17586207, 0.5       ],\n",
       "       [1.        , 1.        ],\n",
       "       [0.34482759, 0.5       ],\n",
       "       [0.68448276, 0.75      ],\n",
       "       [0.06896552, 0.25      ],\n",
       "       [0.10344828, 0.25      ],\n",
       "       [0.5       , 0.5       ],\n",
       "       [0.12931034, 0.25      ],\n",
       "       [0.13103448, 0.5       ],\n",
       "       [0.25517241, 0.5       ],\n",
       "       [0.67931034, 0.5       ],\n",
       "       [0.        , 0.        ]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#scaling\n",
    "\n",
    "from sklearn import preprocessing\n",
    "\n",
    "sx = preprocessing.MinMaxScaler()\n",
    "sy = preprocessing.MinMaxScaler()\n",
    "\n",
    "scaled_x = sx.fit_transform(df.drop('price',axis='columns'))\n",
    "scaled_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "c59631de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 2)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shape of scaled_x it is a 2D array of 20 X 2 Dimensions\n",
    "scaled_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "fd2de523",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20,)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shape of price Column it is a 1D array \n",
    "\n",
    "df['price'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "9f66197a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# row number of df\n",
    "df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "823d1001",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.05237037],\n",
       "       [0.65185185],\n",
       "       [0.22222222],\n",
       "       [0.31851852],\n",
       "       [0.14074074],\n",
       "       [0.04444444],\n",
       "       [0.76296296],\n",
       "       [0.91111111],\n",
       "       [0.13333333],\n",
       "       [1.        ],\n",
       "       [0.37037037],\n",
       "       [0.8       ],\n",
       "       [0.04444444],\n",
       "       [0.05925926],\n",
       "       [0.51111111],\n",
       "       [0.07407407],\n",
       "       [0.11851852],\n",
       "       [0.20740741],\n",
       "       [0.51851852],\n",
       "       [0.        ]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# scaling Y \n",
    "scaled_y = sy.fit_transform(df['price'].values.reshape(df.shape[0],1))\n",
    "scaled_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "9b1b96c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 1)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shape of scaled_x it is a 2D array of 20 X 1 Dimensions\n",
    "scaled_y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "e43381e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20,)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(w,scaled_x.T).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "865f52f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def batch_gradient_descent(x,y_true,epochs,learning_rate=0.01):\n",
    "    \n",
    "    number_of_features = x.shape[1];\n",
    "    \n",
    "    w = np.ones(shape=number_of_features)\n",
    "    b = 0 \n",
    "    \n",
    "    total_samples = x.shape[0]\n",
    "    \n",
    "    cost_list = []\n",
    "    epoch_list = []\n",
    "    \n",
    "    for i in range(epochs):\n",
    "        \n",
    "        y_predicted = np.dot(w,scaled_x.T) + b\n",
    "        \n",
    "        w_grad = (-2/total_samples)*(x.T.dot(y_true-y_predicted))\n",
    "        b_grad = (-2/total_samples)*np.sum(y_true-y_predicted)\n",
    "        \n",
    "        w = w - learning_rate*w_grad\n",
    "        b = b - learning_rate*b_grad\n",
    "        \n",
    "        cost = np.mean(np.square(y_true - y_predicted))\n",
    "        \n",
    "        if i % 10 == 0 :\n",
    "            cost_list.append(cost)\n",
    "            epoch_list.append(i)\n",
    "            \n",
    "    return w,b,cost,cost_list,epoch_list\n",
    "\n",
    "w,b,cost,cost_list,epoch_list = batch_gradient_descent(scaled_x,scaled_y.reshape(scaled_y.shape[0], ),500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "60f85668",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.70712464, 0.67456527]), -0.23034857438407427, 0.0068641890429808105)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w,b,cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "9b0df9be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa07bc90af0>]"
      ]
     },
     "execution_count": 62,
     "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.xlabel('epoch')\n",
    "plt.ylabel('cost')\n",
    "plt.plot(epoch_list,cost_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "de0dbbba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[128.45484403]])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def predict(area,bedroom,w,b):\n",
    "    scaled_x = sx.transform([[area,bedroom]])\n",
    "    \n",
    "    scaled_price = np.dot(scaled_x,w) + b\n",
    "    return sy.inverse_transform([scaled_price])\n",
    "\n",
    "predict(2600,4,w,b)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "49e0e86e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[30.25309446]])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict(1000,2,w,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "19b22667",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[69.47860786]])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict(1500,3,w,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "44993957",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import random\n",
    "random.randint(0,6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "32552727",
   "metadata": {},
   "outputs": [],
   "source": [
    "def stochastic_gradient_descent(x,y_true,epochs,learning_rate=0.01):\n",
    "    \n",
    "    number_of_features = x.shape[1]\n",
    "    \n",
    "    w = np.ones(shape=number_of_features)\n",
    "    b = 0 \n",
    "    \n",
    "    total_samples = x.shape[0]\n",
    "    \n",
    "    cost_list = []\n",
    "    epoch_list = []\n",
    "    \n",
    "    for i in range(epochs):\n",
    "        \n",
    "        random_index = random.randint(0,total_samples-1)\n",
    "        sample_x = x[random_index]\n",
    "        sample_y = y_true[random_index]\n",
    "        \n",
    "        y_predicted = np.dot(w,sample_x.T) + b\n",
    "        \n",
    "        w_grad = (-2/total_samples)*(sample_x.T.dot(sample_y-y_predicted))\n",
    "        b_grad = (-2/total_samples)*(sample_y-y_predicted)\n",
    "        \n",
    "        w = w - learning_rate*w_grad\n",
    "        b = b - learning_rate*b_grad\n",
    "        \n",
    "        cost = np.square(sample_y - y_predicted)\n",
    "        \n",
    "        if i % 100 == 0 :\n",
    "            cost_list.append(cost)\n",
    "            epoch_list.append(i)\n",
    "            \n",
    "    return w,b,cost,cost_list,epoch_list\n",
    "\n",
    "w_sgd,b_sgd,cost_sgd,cost_list_sgd,epoch_list_sgd = stochastic_gradient_descent(scaled_x,scaled_y.reshape(scaled_y.shape[0], ),10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "7fb9b20f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.70537941, 0.67310008]), -0.23043209292316547, 0.0008236106960135617)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_sgd,b_sgd,cost_sgd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "9964254c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.70712464, 0.67456527]), -0.23034857438407427, 0.0068641890429808105)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w,b,cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "3282ba28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa07bdff2e0>]"
      ]
     },
     "execution_count": 85,
     "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.xlabel(\"epoch\")\n",
    "plt.ylabel(\"cost\")\n",
    "plt.plot(epoch_list_sgd,cost_list_sgd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "1608c44f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[128.14897999]])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict(2600,4,w_sgd,b_sgd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "59dd05aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.shuffle(scaled_x)\n",
    "batch_x =  scaled_x[:5, :]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "fe3cb55d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 1.        ],\n",
       "       [0.12931034, 0.25      ],\n",
       "       [0.25517241, 0.5       ],\n",
       "       [0.5       , 0.5       ],\n",
       "       [0.67931034, 0.5       ]])"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "da32fde3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def miniBatch_gradient_descent(x,y_true,epochs,learning_rate=0.01,batch_size = 5):\n",
    "    \n",
    "    number_of_features = x.shape[1]\n",
    "    \n",
    "    w = np.ones(shape=number_of_features)\n",
    "    b = 0 \n",
    "    \n",
    "    total_samples = x.shape[0]\n",
    "    \n",
    "    cost_list = []\n",
    "    epoch_list = []\n",
    "    \n",
    "    for i in range(epochs):\n",
    "        \n",
    "        np.random.shuffle(x)\n",
    "        batch_x =  x[:batch_size, :]\n",
    "        batch_y = y_true[:batch_size, :]\n",
    "        \n",
    "        y_predicted = np.dot(w,batch_x.T) + b\n",
    "        \n",
    "        w_grad = (-2/total_samples)*(batch_x.T.dot(batch_y-y_predicted))\n",
    "        b_grad = (-2/total_samples)*np.sum(batch_y-y_predicted)\n",
    "        \n",
    "        w = w - learning_rate*w_grad\n",
    "        b = b - learning_rate*b_grad\n",
    "        \n",
    "        cost = np.mean(np.square(sample_y - y_predicted))\n",
    "        \n",
    "        if i % 10 == 0 :\n",
    "            cost_list.append(cost)\n",
    "            epoch_list.append(i)\n",
    "            \n",
    "    return w,b,cost,cost_list,epoch_list\n",
    "\n",
    "w_mbgd,b_mbgd,cost_mbgd,cost_list_mbgd,epoch_list_mbgd = stochastic_gradient_descent(scaled_x,scaled_y.reshape(scaled_y.shape[0], ),1900)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "5816091c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.68839445, 0.71523438]), -0.25214896973256645, 0.04736872479845562)"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_mbgd,b_mbgd,cost_mbgd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "8bfcba10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa07dc8c550>]"
      ]
     },
     "execution_count": 167,
     "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.xlabel(\"epoch\")\n",
    "plt.ylabel(\"cost\")\n",
    "plt.plot(epoch_list_mbgd,cost_list_mbgd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "82f57799",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[128.06007766]])"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict(2600,4,w_mbgd,b_mbgd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa1f2864",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}