HousePricePrediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.animation as animation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Generate some house sizes between 1000 and 3500(typically sql ft of house)\n",
    "\n",
    "num_house = 160\n",
    "np.random.seed(42)\n",
    "house_size = np.random.randint(low=1000 , high = 3500, size = num_house)\n",
    "\n",
    "#Generate the price of the houses\n",
    "\n",
    "np.random.seed(42)\n",
    "house_price = house_size * 100.0 + np.random.randint(low=20000,high=70000,size=num_house)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "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(house_size, house_price,\"bx\")\n",
    "plt.ylabel(\"Price\")\n",
    "plt.xlabel(\"Size\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize(array):\n",
    "    return (array - array.mean()) / array.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_train_samples = math.floor(num_house * 0.7)\n",
    "\n",
    "#Define training data\n",
    "train_house_size = np.asarray(house_size[:num_train_samples])\n",
    "train_price = np.asarray(house_price[:num_train_samples])\n",
    "\n",
    "train_house_size_norm = normalize(train_house_size)\n",
    "train_price_norm = normalize(train_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define the  test data\n",
    "test_house_size = np.asarray(house_size[num_train_samples:])\n",
    "test_price = np.asarray(house_price[num_train_samples:])\n",
    "\n",
    "test_house_size_norm = normalize(test_house_size)\n",
    "test_price_norm = normalize(test_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up the Tensorflow placeholder that gets updated as we descend down the gradient\n",
    "tf_house_size = tf.placeholder(\"float\",name=\"house_size\")\n",
    "tf_price = tf.placeholder(\"float\", name=\"price\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\rakap\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    }
   ],
   "source": [
    "# Define the sizefactor and priceoffset\n",
    "tf_size_factor = tf.Variable(np.random.randn(), name = \"size_factor\")\n",
    "tf_price_offset = tf.Variable(np.random.randn(), name=\"price_offset\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inference Function\n",
    "tf_price_pred = tf.add(tf.multiply(tf_size_factor,tf_house_size),tf_price_offset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define  the loss function Mean Square Error\n",
    "tf_cost = tf.reduce_sum(tf.pow(tf_price_pred - tf_price,2))/(2*num_train_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the learning rate\n",
    "learning_rate = 0.15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\rakap\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n"
     ]
    }
   ],
   "source": [
    "# Define a gradient descent optimizer\n",
    "optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(tf_cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the variable\n",
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration #: 0002 cost= 0.592666388 size_factor= 0.17673746 price_offset= -0.7073299\n",
      "iteration #: 0004 cost= 0.333816171 size_factor= 0.38552403 price_offset= -0.52366424\n",
      "iteration #: 0006 cost= 0.191974744 size_factor= 0.54010797 price_offset= -0.38765335\n",
      "iteration #: 0008 cost= 0.114265464 size_factor= 0.6545607 price_offset= -0.28693238\n",
      "iteration #: 0010 cost= 0.071702942 size_factor= 0.73930025 price_offset= -0.21234456\n",
      "iteration #: 0012 cost= 0.048399203 size_factor= 0.8020405 price_offset= -0.15710945\n",
      "iteration #: 0014 cost= 0.035646107 size_factor= 0.8484934 price_offset= -0.11620593\n",
      "iteration #: 0016 cost= 0.028671529 size_factor= 0.8828869 price_offset= -0.08591527\n",
      "iteration #: 0018 cost= 0.024860596 size_factor= 0.9083517 price_offset= -0.06348391\n",
      "iteration #: 0020 cost= 0.022780856 size_factor= 0.9272053 price_offset= -0.046872646\n",
      "iteration #: 0022 cost= 0.021647755 size_factor= 0.94116414 price_offset= -0.03457141\n",
      "iteration #: 0024 cost= 0.021031799 size_factor= 0.9514992 price_offset= -0.02546188\n",
      "iteration #: 0026 cost= 0.020698007 size_factor= 0.9591513 price_offset= -0.018715922\n",
      "iteration #: 0028 cost= 0.020517897 size_factor= 0.96481717 price_offset= -0.013720296\n",
      "iteration #: 0030 cost= 0.020421302 size_factor= 0.9690122 price_offset= -0.010020844\n",
      "Optimization Finished\n",
      "Training Cost= 0.020421302 size_factor= 0.9690122 price_offset= -0.010020844\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    display_every = 2\n",
    "    num_training_iter = 30\n",
    "    \n",
    "    fit_num_plot = math.floor(num_training_iter/display_every)\n",
    "    \n",
    "    fit_size_factor = np.zeros(fit_num_plot)\n",
    "    fit_price_offset = np.zeros(fit_num_plot)\n",
    "    fit_plot_idx = 0\n",
    "    for iteration in range(num_training_iter):\n",
    "        # Fit all the training data\n",
    "        for (x,y) in zip(train_house_size_norm,train_price_norm):\n",
    "            sess.run(optimizer,feed_dict={tf_house_size:x,tf_price:y})\n",
    "        \n",
    "        #Display the current status\n",
    "        if (iteration + 1) % display_every == 0:\n",
    "            c = sess.run(tf_cost, feed_dict={tf_house_size:train_house_size_norm, tf_price:train_price_norm})\n",
    "            print(\"iteration #:\", '%04d' % (iteration + 1), \"cost=\", \"{:.9f}\".format(c),\"size_factor=\",\n",
    "                  sess.run(tf_size_factor),\"price_offset=\",sess.run(tf_price_offset))\n",
    "            fit_size_factor[fit_plot_idx] = sess.run(tf_size_factor)\n",
    "            fit_price_offset[fit_plot_idx] = sess.run(tf_price_offset)\n",
    "            fit_plot_idx = fit_plot_idx + 1\n",
    "    \n",
    "    print(\"Optimization Finished\")\n",
    "    training_cost = sess.run(tf_cost, feed_dict={tf_house_size:train_house_size_norm, tf_price:train_price_norm})\n",
    "    print(\"Training Cost=\", training_cost,\"size_factor=\",\n",
    "                  sess.run(tf_size_factor),\"price_offset=\",sess.run(tf_price_offset))\n",
    "    \n",
    "    train_house_size_mean = train_house_size.mean()\n",
    "    train_house_size_std = train_house_size.std()\n",
    "\n",
    "    train_price_mean = train_price.mean()\n",
    "    train_price_std = train_price.std()\n",
    "\n",
    "    #Plot the graph\n",
    "\n",
    "    plt.rcParams[\"figure.figsize\"] = (10,8)\n",
    "    plt.figure()\n",
    "    plt.ylabel(\"Price\")\n",
    "    plt.xlabel(\"Size (sq.ft)\")\n",
    "    plt.plot(train_house_size,train_price,\"go\",label=\"Traning data\")\n",
    "    plt.plot(test_house_size, test_price,\"mo\", label=\"Testing data\")\n",
    "    plt.plot(train_house_size_norm * train_house_size_std + train_house_size_mean,\n",
    "            (sess.run(tf_size_factor) * train_house_size_norm + sess.run(tf_price_offset)) * train_price_std + train_price_mean,\n",
    "            label = 'Learned Regression')\n",
    "    plt.legend(loc=\"upper left\")\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}