HousePredictionWithRegression.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# authors: Rambod Azimi & Mahdi Moghassemi\n",
    "# Date: May 2023"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing the necessary libraries to the project\n",
    "import numpy as np\n",
    "import math, copy\n",
    "import pandas as pd\n",
    "from sklearn.linear_model import SGDRegressor # linear regression model library in scikit-learn\n",
    "from sklearn.preprocessing import StandardScaler # Z-score scaling library in scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This method will replace non-numeric values in the dataset to numeric and computable values\n",
    "def replace_data(initial_data):\n",
    "    #  yes --> 1\n",
    "    # no --> 0\n",
    "\n",
    "    initial_data['mainroad'] = initial_data['mainroad'].replace(['yes'], 1)\n",
    "    initial_data['mainroad'] = initial_data['mainroad'].replace(['no'], 0)\n",
    "    \n",
    "    initial_data['guestroom'] = initial_data['guestroom'].replace(['yes'], 1)\n",
    "    initial_data['guestroom'] = initial_data['guestroom'].replace(['no'], 0)\n",
    "\n",
    "    initial_data['basement'] = initial_data['basement'].replace(['yes'], 1)\n",
    "    initial_data['basement'] = initial_data['basement'].replace(['no'], 0)\n",
    "\n",
    "    initial_data['hotwaterheating'] = initial_data['hotwaterheating'].replace(['yes'], 1)\n",
    "    initial_data['hotwaterheating'] = initial_data['hotwaterheating'].replace(['no'], 0)\n",
    "\n",
    "    initial_data['airconditioning'] = initial_data['airconditioning'].replace(['yes'], 1)\n",
    "    initial_data['airconditioning'] = initial_data['airconditioning'].replace(['no'], 0)\n",
    "\n",
    "    initial_data['prefarea'] = initial_data['prefarea'].replace(['yes'], 1)\n",
    "    initial_data['prefarea'] = initial_data['prefarea'].replace(['no'], 0)\n",
    "\n",
    "    initial_data['furnishingstatus'] = initial_data['furnishingstatus'].replace(['furnished'], 2) \n",
    "    initial_data['furnishingstatus'] = initial_data['furnishingstatus'].replace(['semi-furnished'], 1)\n",
    "    initial_data['furnishingstatus'] = initial_data['furnishingstatus'].replace(['unfurnished'], 0)\n",
    "\n",
    "    return initial_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(x, w, b): \n",
    "    \"\"\"\n",
    "    prediction of all the training examples using linear regression\n",
    "    Args:\n",
    "      x (ndarray): Shape (n,) example with multiple features\n",
    "      w (ndarray): Shape (n,) model parameters   \n",
    "      b (scalar):             model parameter \n",
    "      \n",
    "    Returns:\n",
    "      p (scalar):  prediction\n",
    "    \"\"\"\n",
    "    m = x.shape[0] # number of training examples (545)\n",
    "    f = np.zeros(m)\n",
    "    for i in range(m):\n",
    "      f[i] = np.dot(x[i,:], w[i,:]) + b\n",
    "    return f # f[i] = x1w1 + x2w2 + ... + b  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_data = pd.read_csv('Housing.csv') # reading the dataset from a csv file in the same directory\n",
    "y_train = initial_data.iloc[:, 0].to_numpy() # target values (1D array)\n",
    "initial_data = replace_data(initial_data) # make all the feature variables numeric using this helper function\n",
    "x_train = initial_data.iloc[:, 1:].to_numpy() # feature values (2D array) --> skip the first row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "[[7420    4    2 ...    2    1    2]\n",
      " [8960    4    4 ...    3    0    2]\n",
      " [9960    3    2 ...    2    1    1]\n",
      " ...\n",
      " [3620    2    1 ...    0    0    0]\n",
      " [2910    3    1 ...    0    0    2]\n",
      " [3850    3    1 ...    0    0    0]]\n",
      "y Shape: (545,), y Type:<class 'numpy.ndarray'>)\n",
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      "  1750000]\n"
     ]
    }
   ],
   "source": [
    "# just for testing purposes\n",
    "# data is stored in numpy array/matrix\n",
    "print(f\"x Shape: {x_train.shape}, X Type:{type(x_train)})\")\n",
    "print(x_train)\n",
    "print(f\"y Shape: {y_train.shape}, y Type:{type(y_train)})\")\n",
    "print(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cost function J(w, b) to check the accuracy of the generated model\n",
    "def compute_cost(x, y, w, b):\n",
    "    m = x.shape[0]\n",
    "    total_cost = 0.0\n",
    "\n",
    "    for i in range(m):\n",
    "        f = np.dot(x[i], w) + b\n",
    "        total_cost += (f - y[i]) ** 2\n",
    "    total_cost /= (2 * m)\n",
    "\n",
    "    return total_cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost at optimal w: 674500372862323.4\n"
     ]
    }
   ],
   "source": [
    "# testing the cost function\n",
    "cost = compute_cost(x_train, y_train, x_train[0], 0.0)\n",
    "print(f\"Cost at optimal w: {cost}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# computing the gradient descent algorithm for linear regression\n",
    "def compute_gradient(x, y, w, b):\n",
    "    m = x.shape[0] # rows\n",
    "    n = x.shape[1] # columns\n",
    "\n",
    "    dj_dw = np.zeros((n,))\n",
    "    dj_db = 0\n",
    "\n",
    "    for i in range(m):\n",
    "        f = np.dot(x[i], w) + b # f = w1x1 + w2x2 + ... + b\n",
    "        error = f - y[i]\n",
    "\n",
    "        for j in range(n):\n",
    "            dj_dw[j] += error * x[i, j]\n",
    "        dj_db += error\n",
    "    dj_dw /= m\n",
    "    dj_db /= m\n",
    "\n",
    "    return dj_db, dj_dw # tuple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dj_db at initial w, b: [2.04995992e+11 1.00481234e+08 4.41048791e+07 6.08844699e+07\n",
      " 3.01497789e+07 6.63421655e+06 1.19201310e+07 1.46696897e+06\n",
      " 1.18261952e+07 2.74702094e+07 9.19611604e+06 3.27828973e+07]\n",
      "dj_dw at initial w, b: 33450311.588990826\n"
     ]
    }
   ],
   "source": [
    "# testying compute_gradient method\n",
    "temp_dj_dw, temp_dj_db = compute_gradient(x_train, y_train, x_train[0], 0.0)\n",
    "print(f\"dj_db at initial w, b: {temp_dj_db}\")\n",
    "print(f\"dj_dw at initial w, b: {temp_dj_dw}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# a method for calculating the parameters w, b\n",
    "def gradient_descent(x, y, w_in, b_in, alpha, iterations):\n",
    "    w = copy.deepcopy(w_in)\n",
    "    b = b_in\n",
    "\n",
    "    for i in range(iterations):\n",
    "        dj_db, dj_dw = compute_gradient(x, y, w, b)\n",
    "\n",
    "        w = w - alpha * dj_dw\n",
    "        b - b - alpha * dj_db\n",
    "\n",
    "    return w, b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction: 7064604, target value: 13300000, accuracy: 53.12%\n",
      "prediction: 8531206, target value: 12250000, accuracy: 69.64%\n",
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      "prediction: 7140930, target value: 12215000, accuracy: 58.46%\n",
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      "prediction: 7139231, target value: 10850000, accuracy: 65.80%\n",
      "prediction: 8168438, target value: 10150000, accuracy: 80.48%\n",
      "prediction: 15407937, target value: 10150000, accuracy: 65.88%\n",
      "prediction: 7710151, target value: 9870000, accuracy: 78.12%\n",
      "prediction: 5475870, target value: 9800000, accuracy: 55.88%\n",
      "prediction: 12557169, target value: 9800000, accuracy: 78.04%\n",
      "prediction: 5713929, target value: 9681000, accuracy: 59.02%\n",
      "prediction: 6235604, target value: 9310000, accuracy: 66.98%\n",
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      "prediction: 3776245, target value: 2275000, accuracy: 60.25%\n",
      "prediction: 3779159, target value: 2275000, accuracy: 60.20%\n",
      "prediction: 1857494, target value: 2240000, accuracy: 82.92%\n",
      "prediction: 5043277, target value: 2233000, accuracy: 44.28%\n",
      "prediction: 2854802, target value: 2135000, accuracy: 74.79%\n",
      "prediction: 2286357, target value: 2100000, accuracy: 91.85%\n",
      "prediction: 2857374, target value: 2100000, accuracy: 73.49%\n",
      "prediction: 3198230, target value: 2100000, accuracy: 65.66%\n",
      "prediction: 3256754, target value: 1960000, accuracy: 60.18%\n",
      "prediction: 1620929, target value: 1890000, accuracy: 85.76%\n",
      "prediction: 3472275, target value: 1890000, accuracy: 54.43%\n",
      "prediction: 2845977, target value: 1855000, accuracy: 65.18%\n",
      "prediction: 2857033, target value: 1820000, accuracy: 63.70%\n",
      "prediction: 2285625, target value: 1767150, accuracy: 77.32%\n",
      "prediction: 3444707, target value: 1750000, accuracy: 50.80%\n",
      "prediction: 2770977, target value: 1750000, accuracy: 63.15%\n",
      "prediction: 3664744, target value: 1750000, accuracy: 47.75%\n",
      "Overal accuracy: 77.53%\n"
     ]
    }
   ],
   "source": [
    "initial_w = np.array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 10, 20, 10, 0, 20, 0])\n",
    "initial_b = 2.0\n",
    "scaler = StandardScaler()\n",
    "x_normalized = scaler.fit_transform(x_train) # normalized (Z-score) version of x_train \n",
    "\n",
    "# number of iterations and step size can be changed depending on the dataset and accuracy\n",
    "iterations = 1900\n",
    "alpha = 5.0e-7\n",
    "\n",
    "w_final, b_final = gradient_descent(x_normalized, y_train, initial_w, initial_b, alpha, iterations)\n",
    "\n",
    "total_accuracy = 0.0\n",
    "\n",
    "# printing the result of the training and their accuracy\n",
    "for i in range(x_train.shape[0]):\n",
    "    accuracy = 0.0\n",
    "    if(((np.dot(x_train[i], w_final) + b_final) / y_train[i]) * 100 > 100):\n",
    "        accuracy = (y_train[i] / (np.dot(x_train[i], w_final) + b_final)) * 100\n",
    "    else:\n",
    "        accuracy = ((np.dot(x_train[i], w_final) + b_final) / y_train[i]) * 100\n",
    "    print(f\"prediction: {np.dot(x_train[i], w_final) + b_final:0.0f}, target value: {y_train[i]}, accuracy: {accuracy:0.2f}%\")\n",
    "    total_accuracy += accuracy\n",
    "\n",
    "print(f\"Overal accuracy: {total_accuracy / x_train.shape[0]:0.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "prediction: 10719886.50, target value: 12250000, accuracy: 87.50927755178975%\n",
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      "prediction: 5924771.50, target value: 3570000, accuracy: 60.25548836318768%\n",
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      "prediction: 4434296.53, target value: 3500000, accuracy: 78.93021976685361%\n",
      "prediction: 3206782.89, target value: 3500000, accuracy: 91.62236816040468%\n",
      "prediction: 3789605.04, target value: 3500000, accuracy: 92.35790963644148%\n",
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      "prediction: 3640768.37, target value: 3500000, accuracy: 96.1335531725129%\n",
      "prediction: 3510787.79, target value: 3500000, accuracy: 99.6927243970232%\n",
      "prediction: 3261635.77, target value: 3500000, accuracy: 93.18959334495631%\n",
      "prediction: 3144524.95, target value: 3500000, accuracy: 89.84356994828353%\n",
      "prediction: 3861480.94, target value: 3500000, accuracy: 90.63880037852819%\n",
      "prediction: 3130919.46, target value: 3500000, accuracy: 89.45484174810518%\n",
      "prediction: 3911316.73, target value: 3500000, accuracy: 89.48393194733032%\n",
      "prediction: 3359992.57, target value: 3500000, accuracy: 95.9997878418506%\n",
      "prediction: 4051448.45, target value: 3500000, accuracy: 86.3888568770703%\n",
      "prediction: 3546196.78, target value: 3500000, accuracy: 98.69728654234422%\n",
      "prediction: 6179112.92, target value: 3500000, accuracy: 56.6424347114416%\n",
      "prediction: 3182352.96, target value: 3500000, accuracy: 90.92437015047865%\n",
      "prediction: 5062543.27, target value: 3500000, accuracy: 69.13521158881802%\n",
      "prediction: 3085654.69, target value: 3493000, accuracy: 88.33823912113562%\n",
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      "prediction: 3994799.59, target value: 3465000, accuracy: 86.73776804357854%\n",
      "prediction: 4389605.23, target value: 3465000, accuracy: 78.93648334411465%\n",
      "prediction: 2715885.44, target value: 3430000, accuracy: 79.18033344138722%\n",
      "prediction: 3083094.46, target value: 3430000, accuracy: 89.88613599774135%\n",
      "prediction: 2806130.71, target value: 3430000, accuracy: 81.81139093093522%\n",
      "prediction: 4389605.23, target value: 3430000, accuracy: 78.13914512851754%\n",
      "prediction: 3982105.63, target value: 3430000, accuracy: 86.13533436978696%\n",
      "prediction: 4822564.86, target value: 3430000, accuracy: 71.12397860027319%\n",
      "prediction: 2725810.28, target value: 3423000, accuracy: 79.6322020991214%\n",
      "prediction: 5753868.57, target value: 3395000, accuracy: 59.003780860359164%\n",
      "prediction: 3642997.99, target value: 3395000, accuracy: 93.19247518468799%\n",
      "prediction: 3065849.39, target value: 3395000, accuracy: 90.30484222070828%\n",
      "prediction: 4727934.30, target value: 3360000, accuracy: 71.06697735894969%\n",
      "prediction: 3897925.52, target value: 3360000, accuracy: 86.19969729239504%\n",
      "prediction: 3195936.34, target value: 3360000, accuracy: 95.11715298301844%\n",
      "prediction: 2901976.20, target value: 3360000, accuracy: 86.3683393538626%\n",
      "prediction: 3076610.57, target value: 3360000, accuracy: 91.56579071810869%\n",
      "prediction: 2781318.61, target value: 3360000, accuracy: 82.77733949100349%\n",
      "prediction: 3225368.65, target value: 3360000, accuracy: 95.99311458439523%\n",
      "prediction: 3889070.02, target value: 3360000, accuracy: 86.3959759562322%\n",
      "prediction: 2528799.59, target value: 3353000, accuracy: 75.4190155291112%\n",
      "prediction: 4306016.88, target value: 3332000, accuracy: 77.38009610469942%\n",
      "prediction: 3010524.11, target value: 3325000, accuracy: 90.54207842046141%\n",
      "prediction: 3613382.46, target value: 3325000, accuracy: 92.01904415741744%\n",
      "prediction: 2773902.92, target value: 3290000, accuracy: 84.31315876357014%\n",
      "prediction: 4445795.95, target value: 3290000, accuracy: 74.0024966964403%\n",
      "prediction: 4473754.17, target value: 3290000, accuracy: 73.54002642230408%\n",
      "prediction: 3365143.86, target value: 3290000, accuracy: 97.76699412688578%\n",
      "prediction: 3589123.26, target value: 3290000, accuracy: 91.66584048978709%\n",
      "prediction: 2725810.28, target value: 3290000, accuracy: 82.8513762265327%\n",
      "prediction: 4306016.88, target value: 3290000, accuracy: 76.40471674203515%\n",
      "prediction: 3594530.21, target value: 3290000, accuracy: 91.52795519697067%\n",
      "prediction: 2628310.24, target value: 3255000, accuracy: 80.74685838750428%\n",
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      "prediction: 3698453.13, target value: 3234000, accuracy: 87.44196246397664%\n",
      "prediction: 3385430.02, target value: 3220000, accuracy: 95.1134709840516%\n",
      "prediction: 3535550.49, target value: 3220000, accuracy: 91.07492623334713%\n",
      "prediction: 2776181.95, target value: 3220000, accuracy: 86.21683073246545%\n",
      "prediction: 3083286.80, target value: 3220000, accuracy: 95.75424844661545%\n",
      "prediction: 2891242.97, target value: 3150000, accuracy: 91.78549125676591%\n",
      "prediction: 4250053.69, target value: 3150000, accuracy: 74.11671070906371%\n",
      "prediction: 2746877.25, target value: 3150000, accuracy: 87.202452459612%\n",
      "prediction: 3528528.36, target value: 3150000, accuracy: 89.27234481424084%\n",
      "prediction: 2645949.41, target value: 3150000, accuracy: 83.99839392746593%\n",
      "prediction: 3743490.48, target value: 3150000, accuracy: 84.14606689832269%\n",
      "prediction: 3617862.00, target value: 3150000, accuracy: 87.06799750311546%\n",
      "prediction: 5323301.77, target value: 3150000, accuracy: 59.17380112509414%\n",
      "prediction: 2769598.60, target value: 3150000, accuracy: 87.9237649757592%\n",
      "prediction: 4279164.24, target value: 3143000, accuracy: 73.44892191646301%\n",
      "prediction: 3580977.82, target value: 3129000, accuracy: 87.37836863455878%\n",
      "prediction: 3332183.32, target value: 3118850, accuracy: 93.59779152197316%\n",
      "prediction: 3025426.86, target value: 3115000, accuracy: 97.12445773450385%\n",
      "prediction: 3256407.09, target value: 3115000, accuracy: 95.65757333814362%\n",
      "prediction: 2591824.93, target value: 3115000, accuracy: 83.20465254808286%\n",
      "prediction: 4017895.45, target value: 3087000, accuracy: 76.83126747295752%\n",
      "prediction: 4182248.13, target value: 3080000, accuracy: 73.64460216589119%\n",
      "prediction: 2992134.89, target value: 3080000, accuracy: 97.14723671824983%\n",
      "prediction: 3170083.79, target value: 3080000, accuracy: 97.15831508981553%\n",
      "prediction: 4660922.81, target value: 3080000, accuracy: 66.08133469103791%\n",
      "prediction: 2666261.23, target value: 3045000, accuracy: 87.56194525362434%\n",
      "prediction: 2856199.41, target value: 3010000, accuracy: 94.89034576525677%\n",
      "prediction: 3894124.69, target value: 3010000, accuracy: 77.29593274170932%\n",
      "prediction: 2646468.21, target value: 3010000, accuracy: 87.92253189992412%\n",
      "prediction: 3304045.57, target value: 3010000, accuracy: 91.10043834371108%\n",
      "prediction: 2679047.61, target value: 3010000, accuracy: 89.0049040309028%\n",
      "prediction: 3231594.99, target value: 3010000, accuracy: 93.14286015127048%\n",
      "prediction: 4403069.14, target value: 3010000, accuracy: 68.36140663933759%\n",
      "prediction: 3720775.57, target value: 3003000, accuracy: 80.70897972735791%\n",
      "prediction: 3361861.63, target value: 2975000, accuracy: 88.49263679641965%\n",
      "prediction: 3137684.56, target value: 2961000, accuracy: 94.36895086316763%\n",
      "prediction: 4404050.99, target value: 2940000, accuracy: 66.75672023684282%\n",
      "prediction: 2954081.62, target value: 2940000, accuracy: 99.52331660435848%\n",
      "prediction: 3047811.20, target value: 2940000, accuracy: 96.46266816503322%\n",
      "prediction: 2905448.05, target value: 2940000, accuracy: 98.82476368126318%\n",
      "prediction: 3237680.44, target value: 2940000, accuracy: 90.80574994785383%\n",
      "prediction: 2401336.17, target value: 2940000, accuracy: 81.67810087583544%\n",
      "prediction: 2651442.91, target value: 2940000, accuracy: 90.18513288834912%\n",
      "prediction: 4162105.47, target value: 2940000, accuracy: 70.63732572986638%\n",
      "prediction: 2046409.79, target value: 2870000, accuracy: 71.30347696827265%\n",
      "prediction: 2624080.66, target value: 2870000, accuracy: 91.4313818646742%\n",
      "prediction: 3431771.24, target value: 2870000, accuracy: 83.6302830133199%\n",
      "prediction: 3768458.10, target value: 2870000, accuracy: 76.15846924077769%\n",
      "prediction: 4169110.27, target value: 2852500, accuracy: 68.4198741058674%\n",
      "prediction: 3193018.65, target value: 2835000, accuracy: 88.78745505381966%\n",
      "prediction: 4100244.85, target value: 2835000, accuracy: 69.14221234360151%\n",
      "prediction: 2671162.73, target value: 2835000, accuracy: 94.2209077304043%\n",
      "prediction: 3610063.73, target value: 2800000, accuracy: 77.56095768888216%\n",
      "prediction: 3272711.54, target value: 2800000, accuracy: 85.55596671553732%\n",
      "prediction: 3410624.29, target value: 2730000, accuracy: 80.04399677864602%\n",
      "prediction: 3578339.07, target value: 2730000, accuracy: 76.29237880469404%\n",
      "prediction: 2715885.44, target value: 2695000, accuracy: 99.2309897628691%\n",
      "prediction: 2699509.45, target value: 2660000, accuracy: 98.53642113747775%\n",
      "prediction: 2889563.54, target value: 2660000, accuracy: 92.05542512341945%\n",
      "prediction: 5092279.66, target value: 2660000, accuracy: 52.235937130257234%\n",
      "prediction: 2545603.64, target value: 2660000, accuracy: 95.6993848714585%\n",
      "prediction: 2022519.39, target value: 2660000, accuracy: 76.03456359125806%\n",
      "prediction: 3091220.10, target value: 2660000, accuracy: 86.05016516879454%\n",
      "prediction: 3062992.62, target value: 2660000, accuracy: 86.84317354589041%\n",
      "prediction: 3375145.29, target value: 2653000, accuracy: 78.60402357336706%\n",
      "prediction: 4155103.73, target value: 2653000, accuracy: 63.849188218688916%\n",
      "prediction: 2014154.06, target value: 2604000, accuracy: 77.34846606551679%\n",
      "prediction: 2616637.03, target value: 2590000, accuracy: 98.98201284274889%\n",
      "prediction: 2815133.85, target value: 2590000, accuracy: 92.00273032989004%\n",
      "prediction: 5161821.90, target value: 2590000, accuracy: 50.17608221508807%\n",
      "prediction: 2134173.85, target value: 2520000, accuracy: 84.6894385189518%\n",
      "prediction: 2208610.16, target value: 2520000, accuracy: 87.64326018958992%\n",
      "prediction: 3357328.32, target value: 2520000, accuracy: 75.05968314568399%\n",
      "prediction: 3392873.65, target value: 2485000, accuracy: 73.24174890411625%\n",
      "prediction: 2833868.52, target value: 2485000, accuracy: 87.68931890202704%\n",
      "prediction: 3464297.67, target value: 2450000, accuracy: 70.72140543660663%\n",
      "prediction: 2694048.27, target value: 2450000, accuracy: 90.94120632776462%\n",
      "prediction: 2752478.25, target value: 2450000, accuracy: 89.01069439017982%\n",
      "prediction: 2905125.38, target value: 2450000, accuracy: 84.3337095831845%\n",
      "prediction: 3374583.47, target value: 2450000, accuracy: 72.60155270257947%\n",
      "prediction: 3633933.21, target value: 2450000, accuracy: 67.42006131881907%\n",
      "prediction: 2194041.80, target value: 2408000, accuracy: 91.11469252856804%\n",
      "prediction: 3354528.21, target value: 2380000, accuracy: 70.94887420008385%\n",
      "prediction: 4563624.50, target value: 2380000, accuracy: 52.15153002124781%\n",
      "prediction: 2533268.37, target value: 2380000, accuracy: 93.9497777603571%\n",
      "prediction: 2626561.87, target value: 2345000, accuracy: 89.2802117869184%\n",
      "prediction: 2512426.20, target value: 2310000, accuracy: 91.94299914813269%\n",
      "prediction: 2334003.87, target value: 2275000, accuracy: 97.47198900208775%\n",
      "prediction: 2149698.91, target value: 2275000, accuracy: 94.49225991024784%\n",
      "prediction: 3652869.64, target value: 2275000, accuracy: 62.279802496533556%\n",
      "prediction: 2769764.19, target value: 2240000, accuracy: 80.87331074435305%\n",
      "prediction: 4262785.78, target value: 2233000, accuracy: 52.38358471842901%\n",
      "prediction: 2036484.95, target value: 2135000, accuracy: 95.38571185836446%\n",
      "prediction: 2896631.61, target value: 2100000, accuracy: 72.49800052610286%\n",
      "prediction: 3172967.65, target value: 2100000, accuracy: 66.18409745719444%\n",
      "prediction: 2841801.81, target value: 2100000, accuracy: 73.89677879402106%\n",
      "prediction: 2973362.43, target value: 1960000, accuracy: 65.91863738891936%\n",
      "prediction: 2722946.90, target value: 1890000, accuracy: 69.4100939978536%\n",
      "prediction: 2628794.96, target value: 1890000, accuracy: 71.8960599646413%\n",
      "prediction: 2318717.57, target value: 1855000, accuracy: 80.00111897715503%\n",
      "prediction: 3403880.11, target value: 1820000, accuracy: 53.46839318999762%\n",
      "prediction: 2234719.52, target value: 1767150, accuracy: 79.0770377224803%\n",
      "prediction: 2621599.45, target value: 1750000, accuracy: 66.75314188285778%\n",
      "prediction: 2580905.00, target value: 1750000, accuracy: 67.80567268597858%\n",
      "prediction: 3256407.09, target value: 1750000, accuracy: 53.74020974053012%\n",
      "Overal accuracy: 85.36%\n"
     ]
    }
   ],
   "source": [
    "# Now, we're using scikit-learn Python library to genrate the linear regression model for our dataset and see the accuracy of the generated model\n",
    "scalerr = StandardScaler()\n",
    "x_normalized_2 = scalerr.fit_transform(x_train) # normalizing the x_train\n",
    "\n",
    "gradient = SGDRegressor(max_iter=int(1e07))\n",
    "\n",
    "gradient.fit(x_normalized_2, y_train)\n",
    "\n",
    "predicted_values = gradient.predict(x_normalized_2)\n",
    "\n",
    "total_accuracy2 = 0.0\n",
    "\n",
    "for i in range(x_train.shape[0]): \n",
    "    accuracy = 0.0\n",
    "    if((predicted_values[i] / y_train[i]) * 100 > 100):\n",
    "        accuracy = (y_train[i] / predicted_values[i]) * 100\n",
    "    else:\n",
    "        accuracy = (predicted_values[i] / y_train[i]) * 100\n",
    "    print(f\"prediction: {predicted_values[i]:0.2f}, target value: {y_train[i]}, accuracy: {accuracy}%\")\n",
    "    total_accuracy2 += accuracy\n",
    "\n",
    "print(f\"Overal accuracy: {total_accuracy2 / x_train.shape[0]:0.2f}%\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrong answer in mainroad!\n",
      "wrong answer in guest room!\n",
      "wrong answer in basement!\n",
      "wrong answer in heating!\n",
      "wrong answer in air conditioning!\n",
      "wrong answer in air prefarea!\n",
      "wrong answer in air furnished!\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'mainroad2' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[12], line 82\u001b[0m\n\u001b[0;32m     79\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m     80\u001b[0m     \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mwrong answer in air furnished!\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m---> 82\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mYou entered the following values: \u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m area: \u001b[39m\u001b[39m{\u001b[39;00marea\u001b[39m}\u001b[39;00m\u001b[39m \u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m bedrooms: \u001b[39m\u001b[39m{\u001b[39;00mbedroom\u001b[39m}\u001b[39;00m\u001b[39m \u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m bathrooms: \u001b[39m\u001b[39m{\u001b[39;00mbathroom\u001b[39m}\u001b[39;00m\u001b[39m \u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m stories: \u001b[39m\u001b[39m{\u001b[39;00mstory\u001b[39m}\u001b[39;00m\u001b[39m \u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m mainroad: \u001b[39m\u001b[39m{\u001b[39;00mmainroad2\u001b[39m}\u001b[39;00m\u001b[39m \u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m guestroom: \u001b[39m\u001b[39m{\u001b[39;00mguestroom2\u001b[39m}\u001b[39;00m\u001b[39m \u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m basement: \u001b[39m\u001b[39m{\u001b[39;00mbasement2\u001b[39m}\u001b[39;00m\u001b[39m \u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m heating system: \u001b[39m\u001b[39m{\u001b[39;00mhot_water_heating2\u001b[39m}\u001b[39;00m\u001b[39m \u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m airconditioning: \u001b[39m\u001b[39m{\u001b[39;00mair_conditioning2\u001b[39m}\u001b[39;00m\u001b[39m \u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m parking: \u001b[39m\u001b[39m{\u001b[39;00mparking\u001b[39m}\u001b[39;00m\u001b[39m \u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m prefarea: \u001b[39m\u001b[39m{\u001b[39;00mprefarea2\u001b[39m}\u001b[39;00m\u001b[39m \u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m furnishing status: \u001b[39m\u001b[39m{\u001b[39;00mfurnished2\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[0;32m     83\u001b[0m x_train_input \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray([\u001b[39mint\u001b[39m(area), \u001b[39mint\u001b[39m(bedroom), \u001b[39mint\u001b[39m(bathroom), \u001b[39mint\u001b[39m(story), \u001b[39mint\u001b[39m(mainroad), \u001b[39mint\u001b[39m(guestroom), \u001b[39mint\u001b[39m(basement), \u001b[39mint\u001b[39m(hot_water_heating), \u001b[39mint\u001b[39m(air_conditioning), \u001b[39mint\u001b[39m(parking), \u001b[39mint\u001b[39m(prefarea), \u001b[39mint\u001b[39m(furnished)])\n\u001b[0;32m     85\u001b[0m result \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mdot(w_final, x_train_input) \u001b[39m+\u001b[39m b_final\n",
      "\u001b[1;31mNameError\u001b[0m: name 'mainroad2' is not defined"
     ]
    }
   ],
   "source": [
    "# getting inputs from the user to predit the price of the house\n",
    "while(True):\n",
    "    area = input(\"Please enter the area (Square foot): \")\n",
    "    bedroom = input(\"Please enter the number if bedrooms: \")\n",
    "    bathroom = input(\"Please enter the number of bathrooms: \")\n",
    "    story = input(\"Please enter the number of stories: \")\n",
    "\n",
    "    mainroad = input(\"Is the house on the main road (y/n)? \")\n",
    "    if (mainroad == 'y'):\n",
    "        mainroad2 = \"yes\"\n",
    "        mainroad = 1\n",
    "    elif (mainroad == 'n'):\n",
    "        mainroad = 0\n",
    "        mainroad2 = \"no\"\n",
    "    else:\n",
    "        print(\"wrong answer in mainroad!\")\n",
    "\n",
    "    guestroom = input(\"Does the house have any guest room (y/n)? \")\n",
    "    if (guestroom == 'y'):\n",
    "        guestroom = 1\n",
    "        guestroom2 = \"yes\"\n",
    "    elif (guestroom == 'n'):\n",
    "        guestroom = 0\n",
    "        guestroom2 = \"no\"\n",
    "    else:\n",
    "        print(\"wrong answer in guest room!\")\n",
    "\n",
    "    basement = input(\"Does the house have basement (y/n)? \")\n",
    "    if (basement == 'y'):\n",
    "        basement = 1\n",
    "        basement2 = \"yes\"\n",
    "    elif (basement == 'n'):\n",
    "        basement = 0\n",
    "        basement2 = \"no\"\n",
    "    else:\n",
    "        print(\"wrong answer in basement!\")\n",
    "\n",
    "    hot_water_heating = input(\"Does the house have heating system (y/n)? \")\n",
    "    if (hot_water_heating == 'y'):\n",
    "        hot_water_heating = 1\n",
    "        hot_water_heating2 = \"yes\"\n",
    "    elif (hot_water_heating == 'n'):\n",
    "        hot_water_heating = 0\n",
    "        hot_water_heating2 = \"no\"\n",
    "    else:\n",
    "        print(\"wrong answer in heating!\")\n",
    "\n",
    "    air_conditioning = input(\"Does the house have air conditioning (y/n)? \")\n",
    "    if (air_conditioning == 'y'):\n",
    "        air_conditioning = 1\n",
    "        air_conditioning2 = \"yes\"\n",
    "    elif (air_conditioning == 'n'):\n",
    "        air_conditioning = 0\n",
    "        air_conditioning2 = \"no\"\n",
    "    else:\n",
    "        print(\"wrong answer in air conditioning!\")\n",
    "\n",
    "    parking = input(\"Please enter the number of parkings: \")\n",
    "    prefarea = input(\"Does the house have prefarea (y/n)? \")\n",
    "    if (prefarea == 'y'):\n",
    "        prefarea = 1\n",
    "        prefarea2 = \"yes\"\n",
    "    elif (prefarea == 'n'):\n",
    "        prefarea = 0\n",
    "        prefarea2 = \"no\"\n",
    "    else:\n",
    "        print(\"wrong answer in air prefarea!\")\n",
    "\n",
    "    furnished = input(\"Is the house furnished (y), semi-furnished (s), or unfurnished (n)? \")\n",
    "    if (furnished == 'y'):\n",
    "        furnished = 2\n",
    "        furnished2 = \"furnished\"\n",
    "    elif (furnished == 's'):\n",
    "        furnished = 1\n",
    "        furnished2 = \"semi-furnished\"\n",
    "    elif (furnished == 'n'):\n",
    "        furnished = 0\n",
    "        furnished2 = \"unfurnished\"\n",
    "    else:\n",
    "        print(\"wrong answer in air furnished!\")\n",
    "\n",
    "    print(f\"You entered the following values: \\n area: {area} \\n bedrooms: {bedroom} \\n bathrooms: {bathroom} \\n stories: {story} \\n mainroad: {mainroad2} \\n guestroom: {guestroom2} \\n basement: {basement2} \\n heating system: {hot_water_heating2} \\n airconditioning: {air_conditioning2} \\n parking: {parking} \\n prefarea: {prefarea2} \\n furnishing status: {furnished2}\")\n",
    "    x_train_input = np.array([int(area), int(bedroom), int(bathroom), int(story), int(mainroad), int(guestroom), int(basement), int(hot_water_heating), int(air_conditioning), int(parking), int(prefarea), int(furnished)])\n",
    "\n",
    "    result = np.dot(w_final, x_train_input) + b_final\n",
    "    print(f\"Prediction: {result:0.2f}\")\n",
    "\n",
    "    break"
   ]
  }
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