Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "nb_path = \"/home/jovyan\"\n",
    "if nb_path not in sys.path:\n",
    "  sys.path.insert(5, nb_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting numpy\n",
      "  Using cached numpy-1.19.4-cp37-cp37m-manylinux2010_x86_64.whl (14.5 MB)\n",
      "Collecting as\n",
      "  Using cached as-0.1-py3-none-any.whl (2.2 kB)\n",
      "Processing ./.cache/pip/wheels/8d/31/5b/f3f27c678f2b3ad7e29903ed09bb7446717fd4c8b35f53973a/np-1.0.2-py3-none-any.whl\n",
      "Installing collected packages: numpy, as, np\n",
      "Successfully installed as-0.1 np-1.0.2 numpy-1.19.4\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.10868761e-16  8.78503652e-01 -4.69166570e-02]]\n",
      "0.1307033696077189\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Error vs. Training Epoch')"
      ]
     },
     "execution_count": 6,
     "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": [
    "# -*- coding: utf-8 -*-\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "my_data = pd.read_csv('car_price.csv')\n",
    "\n",
    "#we need to normalize the features using mean normalization\n",
    "my_data = (my_data - my_data.mean())/my_data.std()\n",
    "\n",
    "\n",
    "#setting the matrixes\n",
    "X = my_data.iloc[:,0:2]\n",
    "ones = np.ones([X.shape[0],1])\n",
    "X = np.concatenate((ones,X),axis=1)\n",
    "\n",
    "y = my_data.iloc[:,2:3].values #.values converts it from pandas.core.frame.DataFrame to numpy.ndarray\n",
    "theta = np.zeros([1,3])\n",
    "\n",
    "#computecost\n",
    "def computeCost(X,y,theta):\n",
    "    tobesummed = np.power(((X @ theta.T)-y),2)\n",
    "    return np.sum(tobesummed)/(2 * len(X))\n",
    "\n",
    "def gradientDescent(X,y,theta,iters,alpha):\n",
    "    cost = np.zeros(iters)\n",
    "    for i in range(iters):\n",
    "        theta = theta - (alpha/len(X)) * np.sum(X * (X @ theta.T - y), axis=0)\n",
    "        cost[i] = computeCost(X, y, theta)\n",
    "    \n",
    "    return theta,cost\n",
    "\n",
    "#set hyper parameters\n",
    "alpha = 0.01\n",
    "iters = 1000\n",
    "\n",
    "g,cost = gradientDescent(X,y,theta,iters,alpha)\n",
    "print(g)\n",
    "\n",
    "finalCost = computeCost(X,y,g)\n",
    "print(finalCost)\n",
    "\n",
    "fig, ax = plt.subplots()  \n",
    "ax.plot(np.arange(iters), cost, 'r')  \n",
    "ax.set_xlabel('Iterations')  \n",
    "ax.set_ylabel('Cost')  \n",
    "ax.set_title('Error vs. Training Epoch')  \n",
    "\n",
    "\n",
    "\n",
    "        \n",
    "\n",
    "\n",
    "\n",
    "\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": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}