linear_reg.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "linear_reg.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ektaarora3501/tensorflow/blob/master/linear_reg.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QxxOzkRO9VKR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install tensorflow-gpu\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EFn9pAL2-Ji9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7XYZYjVSuEbm",
        "colab_type": "text"
      },
      "source": [
        "importing basic files "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I0eNNYDv-c-d",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import files\n",
        "uploaded=files.upload()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UiPMN_LeuKuK",
        "colab_type": "text"
      },
      "source": [
        "to import data file stored in local computer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xLOVl42E-xbh",
        "colab_type": "code",
        "outputId": "ff30ca50-21c4-417b-d859-bfbba8ec1aff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        }
      },
      "source": [
        "import io\n",
        "df2=io.BytesIO(uploaded['kc_house_data.csv']) \n",
        "y_train=pd.read_csv(df2,nrows=7000,usecols=[2],skiprows=[0],header=None)  # importing y parameter from col 2  (price)\n",
        "x_train=pd.read_csv(df2,nrows=7000,usecols=[5],skiprows=[0],header=None)   # importing x parameter from col 5 (sqft living) \n",
        "plt.ylabel('price')\n",
        "plt.xlabel('sqft_living')\n",
        "plt.scatter(x_train,y_train)\n",
        "plt.show()\n",
        "print(x_train.head(),y_train.head())"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAakAAAELCAYAAABj+Hm+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3X+cVPV97/HXh2XBxSQuREp1ESGt\nF66GCrJVUvpDtApqDHtN4o82laap9FGT3phYGmi8F03SSEp7k9gfpjaxwWoj+CMrEQ0hQm7T3AfE\nJQsSFMrGn4w/IMJqI6suy+f+cb4Ds7MzszOzc2bO7L6fj8c89sx3zpzz3bO789nv9/s536+5OyIi\nIkk0qtYVEBERyUdBSkREEktBSkREEktBSkREEktBSkREEktBSkREEktBSkREEktBSkREEktBSkRE\nEmt0rSuQdCeffLJPnTq11tUQEakr27Zt+7m7TxzqcRSkBjF16lQ6OjpqXQ0RkbpiZs9V4jjq7hMR\nkcRSkBIRkcRSkBIRkcRSkBIRkcRSkBIRkcRSdt8w0t6ZYtWGPbzY3cOpzU0sXTCdttktta6WiEjZ\nYmtJmdl0M9ue8XjdzG4wswlmttHM9oav48P+Zma3mVmXmT1hZudkHGtx2H+vmS3OKJ9jZjvDe24z\nMwvlJZ+j3rV3plj+4E5S3T04kOruYfmDO2nvTNW6aiIiZYstSLn7Hnef5e6zgDnAYeDbwDLgMXc/\nA3gsPAe4BDgjPJYAt0MUcIAVwHnAucCKdNAJ+1yX8b6FobykcwwHqzbsoae3r19ZT28fqzbsqVGN\nRESGrlpjUhcCP3P354BFwOpQvhpoC9uLgLs8sgVoNrNTgAXARnc/6O6HgI3AwvDau9x9i7s7cFfW\nsUo5R917sbunpHIRkXpQrSB1NfCtsD3J3V8K2y8Dk8J2C/BCxnv2hbJC5ftylJdzjrp3anNTSeUi\nIvUg9iBlZmOADwD3Zb8WWkAe5/nLOYeZLTGzDjPrOHDgQEw1q6ylC6bT1NjQr6ypsYGlC6bXqEYi\nIkNXjZbUJcBP3P2V8PyVdBdb+Lo/lKeA0zLeNzmUFSqfnKO8nHP04+53uHuru7dOnDjk+RGrom12\nC7deMZOW5iYMaGlu4tYrZiq7T0TqWjVS0K/heFcfwDpgMbAyfH0oo/wTZnYvUZLEa+7+kpltAL6Y\nkSxxMbDc3Q+GjMG5wFbgWuDvyjlHxb/jGmmb3aKgJCLDSqxBysxOBC4C/iSjeCWw1sw+BjwHXBnK\nHwEuBbqIMgE/ChCC0eeBx8N+n3P3g2H7euCbQBPwaHiUfA4REUkmi4ZsJJ/W1lbXUh0iIqUxs23u\n3jrU42haJBERSSwFKRERSSwFKRERSSwFKRERSSwFKRERSSwFKRERSSwFKRERSSwFKRERSSwFKRER\nSSwFKRERSSwFKRERSSwFKRERSSwFKRERSSwFKRERSSwFKRERSSwFKRERSSwFKRERSSwFKRERSSwF\nKRERSSwFKRERSaxYg5SZNZvZ/Wa228yeMrP3mdkEM9toZnvD1/FhXzOz28ysy8yeMLNzMo6zOOy/\n18wWZ5TPMbOd4T23mZmF8pLPISIiyRN3S+qrwHfdfQZwNvAUsAx4zN3PAB4LzwEuAc4IjyXA7RAF\nHGAFcB5wLrAiHXTCPtdlvG9hKC/pHCIikkyxBSkzOwn4beAbAO7+trt3A4uA1WG31UBb2F4E3OWR\nLUCzmZ0CLAA2uvtBdz8EbAQWhtfe5e5b3N2Bu7KOVco5REQkgeJsSU0DDgD/YmadZvZ1MzsRmOTu\nL4V9XgYmhe0W4IWM9+8LZYXK9+Uop4xz9GNmS8ysw8w6Dhw4UOz3KyIiFRZnkBoNnAPc7u6zgTc4\n3u0GQGgBeYx1KOsc7n6Hu7e6e+vEiRNjqpmIiAwmziC1D9jn7lvD8/uJgtYr6S628HV/eD0FnJbx\n/smhrFD55BzllHEOERFJoNiClLu/DLxgZtND0YXAk8A6IJ2htxh4KGyvA64NGXhzgddCl90G4GIz\nGx8SJi4GNoTXXjezuSGr79qsY5VyDhERSaDRMR//z4B7zGwM8DTwUaLAuNbMPgY8B1wZ9n0EuBTo\nAg6HfXH3g2b2eeDxsN/n3P1g2L4e+CbQBDwaHgArSzmHiIgkk0VDNpJPa2urd3R01LoaIiJ1xcy2\nuXvrUI+jGSdERCSxFKRERCSxFKRERCSxFKRERCSx4s7ukxGgvTPFqg17eLG7h1Obm1i6YDptswdM\n5CEiUjIFKRmS9s4Uyx/cSU9vHwCp7h6WP7gTQIFKRIZM3X0yJKs27DkWoNJ6evtYtWFPjWokIsOJ\ngpQMyYvdPSWVi4iUQkFKhuTU5qaSykVESqEgJUOydMF0mhob+pU1NTawdMH0PO8QESmeEidGqEpl\n5KXfo+w+EYmDglSdGkqQqXRGXtvsFgUlEYmFuvvqUDrIpLp7cI4HmfbO4pbGUkaeiNQLBak6NNQg\no4w8EakXClJ1aKhBRhl5IlIvFKTq0FCDjDLyRKReKEjVoaEGmbbZLdx6xUxampswoKW5iVuvmKnk\nBxFJHGX31amxo0cdG5caP66RFZefVVKQUUaeiNQDBak6k50+DvBm79Ea1khEJD6xdveZ2bNmttPM\ntptZRyibYGYbzWxv+Do+lJuZ3WZmXWb2hJmdk3GcxWH/vWa2OKN8Tjh+V3ivlXuOeqH0cREZSaox\nJjXf3We5e2t4vgx4zN3PAB4LzwEuAc4IjyXA7RAFHGAFcB5wLrAiHXTCPtdlvG9hOeeoJ0ofF5GR\npBaJE4uA1WF7NdCWUX6XR7YAzWZ2CrAA2OjuB939ELARWBhee5e7b3F3B+7KOlYp56gbSh8XkZEk\n7iDlwPfMbJuZLQllk9z9pbD9MjApbLcAL2S8d18oK1S+L0d5OeeoG0ofF5GRJO7Eid9095SZ/RKw\n0cx2Z77o7m5mHmcFyjlHCKhLAKZMmRJLvcqlCV1FZCSJNUi5eyp83W9m3yYaU3rFzE5x95dCV9v+\nsHsKOC3j7ZNDWQo4P6v8B6F8co79KeMc2fW+A7gDoLW1NdYgWg6lj4vISBFbd5+ZnWhm70xvAxcD\nPwXWAekMvcXAQ2F7HXBtyMCbC7wWuuw2ABeb2fiQMHExsCG89rqZzQ1ZfddmHauUc4iISALF2ZKa\nBHw7ZIWPBv7N3b9rZo8Da83sY8BzwJVh/0eAS4Eu4DDwUQB3P2hmnwceD/t9zt0Phu3rgW8CTcCj\n4QGwspRziIhIMlmUGCf5tLa2ekdHR62rISJSV8xsW8atR2XT3H0iIpJYClIiIpJYClIiIpJYClIi\nIpJYClIiIpJYClIiIpJYWk+qjrR3pjQdkoiMKApSdSJ7scNUdw/LH9wJoEAlIsOWuvvqhBY7FJGR\nSEGqTmixQxEZiRSk6oQWOxSRkUhBqk5osUMRGYmUOFEntNihiIxEClJ1RIsdishIo+4+ERFJLAUp\nERFJLAUpERFJLAUpERFJLAUpERFJrNiDlJk1mFmnmT0cnk8zs61m1mVma8xsTCgfG553hdenZhxj\neSjfY2YLMsoXhrIuM1uWUV7yOUREJHmq0ZL6JPBUxvMvAV92918FDgEfC+UfAw6F8i+H/TCzM4Gr\ngbOAhcA/hsDXAPwDcAlwJnBN2Lfkc9Sj9s4U81ZuYtqy9cxbuYn2zlStqyQiUnGxBikzmwxcBnw9\nPDfgAuD+sMtqoC1sLwrPCa9fGPZfBNzr7m+5+zNAF3BueHS5+9Pu/jZwL7CozHPUlfSM6KnuHpzj\nM6IrUInIcBN3S+orwF8AR8PzdwPd7n4kPN8HpO9ObQFeAAivvxb2P1ae9Z585eWco65oRnQRGSli\nC1Jm9n5gv7tvi+sccTGzJWbWYWYdBw4cqHV1BtCM6CIyUsQ5LdI84ANmdilwAvAu4KtAs5mNDi2Z\nyUC6jyoFnAbsM7PRwEnAqxnlaZnvyVX+ahnn6Mfd7wDuAGhtbfWyr0BMTm1uIpUjINXzjOhadVhE\ncim6JWVmp5vZ74btJjN7Z6H93X25u09296lEiQ+b3P33gc3Ah8Jui4GHwva68Jzw+iZ391B+dcjM\nmwacAfwYeBw4I2TyjQnnWBfeU+o56spwmxFdY2wikk9RQcrMriNKNPinUDQZaC/znJ8BPm1mXUTj\nQd8I5d8A3h3KPw0sA3D3XcBa4Engu8DH3b0vtJI+AWwgyh5cG/Yt+Rz1pm12C7deMZOW5iYMaGlu\n4tYrZtZty0NjbCKSjxXTkDCz7UTZdFvdfXYo2+nuM2OuX821trZ6R0dHrasxrE1btp5cv4UGPLPy\nsmpXR0QqwMy2uXvrUI9TbHffWyHNO33y0ZDzc0WkZFp1WETyKTZI/V8z+0ugycwuAu4DvhNftWQk\nGW5jbCJSOcVm9y0jmq1hJ/AnwCOEG3Sl9uo9M06rDotIPsWOSZ0IvOnufeF5AzDW3Q/HXL+aS/qY\nVDozLjPxoKmxgQ/OaWHz7gP60BeRmqj2mNRjQOYAQRPw/aGeXIYuX2bcPVueV0q3iNS9YoPUCe7+\ni/STsD0unipJKfLNMpHdPlZKt4jUo2KD1Btmdk76iZnNATQHTwKUkgGnaZNEpN4UG6RuAO4zsx+a\n2X8Aa4hupJUay5UZl29ad6V0i0i9KSq7z90fN7MZQDoneI+798ZXLSlWrsy4+TMm8sC21IBkivkz\nJjJv5SYlU4hI3SgYpMzsAnffZGZXZL3038wMd38wxrpJkdpmtwwINq2nTygYuNLJFOn3i4gk0WAt\nqd8BNgGX53jNAQWphMoOXPNWbso7P56ClIgkVcEg5e4rzGwU8Ki7r61SnSQGWoNKROrRoGNS7n7U\nzP6CaCZySZhiZ5sYjmtQicjwV2x23/fN7M/N7DQzm5B+xFozGVQp6zBpfjwRqUfFzt13FdEY1PVZ\n5e+pbHWkFIXWYcpuTWl+PBGpR8UGqTOJAtRvEgWrHwJfi6tSUpxSx5lyZQGKiCRZsd19q4H/DtwG\n/B1R0FodV6WkOFqHSUSGu2KD1Hvd/Y/dfXN4XAe8N86KyeA0ziQiw12x3X0/MbO57r4FwMzOA5K7\nfsUIUYtxpmqvXVXva2WJyNAUu57UU0RTIj0fiqYAe4AjgLv7r8VWwxpL+npS1ZRv7apbr5gZS+Co\n9vlEpHKqvZ7UQmAa0QwUvxO2FwLvJ/dsFJjZCWb2YzPbYWa7zOyWUD7NzLaaWZeZrTGzMaF8bHje\nFV6fmnGs5aF8j5ktyChfGMq6zGxZRnnJ55DBFcomHIr2zhTzVm5i2rL1zFu56VgKfVznE5H6UVSQ\ncvfnCj3yvO0t4AJ3PxuYBSw0s7nAl4Avu/uvAoeIlqUnfD0Uyr8c9sPMzgSuBs4iCoz/aGYNYXXg\nfwAuIUrkuCbsS6nnkOLEMWtFoXu9NEuGiBTbkiqZR9ILJTaGhwMXAPeH8tVAW9hexPGMwfuBC83M\nQvm97v6Wuz8DdAHnhkeXuz/t7m8D9wKLwntKPUei5GtZ1Pq4cWQTFmotKXtRRGILUgChxbMd2A9s\nBH4GdLv7kbDLPiA9uNACvAAQXn8NeHdmedZ78pW/u4xzZNd7iZl1mFnHgQMHyvvmy5SrZbH0vh3M\n/tz3hhS0ch33hjXbmbZsPTe17yzqGHFkExZqLSl7UURiDVLu3ufus4DJRC2fGXGer1Lc/Q53b3X3\n1okTJ1b13Dev2zWgZdF71Dl0uHfQqY8KydVigahpe/eW54sKVG2zW7j1ipm0NDdhQEtz05CTGAq1\nluI4n4jUl2JT0IfE3bvNbDPwPqDZzEaHlsxkIP1pmwJOA/aZ2WjgJODVjPK0zPfkKn+1jHMkQntn\niu6ewdeSLGeJjcHGce7Z8jybdx8YNNW70rNWLF0wPWcGX7q1pFkyREa22FpSZjbRzJrDdhNwEfAU\nsBn4UNhtMfBQ2F4XnhNe3+RRfvw64OqQmTcNOAP4MfA4cEbI5BtDlFyxLryn1HMkQilZa6UmDww2\njpNupQ2ltVYOtZZEpJA4W1KnAKtDFt4oYK27P2xmTwL3mtkXgE7gG2H/bwD/amZdwEGioIO77zKz\ntcCTRPdlfdzd+wDM7BPABqABuNPdd4VjfaaUcyRFKYGn1OSBXC2WQqq5IKJaSyKST2xByt2fAGbn\nKH+aaHwqu/xN4MN5jvVXwF/lKH8EeKQS50iCfGs+ZSsneaBtdgsdzx3kni3PU2zTUaneIlJrsSZO\nSGnyZbN9ZO4UWkLLqcHsWCunlO649s4UD2xLFR2gQKneIlJ7VUmckOIUmosve4qg9LhR5vsKyZfd\nV0g9p3przj+R4UFBKmHyjc+UssBhLqV23TU3Ndbth/pQA7qIJIe6++rEUKcIytd1N35cY84uxps/\ncFZpFUwQzfknMnyoJVUn8iVVZAafQl1c+e5HWnF5FIzydTHWY5eZ5vwTGT7UkqoTuZIqDJg/I5oR\no9BErVD6/UjtnSmW3rdjwPRM1bh3aqg055/I8FHUelIjWa3Wk8rVismVQp5eX+nmdbtyzlbR3NTI\n9hUXFzxPrhYWOD29R0s+XhJoHSqR2qvUelLq7kugfAP/Y0ePGpBC3tPblzdAAXT39NLemer34XxT\n+06+tfUF+vL8g1IoC7CYaZvS30OtugprsWKxiMRDLalB1KIlNW/lpqJu6i1Wgxl/e+XZAHz22zt5\n4+3SUtGztTQ3Ffzwv6l9Z94WnwKFVFq9jp0Od2pJDWOVHuDvc+eGNdsZZXC0yP9JCu2bDqC5Urvb\nO1M5Z7Wo5jRLtaAPytrQ7QbDnxInEqjUAf5il20sNkA1NTbwe+dNobFh8ANnp3av2rAn76wWwzW7\nbrCkFYmPbjcY/hSkEqjUmR7cGZD5Vw4jum9q7OhR3LPled4xdjTNTY3HsgHzyQw+hQLRcM2u0wdl\n7eh2g+FP3X01lt1NNH/GRDbvLm014JbQvXTLd3Zx6HBxiQ3ZPjJ3Cq2nT+jXdXLocC9NjQ18+apZ\ntM1uyTtWlhl8Ck2Sm06XH270QVk7xdw/KPVNLakaytVNdPeW50tOmpg/YyJts1sYN6a8/zk+MncK\nX2ibOWiLoJjl3AsFoge2pYZlF5juy6qdYn4npb4pSNVQOZO+5rL+iZeGlBG4efcB2jtTg7YIirkh\nuFArsJJdYO2dKeat3MS0ZeuZt3JTTYOfPihrR4tmDn/q7quR9s5UxdLMDx3uHbSbr7mpkTfePkJv\n38C0hvRAf/O4xpzHyWwRDLZA4WBdXJXoAktaRpfuy6otLZo5vClI1UD6Q7YaMu9PSo9/5QqOPb19\nvHmkj8ZRRm9GGmCpLYLBFm6sRBfYUGeEj4M+KEXioe6+Giilm69xlBWVCp5L9gKJbbNb+NGyC8h3\nNHfA6JfRV2rXSa6ur7Smxgbmz5g45G46JSqIjBxqSdVAsR+mDWas+nA0U8SNa3fkncYoF4Nj+2d3\nhxVq7fT2OSeOHT3ofH/5urYyu75S3T00mNHnTkvIXHxgW2rI3XTK6BIZOWJrSZnZaWa22cyeNLNd\nZvbJUD7BzDaa2d7wdXwoNzO7zcy6zOwJMzsn41iLw/57zWxxRvkcM9sZ3nObWXRbaznnqKZiP0yP\nuh/rRvrbK8+mcVTxLap8Mz5A4dYOFA6ixdy4mm6xPbvyMn5266U8u/IyfrTsAjbvPlCR+4mUqCAy\ncsTZ3XcEuNHdzwTmAh83szOBZcBj7n4G8Fh4DnAJcEZ4LAFuhyjgACuA84BzgRXpoBP2uS7jfQtD\neUnnqLbBgkRadsLCO04YWsM3O0uvIc9UFU2N+X8tCo0HDZZxV6luOmV0iYwcsXX3uftLwEth+7/M\n7CmgBVgEnB92Ww38APhMKL/Loxlvt5hZs5mdEvbd6O4HAcxsI7DQzH4AvMvdt4Tyu4A24NFSzxHq\nWjXpD9NCs5dnjt9kdpsNxSizY2NT6Tp8eu32AdMlHe49yk3tO/lC20ygf/devhqkW1SFuvIq2U2n\nRAWRkaEqiRNmNhWYDWwFJmUEhZeBSWG7BXgh4237Qlmh8n05yinjHFXV3pnKGaDS7ZqW5iY+OKeF\nB7YdT1MvdTwqV7JFeqLZWbd871iwynfUu7c8z7Rl65l1y/dYev/xxQ/zSSdpZMruylM3nYiUKvbE\nCTN7B/AAcIO7v24ZXUzu7mYW61oh5ZzDzJYQdQcyZcqUitYn14J8aU4UoH607ALmrdxU9o2+Djnv\nh0rr7ull+YM76XjuIIVin1Pc+lGNDZb3fJldebqfSERKFWuQMrNGogB1j7s/GIpfSXexhe68/aE8\nBZyW8fbJoSzF8a67dPkPQvnkHPuXc45+3P0O4A6I1pMq+hsuwmDp5+kP9bjTqXt6+7hny/OVOZhH\nE9MOdiMw1Gc3nZbhEKmdOLP7DPgG8JS7/5+Ml9YB6Qy9xcBDGeXXhgy8ucBroctuA3CxmY0PCRMX\nAxvCa6+b2dxwrmuzjlXKOapmsODTPK4RgJOaGmOvS6Wib+9RpztHgBoOXXlahkOktuIck5oH/AFw\ngZltD49LgZXARWa2F/jd8BzgEeBpoAv4Z+B6gJAw8Xng8fD4XDqJIuzz9fCenxElTVDqOappsCSB\nQ4d7mXXL9+jtO1qlGlVGdsAbP65xWGTcaRkOkdqKM7vvPyDv5AYX5tjfgY/nOdadwJ05yjuA9+Yo\nf7XUc1TL0gXT845JpRUzDjRURuVaUrmMGzO67gMUaHYLGZmS1MWtaZGqLPMenzg1FLjxt7mpkV/9\npRNLPmYpkzMNlw9xLcMhI03SurgVpGogPSNDnIGq76gzPoxvZQeXN94+wt79b5R8zHT2YXMR42XD\n5UNcafMy0iSti1tBqoaKnXmiXOPGjKaluWlAt16h9PTBpLp7eG2Q7sh6+hAfbJYMzW4hI03Surg1\nwWyNpPt8e3r7YhsfqtR6VaVoyei/ztWvDcm5T6rYdanqMW1epFxJm8BZQarK2jtT3PKdXf3uKYr1\nbuYY5Kpv5rpVADe17+SeLc8f2zfV3cPS+3eAc2y9qnxBoVqDtklcl0qk1nIld9Wyd0TdfVWU/s99\nsFV0602DWb8A1d6Z6heg0nr7vN+CijCwr7uag7ZJ69YQSYKkdXGrJVVFpSx2WE/SS4qkrdqwp6TW\nYWZQqGbrJmndGiJJkaQubrWkqmi4/oee/aFe6lhY5vtLbd0MlvhQiDL3RJJPQaqKhut/6Jkf6u2d\nqYL3U2Uv3JgdFPJdo1FmAwLRULsGk9atISIDmQ9xjaLhrrW11Ts6OipyrPbOFJ9as73uEiUKGTt6\nFCe/YywvdvdwUlMjr73Zm3dm9Y/MnULr6RMKJkUUmiU+LZ2kkV6iPlt6Jvm4JOlufJGkMrNt7t46\n5OMoSBVWySAFMHXZ+oodq948u/KyovZLB4FC3YYtzU15F2E04Jkiz1WqXEE0O7NRRCoXpNTdV0U3\nte+sdRVqamqR40bpGTkKzWyRbsXkUm63ajHjW0m7G19kuFOQqpJ0WvZIV8q4UaGJdtPdbJVKfCh2\nfEtp6yLVpSBVJaWmZQ9nlWh5pMeBKpX4UGwLSRPOilSX7pOqEv2n3V+qu4dfWf4Ife40mNHn3m9K\nJci/2u+4xlHH9qnU/RzFtpCSdje+yHCnllQVtHemGGWlLHQxMvSFpJ301+wuthWXn0VjQ//r1thg\nfPGKX6t4XYptISltXaS61JKKWXqso09ZlEXJnF0i/cFfjXTvUlpIhVpvSk8XqSwFqZgN16mQ4pTZ\nxVZMd14lAkMlAmKxs6qLSPEUpGKmsajSlZKEUMnAMNTxrXqZVV2tPaknsY1JmdmdZrbfzH6aUTbB\nzDaa2d7wdXwoNzO7zcy6zOwJMzsn4z2Lw/57zWxxRvkcM9sZ3nObWTToU8454qSsr9LNnzGx6H2T\ndN9Svn9IUt09Zc0tGIekLQ0uMpg4W1LfBP4euCujbBnwmLuvNLNl4flngEuAM8LjPOB24DwzmwCs\nAFqJljHaZmbr3P1Q2Oc6YCvwCLAQeLTUc8T23QdLF0xn6f07hrQa7kizefeBfs/bO1PcvG7Xsfum\nRhkc9ShpId+sFKnunn4thpOaGjGD7sO9sbUe8s2qDvQLCFC77r96ae2JpMXWknL3fwcOZhUvAlaH\n7dVAW0b5XR7ZAjSb2SnAAmCjux8MgWkjsDC89i533+LRvE53ZR2rlHPEqm12CyeOUa9qKTJbJO2d\nKZbet6Pfjb3pJalS3T0FJ7Ndet+OYy2G7p5eDh3ujbX1kOvm4my1np1CNyNLval2Cvokd38pbL8M\nTArbLcALGfvtC2WFyvflKC/nHLF7rcDMCTJQuou0vTPFjWt3DFgoMVOh9mmh9xUTLEpdBiQ7PT2f\nWgYE3Yws9aZm/+K7u5tZrH1g5Z7DzJYASwCmTJky5HoU6gaS/hobjKULpg9Yfj4OhX4m5SZkZCZf\nzFu5qeaLKmYnScyfMZEHtqV0M7LUjWq3pF5Jd7GFr/tDeQo4LWO/yaGsUPnkHOXlnGMAd7/D3Vvd\nvXXixOIH8fMpphtIIr19zg1rtnN3zAEKomXv86lEQkatF1XMlSTxwLYUH5zTopuRpW5UuyW1DlgM\nrAxfH8oo/4SZ3UuUzPCau79kZhuAL6Yz9ICLgeXuftDMXjezuUSJE9cCf1fOOWL8Xvs5oXGU7pdK\nmD73nOnYkL+VVUpXXTVvRs4lX6DdvPtArOttiVRSbOtJmdm3gPOBk4FXiLL02oG1wBTgOeDKEHCM\nKBNwIXAY+Ki7d4Tj/BHwl+Gwf+Xu/xLKW4kyCJuIsvr+LHTvvbvUcxQy1PWkilnET2qjuamRt44c\n7fezaWww8PzjWQ1mHHWv+f1FxdzrNG3Z+qqvtyWSVqn1pGJrSbn7NXleujDHvg58PM9x7gTuzFHe\nAbw3R/mrpZ4jTppxIpkMMGPAz2awWwWy5xmE6qeTFztelm8sVEkSUk80wWzMlNqbTE50z9RQ1Cqd\nvNjxslqPiYlUgm7giZky+5KpJbQmhvqzieufkELdecXe61TrMTGRSlCQitn8GRO5WyvyJs4bbx3h\n/WefMiAdO9eYVFNjAyc0jsq5tlUcXWeDdefl+8fnpKbGAWWVWm9LpFbU3Rej9s4Uax5/YfAdpeq6\ne3q5e8vzGE5zU+OxdOxVHzqt18XFAAAMtklEQVSbVR8+e0CK9orLz6pa19lg3XlLF0yncdTA9Pk3\n3j6iOfhk2FFLKkarNuzRnH0Jd7j3KI7x5atm9WtxtM1uOdbl9qk12zm1uYkPzmlh8+4DBbvOKjHD\n+GDdeW2zW7jlO7sGtOx6+zwRc/BplnWpJAWpGClpoj709PZx49odx4JRunWU3eWWngEje5n7tEot\nG1JMVl6+pI9cv3PVDBpaU0sqTd19MVKqb/3oc+83+ezN63YN6HJLt4nzTVBbqWVDisnKK3YOvmov\nzZGkpVNkeFCQitHSBdPJMXQgCdfT29dv1vV8+9ywZnu/iWcLLRuSVsyktdkT1eaauqjY9PJqBw3N\nsi6Vpu6+GLXNbuFTa7bXuhoSo1R3D0vv2wFEs1H05ZjBJT1HYCldYYNl5RWbXl7toKEbiKXSFKRi\nkDkGoLSJ4a/3qHPzul05AxREXYnTlq1nVI4gVuqCg6WOL1U7aCxdMH3ANGC6gViGQkGqwjRX38jU\n3dNbcKVgh7xBLN2qGSwAlZOUMH/GxAFLnsQZNHQDsVSaglSFaa6+kSvXWk3FOLW5qagAVOrS7+2d\nKR7YluoXoAz44Jx4b/DVDcRSSUqcqDANEI9c92x5vuQAlW7VFJPgUOr4Uq5jOrB594GS6ihSS2pJ\nVZjm6hu5Sh1/bAkr5a7asKeo9atKHV8qNajpJlxJIrWkKkwDxFKM9A3BD2xLFfynJjMAlTqrebH3\nUkH176cSKZaCVIW1zW5Bt0bJYF7s7hl0/DI7ABVz/1SmUoKabsKVpFJ3XwyUdi6DSbdW8sk39VIp\nSQmlZNrpJlxJKgWpGBRKRRYZTEtzEz9adkFFjlVsUNNNuJJU6u6LQa5uFkkWK9AnOwr4yNwpA5bD\naBxlfOWqWXzlqlnHutyKMa5xFOPHDVzrKZda3fiqVXwlqUZcS8rMFgJfBRqAr7v7ykqfI1c3y6E3\n3uJw79FKn0pKZNBvWY72zlS/ZS+amxq5+QNn0Ta7hdbTJ+TtKkt/val9Z95FLbO77KYtW5+3K9ig\nphl1uglXkso8z13ww5GZNQD/CVwE7AMeB65x9yfzvae1tdU7OjqGfG7NRFF7jaOMVR8+u+IfvDe1\n7+RbW1+gz50GM6457zS+0DZzwH7zVm7K2aVWye49kaQws23u3jrU44y0ltS5QJe7Pw1gZvcCi4C8\nQapS0h+MuRark+KkW0GZ/+3PnzGRNT9+od9y7+lgBNVpGXyhbWbOoJRN89qJlG6kBakWIHM9933A\nedU6eXoQu70zxY1rd+Sdy224GdNgTHznCUNOJjm1uSlnIkAx3XJJoC41kdKNtCBVFDNbAiwBmDJl\nSsWPn/5QyvVfdfq+l/Td/5kf7OmupNbTJ3DjfTvoy2g9NIwy/jarK+um9p0DJhc1OLa67PwZE48t\nh35SUyM9vX28daTwuNm4xlF88YpfAwa2Ck8c08DbR472a9Vkfk8wcFaDdB1S3T3HlroYP66RX7x5\nZMBx8rU46mmuuHqqq0gSjLQxqfcBN7v7gvB8OYC735rvPZUak8plKNPQFPvecs5RjXpV6zgiUhuV\nGpMaaUFqNFHixIVAiihx4vfcfVe+98QZpEREhislTpTB3Y+Y2SeADUQp6HcWClAiIlJbIypIAbj7\nI8Ajta6HiIgMTjNOiIhIYilIiYhIYilIiYhIYo2o7L5ymNkB4Lk8L58M/LyK1akU1bu66rXeUL91\nV72rK1e9T3f3iUM9sILUEJhZRyVSLKtN9a6ueq031G/dVe/qirPe6u4TEZHEUpASEZHEUpAamjtq\nXYEyqd7VVa/1hvqtu+pdXbHVW2NSIiKSWGpJiYhIYilIlcHMFprZHjPrMrNlCajPaWa22cyeNLNd\nZvbJUD7BzDaa2d7wdXwoNzO7LdT/CTM7J+NYi8P+e81scZXq32BmnWb2cHg+zcy2hvqtMbMxoXxs\neN4VXp+acYzloXyPmS2oUr2bzex+M9ttZk+Z2fvq4Zqb2afC78lPzexbZnZCEq+5md1pZvvN7KcZ\nZRW7vmY2x8x2hvfcZmYWY71Xhd+TJ8zs22bWnPFazuuY73Mm388qrrpnvHajmbmZnRyeV+eau7se\nJTyIJqb9GfAeYAywAzizxnU6BTgnbL+TaKb3M4G/BpaF8mXAl8L2pcCjRMtLzQW2hvIJwNPh6/iw\nPb4K9f808G/Aw+H5WuDqsP014E/D9vXA18L21cCasH1m+DmMBaaFn09DFeq9GvjjsD0GaE76NSda\n+PMZoCnjWv9hEq858NvAOcBPM8oqdn2BH4d9Lbz3khjrfTEwOmx/KaPeOa8jBT5n8v2s4qp7KD+N\naGLu54CTq3nNY/0jHo4P4H3Ahozny4Hlta5XVh0fAi4C9gCnhLJTgD1h+5+AazL23xNevwb4p4zy\nfvvFVNfJwGPABcDD4Zf35xl/0Meud/gjeV/YHh32s+yfQeZ+Mdb7JKIPe8sqT/Q15/jq1BPCNXwY\nWJDUaw5Mpf+HfUWub3htd0Z5v/0qXe+s1/4HcE/YznkdyfM5U+jvI866A/cDZwPPcjxIVeWaq7uv\ndLmWoE/ManyhO2Y2sBWY5O4vhZdeBiaF7XzfQy2+t68AfwGklwR+N9Dt7kdy1OFY/cLrr4X9a1Hv\nacAB4F8s6qr8upmdSMKvubungL8BngdeIrqG26iPaw6Vu74tYTu7vBr+iKgVAaXXu9DfRyzMbBGQ\ncvcdWS9V5ZorSA0jZvYO4AHgBnd/PfM1j/51SVQqp5m9H9jv7ttqXZcyjCbqFrnd3WcDbxB1Px2T\n0Gs+HlhEFGRPBU4EFta0UmVK4vUdjJl9FjgC3FPruhTDzMYBfwn871rVQUGqdCmi/tm0yaGspsys\nkShA3ePuD4biV8zslPD6KcD+UJ7ve6j29zYP+ICZPQvcS9Tl91Wg2aJVlLPrcKx+4fWTgFdrUG+I\n/gvc5+5bw/P7iYJW0q/57wLPuPsBd+8FHiT6OdTDNYfKXd9U2M4uj42Z/SHwfuD3Q4BlkPrlKn+V\n/D+rOPwK0T80O8Lf6WTgJ2b2y2XUvbxrXuk+5OH+IPoP+unwg0sPaJ5V4zoZcBfwlazyVfQfZP7r\nsH0Z/Qc8fxzKJxCNs4wPj2eACVX6Hs7neOLEffQfGL4+bH+c/oP4a8P2WfQffH6a6iRO/BCYHrZv\nDtc70dccOA/YBYwLdVkN/FlSrzkDx6Qqdn0ZOIh/aYz1Xgg8CUzM2i/ndaTA50y+n1Vcdc967VmO\nj0lV5ZrH+kc8XB9EWS3/SZR989kE1Oc3ibo9ngC2h8elRP3XjwF7ge9n/KIY8A+h/juB1oxj/RHQ\nFR4freL3cD7Hg9R7wi9zV/iDHBvKTwjPu8Lr78l4/2fD97OHCmVpFVHnWUBHuO7t4Q8y8dccuAXY\nDfwU+NfwAZm4aw58i2jcrJeo5fqxSl5foDVcg58Bf09WEkyF691FNE6T/vv82mDXkTyfM/l+VnHV\nPev1ZzkepKpyzTXjhIiIJJbGpEREJLEUpEREJLEUpEREJLEUpEREJLEUpEREJLEUpEREJLEUpERq\nLCyH8X0z225mV5nZDWE6mkLveTZjyYT/N8i+rWZ2WyXrLFItowffRURiNhvA3WdBFICAu4HDxbzZ\n3X9jkNc7iG46Fqk7akmJxMDMTjSz9Wa2w6LFBa8Ki9jtNrOfhAXfHjazXyIKSL8eWlKfJJr4dbOZ\nbS7yXL8IX+81s8syyr9pZh8ys/Pt+IKSN4eF7X5gZk+b2f/M2P9/hUX2/sOixRD/vJLXRKQcClIi\n8VgIvOjuZ7v7e4HvAv8MXA7MAX4ZwN33A38M/NDdZ7n7V4EXgfnuPr/Ec64BrgQIq7VeCKzPsd8M\nojWkzgVWmFmjmf068EGiNYMuIZq+RqTmFKRE4rETuMjMvmRmv0U0Uegz7r7Xo7nI7o7hnI8C881s\nLFGg+Xd378mx33p3f8vdf040i/gkopnQH3L3N939v4DvxFA/kZIpSInEwN3/k2jpjp3AF4APVOGc\nbwI/IGolXUXUssrlrYztPjQ2LQmmICUSAzM7FTjs7ncTLS/xG8BUM/uVsMs1Bd7+X8A7yzz1GuCj\nwG8RdTEW60fA5WZ2Qlg88/1lnl+kovQflEg8ZgKrzOwo0bIHfwqcDKw3s8NEa1HlC0R3AN81sxfL\nGJf6HtHyGw+5+9vFvsndHzezdUTLjrxC1AJ8rcRzi1ScluoQqQEzOx/4c3dPTIvFzN7h7r8I92j9\nO7DE3X9S63rJyKaWlIik3WFmZxItdLhaAUqSQC0pkQQzs61EK+dm+gN331mL+ohUm4KUiIgklrL7\nREQksRSkREQksRSkREQksRSkREQksRSkREQksf4/wDYIw3/yxUIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "      5\n",
            "0  1250\n",
            "1  2040\n",
            "2  2140\n",
            "3  2510\n",
            "4  2200           2\n",
            "0  221900.0\n",
            "1  538000.0\n",
            "2  180000.0\n",
            "3  604000.0\n",
            "4  510000.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bvNb2yJn_5Yb",
        "colab_type": "code",
        "outputId": "dbaf91d6-c436-48d3-cbfd-f573d95628e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 859
        }
      },
      "source": [
        "learning_rate=0.001\n",
        "X=tf.placeholder(tf.float32)  #declaring placeholders whose value will be feeded during session\n",
        "Y=tf.placeholder(tf.float32)\n",
        "\n",
        "def model(X,w):  \n",
        "  return tf.multiply(X,w)     #creating model function to return X*w\n",
        "\n",
        "w=tf.Variable(0.0,name=\"weight\")    #weight parameter\n",
        "y_model=model(X,w)      # y predicted value\n",
        "cost =tf.square(Y-y_model)   #declaring cost function\n",
        "\n",
        "train_op=tf.train.MomentumOptimizer(learning_rate,momentum=0.9).minimize(cost)  #usin momnetum optimizer to minimize cost with ,moment =0.9\n",
        "\n",
        "sess=tf.Session()   #declaring sessions \n",
        "init=tf.global_variables_initializer()   # initializing global variables\n",
        "sess.run(init)\n",
        "\n",
        "for i in range(0,7000):\n",
        "  for (x,y) in zip(x_train,y_train):\n",
        "    sess.run(train_op,feed_dict={X:x,Y:y})  #fedding placehoder values\n",
        "    \n",
        "w_val=(sess.run(w))   #the final weight of w calculated\n",
        "sess.close()\n",
        "print('w_value',w_val)\n",
        "plt.scatter(x_train,y_train)\n",
        "y_learned=x_train*w_val\n",
        "plt.plot(x_train,y_learned,'r')\n",
        "plt.show()\n",
        "print('y_train',y_train.head(n=10))\n",
        "print('x_train',x_train.head(n=10))\n",
        "print('y_learned',y_learned.head(n=10))\n",
        "\n",
        "\n"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "w_value 0.4\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZsAAAD8CAYAAAChHgmuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "y_train            2\n",
            "0   221900.0\n",
            "1   538000.0\n",
            "2   180000.0\n",
            "3   604000.0\n",
            "4   510000.0\n",
            "5  1225000.0\n",
            "6   257500.0\n",
            "7   291850.0\n",
            "8   229500.0\n",
            "9   323000.0\n",
            "x_train       5\n",
            "0  1250\n",
            "1  2040\n",
            "2  2140\n",
            "3  2510\n",
            "4  2200\n",
            "5  2170\n",
            "6  2980\n",
            "7  1180\n",
            "8  1540\n",
            "9  1540\n",
            "y_learned              5\n",
            "0   500.000007\n",
            "1   816.000012\n",
            "2   856.000013\n",
            "3  1004.000015\n",
            "4   880.000013\n",
            "5   868.000013\n",
            "6  1192.000018\n",
            "7   472.000007\n",
            "8   616.000009\n",
            "9   616.000009\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C4cnzdQgsuXd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}