ML_LAB_3.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "In this practical I have implemented Linear Regression (Supervised Learning) "
      ],
      "metadata": {
        "id": "9BZ_G7PkS62T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import linear_model\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import random"
      ],
      "metadata": {
        "id": "hve6L1nmwosi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# let's create a linear function with some error called f\n",
        "def f(x):\n",
        "    res = x* 25 + 3\n",
        "    error = res * random.uniform(-0.15, 0.15) # you can play with the error to see how it affects the result\n",
        "    return res + error"
      ],
      "metadata": {
        "id": "xVvTgRTGwxoj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "values = []\n",
        "# now using f we are going to create 100 values.\n",
        "for i in range(0, 100):\n",
        "    x = random.uniform(1, 100)\n",
        "    y = f(x)\n",
        "    values.append((x, y))\n",
        "    regr = linear_model.LinearRegression()"
      ],
      "metadata": {
        "id": "fw3To5i0xAwC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x, y = zip(*values)\n",
        "print(x)\n",
        "print(y)\n",
        "max_x = max(x)\n",
        "min_x = min(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DoqRHJhNxWkm",
        "outputId": "262e0017-c5b8-4b78-bd60-99c0245a15bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(11.61061796964851, 78.79993942078539, 84.33365044350846, 8.769810835366155, 61.995662208630655, 22.344701697006467, 37.54374062172276, 49.622331034138575, 8.56355083283393, 88.24091986024494, 29.447512597567247, 74.23468915044847, 22.388428200143697, 89.56966830224972, 22.622054997332466, 81.53557622869731, 46.92455390560475, 68.73443619878582, 38.950580879611834, 75.48288634193734, 84.24182421668371, 45.49476018513862, 65.01711263708756, 81.0462374019092, 29.63102926084367, 60.51958961817853, 79.10163217450615, 12.760427736519599, 82.59821091409681, 42.01607529390482, 94.80435411147654, 58.92132596623872, 63.6186040797605, 86.99404372376009, 2.1957539754970514, 16.3068249268716, 47.71031589246205, 18.635869196995856, 65.69052557193142, 30.4790542355046, 92.5041997527894, 12.681464992285742, 84.85553817487586, 64.53352670774201, 54.73120551149816, 99.73121643845991, 14.206170852590615, 88.51312249606558, 71.736937689131, 51.87102093379277, 6.036192226254468, 75.36084080737551, 4.018436529491886, 53.51478163750031, 68.13450679848576, 74.22569302289536, 5.568660177345212, 2.1495996519848966, 61.58383923520141, 33.90323704257523, 91.22493643772745, 73.85978022679622, 15.00713285007781, 50.958702637301926, 92.13659752030952, 75.20852913849812, 99.46625321376835, 67.61443593432168, 74.49315405556649, 22.77513608895292, 34.63461128781334, 80.44181229253047, 46.40718319773802, 81.16793537886433, 16.512233598448866, 95.45626695143471, 27.76742741298371, 55.18447458052087, 12.349283935345124, 34.58598902580685, 61.06774973723894, 5.1732872011933075, 40.81583076825738, 78.15141177760056, 63.3010042156404, 56.36789864451163, 30.68862470121604, 15.782638683723505, 74.03298717276668, 37.84214733090441, 14.630742956868627, 82.55033733259307, 30.67925437952228, 4.577315839630817, 52.53512101088138, 52.041406506597575, 77.25197954252928, 18.2593839460752, 11.570440516315433, 64.64294358037435)\n",
            "(306.60006165235706, 1818.788362208812, 2320.9400817067535, 207.0011668090295, 1580.1960624469446, 507.08141816577654, 1052.523244060841, 1076.9904436248598, 228.6709086884381, 1899.7788400646505, 845.5167214068274, 2066.356815033389, 634.2047164350262, 1964.3317168373928, 578.402194410606, 1794.0190592282124, 1035.2708535346994, 1950.8585700597018, 999.3204544676485, 1830.2460744945067, 2361.351749723846, 1037.0717467520092, 1447.36429517816, 1750.8188650879356, 749.6936924431408, 1578.5537263491703, 1733.4877687812602, 366.52343858486427, 2095.716705722838, 960.385103534024, 2323.0510183705055, 1523.920239787355, 1368.3743271879948, 1993.2157974662266, 52.60986796442672, 397.87245058963697, 1373.412625478265, 526.3087875052483, 1729.8027533431741, 773.0922997091992, 1983.8654959596274, 294.6003025782246, 2396.2680923860307, 1852.8915164961275, 1490.419773887196, 2809.7295974693106, 395.2447332292574, 2267.114099044459, 1829.0036031465643, 1172.8517191979251, 149.25901517741235, 1928.1987721336513, 118.84289136345399, 1362.601065942825, 1773.4914151451899, 2016.3639434457125, 129.50794357354195, 61.86410591630206, 1740.857344628038, 742.9975025299793, 2010.5910850694393, 2116.5245050977474, 398.6647734693402, 1456.0491937540098, 2471.463747006416, 1883.5779356512035, 2172.0596469722163, 1463.3405814346409, 1640.4005932978143, 495.9079898556735, 759.4712288539004, 2230.4884306716613, 1158.9980774883516, 1755.9078775341463, 395.71097367624685, 2453.617367582742, 687.4071328274562, 1248.1978036272483, 346.39211454089025, 779.9839760804542, 1410.5505866285928, 144.35314577531253, 1107.485697011329, 2165.3937168647344, 1349.990622982688, 1386.4331895448404, 712.094356542546, 364.2576620767129, 1625.6757955406229, 919.8745362329224, 419.5852996596657, 1773.0252143494438, 707.7657391208108, 133.5163293382545, 1393.7273696550035, 1181.7314566117889, 1685.0328601810154, 411.73019906432313, 281.2072288205313, 1598.70380425319)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# split the values in training and test data.\n",
        "\n",
        "train_data_X = list(map(lambda x: [x], list(x[:-20])))\n",
        "train_data_Y = list(y[:-20])\n",
        "\n",
        "test_data_X = list(map(lambda x: [x], list(x[-20:])))\n",
        "test_data_Y = list(y[-20:])"
      ],
      "metadata": {
        "id": "PnCEeg5SxmgJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# feed the linear regression with the train data to obtain a model.\n",
        "regr.fit(train_data_X, train_data_Y)\n",
        "# check that the coeffients are the expected ones.\n",
        "m = regr.coef_[0]\n",
        "b = regr.intercept_\n",
        "print(' y = {0} * x + {1}'.format(m, b))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R5XIvVAryVoN",
        "outputId": "dae12422-8b62-4945-bf52-959a4e244488"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " y = 24.4952729911684 * x + 18.79389040545584\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# now we are going to plot the points and the model obtained\n",
        "plt.scatter(x, y, color='blue') \n",
        "plt.plot([min_x, max_x], [b, m*max_x + b], 'r')\n",
        "plt.title('Fitted linear regression', fontsize=18)\n",
        "plt.xlabel('x', fontsize=15)\n",
        "plt.ylabel('y', fontsize=15)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "cw5Ay3dlybHY",
        "outputId": "b576974c-8373-421f-ee44-9bff41de847b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'y')"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# The mean squared error\n",
        "print(\"Mean squared error: %.2f\" % np.mean((regr.predict(test_data_X) - test_data_Y) ** 2))\n",
        "# Explained variance score: 1 is perfect prediction\n",
        "print('Variance score: %.2f' % regr.score(test_data_X, test_data_Y))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1W43NT0xyi42",
        "outputId": "614157ac-c35a-49fe-f708-c18afd55c006"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean squared error: 254306.67\n",
            "Variance score: 0.64\n"
          ]
        }
      ]
    }
  ]
}