Linear_Regression.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Linear_Regression.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyOsPbRXREqkCyHb7V4IMxWT",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MohammedNayaz/Machine-learning-Internship-2/blob/master/Linear_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4SHIx6YcOMda"
      },
      "source": [
        "#Linear Regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TGxGQOtGOP-e"
      },
      "source": [
        "##Simple Linear Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JL4R38vEORv2",
        "outputId": "0bc6f86d-5789-4351-cd64-075e5234b9bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 602
        }
      },
      "source": [
        "\"\"\"Simple Linear Regression\"\"\"\n",
        "\n",
        "#Importing libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#Loading the dataset\n",
        "dataset = pd.read_csv('Salary_Data-1.csv').values\n",
        "\n",
        "#Specify independent and dependent veraibles\n",
        "x = dataset[:, :-1]\n",
        "y = dataset[:, -1]\n",
        "x,y\n"
      ],
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[ 1.1],\n",
              "        [ 1.3],\n",
              "        [ 1.5],\n",
              "        [ 2. ],\n",
              "        [ 2.2],\n",
              "        [ 2.9],\n",
              "        [ 3. ],\n",
              "        [ 3.2],\n",
              "        [ 3.2],\n",
              "        [ 3.7],\n",
              "        [ 3.9],\n",
              "        [ 4. ],\n",
              "        [ 4. ],\n",
              "        [ 4.1],\n",
              "        [ 4.5],\n",
              "        [ 4.9],\n",
              "        [ 5.1],\n",
              "        [ 5.3],\n",
              "        [ 5.9],\n",
              "        [ 6. ],\n",
              "        [ 6.8],\n",
              "        [ 7.1],\n",
              "        [ 7.9],\n",
              "        [ 8.2],\n",
              "        [ 8.7],\n",
              "        [ 9. ],\n",
              "        [ 9.5],\n",
              "        [ 9.6],\n",
              "        [10.3],\n",
              "        [10.5]]),\n",
              " array([ 39343.,  46205.,  37731.,  43525.,  39891.,  56642.,  60150.,\n",
              "         54445.,  64445.,  57189.,  63218.,  55794.,  56957.,  57081.,\n",
              "         61111.,  67938.,  66029.,  83088.,  81363.,  93940.,  91738.,\n",
              "         98273., 101302., 113812., 109431., 105582., 116969., 112635.,\n",
              "        122391., 121872.]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4nRmiFDeQmrl",
        "outputId": "03aa4da7-932f-43fd-c744-808d03eb5999",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "#Visualising the data before regression\n",
        "plt.scatter(x, y)"
      ],
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fc089da38d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 140
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E1Pu3Ej8QnNd",
        "outputId": "98aeed48-8ab0-4c90-90e1-240a76b9e2b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 150
        }
      },
      "source": [
        "\"\"\"Fitting Simple Linear Regression\"\"\"\n",
        "\n",
        "#Importing linear regreassion class from sklearn library\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "#1 Create linear regression object using the imported class\n",
        "model = LinearRegression()\n",
        "\n",
        "#2 Train the model by applying 'fit fucntion\n",
        "model.fit(x, y)\n",
        "\n",
        "#Predicting the outcome by applying 'predict fucntion on input\n",
        "y_pred = model.predict(x)\n",
        "y_pred\n"
      ],
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 36187.15875227,  38077.15121656,  39967.14368085,  44692.12484158,\n",
              "        46582.11730587,  53197.09093089,  54142.08716303,  56032.07962732,\n",
              "        56032.07962732,  60757.06078805,  62647.05325234,  63592.04948449,\n",
              "        63592.04948449,  64537.04571663,  68317.03064522,  72097.0155738 ,\n",
              "        73987.00803809,  75877.00050238,  81546.97789525,  82491.9741274 ,\n",
              "        90051.94398456,  92886.932681  , 100446.90253816, 103281.8912346 ,\n",
              "       108006.87239533, 110841.86109176, 115566.84225249, 116511.83848464,\n",
              "       123126.81210966, 125016.80457395])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wuSBRAegQnTO",
        "outputId": "2ab604ac-c110-421c-f6b3-6a5074a81bdd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "#Visualising the outcome\n",
        "plt.scatter(x, y)\n",
        "plt.plot(x, y_pred, color='red')\n",
        "\n",
        "model.intercept_\n",
        "model.coef_\n",
        "#"
      ],
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9449.96232146])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 142
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-UHsYPL5QnV_",
        "outputId": "aff6155d-c99e-44e7-d089-a4f7a47716ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "#Visualising the outcome\n",
        "plt.scatter(x, y)\n",
        "plt.plot(x, y_pred, color='green')\n",
        "\n",
        "model.intercept_\n",
        "model.coef_\n",
        "#"
      ],
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9449.96232146])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 143
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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C3VsBCKugyMzKZOi3Q3l49sNEWAQj2o4goWkCZUzzXkoDhYSIT/K7gC1cQmL5xuXEJ8azIGUBVzW4itfbvk7tyrUPvaOUGAoJEZ8E6wK2UEjPTGfQl4N4et7TVClfhfeue49Op3fCu92LlCIKCRGfBOMCtlBYmLKQ+MR4lm1cxo2n38grrV+hRsUavtYk/lGnoohPCnMB25GamJTC+YNmU6/fZM4fNDvPhft2Z+ymz/Q+NBvdjK1pW0nslMh7/35PAVHK6UxCxCcFuYCtIA5ngHzOb3Po9mk3krcm061pN4ZcMYQq5asEtQ4pnhQSIj46kgvYCiq/AfJLT61E35l9GbF4BPWr1md259lcWu/SkNYjxYtCQqSECzQQnrxjLqe91pn1O9fzwL8e4IlLn6BCVIUirk7CnUJCpIQ7cIA8k+1sjRrJ7si5nB59OhNumMC5sef6WKGEMw1ci5RwOQPkDseuiLmsK38XuyPm06nh/SzuvlgBIfnSmYRICde+SSyb09bx4Ky72Zb1NZXsVJ65+FV6X3yZ36VJMaCQECnBslwWo5aMos+8PmSUyeDFy1+k93m9iSgTceidRVBIiJRYyVuT6fZpN+b8NofL6l3GG1e/wYlVT/S7LClmFBIiJcy+rH28/M3LPPr5o5SNKMsbV79BfJN4LakhBaKQEClBlm1YRnxiPAvXLaRdw3a8duVrxFYOj8UCpXhSSIiUAHv37WXgFwMZ+OVAqpavyrh/j6PjaR119iCFppAQKeYWrF1AfGI8yzct55YzbuGlVi9xdIWj/S5LSgiFhEgxtSt9F49+/igvf/MysZVjmXzTZK5scKXfZUkJo5AQKYZm/zqbbp9245dtv3BX3F0MunwQlctV9rssKYEUEiLFSOqeVPpM78OopFE0qNaAubfN5aITLvK7LCnBFBIixcSkHydx1+S72LBrAw82f5DHL3mc6Ch/b1AkJZ9CQiTMbdy1kd7/68345eM549gzSLwxkbhacX6XJaWEQkIkTDnneHfZu9w99W52pu/kqUufou/5fYmKiPK7NClFFBIiYWjN9jXcOflOpqyeQrPazRjdbjSNajTyuywphRQSIocwMSkl5LcYzZHlshixaAR9Z/Yl02XycquX6XVuLy3IJ75RSIjk43DuDx0sq7esJuHTBOb9Po/LT7yckW1HUq9qvaC+h8iR0k2HRPKR3/2hg2Vf1j4Gzx/MGa+fwdINSxnTbgzTb5mugJCwoDMJkXwEuj90oPbDkbv7qkrldeys8Co/b1/Ktadcy7Arh1HzqJoFPrZIsCkkRPJx4P2hc7cXRE731e6MPWyPHMdv6R8RkX4UD5zzOoOv7K4F+STsqLtJJB8594fOLToqgj6tGhboeEOmrWJb5g+sL9eb7VHjqZh5CTX3vMZXyxooICQsFSokzOxeM1tuZj+Y2ftmVt7M6pnZAjNLNrPxZlbW27ac9zzZe71uruP099pXmVmrXO2tvbZkM+tXmFpFCqJ9k1ieva4xsTHRGBAbE82z1zUu0KD1zvSdLNv1MhvKPkgWezhm7xMcnXEvEVQuVPeVSCgVuLvJzGKB3kAj51yamX0AdAKuBF5yzo0zs9eBeGC4932bc+4kM+sEPAfcYGaNvP1OA2oBM83sZO9thgFXAGuBhWaW6JxbUdCaRQqifZPYQs9kmvHzDLp/1p2/In/jqH1ticnoTBkq/P16QbuvREKtsN1NkUC0mUUCFYD1wGXAR97rY4H23uNrvOd4r7ew7PPra4Bxzrm9zrlfgWTgXO8r2Tn3i3MuHRjnbStSbGxL20bXSV1p+U5LykWU45nzJxBLz/0CojDdVyKhVuAzCedcipk9D/wBpAHTgcVAqnNun7fZWiDnT7BYYI237z4z2w5U99q/yXXo3PusOaD9vLxqMbPuQHeAOnXqFPRHEgmqCSsn0HNKTzbt2kT/C/rz2MWPUT6yPI2qF93FeSKFVZjupqpk/2VfD0gFPgRaB6muI+KcGwmMBIiLi3N+1CCS48+df9JrSi8+XvkxZx13FlNumkKTmk3+fj0Y3VciRaUwU2AvB351zm0CMLMJwPlAjJlFemcTtYEUb/sU4Hhgrdc9VQXYkqs9R+59ArWLhB3nHG99/xb3TruX3Rm7GXjZQB5o/oAW5JNirTBjEn8Azcysgje20AJYAXwOdPC26QJM8h4nes/xXp/tnHNeeydv9lM9oAHwLbAQaODNlipL9uB2YiHqFQmZ31N/p827bbht0m00qtGI7+78jv4X9ldASLFXmDGJBWb2EbAE2Ackkd3lMxkYZ2ZPe22jvV1GA2+bWTKwlewPfZxzy72ZUSu84/R0zmUCmFkvYBoQAYxxzi0vaL0ioZDlsnht4Wv0m5k9Q3tom6H0OKcHZUyXIEnJYNl/zJcccXFxbtGiRX6XIaXAj5t/JCExgflr5tOqfitGtB3BCTEn+F2WSIGY2WLn3EF3s9KyHCJHKCMzgyFfDeGJuU9QMaoib17zJp3P7KwrpqVEUkiIHIGk9Ul0TezKd39+R4dGHRjaZijHVTrO77JEQkYhIXIY9uzbw5Nzn2Tw/MHUqFiDjzt+zHWnXud3WSIhp5AQOYQv//iS+MR4ftryE7efdTsvtHyBqtFV/S5LpEgoJEQC+GvvX/Sf1Z9hC4dRN6Yu02+ZzhX1r/C7LJEipZAQycO05Gl0/6w7a7avofe5vXmmxTNUKlvJ77JEipxCQiSXrWlbuXfavbz1/VuccvQpfNn1S5of39zvskR8o5AQ8Xy04iN6TunJ1rStPHLhIzx80cOUjyzvd1kivlJISKm3/q/19PpfLyasnMDZNc9m+i3TOfO4M/0uSyQsKCSk1HLO8eZ3b3Lf9PvYs28Pz13+HPf96z4iy+i/hUgO/W+QUum31N/o/ml3ZvwygwvrXMiodqM4ufrJh95RpJRRSEipkpmVybCFw3ho1kOYGa9d+Rp3xN2hBflEAlBISKmxctNK4hPj+Xrt17Q5qQ2vt32dOlV0J0OR/CgkpMTLyMxg8PzBPDnvSSqVrcTb177NzY1v1oJ8IodBISEl2uJ1i+ma2JWlG5bS8bSODG0zlGMqHuN3WSLFhkJCSqS0jDSemPsEz3/1PMdUPIZPbviE9qe097sskWJHISElzrzf55GQmMDqratJaJLAkJZDiCkf43dZIsWSQkJKjB17d9BvZj+GLxpOvZh6zLx1Ji1ObOF3WSLFmkJCSoQpq6dw52d3snbHWu5tdi9PXfoUFctWPGi7iUkpDJm2inWpadSKiaZPq4a0bxLrQ8UixYNCQoq1zbs3c++0e3ln6Ts0qtGIr+K/olntZnluOzEphf4TlpGWkQlASmoa/ScsA1BQiASgK4ikWHLO8cHyD2g0rBHjfhjHgIsHsKT7koABATBk2qq/AyJHWkYmQ6atCnW5IsWWziSk2Fn31zp6TO7BpFWTiKsVx6x2s2h8bOND75eadkTtIqKQkGLEOcfopNE8MP0B9mbu5fkrnufuZncf9oJ8tWKiSckjEGrFRAe7VJESQyEhxcIv236h26fdmP3rbC6pewlvXP0GJ1U76YiO0adVw/3GJACioyLo06ohoEFtkbwoJCSsZWZl8t8F/+Xh2Q8TFRHFiLYjSGiaUKAF+XI+8PMKAg1qi+RNISFh64eNP5CQmMCClAW0Pbktw68aTu3KtQt1zPZNYvP80M9vUFshIaWZQkIO4ne3S3pmOs9+8SzPfPEMVcpX4b3r3qPT6Z1CuiCfBrVF8qaQkP343e2yMGUhXRO78sPGH7ip8U283OplalSsEfL31aC2SN50nYTsx69rCXZn7KbP9D40G92MbWnbSOyUyLvXvVskAQHZg9rRURH7teUe1BYprXQmIfsJdbdLXl1ZMVVXk5CYwM/bfqZ70+4MvmIwVcpXCcr7Ha78BrVFSjOFhOwnlN0uB3ZlrUndzO0TXyC1zBTqV63P510+55K6lxT6fQoq0KC2SGmm7ibZTyi7XXJ3Ze0u8y3ryvcg1aZSK6IjS+9a6mtAiEjedCYh+wllt8u61DQy2c7WqJHsjpxLVFZdaqQ/TNk9J1MhqkKhjy8iwaeQkIOEotvFOUfUUV/zR8ZQsthNlYybqbKvA0ZUgbuy/J6qK1IaKCQk5NbuWMtdk+9i9b7PKE9Dqu7tTVl3AlDwriy/p+qKlBYak5CQyXJZjFg0gkbDGjHrl1m82PJF3mk3nXpVTsGA2Jhonr2ucYE+1LXst0jRKNSZhJnFAKOA0wEHdAVWAeOBusBvQEfn3DbLvlz2FeBKYDdwm3NuiXecLsAj3mGfds6N9drPBt4EooEpwN3OOVeYmuUfoeyuSd6aTLdPuzHntzlcVu8y3rj6DU6seiIA/25ap9DH1xXSIkWjsGcSrwBTnXOnAGcCK4F+wCznXANglvccoA3QwPvqDgwHMLNqwADgPOBcYICZVfX2GQ50y7Vf60LWK56c7pqU1DQc/3TXTExKKdRx92Xt4/mvnqfx8MYsWb+EN65+g5m3zvw7IIIl0DiGrpAWCa4Ch4SZVQEuAkYDOOfSnXOpwDXAWG+zsUB77/E1wFsu2zdAjJnVBFoBM5xzW51z24AZQGvvtcrOuW+8s4e3ch1LCikU3TXLNiyj+ejm9JnRh5b1W7KixwoSmiaEZM0lXSEtUjQK091UD9gE/J+ZnQksBu4GjnXOrfe2+RM41nscC6zJtf9ary2/9rV5tB/EzLqTfXZCnTqF78ooDYLZXbN3314GfjGQgV8OpGr5qozvMJ7rG10f0gX5dIW0SNEoTEhEAk2B/zjnFpjZK/zTtQSAc86ZWcjHEJxzI4GRAHFxcRqzOAzBurL6m7XfEJ8Yz4pNK7jljFt4udXLVK9QPVhl5ktXSIuEXmHGJNYCa51zC7znH5EdGhu8riK87xu911OA43PtX9try6+9dh7tEgSF7a7Zlb6L+6bdR/PRzdmxdweTb5rM29e+XWQBISJFo8BnEs65P81sjZk1dM6tAloAK7yvLsAg7/skb5dEoJeZjSN7kHq7c269mU0DBuYarG4J9HfObTWzHWbWDFgAdAaGFrRe2V9humtm/TKLbp9249fUX7kr7i4GXT6IyuUq//16QWZN6cI4kfBU2Ivp/gO8a2ZlgV+A28k+O/nAzOKB34GO3rZTyJ7+mkz2FNjbAbwweApY6G33pHNuq/e4B/9Mgf2f9yUFlNcH8fx+lx32/ql7Unlg+gOMThpNg2oNmHvbXC464aKD3uNwL3LLqSclNQ0jew71ofYRkaJlJe2yg7i4OLdo0SK/ywg7B354Q3b30uFezDbxx4n0mNyDjbs28kDzBxhw8QCiow4evzh/0Ow8xzpiY6L3C6S86jnUPiISOma22DkXd2C7rrguJQo65XXDzg10/LAj146/lmMqHsOChAUMunxQngEBhz9rKq96DvdYIlJ0tHZTKXGkU16dc7yz9B3umXYPO9N38vSlT/Pg+Q8SFRGV7/sc7qypvLY51D4iUvR0JlFKHMkVymu2r6Ht+23pPLEzDas35Ls7vuPhix4+ZEDA4c+aijjENRS6ME4kPCgkSonD+fDOclkMXzick4eeytTVs6mW3h02PsGqtZUPPFxA7ZvE8ux1jYmNic53Eb/MfMbCCrPwn4gEl7qbSolDTXn9actPJCQm8MUfX1Ahqwk103sR6Y5l3fb0I55pdDgXucUG6JbSYLVIeFFIlCJ5fXjvy9rHi1+/yIA5AygfWZ6TIvuQ/tdFGP90B+UMcAfzL/s+rRrmOdtKXUwi4UXdTaXY939+z3mjzqPvzL60OakNK3qsYN9fF+8XEDmCPdPocLulRMRfOpMohfbs28PT857mufnPUS26Gh9e/yEdGnUAgrem0+HQ2ksi4U9nEqXMV2u+osmIJjzzxTPc3PhmVvZc+XdAgJbgFpH96UyilNiZvpOHZj3Eq9++yvFVjmfqzVNpdVKrg7bTEtwikptCohSY/vN0un/anT+2/0HPc3oysMVAjip3VMDt1Q0kIjkUEiXYtrRt3Df9Pt787k0aVm/IvNvncUGdC/wuS0SKEYVECTVh5QR6TunJpl2b6H9Bfx67+DHKR5b3uywRKWYUEiXMnzv/pNeUXny88mPOOu4splUiFxIAAAoASURBVNw0hSY1m/hdlogUUwqJEsI5x9jvx3LftPvYnbGbZ1s8y/3/uv+w1lsSEQlEIVEC/Jb6G3d8dgfTf57OBXUuYNTVo2h4tKasikjhKSSKsSyXxbBvh9F/Vn/MjFfbvMpd59xFGdPlLyISHAqJYurHzT+SkJjA/DXzaVW/FSPajuCEmBP8LktEShiFRDGTkZnBkK+G8MTcJ6gYVZGx7cdy6xm3Yoe4P4OISEEoJIqRpPVJdE3synd/fkeHRh14tc2rHFvpWL/LEpESTCFRDKRlpPHk3CcZ8tUQalSswccdP+a6U6/zuywRKQUUEmHuyz++JD4xnp+2/MTtZ93OCy1foGp0Vb/LEpFSQiFxCBOTUnxZ7O6vvX/Rf1Z/hi0cRt2Yuky/ZTpX1L8i5O8rIpKbQiIfE5NS9rt7Wkpq2hHfyrMgpiZP5Y7P7mDN9jX0Prc3z7R4hkplK4Xs/UREAtGE+nwMmbZqv9trwj+38gyFLbu30GViF9q824YKURX4suuXvNLmFQWEiPhGZxL5CHTLzmDfytM5x0crPqLX/3qxNW0rj1z4CA9f9LAW5BMR3ykk8lEUt/Jc/9d6ekzpwcQfJ3J2zbOZfst0zjzuzKAdX0SkMNTdlI9Q3srTOceYpDGcOuxUpiZP5bnLn+ObhG8UECISVnQmkY9Q3crz122/cu17Xfh+8xeUyzyNRuX7cHKFy4kso38OEQkv+lQ6hGDeyjMzK5NXv32VvjP7k7EPqmX0oFJma7amlymSWVMiIkdK3U1FZMWmFVzwfxdwz7R7iHaNqbn3NY7KvBLz/glCOWtKRKSgFBIhlp6ZzlNzn6LJiCas3rKad659hyq7HiXS1Tho22DPmhIRKSyFRAgtWreIc944h8fmPMa1p1zLip4ruPmMm4mNqZDn9sGcNSUiEgwKiRBIy0jjwRkPct6o89i8ezMTb5jIuA7jOKbiMUBoZ02JiASTBq6DbO5vc0n4NIHkrcl0a9qNwVcMJqZ8zH7bhGrWlIhIsBU6JMwsAlgEpDjn2ppZPWAcUB1YDNzqnEs3s3LAW8DZwBbgBufcb94x+gPxQCbQ2zk3zWtvDbwCRACjnHODCltvqOzYu4O+M/ry+uLXObHqiczqPIvL6l0WcPtgzpoSEQmVYHQ33Q2szPX8OeAl59xJwDayP/zxvm/z2l/ytsPMGgGdgNOA1sBrZhbhhc8woA3QCLjR2zbsTP5pMqe9dhojl4zkvmb3sfTOpfkGxKFMTErh/EGzqddvMucPms3EpJQgVisicvgKFRJmVhu4ChjlPTfgMuAjb5OxQHvv8TXec7zXW3jbXwOMc87tdc79CiQD53pfyc65X5xz6WSfnVxTmHoDKeiH8ubdm7llwi20fb8tVcpV4auuX/FCqxeoWLZioWrpP2EZKalpOP5ZeVZBISJ+KOyZxMvAg0CW97w6kOqc2+c9Xwvk9KnEAmsAvNe3e9v/3X7APoHaD2Jm3c1skZkt2rRp0xH9AAX5UHbOMe6HcZw67FQ+WP4BAy4ewJI7lnBe7fOO6L3zUtQrz4qI5KfAIWFmbYGNzrnFQaynQJxzI51zcc65uBo1Dr7+ID9H+qGcsiOF9uPbc+PHN1Ivph6Luy/m8Usep2xE2QLXn1tRrTwrInI4CjNwfT7QzsyuBMoDlckeZI4xs0jvbKE2kPMneQpwPLDWzCKBKmQPYOe058i9T6D2oDncD2XnHKOWjOKBGQ+QkZnBbY0eZVXyhbR76Q9qxWwK2uykolh5VkTkcBX4TMI51985V9s5V5fsgefZzrmbgc+BDt5mXYBJ3uNE7zne67Odc85r72Rm5byZUQ2Ab4GFQAMzq2dmZb33SCxovYEE+vDN3f7z1p9p8VYLun/WnaY1m/LCRdP5Zmlz1m1PD/q4ga6hEJFwEoqL6foC95lZMtljDqO99tFAda/9PqAfgHNuOfABsAKYCvR0zmV6ZyK9gGlkz576wNs2qPL7UM7MyuTFr1+k8fDGLF6/mJFtRzKr8yzemZ8esnGD9k1iefa6xsTGRGNAbEw0z17XWNNlRcQXlv3HfMkRFxfnFi1adET7TExKOejCtpNitxGfGM+3Kd/S9uS2DL9qOLUr1wagXr/J5PVbM+DXQVcV/ocQESliZrbYORd3YLuuuGb/C9vSM9N59otn6fjZM1QpX4X3//0+N5x2A9mzdbNp3EBESgut3ZTLtynfcvbIs3l87uNcf9r1rOy5kk6nd9ovIEDjBiJSeuhMwvP0vKcZMGcANSvV5NMbP6XtyW0Dbqu1l0SktFBIeOpXrU+3pt147vLnqFK+yiG319pLIlIaaOBaREQCDlxrTEJERAJSSIiISEAKCRERCUghISIiASkkREQkIIWEiIgEpJAQEZGAFBIiIhJQibuYzsw2Ab/7XccROBrY7HcRPtPvQL8D0O/A75//BOfcQbf2LHEhUdyY2aK8rnIsTfQ70O8A9DsI159f3U0iIhKQQkJERAJSSPhvpN8FhAH9DvQ7AP0OwvLn15iEiIgEpDMJEREJSCEhIiIBKSR8YmbHm9nnZrbCzJab2d1+1+QHM4swsyQz+8zvWvxgZjFm9pGZ/WhmK83sX37XVNTM7F7v/8APZva+mZX3u6ZQM7MxZrbRzH7I1VbNzGaY2Wrve1U/a8yhkPDPPuB+51wjoBnQ08wa+VyTH+4GVvpdhI9eAaY6504BzqSU/S7MLBboDcQ5504HIoBO/lZVJN4EWh/Q1g+Y5ZxrAMzynvtOIeET59x659wS7/FfZH84lKqbZptZbeAqYJTftfjBzKoAFwGjAZxz6c65VH+r8kUkEG1mkUAFYJ3P9YScc24esPWA5muAsd7jsUD7Ii0qAIVEGDCzukATYIG/lRS5l4EHgSy/C/FJPWAT8H9el9soM6vod1FFyTmXAjwP/AGsB7Y756b7W5VvjnXOrfce/wkc62cxORQSPjOzSsDHwD3OuR1+11NUzKwtsNE5t9jvWnwUCTQFhjvnmgC7CJMuhqLi9btfQ3Zg1gIqmtkt/lblP5d9bUJYXJ+gkPCRmUWRHRDvOucm+F1PETsfaGdmvwHjgMvM7B1/Sypya4G1zrmcM8iPyA6N0uRy4Ffn3CbnXAYwAWjuc01+2WBmNQG87xt9rgdQSPjGzIzsvuiVzrkX/a6nqDnn+jvnajvn6pI9UDnbOVeq/oJ0zv0JrDGzhl5TC2CFjyX54Q+gmZlV8P5PtKCUDd7nkgh08R53ASb5WMvfFBL+OR+4ley/oL/zvq70uygpcv8B3jWzpcBZwECf6ylS3lnUR8ASYBnZn0lhuTxFMJnZ+8DXQEMzW2tm8cAg4AozW032GdYgP2vMoWU5REQkIJ1JiIhIQAoJEREJSCEhIiIBKSRERCQghYSIiASkkBARkYAUEiIiEtD/Awe0vpNi7qBoAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VN2uBz0_RE1k",
        "outputId": "23e8a4f5-3c50-4c0f-e27d-95129dd8d999",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 602
        }
      },
      "source": [
        "##Simple Linear Regression _Example 2\n",
        "#Importing libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "#Importing the dataset\n",
        "dataset= pd.read_csv('Salary_Data-1.csv')\n",
        "\n",
        "#Matrix creation\n",
        "X= dataset.iloc[:, :-1].values\n",
        "Y= dataset.iloc[:, 1].values\n",
        "X, Y"
      ],
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[ 1.1],\n",
              "        [ 1.3],\n",
              "        [ 1.5],\n",
              "        [ 2. ],\n",
              "        [ 2.2],\n",
              "        [ 2.9],\n",
              "        [ 3. ],\n",
              "        [ 3.2],\n",
              "        [ 3.2],\n",
              "        [ 3.7],\n",
              "        [ 3.9],\n",
              "        [ 4. ],\n",
              "        [ 4. ],\n",
              "        [ 4.1],\n",
              "        [ 4.5],\n",
              "        [ 4.9],\n",
              "        [ 5.1],\n",
              "        [ 5.3],\n",
              "        [ 5.9],\n",
              "        [ 6. ],\n",
              "        [ 6.8],\n",
              "        [ 7.1],\n",
              "        [ 7.9],\n",
              "        [ 8.2],\n",
              "        [ 8.7],\n",
              "        [ 9. ],\n",
              "        [ 9.5],\n",
              "        [ 9.6],\n",
              "        [10.3],\n",
              "        [10.5]]),\n",
              " array([ 39343.,  46205.,  37731.,  43525.,  39891.,  56642.,  60150.,\n",
              "         54445.,  64445.,  57189.,  63218.,  55794.,  56957.,  57081.,\n",
              "         61111.,  67938.,  66029.,  83088.,  81363.,  93940.,  91738.,\n",
              "         98273., 101302., 113812., 109431., 105582., 116969., 112635.,\n",
              "        122391., 121872.]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jb7eXO1lRFSI",
        "outputId": "97946468-f846-45bb-85bb-35fceb916457",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "plt.scatter(X, Y)"
      ],
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fc089cb03c8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 145
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IABuij1FQnbD"
      },
      "source": [
        "#Splitting the data into Training and Test set\n",
        "\n",
        "from sklearn.model_selection import train_test_split \n",
        "X_train,X_test,Y_train,Y_test = train_test_split(X, Y, test_size= 0.3 ,random_state=0)"
      ],
      "execution_count": 146,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-C4UfX7sQndQ",
        "outputId": "377bf87a-9b33-4444-ac9a-5ad571639d0f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "#Fitting Simple Linear Regression to the Training Set\n",
        "from sklearn.linear_model import LinearRegression\n",
        "regressor = LinearRegression()\n",
        "regressor.fit(X_train, Y_train)"
      ],
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6BlA9SuNQnh-",
        "outputId": "994aa63d-416a-4655-e1d8-efbf2dc2a92d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "#Predicting the Test Set Results\n",
        "Y_pred = regressor.predict(X_test)\n",
        "Y_pred"
      ],
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 40817.78327049, 123188.08258899,  65154.46261459,  63282.41035735,\n",
              "       115699.87356004, 108211.66453108, 116635.89968866,  64218.43648597,\n",
              "        76386.77615802])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DDtOErciQnkp",
        "outputId": "77160d0f-1ef1-41fe-93be-c365b9f913b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "#Visualising the Training & Test Set Results\n",
        "plt.scatter(X_train, Y_train, color='red')\n",
        "plt.scatter(X_test, Y_test, color='green')\n",
        "plt.plot(X_train, regressor.predict(X_train), color='blue')\n",
        "plt.title('Salary Vs Experince(Training set)')\n",
        "plt.xlabel('Years of exp')\n",
        "plt.ylabel('Salary')\n",
        "plt.show()"
      ],
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DepdtWpLT6lv"
      },
      "source": [
        "##Multivariable Linear Regression Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OulCWIrhQnm7"
      },
      "source": [
        "import numpy as np\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "x = [[0, 1], [5, 1], [15, 2], [25, 5], [35, 11], [45, 15], [55, 34], [60, 35]]\n",
        "y = [4, 5, 20, 14, 32, 22, 38, 43]\n",
        "x, y = np.array(x), np.array(y)"
      ],
      "execution_count": 150,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iiQ2OkX9QnpD",
        "outputId": "9a0f3633-2f93-43a7-c81b-ca6627767717",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        }
      },
      "source": [
        " print(x)\n",
        " print(y)\n",
        "\n",
        " #In multiple linear regression, x is a two-dimensional array with at least two columns, \n",
        " #while y is usually a one-dimensional array. This is a simple example of multiple linear regression,\n",
        " # and x has exactly two columns."
      ],
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 0  1]\n",
            " [ 5  1]\n",
            " [15  2]\n",
            " [25  5]\n",
            " [35 11]\n",
            " [45 15]\n",
            " [55 34]\n",
            " [60 35]]\n",
            "[ 4  5 20 14 32 22 38 43]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OEOVkLAHQnrz"
      },
      "source": [
        "model = LinearRegression().fit(x, y)"
      ],
      "execution_count": 152,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fte_C_CDQnt8",
        "outputId": "9510b1b0-d091-45f1-fd31-1a67b051e03f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "r_sq = model.score(x, y)\n",
        "print('coefficient of determination:', r_sq)\n",
        "print('intercept:', model.intercept_)\n",
        "print('slope:', model.coef_)\n",
        "\n",
        "#You obtain the value of 𝑅² using .score() and the values of the estimators of regression coefficients\n",
        "# with .intercept_ and .coef_. Again, .intercept_ holds the bias 𝑏₀, \n",
        "#while now .coef_ is an array containing 𝑏₁ and 𝑏₂ respectively.\n",
        "\n",
        "#In this example, the intercept is approximately 5.52, \n",
        "#and this is the value of the predicted response when 𝑥₁ = 𝑥₂ = 0.\n",
        "# The increase of 𝑥₁ by 1 yields the rise of the predicted response by 0.45. Similarly, when 𝑥₂ grows by 1, \n",
        "#the response rises by 0.26."
      ],
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "coefficient of determination: 0.8615939258756776\n",
            "intercept: 5.52257927519819\n",
            "slope: [0.44706965 0.25502548]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tjVDWA2gQnwV",
        "outputId": "6aae009d-8807-4371-aeee-3e9676fe78d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "#Predictions also work the same way as in the case of simple linear regression:\n",
        "y_pred = model.predict(x)\n",
        "print('predicted response:', y_pred, sep='\\n')"
      ],
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "predicted response:\n",
            "[ 5.77760476  8.012953   12.73867497 17.9744479  23.97529728 29.4660957\n",
            " 38.78227633 41.27265006]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OYMfOYDAQnyb",
        "outputId": "14748f12-e1c5-494f-c356-8ef4af878eef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "#The predicted response is obtained with .predict(), which is very similar to the following:\n",
        "y_pred = model.intercept_ + np.sum(model.coef_ * x, axis=1)\n",
        "print('predicted response:', y_pred, sep='\\n')"
      ],
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "predicted response:\n",
            "[ 5.77760476  8.012953   12.73867497 17.9744479  23.97529728 29.4660957\n",
            " 38.78227633 41.27265006]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "auMD0intQn0f",
        "outputId": "b6eeb25f-e293-4ce8-9033-d81c5e92083e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        }
      },
      "source": [
        "#You can apply this model to new data as well\n",
        "x_new = np.arange(10).reshape((-1, 2))\n",
        "print(x_new)\n",
        "\n",
        "y_new = model.predict(x_new)\n",
        "print(y_new)"
      ],
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0 1]\n",
            " [2 3]\n",
            " [4 5]\n",
            " [6 7]\n",
            " [8 9]]\n",
            "[ 5.77760476  7.18179502  8.58598528  9.99017554 11.3943658 ]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dD9zDo8rQn3A"
      },
      "source": [
        "#Example-2\n",
        "import pandas as pd  \n",
        "import numpy as np  \n",
        "import matplotlib.pyplot as plt  \n",
        "import seaborn as seabornInstance \n",
        "from sklearn.model_selection import train_test_split \n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn import metrics"
      ],
      "execution_count": 157,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ojjqBe3zUshd"
      },
      "source": [
        "dataset = pd.read_csv('winequality-red.csv')"
      ],
      "execution_count": 158,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YxxPzt2UUsmP",
        "outputId": "26646e59-66e4-457d-b20b-dc97e097f430",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "dataset.shape"
      ],
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1599, 12)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i4U_2av5Us7q",
        "outputId": "c47e9e34-4756-4b31-b5a7-52cbd0df6ae8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        }
      },
      "source": [
        "dataset.describe()"
      ],
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>8.319637</td>\n",
              "      <td>0.527821</td>\n",
              "      <td>0.270976</td>\n",
              "      <td>2.538806</td>\n",
              "      <td>0.087467</td>\n",
              "      <td>15.874922</td>\n",
              "      <td>46.467792</td>\n",
              "      <td>0.996747</td>\n",
              "      <td>3.311113</td>\n",
              "      <td>0.658149</td>\n",
              "      <td>10.422983</td>\n",
              "      <td>5.636023</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.741096</td>\n",
              "      <td>0.179060</td>\n",
              "      <td>0.194801</td>\n",
              "      <td>1.409928</td>\n",
              "      <td>0.047065</td>\n",
              "      <td>10.460157</td>\n",
              "      <td>32.895324</td>\n",
              "      <td>0.001887</td>\n",
              "      <td>0.154386</td>\n",
              "      <td>0.169507</td>\n",
              "      <td>1.065668</td>\n",
              "      <td>0.807569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.600000</td>\n",
              "      <td>0.120000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.012000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.990070</td>\n",
              "      <td>2.740000</td>\n",
              "      <td>0.330000</td>\n",
              "      <td>8.400000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>7.100000</td>\n",
              "      <td>0.390000</td>\n",
              "      <td>0.090000</td>\n",
              "      <td>1.900000</td>\n",
              "      <td>0.070000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>0.995600</td>\n",
              "      <td>3.210000</td>\n",
              "      <td>0.550000</td>\n",
              "      <td>9.500000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>7.900000</td>\n",
              "      <td>0.520000</td>\n",
              "      <td>0.260000</td>\n",
              "      <td>2.200000</td>\n",
              "      <td>0.079000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>0.996750</td>\n",
              "      <td>3.310000</td>\n",
              "      <td>0.620000</td>\n",
              "      <td>10.200000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>9.200000</td>\n",
              "      <td>0.640000</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>2.600000</td>\n",
              "      <td>0.090000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>0.997835</td>\n",
              "      <td>3.400000</td>\n",
              "      <td>0.730000</td>\n",
              "      <td>11.100000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>15.900000</td>\n",
              "      <td>1.580000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>15.500000</td>\n",
              "      <td>0.611000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>289.000000</td>\n",
              "      <td>1.003690</td>\n",
              "      <td>4.010000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>14.900000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       fixed acidity  volatile acidity  ...      alcohol      quality\n",
              "count    1599.000000       1599.000000  ...  1599.000000  1599.000000\n",
              "mean        8.319637          0.527821  ...    10.422983     5.636023\n",
              "std         1.741096          0.179060  ...     1.065668     0.807569\n",
              "min         4.600000          0.120000  ...     8.400000     3.000000\n",
              "25%         7.100000          0.390000  ...     9.500000     5.000000\n",
              "50%         7.900000          0.520000  ...    10.200000     6.000000\n",
              "75%         9.200000          0.640000  ...    11.100000     6.000000\n",
              "max        15.900000          1.580000  ...    14.900000     8.000000\n",
              "\n",
              "[8 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KdsTRDXPUtAs",
        "outputId": "68a99ede-3675-43d4-95bc-8846b1c7d362",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        }
      },
      "source": [
        "dataset.isnull().any()"
      ],
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "fixed acidity           False\n",
              "volatile acidity        False\n",
              "citric acid             False\n",
              "residual sugar          False\n",
              "chlorides               False\n",
              "free sulfur dioxide     False\n",
              "total sulfur dioxide    False\n",
              "density                 False\n",
              "pH                      False\n",
              "sulphates               False\n",
              "alcohol                 False\n",
              "quality                 False\n",
              "dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 161
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CnyKpQBmUs-I"
      },
      "source": [
        "dataset = dataset.fillna(method='ffill')"
      ],
      "execution_count": 162,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vqL8YggBUs5E"
      },
      "source": [
        "X = dataset[['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar', 'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density', 'pH', 'sulphates','alcohol']].values\n",
        "y = dataset['quality'].values"
      ],
      "execution_count": 163,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lmn4kn6aUskC",
        "outputId": "f06529a0-9964-4a59-e7d8-0ad14d823945",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        }
      },
      "source": [
        "plt.figure(figsize=(15,10))\n",
        "plt.tight_layout()\n",
        "seabornInstance.distplot(dataset['quality'])"
      ],
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc089c3d240>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 164
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eJmLIP24UsfQ"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
      ],
      "execution_count": 165,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lzgvnngkZ0Kl",
        "outputId": "c565e7d1-5ea5-4665-dbca-d8fbdfb178f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "regressor = LinearRegression()  \n",
        "regressor.fit(X_train, y_train)"
      ],
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 166
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eJBAiO9eZz1B"
      },
      "source": [
        "y_pred = regressor.predict(X_test)"
      ],
      "execution_count": 167,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GqKk1HoNZzrS",
        "outputId": "cb63ec58-6855-4842-de0d-3db29d106660",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        }
      },
      "source": [
        "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})\n",
        "df1 = df.head(25)\n",
        "print(df1)"
      ],
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "    Actual  Predicted\n",
            "0        6   5.782930\n",
            "1        5   5.036193\n",
            "2        7   6.596989\n",
            "3        6   5.339126\n",
            "4        5   5.939529\n",
            "5        6   5.007207\n",
            "6        5   5.396162\n",
            "7        6   6.052112\n",
            "8        4   4.867603\n",
            "9        5   4.950676\n",
            "10       5   5.285804\n",
            "11       5   5.412653\n",
            "12       6   5.705742\n",
            "13       5   5.129217\n",
            "14       6   5.528852\n",
            "15       6   6.380524\n",
            "16       7   6.810125\n",
            "17       5   5.738033\n",
            "18       5   5.976188\n",
            "19       4   5.086134\n",
            "20       7   6.344799\n",
            "21       6   5.164010\n",
            "22       6   5.642040\n",
            "23       4   6.146290\n",
            "24       6   5.481780\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EekzAISybiO3",
        "outputId": "cf07a6f6-b8e7-41cd-b102-9edb30e2f68e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        }
      },
      "source": [
        "df1.plot(kind='bar',figsize=(10,8))\n",
        "plt.grid(which='major', linestyle='-', linewidth='0.5', color='green')\n",
        "plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n",
        "plt.show()"
      ],
      "execution_count": 169,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OPzucTVzbioC",
        "outputId": "40eb24c1-3393-4472-beae-8f0e795d19ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))  \n",
        "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))  \n",
        "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
      ],
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 0.4696330928661087\n",
            "Mean Squared Error: 0.3844711978201226\n",
            "Root Mean Squared Error: 0.6200574149384254\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cudJPmNabquy"
      },
      "source": [
        "'''There are many factors that may have contributed to this inaccuracy, for example :\n",
        "Need more data: We need to have a huge amount of data to get the best possible prediction.\n",
        "Bad assumptions: We made the assumption that this data has a linear relationship, but that might not be the case. Visualizing the data may help you determine that.\n",
        "Poor features: The features we used may not have had a high enough correlation to the values we were trying to predict.'''"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Whgbs1vabvDx"
      },
      "source": [
        "##Example"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JIWFuAhbbiq9",
        "outputId": "3dda2041-2477-499b-de90-5a788997b299",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 903
        }
      },
      "source": [
        "\"\"\"Simple Linear Regression\"\"\"\n",
        "\n",
        "#Importing libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#Loading the dataset\n",
        "dataset = pd.read_csv('House_MLR.csv').values\n",
        "\n",
        "#Specify independent and dependent veraibles\n",
        "x = dataset[:, :-1]\n",
        "y = dataset[:, -1]\n",
        "x,y\n"
      ],
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[2104,    3],\n",
              "        [1600,    3],\n",
              "        [2400,    3],\n",
              "        [1416,    2],\n",
              "        [3000,    4],\n",
              "        [1985,    4],\n",
              "        [1534,    3],\n",
              "        [1427,    3],\n",
              "        [1380,    3],\n",
              "        [1494,    3],\n",
              "        [1940,    4],\n",
              "        [2000,    3],\n",
              "        [1890,    3],\n",
              "        [4478,    5],\n",
              "        [1268,    3],\n",
              "        [2300,    4],\n",
              "        [1320,    2],\n",
              "        [1236,    3],\n",
              "        [2609,    4],\n",
              "        [3031,    4],\n",
              "        [1767,    3],\n",
              "        [1888,    2],\n",
              "        [1604,    3],\n",
              "        [1962,    4],\n",
              "        [3890,    3],\n",
              "        [1100,    3],\n",
              "        [1458,    3],\n",
              "        [2526,    3],\n",
              "        [2200,    3],\n",
              "        [2637,    3],\n",
              "        [1839,    2],\n",
              "        [1000,    1],\n",
              "        [2040,    4],\n",
              "        [3137,    3],\n",
              "        [1811,    4],\n",
              "        [1437,    3],\n",
              "        [1239,    3],\n",
              "        [2132,    4],\n",
              "        [4215,    4],\n",
              "        [2162,    4],\n",
              "        [1664,    2],\n",
              "        [2238,    3],\n",
              "        [2567,    4],\n",
              "        [1200,    3],\n",
              "        [ 852,    2],\n",
              "        [1852,    4],\n",
              "        [1203,    3]]),\n",
              " array([399900, 329900, 369000, 232000, 539900, 299900, 314900, 198999,\n",
              "        212000, 242500, 239999, 347000, 329999, 699900, 259900, 449900,\n",
              "        299900, 199900, 499998, 599000, 252900, 255000, 242900, 259900,\n",
              "        573900, 249900, 464500, 469000, 475000, 299900, 349900, 169900,\n",
              "        314900, 579900, 285900, 249900, 229900, 345000, 549000, 287000,\n",
              "        368500, 329900, 314000, 299000, 179900, 299900, 239500]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "szb40Z1TbivJ",
        "outputId": "c47e4e91-9d28-4fc5-a041-85ee166e0be9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y , test_size = 0.1)\n",
        "\n",
        "#fir simple linear model to training test\n",
        "from sklearn.linear_model import LinearRegression\n",
        "model = LinearRegression()\n",
        "model.fit(x_train, y_train)"
      ],
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 172
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xh4cHdRnbix8",
        "outputId": "35beb091-77b0-441c-e279-b61382c36551",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "#Predicting test set\n",
        "y_pred = model.predict(x_test)\n",
        "\n",
        "#accuracy score\n",
        "model.score(x,y)"
      ],
      "execution_count": 173,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7321868563327754"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 173
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QuiAsW8ufHNv",
        "outputId": "d1ae381a-54a6-4613-bbaf-4c7bd4917361",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "model.coef_ , model.intercept_"
      ],
      "execution_count": 174,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([   138.40823862, -10757.28566951]), 94808.66059188859)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 174
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bvCrFZ4UfHgW",
        "outputId": "214c734d-dc93-48fd-877d-b5a16ba61957",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "from sklearn.metrics import r2_score\n",
        "r2_score = r2_score(y_test,y_pred)\n",
        "r2_score"
      ],
      "execution_count": 175,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.700442721912183"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 175
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iHZY9bJKf07-"
      },
      "source": [
        "##Implementing Autucorelation\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TvxLrLHKfHip"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from pandas.plotting import scatter_matrix\n",
        "\n",
        "from scipy import stats\n",
        "from scipy.stats import norm\n",
        "\n",
        "from sklearn.datasets import fetch_california_housing\n",
        "from sklearn.metrics import r2_score, mean_squared_error\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression \n"
      ],
      "execution_count": 176,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HsHgUXTifHlD",
        "outputId": "eb10e88e-2cee-4988-a751-da0c391b2447",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "fetch_california_housing = fetch_california_housing()\n",
        "print(fetch_california_housing.keys())"
      ],
      "execution_count": 177,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dict_keys(['data', 'target', 'feature_names', 'DESCR'])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gkUU6HXOhLXa",
        "outputId": "26e77346-82de-4c4d-f700-99d8ab1055dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 702
        }
      },
      "source": [
        "print(fetch_california_housing.DESCR)"
      ],
      "execution_count": 178,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            ".. _california_housing_dataset:\n",
            "\n",
            "California Housing dataset\n",
            "--------------------------\n",
            "\n",
            "**Data Set Characteristics:**\n",
            "\n",
            "    :Number of Instances: 20640\n",
            "\n",
            "    :Number of Attributes: 8 numeric, predictive attributes and the target\n",
            "\n",
            "    :Attribute Information:\n",
            "        - MedInc        median income in block\n",
            "        - HouseAge      median house age in block\n",
            "        - AveRooms      average number of rooms\n",
            "        - AveBedrms     average number of bedrooms\n",
            "        - Population    block population\n",
            "        - AveOccup      average house occupancy\n",
            "        - Latitude      house block latitude\n",
            "        - Longitude     house block longitude\n",
            "\n",
            "    :Missing Attribute Values: None\n",
            "\n",
            "This dataset was obtained from the StatLib repository.\n",
            "http://lib.stat.cmu.edu/datasets/\n",
            "\n",
            "The target variable is the median house value for California districts.\n",
            "\n",
            "This dataset was derived from the 1990 U.S. census, using one row per census\n",
            "block group. A block group is the smallest geographical unit for which the U.S.\n",
            "Census Bureau publishes sample data (a block group typically has a population\n",
            "of 600 to 3,000 people).\n",
            "\n",
            "It can be downloaded/loaded using the\n",
            ":func:`sklearn.datasets.fetch_california_housing` function.\n",
            "\n",
            ".. topic:: References\n",
            "\n",
            "    - Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,\n",
            "      Statistics and Probability Letters, 33 (1997) 291-297\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zOstrfAKhtdX"
      },
      "source": [
        "pd = pd.DataFrame(fetch_california_housing.data, columns = fetch_california_housing.feature_names)\n"
      ],
      "execution_count": 179,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QEUzlkV6h-I9",
        "outputId": "23a7a6a3-ff28-4453-cdc0-219434ef68d5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        }
      },
      "source": [
        "pd.head()"
      ],
      "execution_count": 180,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MedInc</th>\n",
              "      <th>HouseAge</th>\n",
              "      <th>AveRooms</th>\n",
              "      <th>AveBedrms</th>\n",
              "      <th>Population</th>\n",
              "      <th>AveOccup</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8.3252</td>\n",
              "      <td>41.0</td>\n",
              "      <td>6.984127</td>\n",
              "      <td>1.023810</td>\n",
              "      <td>322.0</td>\n",
              "      <td>2.555556</td>\n",
              "      <td>37.88</td>\n",
              "      <td>-122.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8.3014</td>\n",
              "      <td>21.0</td>\n",
              "      <td>6.238137</td>\n",
              "      <td>0.971880</td>\n",
              "      <td>2401.0</td>\n",
              "      <td>2.109842</td>\n",
              "      <td>37.86</td>\n",
              "      <td>-122.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.2574</td>\n",
              "      <td>52.0</td>\n",
              "      <td>8.288136</td>\n",
              "      <td>1.073446</td>\n",
              "      <td>496.0</td>\n",
              "      <td>2.802260</td>\n",
              "      <td>37.85</td>\n",
              "      <td>-122.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.6431</td>\n",
              "      <td>52.0</td>\n",
              "      <td>5.817352</td>\n",
              "      <td>1.073059</td>\n",
              "      <td>558.0</td>\n",
              "      <td>2.547945</td>\n",
              "      <td>37.85</td>\n",
              "      <td>-122.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.8462</td>\n",
              "      <td>52.0</td>\n",
              "      <td>6.281853</td>\n",
              "      <td>1.081081</td>\n",
              "      <td>565.0</td>\n",
              "      <td>2.181467</td>\n",
              "      <td>37.85</td>\n",
              "      <td>-122.25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   MedInc  HouseAge  AveRooms  ...  AveOccup  Latitude  Longitude\n",
              "0  8.3252      41.0  6.984127  ...  2.555556     37.88    -122.23\n",
              "1  8.3014      21.0  6.238137  ...  2.109842     37.86    -122.22\n",
              "2  7.2574      52.0  8.288136  ...  2.802260     37.85    -122.24\n",
              "3  5.6431      52.0  5.817352  ...  2.547945     37.85    -122.25\n",
              "4  3.8462      52.0  6.281853  ...  2.181467     37.85    -122.25\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 180
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LyIiDM1diUM2",
        "outputId": "48add9ff-d5ab-43e0-86c1-1458a71e4a1d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        }
      },
      "source": [
        "pd.tail()"
      ],
      "execution_count": 181,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MedInc</th>\n",
              "      <th>HouseAge</th>\n",
              "      <th>AveRooms</th>\n",
              "      <th>AveBedrms</th>\n",
              "      <th>Population</th>\n",
              "      <th>AveOccup</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>20635</th>\n",
              "      <td>1.5603</td>\n",
              "      <td>25.0</td>\n",
              "      <td>5.045455</td>\n",
              "      <td>1.133333</td>\n",
              "      <td>845.0</td>\n",
              "      <td>2.560606</td>\n",
              "      <td>39.48</td>\n",
              "      <td>-121.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20636</th>\n",
              "      <td>2.5568</td>\n",
              "      <td>18.0</td>\n",
              "      <td>6.114035</td>\n",
              "      <td>1.315789</td>\n",
              "      <td>356.0</td>\n",
              "      <td>3.122807</td>\n",
              "      <td>39.49</td>\n",
              "      <td>-121.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20637</th>\n",
              "      <td>1.7000</td>\n",
              "      <td>17.0</td>\n",
              "      <td>5.205543</td>\n",
              "      <td>1.120092</td>\n",
              "      <td>1007.0</td>\n",
              "      <td>2.325635</td>\n",
              "      <td>39.43</td>\n",
              "      <td>-121.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20638</th>\n",
              "      <td>1.8672</td>\n",
              "      <td>18.0</td>\n",
              "      <td>5.329513</td>\n",
              "      <td>1.171920</td>\n",
              "      <td>741.0</td>\n",
              "      <td>2.123209</td>\n",
              "      <td>39.43</td>\n",
              "      <td>-121.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20639</th>\n",
              "      <td>2.3886</td>\n",
              "      <td>16.0</td>\n",
              "      <td>5.254717</td>\n",
              "      <td>1.162264</td>\n",
              "      <td>1387.0</td>\n",
              "      <td>2.616981</td>\n",
              "      <td>39.37</td>\n",
              "      <td>-121.24</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       MedInc  HouseAge  AveRooms  ...  AveOccup  Latitude  Longitude\n",
              "20635  1.5603      25.0  5.045455  ...  2.560606     39.48    -121.09\n",
              "20636  2.5568      18.0  6.114035  ...  3.122807     39.49    -121.21\n",
              "20637  1.7000      17.0  5.205543  ...  2.325635     39.43    -121.22\n",
              "20638  1.8672      18.0  5.329513  ...  2.123209     39.43    -121.32\n",
              "20639  2.3886      16.0  5.254717  ...  2.616981     39.37    -121.24\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 181
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_kSXAyetio0M",
        "outputId": "37b19b1a-1477-47e4-fc67-5ff63ae8481d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        }
      },
      "source": [
        "pd.isnull().sum()"
      ],
      "execution_count": 182,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MedInc        0\n",
              "HouseAge      0\n",
              "AveRooms      0\n",
              "AveBedrms     0\n",
              "Population    0\n",
              "AveOccup      0\n",
              "Latitude      0\n",
              "Longitude     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 182
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cQBsjs6mio9M"
      },
      "source": [
        "pd['AveHouseval'] = (fetch_california_housing.target)"
      ],
      "execution_count": 183,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vrI1SK8Uj3uz",
        "outputId": "9636b1da-7bdf-47cf-bda6-a50b26bd38a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        }
      },
      "source": [
        "pd.head()"
      ],
      "execution_count": 184,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MedInc</th>\n",
              "      <th>HouseAge</th>\n",
              "      <th>AveRooms</th>\n",
              "      <th>AveBedrms</th>\n",
              "      <th>Population</th>\n",
              "      <th>AveOccup</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "      <th>AveHouseval</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8.3252</td>\n",
              "      <td>41.0</td>\n",
              "      <td>6.984127</td>\n",
              "      <td>1.023810</td>\n",
              "      <td>322.0</td>\n",
              "      <td>2.555556</td>\n",
              "      <td>37.88</td>\n",
              "      <td>-122.23</td>\n",
              "      <td>4.526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8.3014</td>\n",
              "      <td>21.0</td>\n",
              "      <td>6.238137</td>\n",
              "      <td>0.971880</td>\n",
              "      <td>2401.0</td>\n",
              "      <td>2.109842</td>\n",
              "      <td>37.86</td>\n",
              "      <td>-122.22</td>\n",
              "      <td>3.585</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.2574</td>\n",
              "      <td>52.0</td>\n",
              "      <td>8.288136</td>\n",
              "      <td>1.073446</td>\n",
              "      <td>496.0</td>\n",
              "      <td>2.802260</td>\n",
              "      <td>37.85</td>\n",
              "      <td>-122.24</td>\n",
              "      <td>3.521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.6431</td>\n",
              "      <td>52.0</td>\n",
              "      <td>5.817352</td>\n",
              "      <td>1.073059</td>\n",
              "      <td>558.0</td>\n",
              "      <td>2.547945</td>\n",
              "      <td>37.85</td>\n",
              "      <td>-122.25</td>\n",
              "      <td>3.413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.8462</td>\n",
              "      <td>52.0</td>\n",
              "      <td>6.281853</td>\n",
              "      <td>1.081081</td>\n",
              "      <td>565.0</td>\n",
              "      <td>2.181467</td>\n",
              "      <td>37.85</td>\n",
              "      <td>-122.25</td>\n",
              "      <td>3.422</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   MedInc  HouseAge  AveRooms  ...  Latitude  Longitude  AveHouseval\n",
              "0  8.3252      41.0  6.984127  ...     37.88    -122.23        4.526\n",
              "1  8.3014      21.0  6.238137  ...     37.86    -122.22        3.585\n",
              "2  7.2574      52.0  8.288136  ...     37.85    -122.24        3.521\n",
              "3  5.6431      52.0  5.817352  ...     37.85    -122.25        3.413\n",
              "4  3.8462      52.0  6.281853  ...     37.85    -122.25        3.422\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 184
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TxYD14lbipDk"
      },
      "source": [
        "pd['AveHouseval'] = (fetch_california_housing.target)*1000"
      ],
      "execution_count": 185,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v62FW1lkipGY",
        "outputId": "b0883f26-4095-4741-b960-703dd74f367b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        }
      },
      "source": [
        "pd.head()"
      ],
      "execution_count": 186,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MedInc</th>\n",
              "      <th>HouseAge</th>\n",
              "      <th>AveRooms</th>\n",
              "      <th>AveBedrms</th>\n",
              "      <th>Population</th>\n",
              "      <th>AveOccup</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "      <th>AveHouseval</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8.3252</td>\n",
              "      <td>41.0</td>\n",
              "      <td>6.984127</td>\n",
              "      <td>1.023810</td>\n",
              "      <td>322.0</td>\n",
              "      <td>2.555556</td>\n",
              "      <td>37.88</td>\n",
              "      <td>-122.23</td>\n",
              "      <td>4526.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8.3014</td>\n",
              "      <td>21.0</td>\n",
              "      <td>6.238137</td>\n",
              "      <td>0.971880</td>\n",
              "      <td>2401.0</td>\n",
              "      <td>2.109842</td>\n",
              "      <td>37.86</td>\n",
              "      <td>-122.22</td>\n",
              "      <td>3585.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.2574</td>\n",
              "      <td>52.0</td>\n",
              "      <td>8.288136</td>\n",
              "      <td>1.073446</td>\n",
              "      <td>496.0</td>\n",
              "      <td>2.802260</td>\n",
              "      <td>37.85</td>\n",
              "      <td>-122.24</td>\n",
              "      <td>3521.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.6431</td>\n",
              "      <td>52.0</td>\n",
              "      <td>5.817352</td>\n",
              "      <td>1.073059</td>\n",
              "      <td>558.0</td>\n",
              "      <td>2.547945</td>\n",
              "      <td>37.85</td>\n",
              "      <td>-122.25</td>\n",
              "      <td>3413.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.8462</td>\n",
              "      <td>52.0</td>\n",
              "      <td>6.281853</td>\n",
              "      <td>1.081081</td>\n",
              "      <td>565.0</td>\n",
              "      <td>2.181467</td>\n",
              "      <td>37.85</td>\n",
              "      <td>-122.25</td>\n",
              "      <td>3422.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   MedInc  HouseAge  AveRooms  ...  Latitude  Longitude  AveHouseval\n",
              "0  8.3252      41.0  6.984127  ...     37.88    -122.23       4526.0\n",
              "1  8.3014      21.0  6.238137  ...     37.86    -122.22       3585.0\n",
              "2  7.2574      52.0  8.288136  ...     37.85    -122.24       3521.0\n",
              "3  5.6431      52.0  5.817352  ...     37.85    -122.25       3413.0\n",
              "4  3.8462      52.0  6.281853  ...     37.85    -122.25       3422.0\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 186
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ahRA7GRhj8GX",
        "outputId": "af1cb414-b917-4497-ddcb-fc605cf27c5b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 184
        }
      },
      "source": [
        "pd.isnull().sum()"
      ],
      "execution_count": 187,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MedInc         0\n",
              "HouseAge       0\n",
              "AveRooms       0\n",
              "AveBedrms      0\n",
              "Population     0\n",
              "AveOccup       0\n",
              "Latitude       0\n",
              "Longitude      0\n",
              "AveHouseval    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 187
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7_wPSUTOj77W",
        "outputId": "fc0755ae-bc70-4755-ec98-b3ac1a9e76e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "plt.figure(figsize=(10,8))"
      ],
      "execution_count": 188,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 188
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rzw-Q4TvkQD8",
        "outputId": "ab8f2c2d-1963-42be-8574-7859a8bc5fde",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        }
      },
      "source": [
        "plt.figure(figsize=(10,8))\n",
        "plt.scatter(pd['Latitude'], pd['Longitude'], c=pd['AveHouseval'], s=pd['Population']/100)\n",
        "plt.colorbar()"
      ],
      "execution_count": 189,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.colorbar.Colorbar at 0x7fc08c3c7eb8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 189
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sVflKJ5Ql-Qo",
        "outputId": "16e35962-222c-4d16-91ab-9fa9845aa71d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        }
      },
      "source": [
        "plt.figure(figsize=(10,8))\n",
        "plt.figure(figsize=(10,8))\n",
        "plt.scatter(pd['Latitude'], pd['Longitude'], c=pd['AveHouseval'], s=pd['Population']/100)\n",
        "plt.colorbar()"
      ],
      "execution_count": 190,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.colorbar.Colorbar at 0x7fc08b926c88>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 190
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7vy750lxkQWQ",
        "outputId": "cba2b744-1249-4c30-d44b-6a91117367d5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        }
      },
      "source": [
        "corrmat = pd.corr()\n",
        "plt.subplots(figsize=(12,9))\n",
        "mask = np.zeros_like(corrmat, dtype =np.bool)\n",
        "mask[np.triu_indices_from(mask)] = True\n",
        "sns.heatmap(corrmat, vmax=0.9, square =True, annot =True, mask=mask, cbar_kws={\"shrink\": 0.5})"
      ],
      "execution_count": 191,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc08a0bdeb8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 191
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nhdfdNSjkQbD",
        "outputId": "8cde8c90-d92b-42c4-d0cc-7479cf581d66",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "X = pd[['MedInc', 'Latitude', 'AveRooms']]\n",
        "Y = pd['AveHouseval']\n",
        "\n",
        "X_train,X_test,Y_train,Y_test = train_test_split(X, Y, test_size= 0.2 ,random_state=5)\n",
        "lin_model = LinearRegression()\n",
        "lin_model.fit(X_train,Y_train)"
      ],
      "execution_count": 195,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 195
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bQLMdAxxkQkF",
        "outputId": "b1250d12-387f-47a4-e6c3-bae7339a4fe3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "#model evaluatioino for training\n",
        "Y_train_predict = lin_model.predict(X_train)\n",
        "rmse = (np.sqrt(mean_squared_error(Y_train,Y_train_predict)))\n",
        "r2 = r2_score(Y_train,Y_train_predict)\n",
        "Y_train_predict, r2"
      ],
      "execution_count": 197,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([2134.77417257, 2258.04596015, 1451.05756492, ..., 2870.32501904,\n",
              "        1899.80396946, 2269.8050493 ]), 0.4822870285735623)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 197
        }
      ]
    }
  ]
}