Machine_Learning_Regression.ipynb

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    "colab": {
      "name": "Machine_Learning_Regression.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Elgendi/Machine-Learning-and-Data-Viz-Workshop/blob/master/Machine_Learning_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LPyDQYBWkYQR"
      },
      "source": [
        "# Copyright (c) Mohamed Elgendi 2020. All rights reserved\n",
        "# Author: Mohamed Elgendi \n",
        "# Version: 1.0 (2020.11.29)\n",
        "# Email: moe.elgendi@gmail.com\n",
        "# Platform: Tested on Windows 10 with Python 3.6"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yV16QvxCkYQa"
      },
      "source": [
        "from sklearn.datasets import load_diabetes"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u3krCtYNkYQb"
      },
      "source": [
        "data = load_diabetes()"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c0tLxZ6nkYQb"
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "DongXWqwkYQc",
        "outputId": "998b0f4b-a49e-450f-fdca-444da2d126a5"
      },
      "source": [
        "X = pd.DataFrame(data.data, columns=data.feature_names)\n",
        "X.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>bmi</th>\n",
              "      <th>bp</th>\n",
              "      <th>s1</th>\n",
              "      <th>s2</th>\n",
              "      <th>s3</th>\n",
              "      <th>s4</th>\n",
              "      <th>s5</th>\n",
              "      <th>s6</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.038076</td>\n",
              "      <td>0.050680</td>\n",
              "      <td>0.061696</td>\n",
              "      <td>0.021872</td>\n",
              "      <td>-0.044223</td>\n",
              "      <td>-0.034821</td>\n",
              "      <td>-0.043401</td>\n",
              "      <td>-0.002592</td>\n",
              "      <td>0.019908</td>\n",
              "      <td>-0.017646</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.001882</td>\n",
              "      <td>-0.044642</td>\n",
              "      <td>-0.051474</td>\n",
              "      <td>-0.026328</td>\n",
              "      <td>-0.008449</td>\n",
              "      <td>-0.019163</td>\n",
              "      <td>0.074412</td>\n",
              "      <td>-0.039493</td>\n",
              "      <td>-0.068330</td>\n",
              "      <td>-0.092204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.085299</td>\n",
              "      <td>0.050680</td>\n",
              "      <td>0.044451</td>\n",
              "      <td>-0.005671</td>\n",
              "      <td>-0.045599</td>\n",
              "      <td>-0.034194</td>\n",
              "      <td>-0.032356</td>\n",
              "      <td>-0.002592</td>\n",
              "      <td>0.002864</td>\n",
              "      <td>-0.025930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.089063</td>\n",
              "      <td>-0.044642</td>\n",
              "      <td>-0.011595</td>\n",
              "      <td>-0.036656</td>\n",
              "      <td>0.012191</td>\n",
              "      <td>0.024991</td>\n",
              "      <td>-0.036038</td>\n",
              "      <td>0.034309</td>\n",
              "      <td>0.022692</td>\n",
              "      <td>-0.009362</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.005383</td>\n",
              "      <td>-0.044642</td>\n",
              "      <td>-0.036385</td>\n",
              "      <td>0.021872</td>\n",
              "      <td>0.003935</td>\n",
              "      <td>0.015596</td>\n",
              "      <td>0.008142</td>\n",
              "      <td>-0.002592</td>\n",
              "      <td>-0.031991</td>\n",
              "      <td>-0.046641</td>\n",
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              "  </tbody>\n",
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            ],
            "text/plain": [
              "        age       sex       bmi  ...        s4        s5        s6\n",
              "0  0.038076  0.050680  0.061696  ... -0.002592  0.019908 -0.017646\n",
              "1 -0.001882 -0.044642 -0.051474  ... -0.039493 -0.068330 -0.092204\n",
              "2  0.085299  0.050680  0.044451  ... -0.002592  0.002864 -0.025930\n",
              "3 -0.089063 -0.044642 -0.011595  ...  0.034309  0.022692 -0.009362\n",
              "4  0.005383 -0.044642 -0.036385  ... -0.002592 -0.031991 -0.046641\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BBoPcRFfkYQe",
        "outputId": "7589dd55-a1b7-4636-9a24-f2fef1c9c6e2"
      },
      "source": [
        "print(data.DESCR)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            ".. _diabetes_dataset:\n",
            "\n",
            "Diabetes dataset\n",
            "----------------\n",
            "\n",
            "Ten baseline variables, age, sex, body mass index, average blood\n",
            "pressure, and six blood serum measurements were obtained for each of n =\n",
            "442 diabetes patients, as well as the response of interest, a\n",
            "quantitative measure of disease progression one year after baseline.\n",
            "\n",
            "**Data Set Characteristics:**\n",
            "\n",
            "  :Number of Instances: 442\n",
            "\n",
            "  :Number of Attributes: First 10 columns are numeric predictive values\n",
            "\n",
            "  :Target: Column 11 is a quantitative measure of disease progression one year after baseline\n",
            "\n",
            "  :Attribute Information:\n",
            "      - Age\n",
            "      - Sex\n",
            "      - Body mass index\n",
            "      - Average blood pressure\n",
            "      - S1\n",
            "      - S2\n",
            "      - S3\n",
            "      - S4\n",
            "      - S5\n",
            "      - S6\n",
            "\n",
            "Note: Each of these 10 feature variables have been mean centered and scaled by the standard deviation times `n_samples` (i.e. the sum of squares of each column totals 1).\n",
            "\n",
            "Source URL:\n",
            "https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html\n",
            "\n",
            "For more information see:\n",
            "Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani (2004) \"Least Angle Regression,\" Annals of Statistics (with discussion), 407-499.\n",
            "(https://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YIcBR1yAkYQe",
        "outputId": "41a8ffb0-39c7-4f68-c8bf-cdbda4978f68"
      },
      "source": [
        "y = data.target\n",
        "y"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([151.,  75., 141., 206., 135.,  97., 138.,  63., 110., 310., 101.,\n",
              "        69., 179., 185., 118., 171., 166., 144.,  97., 168.,  68.,  49.,\n",
              "        68., 245., 184., 202., 137.,  85., 131., 283., 129.,  59., 341.,\n",
              "        87.,  65., 102., 265., 276., 252.,  90., 100.,  55.,  61.,  92.,\n",
              "       259.,  53., 190., 142.,  75., 142., 155., 225.,  59., 104., 182.,\n",
              "       128.,  52.,  37., 170., 170.,  61., 144.,  52., 128.,  71., 163.,\n",
              "       150.,  97., 160., 178.,  48., 270., 202., 111.,  85.,  42., 170.,\n",
              "       200., 252., 113., 143.,  51.,  52., 210.,  65., 141.,  55., 134.,\n",
              "        42., 111.,  98., 164.,  48.,  96.,  90., 162., 150., 279.,  92.,\n",
              "        83., 128., 102., 302., 198.,  95.,  53., 134., 144., 232.,  81.,\n",
              "       104.,  59., 246., 297., 258., 229., 275., 281., 179., 200., 200.,\n",
              "       173., 180.,  84., 121., 161.,  99., 109., 115., 268., 274., 158.,\n",
              "       107.,  83., 103., 272.,  85., 280., 336., 281., 118., 317., 235.,\n",
              "        60., 174., 259., 178., 128.,  96., 126., 288.,  88., 292.,  71.,\n",
              "       197., 186.,  25.,  84.,  96., 195.,  53., 217., 172., 131., 214.,\n",
              "        59.,  70., 220., 268., 152.,  47.,  74., 295., 101., 151., 127.,\n",
              "       237., 225.,  81., 151., 107.,  64., 138., 185., 265., 101., 137.,\n",
              "       143., 141.,  79., 292., 178.,  91., 116.,  86., 122.,  72., 129.,\n",
              "       142.,  90., 158.,  39., 196., 222., 277.,  99., 196., 202., 155.,\n",
              "        77., 191.,  70.,  73.,  49.,  65., 263., 248., 296., 214., 185.,\n",
              "        78.,  93., 252., 150.,  77., 208.,  77., 108., 160.,  53., 220.,\n",
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              "        71.,  47., 187., 125.,  78.,  51., 258., 215., 303., 243.,  91.,\n",
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              "       145.,  74.,  45., 115., 264.,  87., 202., 127., 182., 241.,  66.,\n",
              "        94., 283.,  64., 102., 200., 265.,  94., 230., 181., 156., 233.,\n",
              "        60., 219.,  80.,  68., 332., 248.,  84., 200.,  55.,  85.,  89.,\n",
              "        31., 129.,  83., 275.,  65., 198., 236., 253., 124.,  44., 172.,\n",
              "       114., 142., 109., 180., 144., 163., 147.,  97., 220., 190., 109.,\n",
              "       191., 122., 230., 242., 248., 249., 192., 131., 237.,  78., 135.,\n",
              "       244., 199., 270., 164.,  72.,  96., 306.,  91., 214.,  95., 216.,\n",
              "       263., 178., 113., 200., 139., 139.,  88., 148.,  88., 243.,  71.,\n",
              "        77., 109., 272.,  60.,  54., 221.,  90., 311., 281., 182., 321.,\n",
              "        58., 262., 206., 233., 242., 123., 167.,  63., 197.,  71., 168.,\n",
              "       140., 217., 121., 235., 245.,  40.,  52., 104., 132.,  88.,  69.,\n",
              "       219.,  72., 201., 110.,  51., 277.,  63., 118.,  69., 273., 258.,\n",
              "        43., 198., 242., 232., 175.,  93., 168., 275., 293., 281.,  72.,\n",
              "       140., 189., 181., 209., 136., 261., 113., 131., 174., 257.,  55.,\n",
              "        84.,  42., 146., 212., 233.,  91., 111., 152., 120.,  67., 310.,\n",
              "        94., 183.,  66., 173.,  72.,  49.,  64.,  48., 178., 104., 132.,\n",
              "       220.,  57.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ymWXTN46kYQf"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y) # default 25 testing 75 training"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "as1BpPRZkYQf"
      },
      "source": [
        "from sklearn.dummy import DummyRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UPEc6SkskYQf"
      },
      "source": [
        "Reg1 = DummyRegressor(strategy='median')\n",
        "Reg2 = RandomForestRegressor(n_estimators=10)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i0jdYNiekYQf"
      },
      "source": [
        "Reg1.fit(X_train, y_train)\n",
        "y_pred1 = Reg1.predict(X_test)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2YiRLK7BkYQg"
      },
      "source": [
        "Reg2.fit(X_train, y_train)\n",
        "y_pred2 = Reg2.predict(X_test)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q1B5z0nSkYQg",
        "outputId": "9c71c2dc-c4b1-4591-b364-e71acd0b10c4"
      },
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "mse1=mean_squared_error(y_test, y_pred1)\n",
        "mse1"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6021.18018018018"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rPODJrvhkYQg",
        "outputId": "8bed9bb9-69b6-4380-b733-71499ac1b427"
      },
      "source": [
        "mse2=mean_squared_error(y_test, y_pred2)\n",
        "mse2"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3806.945315315316"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fb8uNnZLkYQh",
        "outputId": "a2b4f155-0100-40df-86a3-83a710698ce0"
      },
      "source": [
        "from math import sqrt\n",
        "rmse1 = sqrt(mse1)\n",
        "rmse1"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "77.59626395761705"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p6iXGC6skYQh",
        "outputId": "6ee57d68-9708-49ca-e50e-8fb87abd4d8c"
      },
      "source": [
        "rmse2 = sqrt(mse2)\n",
        "rmse2"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "61.70044825862544"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Bmk0mBrkYQh"
      },
      "source": [
        ""
      ],
      "execution_count": 20,
      "outputs": []
    }
  ]
}