Quantitative_Model_Assignment_ML6.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.5.6"
    },
    "colab": {
      "name": "Quantitative_Model_Assignment_ML6.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Hutaf/SDA-Machine-Learning/blob/main/Quantitative_Model_Assignment_ML6.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N8bJQ9wjpylY"
      },
      "source": [
        "## Assignment ML6\n",
        "Use the same project from the previous assignment (the heuristic modeling), then:\n",
        "1. Modify the heuristic model to a basic quantitative model.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7zPVWwdIqWDR"
      },
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, mean_squared_error, confusion_matrix\n",
        "from sklearn.preprocessing import StandardScaler"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wNgf3XkbBZp8"
      },
      "source": [
        "filepath = 'https://raw.githubusercontent.com/daniel-dc-cd/data_science/master/module_4_ML/data/seattle_weather_1948-2017.csv'\n",
        "df = pd.read_csv(filepath)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NIvpsvxKGA7r",
        "outputId": "bcbd1216-d05e-4075-b890-449199a4341a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 4,
      "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>DATE</th>\n",
              "      <th>PRCP</th>\n",
              "      <th>TMAX</th>\n",
              "      <th>TMIN</th>\n",
              "      <th>RAIN</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1948-01-01</td>\n",
              "      <td>0.47</td>\n",
              "      <td>51</td>\n",
              "      <td>42</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1948-01-02</td>\n",
              "      <td>0.59</td>\n",
              "      <td>45</td>\n",
              "      <td>36</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1948-01-03</td>\n",
              "      <td>0.42</td>\n",
              "      <td>45</td>\n",
              "      <td>35</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1948-01-04</td>\n",
              "      <td>0.31</td>\n",
              "      <td>45</td>\n",
              "      <td>34</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1948-01-05</td>\n",
              "      <td>0.17</td>\n",
              "      <td>45</td>\n",
              "      <td>32</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         DATE  PRCP  TMAX  TMIN  RAIN\n",
              "0  1948-01-01  0.47    51    42  True\n",
              "1  1948-01-02  0.59    45    36  True\n",
              "2  1948-01-03  0.42    45    35  True\n",
              "3  1948-01-04  0.31    45    34  True\n",
              "4  1948-01-05  0.17    45    32  True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oBfa1dJxiVjp",
        "outputId": "c5bc8cc3-24c0-49cb-efc2-27f5742ff77a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        }
      },
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "sns.boxplot(data=df)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f36a3dd2e48>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8x3QE_JerfpJ",
        "outputId": "5af5e79a-3820-48f5-8786-6dff38c29e4e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        }
      },
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "sns.barplot(data=df)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f36c859ec18>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "txNmnV8-yNGZ",
        "outputId": "113ae2b1-ec23-48bb-dadd-f2704c2f08ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        }
      },
      "source": [
        "sns.countplot(df['RAIN'])"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f36a3240978>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEGCAYAAACkQqisAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAVLElEQVR4nO3df7BfdX3n8edLIopaDMgVacIaZs3aRfpDuAvZsu220gnBdg3bRQvqEjXT7Ezpr61bC63TOCidulpZqZVuKpHguCBiLXEXixnUZXaHXxeh/NRyi/xIFuRqAKv4K/S9f3w/F7+Ge5Obk/v9frnc52PmO/ec9/mccz5n5pu85vz6fFNVSJLUxXNG3QFJ0sJliEiSOjNEJEmdGSKSpM4MEUlSZ0tG3YFhO+yww2rFihWj7oYkLSg333zz16tqbPf6oguRFStWMDExMepuSNKCkuT+mepezpIkdWaISJI6M0QkSZ0ZIpKkzgwRSVJnhogkqTNDRJLUmSEiSerMEJEkdbbo3ljfX8f9/iWj7oKegW5+35mj7oI0Ep6JSJI6M0QkSZ0ZIpKkzgwRSVJnhogkqbOBhUiSzUkeSXLHDMvenqSSHNbmk+SCJJNJbktybF/bdUnuaZ91ffXjktze1rkgSQZ1LJKkmQ3yTORiYM3uxSRHAquBB/rKpwAr22cDcGFreyiwETgBOB7YmOSQts6FwK/3rfe0fUmSBmtgIVJV1wI7Z1h0PvAOoPpqa4FLqud6YGmSI4CTgW1VtbOqHgW2AWvasoOr6vqqKuAS4NRBHYskaWZDvSeSZC2wo6r+brdFy4AH++a3t9qe6ttnqM+23w1JJpJMTE1N7ccRSJL6DS1EkrwA+EPgj4e1z2lVtamqxqtqfGzsab8zL0nqaJhnIv8cOAr4uyT3AcuBLyV5GbADOLKv7fJW21N9+Qx1SdIQDS1Equr2qnppVa2oqhX0LkEdW1UPA1uBM9tTWquAx6vqIeBqYHWSQ9oN9dXA1W3ZN5Osak9lnQlcOaxjkST1DPIR30uB64BXJtmeZP0eml8F3AtMAn8F/AZAVe0E3g3c1D7nthqtzUfaOv8AfHYQxyFJmt3ARvGtqjP2snxF33QBZ83SbjOweYb6BHDM/vVSkrQ/fGNdktSZISJJ6swQkSR1ZohIkjozRCRJnRkikqTODBFJUmeGiCSpM0NEktSZISJJ6swQkSR1ZohIkjozRCRJnRkikqTODBFJUmeGiCSpM0NEktSZISJJ6swQkSR1NrAQSbI5ySNJ7uirvS/Jl5PcluTTSZb2LTsnyWSSryQ5ua++ptUmk5zdVz8qyQ2t/okkBw7qWCRJMxvkmcjFwJrdatuAY6rqp4C/B84BSHI0cDrwqrbOh5MckOQA4C+AU4CjgTNaW4D3AudX1SuAR4H1AzwWSdIMBhYiVXUtsHO32ueqalebvR5Y3qbXApdV1feq6qvAJHB8+0xW1b1V9X3gMmBtkgCvAa5o628BTh3UsUiSZjbKeyJvAz7bppcBD/Yt295qs9VfAjzWF0jT9Rkl2ZBkIsnE1NTUPHVfkjSSEEnyR8Au4OPD2F9Vbaqq8aoaHxsbG8YuJWlRWDLsHSZ5C/ArwElVVa28Aziyr9nyVmOW+jeApUmWtLOR/vaSpCEZ6plIkjXAO4DXVdUTfYu2AqcneV6So4CVwI3ATcDK9iTWgfRuvm9t4fMF4LS2/jrgymEdhySpZ5CP+F4KXAe8Msn2JOuBDwE/BmxLcmuSvwSoqjuBy4G7gL8FzqqqJ9tZxm8CVwN3A5e3tgB/APxekkl690guGtSxSJJmNrDLWVV1xgzlWf+jr6rzgPNmqF8FXDVD/V56T29JkkbEN9YlSZ0ZIpKkzgwRSVJnhogkqTNDRJLUmSEiSerMEJEkdWaISJI6M0QkSZ0ZIpKkzoY+iq+kwXng3J8cdRf0DPTP/vj2gW3bMxFJUmeGiCSpM0NEktSZISJJ6swQkSR1ZohIkjozRCRJnRkikqTOBhYiSTYneSTJHX21Q5NsS3JP+3tIqyfJBUkmk9yW5Ni+dda19vckWddXPy7J7W2dC5JkUMciSZrZIM9ELgbW7FY7G7imqlYC17R5gFOAle2zAbgQeqEDbAROAI4HNk4HT2vz633r7b4vSdKADSxEqupaYOdu5bXAlja9BTi1r35J9VwPLE1yBHAysK2qdlbVo8A2YE1bdnBVXV9VBVzSty1J0pAM+57I4VX1UJt+GDi8TS8DHuxrt73V9lTfPkN9Rkk2JJlIMjE1NbV/RyBJesrIbqy3M4ga0r42VdV4VY2PjY0NY5eStCgMO0S+1i5F0f4+0uo7gCP72i1vtT3Vl89QlyQN0bBDZCsw/YTVOuDKvvqZ7SmtVcDj7bLX1cDqJIe0G+qrgavbsm8mWdWeyjqzb1uSpCEZ2O+JJLkU+AXgsCTb6T1l9afA5UnWA/cDb2jNrwJeC0wCTwBvBaiqnUneDdzU2p1bVdM363+D3hNgBwGfbR9J0hANLESq6oxZFp00Q9sCzpplO5uBzTPUJ4Bj9qePkqT94xvrkqTODBFJUmeGiCSpM0NEktSZISJJ6swQkSR1ZohIkjozRCRJnRkikqTODBFJUmeGiCSpM0NEktSZISJJ6swQkSR1ZohIkjozRCRJnc0pRJJcM5eaJGlx2eMvGyZ5PvACej9xewiQtuhgYNmA+yZJeobb28/j/ifgd4EfB27mhyHyTeBDA+yXJGkB2GOIVNUHgQ8m+a2q+vMh9UmStEDM6Z5IVf15kp9N8sYkZ05/uu40yX9OcmeSO5JcmuT5SY5KckOSySSfSHJga/u8Nj/Zlq/o2845rf6VJCd37Y8kqZu53lj/GPB+4N8A/6p9xrvsMMky4LeB8ao6BjgAOB14L3B+Vb0CeBRY31ZZDzza6ue3diQ5uq33KmAN8OEkB3TpkySpm73dE5k2DhxdVTWP+z0oyQ/o3bh/CHgN8Ma2fAvwLuBCYG2bBrgC+FCStPplVfU94KtJJoHjgevmqY+SpL2Y63sidwAvm48dVtUOemc1D9ALj8fp3bR/rKp2tWbb+eHTX8uAB9u6u1r7l/TXZ1jnRyTZkGQiycTU1NR8HIYkibmfiRwG3JXkRuB708Wqet2+7rA9KrwWOAp4DPgkvctRA1NVm4BNAOPj4/N1NiVJi95cQ+Rd87jPXwK+WlVTAEn+GjgRWJpkSTvbWA7saO13AEcC25MsAV4MfKOvPq1/HUnSEMwpRKrqf8/jPh8AViV5AfAd4CRgAvgCcBpwGbAOuLK139rmr2vLP19VlWQr8D+SfIDeeywrgRvnsZ+SpL2YU4gk+Udg+jLQgcBzgW9X1cH7usOquiHJFcCXgF3ALfQuNf0v4LIk72m1i9oqFwEfazfOd9J7IouqujPJ5cBdbTtnVdWT+9ofSVJ3cz0T+bHp6b4no1Z13WlVbQQ27la+l97TVbu3/S7w+lm2cx5wXtd+SJL2zz6P4ls9fwP4cp8kLXJzvZz1q32zz6H33sh3B9IjSdKCMdens/5d3/Qu4D56l7QkSYvYXO+JvHXQHZEkLTxzHTtreZJPJ3mkfT6VZPmgOydJemab6431j9J7X+PH2+czrSZJWsTmGiJjVfXRqtrVPhcDYwPslyRpAZhriHwjyZuTHNA+b6Y39IgkaRGba4i8DXgD8DC9kXdPA94yoD5JkhaIuT7iey6wrqoeBUhyKL3h3N82qI5Jkp755nom8lPTAQJQVTuBVw+mS5KkhWKuIfKc9jsgwFNnInM9i5EkPUvNNQj+DLguySfb/Otx4ENJWvTm+sb6JUkm6P0OOsCvVtVdg+uWJGkhmPMlqRYaBock6Sn7PBS8JEnTDBFJUmeGiCSpM0NEktTZSEIkydIkVyT5cpK7k/zrJIcm2Zbknvb3kNY2SS5IMpnktiTH9m1nXWt/T5J1ozgWSVrMRnUm8kHgb6vqJ4CfBu4GzgauqaqVwDVtHuAUYGX7bAAuhKdeeNwInAAcD2zsfyFSkjR4Qw+RJC8Gfh64CKCqvl9Vj9H7ud0trdkW4NQ2vRa4pHquB5YmOQI4GdhWVTvbkCzbgDVDPBRJWvRGcSZyFDAFfDTJLUk+kuSFwOFV9VBr8zBweJteBjzYt/72Vput/jRJNiSZSDIxNTU1j4ciSYvbKEJkCXAscGFVvRr4Nj+8dAVAVRVQ87XDqtpUVeNVNT425m9pSdJ8GUWIbAe2V9UNbf4KeqHytXaZivb3kbZ8B3Bk3/rLW222uiRpSIYeIlX1MPBgkle20kn0hlPZCkw/YbUOuLJNbwXObE9prQIeb5e9rgZWJzmk3VBf3WqSpCEZ1XDuvwV8PMmBwL3AW+kF2uVJ1gP30/slRYCrgNcCk8ATrS1VtTPJu4GbWrtz2++cSJKGZCQhUlW3AuMzLDpphrYFnDXLdjYDm+e3d5KkufKNdUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1NnIQiTJAUluSfI/2/xRSW5IMpnkE0kObPXntfnJtnxF3zbOafWvJDl5NEciSYvXKM9Efge4u2/+vcD5VfUK4FFgfauvBx5t9fNbO5IcDZwOvApYA3w4yQFD6rskiRGFSJLlwC8DH2nzAV4DXNGabAFObdNr2zxt+Umt/Vrgsqr6XlV9FZgEjh/OEUiSYHRnIv8NeAfwT23+JcBjVbWrzW8HlrXpZcCDAG354639U/UZ1vkRSTYkmUgyMTU1NZ/HIUmL2tBDJMmvAI9U1c3D2mdVbaqq8aoaHxsbG9ZuJelZb8kI9nki8LokrwWeDxwMfBBYmmRJO9tYDuxo7XcARwLbkywBXgx8o68+rX8dSdIQDP1MpKrOqarlVbWC3o3xz1fVm4AvAKe1ZuuAK9v01jZPW/75qqpWP709vXUUsBK4cUiHIUliNGcis/kD4LIk7wFuAS5q9YuAjyWZBHbSCx6q6s4klwN3AbuAs6rqyeF3W5IWr5GGSFV9Efhim76XGZ6uqqrvAq+fZf3zgPMG10NJ0p74xrokqTNDRJLUmSEiSerMEJEkdWaISJI6M0QkSZ0ZIpKkzgwRSVJnhogkqTNDRJLUmSEiSerMEJEkdWaISJI6M0QkSZ0ZIpKkzgwRSVJnhogkqTNDRJLUmSEiSeps6CGS5MgkX0hyV5I7k/xOqx+aZFuSe9rfQ1o9SS5IMpnktiTH9m1rXWt/T5J1wz4WSVrsRnEmsgt4e1UdDawCzkpyNHA2cE1VrQSuafMApwAr22cDcCH0QgfYCJwAHA9snA4eSdJwDD1EquqhqvpSm/5H4G5gGbAW2NKabQFObdNrgUuq53pgaZIjgJOBbVW1s6oeBbYBa4Z4KJK06I30nkiSFcCrgRuAw6vqobboYeDwNr0MeLBvte2tNltdkjQkIwuRJC8CPgX8blV9s39ZVRVQ87ivDUkmkkxMTU3N12YladEbSYgkeS69APl4Vf11K3+tXaai/X2k1XcAR/atvrzVZqs/TVVtqqrxqhofGxubvwORpEVuFE9nBbgIuLuqPtC3aCsw/YTVOuDKvvqZ7SmtVcDj7bLX1cDqJIe0G+qrW02SNCRLRrDPE4H/CNye5NZW+0PgT4HLk6wH7gfe0JZdBbwWmASeAN4KUFU7k7wbuKm1O7eqdg7nECRJMIIQqar/A2SWxSfN0L6As2bZ1mZg8/z1TpK0L3xjXZLUmSEiSerMEJEkdWaISJI6M0QkSZ0ZIpKkzgwRSVJnhogkqTNDRJLUmSEiSerMEJEkdWaISJI6M0QkSZ0ZIpKkzgwRSVJnhogkqTNDRJLUmSEiSerMEJEkdWaISJI6W/AhkmRNkq8kmUxy9qj7I0mLyYIOkSQHAH8BnAIcDZyR5OjR9kqSFo8FHSLA8cBkVd1bVd8HLgPWjrhPkrRoLBl1B/bTMuDBvvntwAm7N0qyAdjQZr+V5CtD6NticBjw9VF34pkg71836i7o6fx+TtuY+djKy2cqLvQQmZOq2gRsGnU/nm2STFTV+Kj7Ic3E7+dwLPTLWTuAI/vml7eaJGkIFnqI3ASsTHJUkgOB04GtI+6TJC0aC/pyVlXtSvKbwNXAAcDmqrpzxN1aTLxEqGcyv59DkKoadR8kSQvUQr+cJUkaIUNEktTZgr4novmX5Eng9r7SqVV13yxtv1VVLxpKxyQgyUuAa9rsy4Angak2f3x76VhD5D0R/Yh9CQZDRKOU5F3At6rq/X21JVW1a3S9Wny8nKU9SvKiJNck+VKS25M8bViZJEckuTbJrUnuSPJzrb46yXVt3U8mMXA075JcnOQvk9wA/Nck70ryX/qW35FkRZt+c5Ib23f1v7fx97QfDBHt7qD2D+zWJJ8Gvgv8+6o6FvhF4M+S7D6GwhuBq6vqZ4CfBm5NchjwTuCX2roTwO8N7zC0yCwHfraqZv2OJfmXwK8BJ7bv6pPAm4bUv2ct74lod99p/8AASPJc4E+S/DzwT/TGKzsceLhvnZuAza3t31TVrUn+Lb2Rlf9vy5wDgeuGdAxafD5ZVU/upc1JwHHATe07eRDwyKA79mxniGhv3gSMAcdV1Q+S3Ac8v79BVV3bQuaXgYuTfAB4FNhWVWcMu8NalL7dN72LH73KMv19DbClqs4ZWq8WAS9naW9eDDzSAuQXmWEkzyQvB75WVX8FfAQ4FrgeODHJK1qbFyb5F0Pstxav++h9B0lyLHBUq18DnJbkpW3Zoe27q/3gmYj25uPAZ5LcTu++xpdnaPMLwO8n+QHwLeDMqppK8hbg0iTPa+3eCfz94LusRe5TwJlJ7gRuoH3nququJO8EPpfkOcAPgLOA+0fW02cBH/GVJHXm5SxJUmeGiCSpM0NEktSZISJJ6swQkSR1ZohIA5Tkyb4xxT6TZOluy29NctlutYuTnNamv5hkom/ZeJIvDqXz0hwYItJgfaeqfqaqjgF20nsvAXhqLKcDgJ9L8sI9bOOlSU4ZcD+lTgwRaXiuozf22LQzgI8BnwOeNjpyn/cBfzTAfkmdGSLSELQhx08CtvaVfw24DLiUXqDM5jrg+23YGekZxRCRBuugJLfSG/X4cGAb9O5tAF+vqgfojen06iSH7mE776E3bIz0jGKISIM1PbT+y+mNIjt9T+QM4CfaqMj/ABwM/IfZNlJVn6c3dPmqgfZW2keGiDQEVfUE8NvA25McCLwB+MmqWlFVK+jdE9nbsPnvAd4x0I5K+8gQkYakqm4BbgPOAXZU1f/rW3wtcHSSI/aw/lXA1GB7Ke0bR/GVJHXmmYgkqTNDRJLUmSEiSerMEJEkdWaISJI6M0QkSZ0ZIpKkzv4/X/XjRyK97ncAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i8bBv6rIyVgS",
        "outputId": "be99247b-b8ea-4114-fc4f-7053627c5188",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        }
      },
      "source": [
        "sns.scatterplot(df['PRCP'], df['RAIN'])"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f36a3254320>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "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": "1-enexSYyf0q",
        "outputId": "dc5cec33-beca-4fbf-a68c-b9c4658c9640",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        }
      },
      "source": [
        "sns.scatterplot(df['TMAX'], df['RAIN'])"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f36a370e860>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "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": "7wQoIB9UyjM0",
        "outputId": "c9fd290b-b622-4571-d017-8301e5e29b71",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        }
      },
      "source": [
        "sns.scatterplot(df['TMIN'], df['RAIN'])"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f36a1ebbf28>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "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": "y0Trs6zRuLBA"
      },
      "source": [
        "### Preparing the Seattle weather data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q-9j3I5toPws",
        "outputId": "92b8c5c4-db28-4f9a-d2a6-b20eed5882b6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df.isna().sum()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DATE    0\n",
              "PRCP    3\n",
              "TMAX    0\n",
              "TMIN    0\n",
              "RAIN    3\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xj6W7goNuYcy"
      },
      "source": [
        "def isNull(df, column):\n",
        "  return df[pd.isnull(df[column])]"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bVexSEyeut3F",
        "outputId": "24642ce6-ee87-4fae-f6d4-7d403c6cb1da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        }
      },
      "source": [
        "isNull(df, 'PRCP')"
      ],
      "execution_count": 13,
      "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>DATE</th>\n",
              "      <th>PRCP</th>\n",
              "      <th>TMAX</th>\n",
              "      <th>TMIN</th>\n",
              "      <th>RAIN</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>18415</th>\n",
              "      <td>1998-06-02</td>\n",
              "      <td>NaN</td>\n",
              "      <td>72</td>\n",
              "      <td>52</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18416</th>\n",
              "      <td>1998-06-03</td>\n",
              "      <td>NaN</td>\n",
              "      <td>66</td>\n",
              "      <td>51</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21067</th>\n",
              "      <td>2005-09-05</td>\n",
              "      <td>NaN</td>\n",
              "      <td>70</td>\n",
              "      <td>52</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             DATE  PRCP  TMAX  TMIN RAIN\n",
              "18415  1998-06-02   NaN    72    52  NaN\n",
              "18416  1998-06-03   NaN    66    51  NaN\n",
              "21067  2005-09-05   NaN    70    52  NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RtMhRklIuRpW",
        "outputId": "34ac44f2-9fd1-4d0e-812f-df60648baf72",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        }
      },
      "source": [
        "isNull(df, 'RAIN')"
      ],
      "execution_count": 14,
      "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>DATE</th>\n",
              "      <th>PRCP</th>\n",
              "      <th>TMAX</th>\n",
              "      <th>TMIN</th>\n",
              "      <th>RAIN</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>18415</th>\n",
              "      <td>1998-06-02</td>\n",
              "      <td>NaN</td>\n",
              "      <td>72</td>\n",
              "      <td>52</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18416</th>\n",
              "      <td>1998-06-03</td>\n",
              "      <td>NaN</td>\n",
              "      <td>66</td>\n",
              "      <td>51</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21067</th>\n",
              "      <td>2005-09-05</td>\n",
              "      <td>NaN</td>\n",
              "      <td>70</td>\n",
              "      <td>52</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             DATE  PRCP  TMAX  TMIN RAIN\n",
              "18415  1998-06-02   NaN    72    52  NaN\n",
              "18416  1998-06-03   NaN    66    51  NaN\n",
              "21067  2005-09-05   NaN    70    52  NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DG-gAnCeu-PN"
      },
      "source": [
        "#df['PRCP'] = df['PRCP'].fillna(df['PRCP'].median)\n",
        "#isNull(df, 'PRCP')"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mEx8j0LrvmPn"
      },
      "source": [
        "#df['RAIN'] = df['RAIN'].fillna(False)\n",
        "#isNull(df, 'RAIN')"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mgdMiYWby235"
      },
      "source": [
        "df = df.dropna()"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RUNes7-thXp2"
      },
      "source": [
        "# Regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wCQqlDfVsMFP"
      },
      "source": [
        "---\n",
        "### 1. Break the dataset into two parts, training and testing.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PHy5P29sx4w6"
      },
      "source": [
        "X = df.loc[:, ['PRCP', 'TMAX', 'TMIN']].values\n",
        "y = df['RAIN'].astype('int')\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state = 42)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oXsD5uHCqnaQ"
      },
      "source": [
        "### Scaling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eVWmeMSjo07S"
      },
      "source": [
        "scaler=StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XlM_PzZ6sYdJ"
      },
      "source": [
        "---\n",
        "### 2. Use the first 80% of the dataset for training and the last 20% for testing."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AXJJ6xTIE5aJ"
      },
      "source": [
        "### Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4niHxg8xtjCs",
        "outputId": "e95886bb-dcd9-438d-ad25-686dd7c21007",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "reg= LogisticRegression(random_state=42,max_iter=200)\n",
        "reg.fit(X_train,y_train)\n",
        "print(\"Training Accuracy: {}%\".format(round(accuracy_score(y_train, reg.predict(X_train))*100)))"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Accuracy: 93.0%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Is4SZzmy03b",
        "outputId": "d892e7cb-581a-42e5-85ad-c76adbb3140e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "reg= LogisticRegression(random_state=42,max_iter=200)\n",
        "reg.fit(X_train_scaled,y_train)\n",
        "print(\"Training Accuracy with Scaled data: {}%\".format(round(accuracy_score(y_train, reg.predict(X_train_scaled))*100)))"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Accuracy with Scaled data: 97.0%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cFmGb2oqseIZ"
      },
      "source": [
        "---\n",
        "### 3. Evaluate both sets of data using your function."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t-AfU2EdtNCD"
      },
      "source": [
        "import numpy as np\n",
        "def SSE(y_test, y_pred):\n",
        "  difference = y_test - y_pred\n",
        "  squared_errors = difference ** 2\n",
        "  return np.sum(squared_errors)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vuOS5oc3zh0M"
      },
      "source": [
        "def evaluation(model, X, y):\n",
        "  print(\"Accuracy: {}%\".format(round(accuracy_score(y, model.predict(X))*100)))\n",
        "  print(\"Precision: {}%\".format(round(precision_score(y, model.predict(X))*100)))\n",
        "  print(\"Recall: {}%\".format(round(recall_score(y, model.predict(X))*100)))\n",
        "  print(\"MSE: {}\".format(mean_squared_error(y, model.predict(X))))\n",
        "  sse = SSE(y_pred=model.predict(X), y_test=y.astype('int'))\n",
        "  print(\"SSE: {}\".format(sse))"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GtAM7Qt624Px",
        "outputId": "0a40b2f7-294b-42bd-e920-ed8a938a39bb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Logistic Regression\n",
        "print(\"*\"*50)\n",
        "print('Logistic Regression')\n",
        "print(\"*\"*50)\n",
        "print('*'*30)\n",
        "print(\"Training Performance\")\n",
        "print('*'*30)\n",
        "evaluation(reg, X_train, y_train)\n",
        "print('*'*15)\n",
        "print('Scaled Data')\n",
        "evaluation(reg, X_train_scaled, y_train)\n",
        "print('*'*30)\n",
        "print('\\n')\n",
        "print('*'*30)\n",
        "print(\"Testing Performance\")\n",
        "print('*'*30)\n",
        "evaluation(reg, X_test, y_test)\n",
        "print('*'*15)\n",
        "print('Scaled Data')\n",
        "evaluation(reg, X_test_scaled, y_test)\n",
        "print('*'*30)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "**************************************************\n",
            "Logistic Regression\n",
            "**************************************************\n",
            "******************************\n",
            "Training Performance\n",
            "******************************\n",
            "Accuracy: 64.0%\n",
            "Precision: 100.0%\n",
            "Recall: 16.0%\n",
            "MSE: 0.36128779724043447\n",
            "SSE: 7384\n",
            "***************\n",
            "Scaled Data\n",
            "Accuracy: 97.0%\n",
            "Precision: 100.0%\n",
            "Recall: 92.0%\n",
            "MSE: 0.03395635580780898\n",
            "SSE: 694\n",
            "******************************\n",
            "\n",
            "\n",
            "******************************\n",
            "Testing Performance\n",
            "******************************\n",
            "Accuracy: 65.0%\n",
            "Precision: 100.0%\n",
            "Recall: 16.0%\n",
            "MSE: 0.35401174168297456\n",
            "SSE: 1809\n",
            "***************\n",
            "Scaled Data\n",
            "Accuracy: 97.0%\n",
            "Precision: 100.0%\n",
            "Recall: 92.0%\n",
            "MSE: 0.03444227005870842\n",
            "SSE: 176\n",
            "******************************\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nf_6d40-0jqI"
      },
      "source": [
        "### Extra: Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "emZCvMHQ0ocq",
        "outputId": "97eb3959-bba3-46f2-e82e-f4fced6dafac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "sns.heatmap(confusion_matrix(y_test, reg.predict(X_test_scaled)) / len(y), cmap='Blues', annot=True)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f36a1e56630>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gHrw1DMxheTr"
      },
      "source": [
        "# Classification"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s989dXOSheTs"
      },
      "source": [
        "---\n",
        "### 1. Break the dataset into two parts, training and testing.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "axulOcYqhjIN"
      },
      "source": [
        "X = df.loc[:, ['PRCP', 'TMAX', 'TMIN']].values\n",
        "y = df['RAIN'].astype('int')\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state = 42)"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wGGkasikheTt"
      },
      "source": [
        "---\n",
        "### 2. Use the first 80% of the dataset for training and the last 20% for testing."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wOFkgRuuEDPL"
      },
      "source": [
        "### Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BecYjr9zjBOo",
        "outputId": "0bdb9b6c-164b-4c7e-cd78-24c9d209a8bf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "rf=RandomForestClassifier()\n",
        "rf.fit(X_train,y_train)\n",
        "print(\"Training Accuracy: {}%\".format(round(accuracy_score(y_train, rf.predict(X_train))*100)))"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Accuracy: 100.0%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bV16c9DDzwJr",
        "outputId": "d9187c59-2595-426c-f686-a5ad7ce09a41",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Scaled data\n",
        "rf=RandomForestClassifier()\n",
        "rf.fit(X_train_scaled,y_train)\n",
        "print(\"Training Accuracy with scaled data: {}%\".format(round(accuracy_score(y_train, rf.predict(X_train_scaled))*100)))"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Accuracy with scaled data: 100.0%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u4h0w7QND_7B"
      },
      "source": [
        "### KNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mkRCW9JVDru0",
        "outputId": "32767f4e-5a01-4e77-c9e2-c9da536a7d3e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "knn = KNeighborsClassifier(n_neighbors=3)\n",
        "knn.fit(X_train,y_train)\n",
        "print(\"Training Accuracy: {}%\".format(round(accuracy_score(y_train, knn.predict(X_train))*100)))"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Accuracy: 98.0%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GNa4yzz3D36q",
        "outputId": "4c1dd53a-0949-4b17-8839-5fffd35f1dc3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Scaled data\n",
        "knn = KNeighborsClassifier(n_neighbors=3)\n",
        "knn.fit(X_train_scaled,y_train)\n",
        "print(\"Training Accuracy with scaled data: {}%\".format(round(accuracy_score(y_train, knn.predict(X_train_scaled))*100)))"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Accuracy with scaled data: 99.0%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G-hlQ3tpEL-i"
      },
      "source": [
        "### Gradient Boosting "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "neW7IMYGD_FD",
        "outputId": "d6e8085c-8a82-44f3-f4fc-58c0e8bb673e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "gb = GradientBoostingClassifier()\n",
        "gb.fit(X_train,y_train)\n",
        "print(\"Training Accuracy: {}%\".format(round(accuracy_score(y_train, gb.predict(X_train))*100)))"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Accuracy: 100.0%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rvYqQW_jEYc0",
        "outputId": "0473fed6-31a6-482d-ace4-eba7b38eed16",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "gb = GradientBoostingClassifier()\n",
        "gb.fit(X_train_scaled,y_train)\n",
        "print(\"Training Accuracy with scaled data: {}%\".format(round(accuracy_score(y_train, gb.predict(X_train_scaled))*100)))"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Accuracy with scaled data: 100.0%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e5oR3O-eEhtd"
      },
      "source": [
        "### SVC"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gT2ZSoBA9i4j",
        "outputId": "6ea157c1-b09d-49ee-f309-c5581cfbc0ef",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "svc= SVC()\n",
        "svc.fit(X_train,y_train)\n",
        "print(\"Training Accuracy: {}%\".format(round(accuracy_score(y_train, svc.predict(X_train))*100)))"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Accuracy: 78.0%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2QtpaqUzAsEp",
        "outputId": "e1c57fbe-f872-42cc-fe86-824a42821ffb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "svc= SVC()\n",
        "svc.fit(X_train_scaled,y_train)\n",
        "print(\"Training Accuracy with Scaled data (SVC): {}%\".format(round(accuracy_score(y_train, svc.predict(X_train_scaled))*100)))"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Accuracy with Scaled data (SVC): 98.0%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RIipStx7heTt"
      },
      "source": [
        "---\n",
        "### 3. Evaluate both sets of data using your function."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vTjRU3ux3sNN",
        "outputId": "89826bfa-2b3a-4fbc-d80d-ffe9e398fb10",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(\"*\"*50)\n",
        "print('Random Forest')\n",
        "print(\"*\"*50)\n",
        "print('\\n')\n",
        "print('*'*30)\n",
        "print(\"Training Performance\")\n",
        "print('*'*30)\n",
        "evaluation(rf, X_train, y_train)\n",
        "print('*'*15)\n",
        "print('Scaled Data')\n",
        "evaluation(rf, X_train_scaled, y_train)\n",
        "print('*'*30)\n",
        "print('\\n')\n",
        "print('*'*30)\n",
        "print(\"Testing Performance\")\n",
        "print('*'*30)\n",
        "evaluation(rf, X_test, y_test)\n",
        "print('*'*15)\n",
        "print('Scaled Data')\n",
        "evaluation(rf, X_test_scaled, y_test)\n",
        "print('*'*30)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "**************************************************\n",
            "Random Forest\n",
            "**************************************************\n",
            "\n",
            "\n",
            "******************************\n",
            "Training Performance\n",
            "******************************\n",
            "Accuracy: 43.0%\n",
            "Precision: 43.0%\n",
            "Recall: 100.0%\n",
            "MSE: 0.5722184166748214\n",
            "SSE: 11695\n",
            "***************\n",
            "Scaled Data\n",
            "Accuracy: 100.0%\n",
            "Precision: 100.0%\n",
            "Recall: 100.0%\n",
            "MSE: 0.0\n",
            "SSE: 0\n",
            "******************************\n",
            "\n",
            "\n",
            "******************************\n",
            "Testing Performance\n",
            "******************************\n",
            "Accuracy: 42.0%\n",
            "Precision: 42.0%\n",
            "Recall: 100.0%\n",
            "MSE: 0.5778864970645793\n",
            "SSE: 2953\n",
            "***************\n",
            "Scaled Data\n",
            "Accuracy: 100.0%\n",
            "Precision: 100.0%\n",
            "Recall: 100.0%\n",
            "MSE: 0.0\n",
            "SSE: 0\n",
            "******************************\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SCjCL8PcGIBJ",
        "outputId": "b09d50ae-133a-44f0-cd4a-dd58f79a8fbc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(\"*\"*50)\n",
        "print('KNN')\n",
        "print(\"*\"*50)\n",
        "print('\\n')\n",
        "print('*'*30)\n",
        "print(\"Training Performance\")\n",
        "print('*'*30)\n",
        "evaluation(knn, X_train, y_train)\n",
        "print('*'*15)\n",
        "print('Scaled Data')\n",
        "evaluation(knn, X_train_scaled, y_train)\n",
        "print('*'*30)\n",
        "print('\\n')\n",
        "print('*'*30)\n",
        "print(\"Testing Performance\")\n",
        "print('*'*30)\n",
        "evaluation(knn, X_test, y_test)\n",
        "print('*'*15)\n",
        "print('Scaled Data')\n",
        "evaluation(knn, X_test_scaled, y_test)\n",
        "print('*'*30)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "**************************************************\n",
            "KNN\n",
            "**************************************************\n",
            "\n",
            "\n",
            "******************************\n",
            "Training Performance\n",
            "******************************\n",
            "Accuracy: 57.0%\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Precision: 0.0%\n",
            "Recall: 0.0%\n",
            "MSE: 0.42778158332517857\n",
            "SSE: 8743\n",
            "***************\n",
            "Scaled Data\n",
            "Accuracy: 99.0%\n",
            "Precision: 100.0%\n",
            "Recall: 98.0%\n",
            "MSE: 0.0076817692533516\n",
            "SSE: 157\n",
            "******************************\n",
            "\n",
            "\n",
            "******************************\n",
            "Testing Performance\n",
            "******************************\n",
            "Accuracy: 58.0%\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Precision: 0.0%\n",
            "Recall: 0.0%\n",
            "MSE: 0.42211350293542077\n",
            "SSE: 2157\n",
            "***************\n",
            "Scaled Data\n",
            "Accuracy: 98.0%\n",
            "Precision: 100.0%\n",
            "Recall: 96.0%\n",
            "MSE: 0.018003913894324854\n",
            "SSE: 92\n",
            "******************************\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uXY0qlYEGn1o",
        "outputId": "5f9e1891-51b0-402b-9920-8fcc7faed87f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(\"*\"*50)\n",
        "print('Gradient Boosting')\n",
        "print(\"*\"*50)\n",
        "print('\\n')\n",
        "print('*'*30)\n",
        "print(\"Training Performance\")\n",
        "print('*'*30)\n",
        "evaluation(gb, X_train, y_train)\n",
        "print('*'*15)\n",
        "print('Scaled Data')\n",
        "evaluation(gb, X_train_scaled, y_train)\n",
        "print('*'*30)\n",
        "print('\\n')\n",
        "print('*'*30)\n",
        "print(\"Testing Performance\")\n",
        "print('*'*30)\n",
        "evaluation(gb, X_test, y_test)\n",
        "print('*'*15)\n",
        "print('Scaled Data')\n",
        "evaluation(gb, X_test_scaled, y_test)\n",
        "print('*'*30)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "**************************************************\n",
            "Gradient Boosting\n",
            "**************************************************\n",
            "\n",
            "\n",
            "******************************\n",
            "Training Performance\n",
            "******************************\n",
            "Accuracy: 43.0%\n",
            "Precision: 43.0%\n",
            "Recall: 100.0%\n",
            "MSE: 0.5722184166748214\n",
            "SSE: 11695\n",
            "***************\n",
            "Scaled Data\n",
            "Accuracy: 100.0%\n",
            "Precision: 100.0%\n",
            "Recall: 100.0%\n",
            "MSE: 0.0\n",
            "SSE: 0\n",
            "******************************\n",
            "\n",
            "\n",
            "******************************\n",
            "Testing Performance\n",
            "******************************\n",
            "Accuracy: 42.0%\n",
            "Precision: 42.0%\n",
            "Recall: 100.0%\n",
            "MSE: 0.5778864970645793\n",
            "SSE: 2953\n",
            "***************\n",
            "Scaled Data\n",
            "Accuracy: 100.0%\n",
            "Precision: 100.0%\n",
            "Recall: 100.0%\n",
            "MSE: 0.0\n",
            "SSE: 0\n",
            "******************************\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xerHRFqVA3eW",
        "outputId": "b19cb8be-cda9-4575-a38c-aede697cf653",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(\"*\"*50)\n",
        "print('SVC')\n",
        "print(\"*\"*50)\n",
        "print('\\n')\n",
        "print('*'*30)\n",
        "print(\"Training Performance\")\n",
        "print('*'*30)\n",
        "evaluation(svc, X_train, y_train)\n",
        "print('*'*15)\n",
        "print('Scaled Data')\n",
        "evaluation(svc, X_train_scaled, y_train)\n",
        "print('*'*30)\n",
        "print('\\n')\n",
        "print('*'*30)\n",
        "print(\"Testing Performance\")\n",
        "print('*'*30)\n",
        "evaluation(svc, X_test, y_test)\n",
        "print('*'*15)\n",
        "print('Scaled Data')\n",
        "evaluation(svc, X_test_scaled, y_test)\n",
        "print('*'*30)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "**************************************************\n",
            "SVC\n",
            "**************************************************\n",
            "\n",
            "\n",
            "******************************\n",
            "Training Performance\n",
            "******************************\n",
            "Accuracy: 43.0%\n",
            "Precision: 43.0%\n",
            "Recall: 100.0%\n",
            "MSE: 0.5722184166748214\n",
            "SSE: 11695\n",
            "***************\n",
            "Scaled Data\n",
            "Accuracy: 98.0%\n",
            "Precision: 100.0%\n",
            "Recall: 95.0%\n",
            "MSE: 0.023289950092964088\n",
            "SSE: 476\n",
            "******************************\n",
            "\n",
            "\n",
            "******************************\n",
            "Testing Performance\n",
            "******************************\n",
            "Accuracy: 42.0%\n",
            "Precision: 42.0%\n",
            "Recall: 100.0%\n",
            "MSE: 0.5778864970645793\n",
            "SSE: 2953\n",
            "***************\n",
            "Scaled Data\n",
            "Accuracy: 98.0%\n",
            "Precision: 100.0%\n",
            "Recall: 95.0%\n",
            "MSE: 0.02172211350293542\n",
            "SSE: 111\n",
            "******************************\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ZLNoDjMheTu"
      },
      "source": [
        "### Extra: Confusion Matrix"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X38EGKUEGBSS"
      },
      "source": [
        "### RF"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CSBmMog0m8zY",
        "outputId": "6e9cb9d0-c5e1-4fe3-c416-fac79d498bd7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "sns.heatmap(confusion_matrix(y_test, rf.predict(X_test_scaled)) / len(y), cmap='Blues', annot=True)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f369f21f278>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9qJxPHQoHEHW"
      },
      "source": [
        "### KNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8ac7Jy3sHJx6",
        "outputId": "f0b126e4-e33e-4b1b-df0e-1ab69a7a0e5d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "sns.heatmap(confusion_matrix(y_test, knn.predict(X_test_scaled)) / len(y), cmap='Blues', annot=True)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f369f214208>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lugbOCoPHFlA"
      },
      "source": [
        "### Gradient Boosting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MT86avQWHNun",
        "outputId": "d3187cb0-10e5-4736-f2b1-42c1ed17bd63",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "sns.heatmap(confusion_matrix(y_test, gb.predict(X_test_scaled)) / len(y), cmap='Blues', annot=True)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f369f2a37b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWMAAAD4CAYAAAA5FIfVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAVYElEQVR4nO3df5RfdX3n8ed7ZvIDsQSMZIAkAmuCmBho+WW7VURYIIglsiQLeHpku9GsnKa71dUKyxptVlxTW+j2gGcbmyBLpYTSsqaSkloQ+SHiBCyEkKYdIiYTwwSSGORHTDLz3j/ma/xmmMx8R+bHvTfPB+ce7v3cz7338z3kvPLhc+/n3shMJEmjq2m0GyBJMowlqRAMY0kqAMNYkgrAMJakAmgZ7gsc9msLfVxDr7Oz7abRboIKaHwL8UbPMZjMee0HN73h6w0Ve8aSVADD3jOWpBEV5exjGsaSqqWpebRb8EsxjCVVSxRmGHhQDGNJ1eIwhSQVgD1jSSoAe8aSVAD2jCWpAHyaQpIKwGEKSSoAhykkqQDsGUtSARjGklQAzd7Ak6TR55ixJBWAwxSSVAD2jCWpAOwZS1IB2DOWpAJwOrQkFYDDFJJUAA5TSFIB2DOWpAIwjCWpALyBJ0kFUNIx43L25yXpYKKp8WWgU0XMjogNEdEeEdf0sf/siHgiIvZFxNxe+66KiH+tLVcNdC17xpKqZYh6xhHRDNwMnA90AG0RsTIzn6mrtgn4j8Cneh37FuBzwBlAAo/Xjt15sOvZM5ZUKRHR8DKAs4D2zNyYmXuAO4A59RUy87nMfAro7nXshcC3MnNHLYC/Bczu72KGsaRKGUwYR8SCiFhTtyyoO9VkYHPddketrBGDPtZhCkmVEk2ND1Nk5lJg6fC1pnH2jCVVyhAOU2wBptZtT6mVNWLQxxrGkiplCMO4DZgeESdGxFjgCmBlg81YDVwQEUdFxFHABbWygzKMJVXKUIVxZu4DFtITouuBOzNzXUQsjohLatc6MyI6gHnAn0fEutqxO4D/SU+gtwGLa2UH5ZixpGoZwjkfmbkKWNWrbFHdehs9QxB9HbscWN7otQxjSZXSwPBDIRnGkiqlqamco6+GsaRKsWcsSUVQziw2jCVViz1jSSoAw1iSCmAw06GLxDCWVCn2jCWpAAxjSSoAw1iSCsAwlqQiKGcWG8aSqsXp0JJUAA5TSFIRlDOLfbn8UDn/376TJ+/+LE9/43N86nfOf93+3zzt7Xz39s/w07b/zaX/7lf3l59y0mQeuPW/8fhd1/H9Fdcy94LTRrLZGmWPPPQgl1x8IR+cfT7LvlqIT7GV3hB+6WNE2TMeAk1NwZ9e8x+4+Oqb2NL5Ex7++qf55nfW8s8bn99fZ/PWnSz43G38/kfOO+DYV3fvZf5n/y/PbnqBY4+ewCNf/wO+9d317Hr5tZH+GRphXV1dfPH6xfz5V2+htbWVD18+l3Pefy5vnzZttJtWakUL2UYNGMYRcTIwh198ZnoLsDIz1w9nw8rkzHedwLObX+S5LdsB+OvVT/DBc045IIw3be354kp3dx5wbPumbfvXt76wixd2/pS3vuXNhvEh4Om1TzF16vFMmdrz3crZH7iYB759n2H8BpU1jPsdpoiIzwB30DMK8/3aEsBfRcQ1w9+8cjhu0gQ6Onfu397SuZPJR08Y9HnOmHk8Y1ta2Lj5xaFsngpqW2cnxxx7zP7tSa2tdHZ2jmKLqiGaouGlSAbqGc8HZmbm3vrCiLgBWAd8qa+DImIBsACgZco5tLx15hA0tdqOeesRLPvCR/jYotvIzIEPkNSnSvaMgW7guD7Kj63t61NmLs3MMzLzjEMhiH+8bRdTWo/avz259Si2vLCr4eN/5fDx/O2fXc3nb/47vr/2uWFooYpoUmsrz2/9xVDWts5OWltbR7FF1VDWG3gDhfHvA/dFxN9HxNLaci9wH/Bfh7955bBm3Y+Y9rajOf64iYxpaWbehadxzwNPNXTsmJZmVvzJx7j9m49x9z/+0zC3VEUy812z2LTpOTo6NrN3zx7uXXUP73v/uaPdrNKLaHwpkn6HKTLz3og4CTiLA2/gtWVm13A3riy6urr5xJI7+buv/C7NTcGt3/ge6zc+z2evvpgnntnEPd9Zy+kz3saKGz7GkUe8iQ+cPYv/8fGLOX3u9Vx2wWm857RpvOXIw/ntS34dgAWLbuOpf9kyyr9Kw62lpYVrr1vE1Qs+Snd3Fx+69DKmTZs+2s0qvaL1eBsVwz0+edivLXQAVK+zs+2m0W6CCmh8yxufsvGOz6xuOHM2LLmwMMntc8aSKqWkHWPDWFK1NBXskbVGGcaSKsWesSQVQFlv4PmiIEmVMpSPtkXE7IjYEBHtfc06johxEbGitv+xiDihVj4mIm6NiLURsT4irh3oWoaxpEppampqeOlPRDQDNwMXATOAKyNiRq9q84GdmTkNuBFYUiufB4zLzFnA6cB//nlQH7Tdg/ydklRoQ9gzPgtoz8yNmbmHnvf0zOlVZw5wa239LuC86BknSeDwiGgBDgP2AC/1dzHDWFKlDGY6dEQsiIg1dcuCulNNBjbXbXfwi8lvr6uTmfuAXcBEeoL5FWArsAn448zc0V+7vYEnqVIGc/8uM5cCw/FW/7OALnre7XMU8FBE/GNmbjzYAfaMJVXKEL4oaAswtW57Sq2szzq1IYkJwHbgw8C9mbk3M7cBjwBn9Hcxw1hSpQzhmHEbMD0iToyIscAVwMpedVYCV9XW5wL3Z887JjYB5/a0Jw4Hfh345/4u5jCFpEoZqhl4mbkvIhYCq4FmYHlmrouIxcCazFwJLANui4h2YAc9gQ09T2HcEhHr6Pkgxy2Z2e+rHA1jSZUylJM+MnMVsKpX2aK69d30PMbW+7iX+yrvj2EsqVJKOgHPMJZULWWdDm0YS6qUkmaxYSypWnyFpiQVgMMUklQAhrEkFUBJs9gwllQt9owlqQBKmsWGsaRq8WkKSSqAppJ2jQ1jSZVS0iw2jCVVizfwJKkASjpkbBhLqhZv4ElSAQSGsSSNupJ2jA1jSdXiDTxJKoCSZrFhLKlanPQhSQXg0xSSVAAl7RgbxpKqxWEKSSqAckaxYSypYny0TZIKoKT37wxjSdXi0xSSVAAOU0hSAZS0Y0zTaDdAkoZSRDS8NHCu2RGxISLaI+KaPvaPi4gVtf2PRcQJdftOiYhHI2JdRKyNiPH9XcswllQpMYil3/NENAM3AxcBM4ArI2JGr2rzgZ2ZOQ24EVhSO7YF+Evg45k5EzgH2Nvf9QxjSZXS3BQNLwM4C2jPzI2ZuQe4A5jTq84c4Nba+l3AedHT5b4AeCoznwTIzO2Z2dXfxQxjSZUymGGKiFgQEWvqlgV1p5oMbK7b7qiV0VedzNwH7AImAicBGRGrI+KJiPiDgdrtDTxJlTKYhykycymwdBia0QK8BzgTeBW4LyIez8z7DnaAPWNJldIU0fAygC3A1LrtKbWyPuvUxoknANvp6UU/mJkvZuarwCrgtH7b3fAvlKQSiGh8GUAbMD0iToyIscAVwMpedVYCV9XW5wL3Z2YCq4FZEfGmWki/D3imv4sN+zDFzrabhvsSKqFTr1s92k1QAW1YcuEbPsdQTfrIzH0RsZCeYG0GlmfmuohYDKzJzJXAMuC2iGgHdtAT2GTmzoi4gZ5AT2BVZt7T3/UcM5ZUKc1DOAMvM1fRM8RQX7aobn03MO8gx/4lPY+3NcQwllQpZZ2BZxhLqhTDWJIKwBcFSVIB2DOWpAIoacfYMJZULS0lTWPDWFKllDSLDWNJ1dLANOdCMowlVUpJs9gwllQtPk0hSQXQwEvjC8kwllQpJc1iw1hStcSAX7crJsNYUqXYM5akAjCMJakAfFGQJBVAc0k/JmcYS6oUZ+BJUgE4ZixJBVDSjrFhLKlamnzOWJJGnz1jSSqAlpIOGhvGkirFnrEkFYCPtklSAZQ0iw1jSdVS0gl4hrGkainrMEVZ/xKRpD41RTS8DCQiZkfEhohoj4hr+tg/LiJW1PY/FhEn9Nr/toh4OSI+NWC7B/EbJanwYhBLv+eJaAZuBi4CZgBXRsSMXtXmAzszcxpwI7Ck1/4bgL9vpN2GsaRKiWh8GcBZQHtmbszMPcAdwJxedeYAt9bW7wLOi9o7PCPiQ8APgXWNtNswllQpEdHwMoDJwOa67Y5aWZ91MnMfsAuYGBFvBj4D/GGj7TaMJVVK0yCWiFgQEWvqlgVD1IzPAzdm5suNHuDTFJIqZTBPU2TmUmDpQXZvAabWbU+plfVVpyMiWoAJwHbg3cDciPgj4EigOyJ2Z+ZNB2uLYSypUobws0ttwPSIOJGe0L0C+HCvOiuBq4BHgbnA/ZmZwHvr2vN54OX+ghgMY0kVM1Rjr5m5LyIWAquBZmB5Zq6LiMXAmsxcCSwDbouIdmAHPYH9SzGMJVXKUH6QNDNXAat6lS2qW98NzBvgHJ9v5FqGsaRKKef8O8NYUsU0l3Q6tGEsqVJKmsWGsaRqiZIOVBjGkirFnrEkFYBfh5akArBnLEkFUNaXyxvGkiqlqZxZbBhLqhafppCkAijpKIVhPFIeeehBlnzperq7urn0snnM/9hQvTZVRfPek97KdZecTFMEf93WwVcf+OEB+8c0B390+SxmTp7AT17dwyduf5ItO3fT0hR8Ye5MZhx3BC3Nwf97/McsrTu2KeBvfu836HxpNx//2g9G+meVRll7xr5cfgR0dXXxxesX85X/8xfcvfIe7l31TZ5tbx/tZmkYNAUs+tA7+ejyx7n4hof54KnH8vZJhx9QZ96ZU3jptX1c8OWH+NrDP+JTF50EwOxTjmFsSxOX/Ol3+fd/9iiXv3sqk48av/+4j7zneJ7d9sqI/p4yaorGlyIxjEfA02ufYurU45kydSpjxo5l9gcu5oFv3zfazdIwOGXqBH60/VU6drzG3q7knie3ct6MSQfUOXfmJO5+vOcd5avXdvIb0yYCkJkcNqaZ5qZg/Jhm9nZ18/LuLgBaJ4zjnJOP5q62jpH9QSU0lF+HHkmG8QjY1tnJMcces397UmsrnZ2do9giDZfWCeN5/ie792937tpN64TxB9Y5Yhxbd/XU6epOfrp7H0e9aQyr13by2t4uHr7uHL597dksf/A5dr22F4D//lsn8+VV/0J35sj9mJIaqq9Dj7RfOowj4nf62bf/u1LLvnqwL5pIqnfK1Al0dyfvvf4BzvvSQ/yns09gylsO45yTj2bHy3tYt+Wl0W5iKZS1Z/xGbuD9IXBLXzvqvyu1ex+H/F/lk1pbeX7r8/u3t3V20traOoot0nDp3LWbY478RU+4dcJ4OnftPrDOSz/j2Anj6dz1M5qbgl8Z38LOV/fye796LA9teJF93cmOV/bwxHM7mTXlCN553BGcO2MSZ7/jaMaNaeLN41r48uWz+PSKtSP980qhWBHbuH57xhHx1EGWtYBp0qCZ75rFpk3P0dGxmb179nDvqnt43/vPHe1maRis7XiJEya+iSlHHcaY5uDiU4/l/vXbDqhz/zPbuPT0ni++Xzirle89uwOArT/Zzbtr48eHjWnm1LcdycZtr3DDvf/K+774Hc5b8iCfvP1JvvfsdoO4PyUdpxioZ9wKXAjs7FUewHeHpUUV1NLSwrXXLeLqBR+lu7uLD116GdOmTR/tZmkYdHUni7+xnr+YfzrNTcHftG2hvfMV/sv503i6Yxf3r3+Bu9q28OXLZ/EPn34vu17byydufxKArz+6if81711885O/SQB/u2YLG55v+Evvqina8EOjIvu5IRARy4BbMvPhPvbdnpm9v5T6Og5TqC+nXrd6tJugAtqw5MI3nKRtG3c1nDln/psJhUnufnvGmTm/n30DBrEkjbjCxOvgOANPUqWUdQaeYSypUko6ZGwYS6qWkmaxYSypWqKkXWPDWFKllDSLDWNJ1VLSLDaMJVVMSdPYMJZUKWV9tM1XaEqqlIjGl4HPFbMjYkNEtEfENX3sHxcRK2r7H4uIE2rl50fE4xGxtvbvAV9GYxhLqpShCuOIaAZuBi4CZgBXRsSMXtXmAzszcxpwI7CkVv4i8FuZOQu4CrhtoHYbxpIqJQbxzwDOAtozc2Nm7gHuAOb0qjMHuLW2fhdwXkREZv4gM39cK18HHBYR4/q7mGEsqVIG0zOu/xBGban/UvBkYHPddketjL7qZOY+YBcwsVedy4AnMvNn/bXbG3iSKmUwt+/qP4QxLG2JmEnP0MUFA9W1ZyypWobu5fJbgKl121NqZX3WiYgWYAKwvbY9Bbgb+EhmPjvQxQxjSZUyhN/AawOmR8SJETEWuAJY2avOSnpu0AHMBe7PzIyII4F7gGsy85GG2t3wL5SkEhiqjnFtDHghsBpYD9yZmesiYnFEXFKrtgyYGBHtwCeBnz/+thCYBiyKiH+qLZP6u55jxpKqZQjnfGTmKmBVr7JFdeu7gXl9HPcF4AuDuZZhLKlSyjoDzzCWVCm+tU2SCqCkWWwYS6oWXy4vSQVQ0iw2jCVVS0mz2DCWVDElTWPDWFKl+GibJBWAY8aSVABNhrEkFUE509gwllQpDlNIUgGUNIsNY0nVYs9YkgrA6dCSVADljGLDWFLFlLRjbBhLqhZn4ElSEZQziw1jSdVS0iw2jCVVS1NJB40NY0mVUtIspmm0GyBJsmcsqWLK2jM2jCVVio+2SVIB2DOWpAIwjCWpABymkKQCKGvP2EfbJFVKDGIZ8FwRsyNiQ0S0R8Q1fewfFxEravsfi4gT6vZdWyvfEBEXDnQtw1hStQxRGkdEM3AzcBEwA7gyImb0qjYf2JmZ04AbgSW1Y2cAVwAzgdnAV2rnOyjDWFKlNEU0vAzgLKA9Mzdm5h7gDmBOrzpzgFtr63cB50XP2+3nAHdk5s8y84dAe+18BzXsY8bjW0o6mj4MImJBZi4d7XYUwYYlA/5f2yHDPxdDazCZExELgAV1RUvr/ltMBjbX7esA3t3rFPvrZOa+iNgFTKyVf6/XsZP7a4s945G1YOAqOgT552KUZObSzDyjbhm1vxQNY0nq2xZgat32lFpZn3UiogWYAGxv8NgDGMaS1Lc2YHpEnBgRY+m5IbeyV52VwFW19bnA/ZmZtfIrak9bnAhMB77f38V8znhkOS6ovvjnooBqY8ALgdVAM7A8M9dFxGJgTWauBJYBt0VEO7CDnsCmVu9O4BlgH/C7mdnV3/WiJ8QlSaPJYQpJKgDDWJIKwDAeIQNNq9ShJyKWR8S2iHh6tNui0WcYj4AGp1Xq0PM1eqbKSobxCGlkWqUOMZn5ID134CXDeIT0Na2y36mRkg4thrEkFYBhPDIGPTVS0qHFMB4ZjUyrlHQIM4xHQGbuA34+rXI9cGdmrhvdVmm0RcRfAY8C74iIjoiYP9pt0uhxOrQkFYA9Y0kqAMNYkgrAMJakAjCMJakADGNJKgDDWJIKwDCWpAL4/780deV6jFCPAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QQEf3byAHAvb"
      },
      "source": [
        "### SVC"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BYMAfLyfDZer",
        "outputId": "0334c3be-9883-48ee-b2c8-b8c03847f325",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "sns.heatmap(confusion_matrix(y_test, svc.predict(X_test_scaled)) / len(y), cmap='Blues', annot=True)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f369f124978>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}