SpaceX_Machine Learning Prediction_Part_5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align:center\">\n",
    "    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\" target=\"_blank\">\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n",
    "    </a>\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Space X  Falcon 9 First Stage Landing Prediction**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assignment:  Machine Learning Prediction\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimated time needed: **60** minutes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Space X advertises Falcon 9 rocket launches on its website with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is because Space X can reuse the first stage. Therefore if we can determine if the first stage will land, we can determine the cost of a launch. This information can be used if an alternate company wants to bid against space X for a rocket launch.   In this lab, you will create a machine learning pipeline  to predict if the first stage will land given the data from the preceding labs.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/landing\\_1.gif)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Several examples of an unsuccessful landing are shown here:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most unsuccessful landings are planed. Space X; performs a controlled landing in the oceans.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objectives\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform exploratory  Data Analysis and determine Training Labels\n",
    "\n",
    "*   create a column for the class\n",
    "*   Standardize the data\n",
    "*   Split into training data and test data\n",
    "\n",
    "\\-Find best Hyperparameter for SVM, Classification Trees and Logistic Regression\n",
    "\n",
    "*   Find the method performs best using test data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries and Define Auxiliary Functions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will import the following libraries for the lab\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "jupyter": {
     "outputs_hidden": true
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    "tags": []
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   "source": [
    "# Pandas is a software library written for the Python programming language for data manipulation and analysis.\n",
    "import pandas as pd\n",
    "# NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays\n",
    "import numpy as np\n",
    "# Matplotlib is a plotting library for python and pyplot gives us a MatLab like plotting framework. We will use this in our plotter function to plot data.\n",
    "import matplotlib.pyplot as plt\n",
    "#Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics\n",
    "import seaborn as sns\n",
    "# Preprocessing allows us to standarsize our data\n",
    "from sklearn import preprocessing\n",
    "# Allows us to split our data into training and testing data\n",
    "from sklearn.model_selection import train_test_split\n",
    "# Allows us to test parameters of classification algorithms and find the best one\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "# Logistic Regression classification algorithm\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "# Support Vector Machine classification algorithm\n",
    "from sklearn.svm import SVC\n",
    "# Decision Tree classification algorithm\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "# K Nearest Neighbors classification algorithm\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function is to plot the confusion matrix.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(y,y_predict):\n",
    "    \"this function plots the confusion matrix\"\n",
    "    from sklearn.metrics import confusion_matrix\n",
    "\n",
    "    cm = confusion_matrix(y, y_predict)\n",
    "    ax= plt.subplot()\n",
    "    sns.heatmap(cm, annot=True, ax = ax); #annot=True to annotate cells\n",
    "    ax.set_xlabel('Predicted labels')\n",
    "    ax.set_ylabel('True labels')\n",
    "    ax.set_title('Confusion Matrix'); \n",
    "    ax.xaxis.set_ticklabels(['did not land', 'land']); ax.yaxis.set_ticklabels(['did not land', 'landed'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the dataframe\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FlightNumber</th>\n",
       "      <th>Date</th>\n",
       "      <th>BoosterVersion</th>\n",
       "      <th>PayloadMass</th>\n",
       "      <th>Orbit</th>\n",
       "      <th>LaunchSite</th>\n",
       "      <th>Outcome</th>\n",
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       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-06-04</td>\n",
       "      <td>Falcon 9</td>\n",
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       "      <td>LEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2012-05-22</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>LEO</td>\n",
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       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2013-03-01</td>\n",
       "      <td>Falcon 9</td>\n",
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       "      <td>CCAFS SLC 40</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2013-09-29</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>PO</td>\n",
       "      <td>VAFB SLC 4E</td>\n",
       "      <td>False Ocean</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B1003</td>\n",
       "      <td>-120.610829</td>\n",
       "      <td>34.632093</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2013-12-03</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>3170.000000</td>\n",
       "      <td>GTO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
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       "   FlightNumber        Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\n",
       "0             1  2010-06-04       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n",
       "1             2  2012-05-22       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n",
       "2             3  2013-03-01       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n",
       "3             4  2013-09-29       Falcon 9   500.000000    PO   VAFB SLC 4E   \n",
       "4             5  2013-12-03       Falcon 9  3170.000000   GTO  CCAFS SLC 40   \n",
       "\n",
       "       Outcome  Flights  GridFins  Reused   Legs LandingPad  Block  \\\n",
       "0    None None        1     False   False  False        NaN    1.0   \n",
       "1    None None        1     False   False  False        NaN    1.0   \n",
       "2    None None        1     False   False  False        NaN    1.0   \n",
       "3  False Ocean        1     False   False  False        NaN    1.0   \n",
       "4    None None        1     False   False  False        NaN    1.0   \n",
       "\n",
       "   ReusedCount Serial   Longitude   Latitude  Class  \n",
       "0            0  B0003  -80.577366  28.561857      0  \n",
       "1            0  B0005  -80.577366  28.561857      0  \n",
       "2            0  B0007  -80.577366  28.561857      0  \n",
       "3            0  B1003 -120.610829  34.632093      0  \n",
       "4            0  B1004  -80.577366  28.561857      0  "
      ]
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#data = pd.read_csv(\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv\")\n",
    "\n",
    "# If you were unable to complete the previous lab correctly you can uncomment and load this csv\n",
    "\n",
    "data = pd.read_csv('dataset_part_2.csv')\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>87.0</td>\n",
       "      <td>15400.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>88.0</td>\n",
       "      <td>15400.000000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>89.0</td>\n",
       "      <td>15400.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3681.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    FlightNumber   PayloadMass  Flights  GridFins  Reused  Legs  Block  \\\n",
       "0            1.0   6104.959412      1.0       0.0     0.0   0.0    1.0   \n",
       "1            2.0    525.000000      1.0       0.0     0.0   0.0    1.0   \n",
       "2            3.0    677.000000      1.0       0.0     0.0   0.0    1.0   \n",
       "3            4.0    500.000000      1.0       0.0     0.0   0.0    1.0   \n",
       "4            5.0   3170.000000      1.0       0.0     0.0   0.0    1.0   \n",
       "..           ...           ...      ...       ...     ...   ...    ...   \n",
       "85          86.0  15400.000000      2.0       1.0     1.0   1.0    5.0   \n",
       "86          87.0  15400.000000      3.0       1.0     1.0   1.0    5.0   \n",
       "87          88.0  15400.000000      6.0       1.0     1.0   1.0    5.0   \n",
       "88          89.0  15400.000000      3.0       1.0     1.0   1.0    5.0   \n",
       "89          90.0   3681.000000      1.0       1.0     0.0   1.0    5.0   \n",
       "\n",
       "    ReusedCount  Orbit_ES-L1  Orbit_GEO  ...  Serial_B1048  Serial_B1049  \\\n",
       "0           0.0          0.0        0.0  ...           0.0           0.0   \n",
       "1           0.0          0.0        0.0  ...           0.0           0.0   \n",
       "2           0.0          0.0        0.0  ...           0.0           0.0   \n",
       "3           0.0          0.0        0.0  ...           0.0           0.0   \n",
       "4           0.0          0.0        0.0  ...           0.0           0.0   \n",
       "..          ...          ...        ...  ...           ...           ...   \n",
       "85          2.0          0.0        0.0  ...           0.0           0.0   \n",
       "86          2.0          0.0        0.0  ...           0.0           0.0   \n",
       "87          5.0          0.0        0.0  ...           0.0           0.0   \n",
       "88          2.0          0.0        0.0  ...           0.0           0.0   \n",
       "89          0.0          0.0        0.0  ...           0.0           0.0   \n",
       "\n",
       "    Serial_B1050  Serial_B1051  Serial_B1054  Serial_B1056  Serial_B1058  \\\n",
       "0            0.0           0.0           0.0           0.0           0.0   \n",
       "1            0.0           0.0           0.0           0.0           0.0   \n",
       "2            0.0           0.0           0.0           0.0           0.0   \n",
       "3            0.0           0.0           0.0           0.0           0.0   \n",
       "4            0.0           0.0           0.0           0.0           0.0   \n",
       "..           ...           ...           ...           ...           ...   \n",
       "85           0.0           0.0           0.0           0.0           0.0   \n",
       "86           0.0           0.0           0.0           0.0           1.0   \n",
       "87           0.0           1.0           0.0           0.0           0.0   \n",
       "88           0.0           0.0           0.0           0.0           0.0   \n",
       "89           0.0           0.0           0.0           0.0           0.0   \n",
       "\n",
       "    Serial_B1059  Serial_B1060  Serial_B1062  \n",
       "0            0.0           0.0           0.0  \n",
       "1            0.0           0.0           0.0  \n",
       "2            0.0           0.0           0.0  \n",
       "3            0.0           0.0           0.0  \n",
       "4            0.0           0.0           0.0  \n",
       "..           ...           ...           ...  \n",
       "85           0.0           1.0           0.0  \n",
       "86           0.0           0.0           0.0  \n",
       "87           0.0           0.0           0.0  \n",
       "88           0.0           1.0           0.0  \n",
       "89           0.0           0.0           1.0  \n",
       "\n",
       "[90 rows x 80 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#X = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_3.csv')\n",
    "\n",
    "# If you were unable to complete the previous lab correctly you can uncomment and load this csv\n",
    "\n",
    "X = pd.read_csv('dataset_part_3.csv')\n",
    "\n",
    "X.head(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a NumPy array from the column <code>Class</code> in <code>data</code>, by applying the method <code>to_numpy()</code>  then\n",
    "assign it  to the variable <code>Y</code>,make sure the output is a  Pandas series (only one bracket df\\['name of  column']).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['FlightNumber', 'Date', 'BoosterVersion', 'PayloadMass', 'Orbit',\n",
       "       'LaunchSite', 'Outcome', 'Flights', 'GridFins', 'Reused', 'Legs',\n",
       "       'LandingPad', 'Block', 'ReusedCount', 'Serial', 'Longitude', 'Latitude',\n",
       "       'Class'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = data['Class'].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Standardize the data in <code>X</code> then reassign it to the variable  <code>X</code> using the transform provided below.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# students get this \n",
    "transform = preprocessing.StandardScaler()\n",
    "X = transform.fit(X).transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.71291154e+00, -1.94814463e-16, -6.53912840e-01, ...,\n",
       "        -2.15665546e-01, -1.85695338e-01, -1.05999788e-01],\n",
       "       [-1.67441914e+00, -1.19523159e+00, -6.53912840e-01, ...,\n",
       "        -2.15665546e-01, -1.85695338e-01, -1.05999788e-01],\n",
       "       [-1.63592675e+00, -1.16267307e+00, -6.53912840e-01, ...,\n",
       "        -2.15665546e-01, -1.85695338e-01, -1.05999788e-01],\n",
       "       ...,\n",
       "       [ 1.63592675e+00,  1.99100483e+00,  3.49060516e+00, ...,\n",
       "        -2.15665546e-01, -1.85695338e-01, -1.05999788e-01],\n",
       "       [ 1.67441914e+00,  1.99100483e+00,  1.00389436e+00, ...,\n",
       "        -2.15665546e-01,  5.38516481e+00, -1.05999788e-01],\n",
       "       [ 1.71291154e+00, -5.19213966e-01, -6.53912840e-01, ...,\n",
       "        -2.15665546e-01, -1.85695338e-01,  9.43398113e+00]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We split the data into training and testing data using the  function  <code>train_test_split</code>.   The training data is divided into validation data, a second set used for training  data; then the models are trained and hyperparameters are selected using the function <code>GridSearchCV</code>.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  3\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the function train_test_split to split the data X and Y into training and test data. Set the parameter test_size to  0.2 and random_state to 2. The training data and test data should be assigned to the following labels.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<code>X_train, X_test, Y_train, Y_test</code>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size=0.2,random_state=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can see we only have 18 test samples.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18,)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  4\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a logistic regression object  then create a  GridSearchCV object  <code>logreg_cv</code> with cv = 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters ={'C':[0.01,0.1,1],\n",
    "             'penalty':['l2'],\n",
    "             'solver':['lbfgs']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=LogisticRegression(),\n",
       "             param_grid={'C': [0.01, 0.1, 1], 'penalty': ['l2'],\n",
       "                         'solver': ['lbfgs']})"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters ={\"C\":[0.01,0.1,1],'penalty':['l2'], 'solver':['lbfgs']}# l1 lasso l2 ridge\n",
    "lr=LogisticRegression()\n",
    "logreg_cv = GridSearchCV(lr, parameters, cv=10)\n",
    "logreg_cv.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We output the <code>GridSearchCV</code> object for logistic regression. We display the best parameters using the data attribute <code>best_params\\_</code> and the accuracy on the validation data using the data attribute <code>best_score\\_</code>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tuned hpyerparameters :(best parameters)  {'C': 1, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "accuracy : 0.8214285714285714\n"
     ]
    }
   ],
   "source": [
    "print(\"tuned hpyerparameters :(best parameters) \",logreg_cv.best_params_)\n",
    "print(\"accuracy :\",logreg_cv.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  5\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the accuracy on the test data using the method <code>score</code>:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1-Score:0.89\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "def score(model):\n",
    "    yhat = model.predict(X_test)\n",
    "    print(f'F1-Score:{f1_score(Y_test,yhat):.2f}')\n",
    "score(logreg_cv)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8333333333333334"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_score = logreg_cv.score(X_test,Y_test)\n",
    "log_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets look at the confusion matrix:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yhat=logreg_cv.predict(X_test)\n",
    "plot_confusion_matrix(Y_test,yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Examining the confusion matrix, we see that logistic regression can distinguish between the different classes.  We see that the major problem is false positives.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  6\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a support vector machine object then  create a  <code>GridSearchCV</code> object  <code>svm_cv</code> with cv - 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {'kernel':('linear', 'rbf','poly','rbf', 'sigmoid'),\n",
    "              'C': np.logspace(-3, 3, 5),\n",
    "              'gamma':np.logspace(-3, 3, 5)}\n",
    "svm = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=SVC(),\n",
       "             param_grid={'C': array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n",
       "       1.00000000e+03]),\n",
       "                         'gamma': array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n",
       "       1.00000000e+03]),\n",
       "                         'kernel': ('linear', 'rbf', 'poly', 'rbf', 'sigmoid')})"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_cv = GridSearchCV(svm, parameters, cv=10)\n",
    "svm_cv.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tuned hpyerparameters :(best parameters)  {'C': 1.0, 'gamma': 0.03162277660168379, 'kernel': 'sigmoid'}\n",
      "accuracy : 0.8482142857142858\n"
     ]
    }
   ],
   "source": [
    "print(\"tuned hpyerparameters :(best parameters) \",svm_cv.best_params_)\n",
    "print(\"accuracy :\",svm_cv.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  7\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the accuracy on the test data using the method <code>score</code>:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1-Score:0.89\n"
     ]
    }
   ],
   "source": [
    "score(svm_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8333333333333334"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_score = svm_cv.score(X_test,Y_test)\n",
    "svm_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the confusion matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yhat=svm_cv.predict(X_test)\n",
    "plot_confusion_matrix(Y_test,yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  8\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a decision tree classifier object then  create a  <code>GridSearchCV</code> object  <code>tree_cv</code> with cv = 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {'criterion': ['gini', 'entropy'],\n",
    "     'splitter': ['best', 'random'],\n",
    "     'max_depth': [2*n for n in range(1,10)],\n",
    "     'max_features': ['auto', 'sqrt'],\n",
    "     'min_samples_leaf': [1, 2, 4],\n",
    "     'min_samples_split': [2, 5, 10]}\n",
    "\n",
    "tree = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=DecisionTreeClassifier(),\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': [2, 4, 6, 8, 10, 12, 14, 16, 18],\n",
       "                         'max_features': ['auto', 'sqrt'],\n",
       "                         'min_samples_leaf': [1, 2, 4],\n",
       "                         'min_samples_split': [2, 5, 10],\n",
       "                         'splitter': ['best', 'random']})"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_cv = GridSearchCV(tree, parameters, cv=10)\n",
    "tree_cv.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tuned hpyerparameters :(best parameters)  {'criterion': 'gini', 'max_depth': 4, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'splitter': 'best'}\n",
      "accuracy : 0.8874999999999998\n"
     ]
    }
   ],
   "source": [
    "print(\"tuned hpyerparameters :(best parameters) \",tree_cv.best_params_)\n",
    "print(\"accuracy :\",tree_cv.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  9\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the accuracy of tree_cv on the test data using the method <code>score</code>:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1-Score:0.92\n"
     ]
    }
   ],
   "source": [
    "score(tree_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8888888888888888"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_score = tree_cv.score(X_test,Y_test)\n",
    "tree_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the confusion matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yhat = tree_cv.predict(X_test)\n",
    "plot_confusion_matrix(Y_test,yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  10\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a k nearest neighbors object then  create a  <code>GridSearchCV</code> object  <code>knn_cv</code> with cv = 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
    "              'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n",
    "              'p': [1,2]}\n",
    "\n",
    "KNN = KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n",
       "             param_grid={'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n",
       "                         'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
       "                         'p': [1, 2]})"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_cv = GridSearchCV(KNN, parameters, cv=10)\n",
    "knn_cv.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tuned hpyerparameters :(best parameters)  {'algorithm': 'auto', 'n_neighbors': 6, 'p': 1}\n",
      "accuracy : 0.8339285714285714\n"
     ]
    }
   ],
   "source": [
    "print(\"tuned hpyerparameters :(best parameters) \",knn_cv.best_params_)\n",
    "print(\"accuracy :\",knn_cv.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  11\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the accuracy of tree_cv on the test data using the method <code>score</code>:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1-Score:0.89\n"
     ]
    }
   ],
   "source": [
    "score(knn_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8333333333333334"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_score = knn_cv.score(X_test,Y_test)\n",
    "knn_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the confusion matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yhat = knn_cv.predict(X_test)\n",
    "plot_confusion_matrix(Y_test,yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  12\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the method performs best:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 864x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+4O4c1GZmZh3A11u51z01osU2Tz31FA8++CBPP/00p5xyCrt27WL//v3Hvc2DBw9SUJDNtdttq8medkTMiIjTgNsj4vTk57SIODsibs1hjWZmZsdk586d9OjRg1NOOQWAHj16UFhYyMqVK/ngBz9ISUkJF110EW+88QY1NTVce+21DB48mLKyMh599FEA5s2bx9/8zd/wsY99jJEjRwJw++23U15ezpAhQ5g6dSoAb775JldccQUlJSVceOGFLFy4sM32q7me9gUR8QfgPknvbzjfLwwxM7P2auTIkUyfPp1+/fpx+eWXM3bsWIYPH87YsWNZuHAh5eXl7Nmzh1NPPZXvfve7AKxZs4Y//OEPjBw5khdeeAHI9Niff/55zjrrLJYsWcKmTZtYsWIFEcHo0aP53e9+xyuvvEJhYSG//vWvAdi9e3eb7Vdzff0vAxOBWY3M8wtDzMys3erevTurV6/m8ccf59FHH2Xs2LHcdtttnHvuuZSXlwNw+umnA7Bs2TImT54MwAUXXMB55513JLQ/8pGPcNZZZwGwZMkSlixZQllZ5j1ae/fuZdOmTVxyySXcfPPNfOUrX+HKK6/kkksuabP9au7q8YmSTgL+ISKeaLMKzMzM2sDJJ5/MpZdeyqWXXsrgwYO58847G72SO5o5R96tW7d67W699VZuuOGGo9qtXr2aqqoqbr31VkaOHMmUKVNaZycaaPbq8Yg4DHy7TbZsZmbWRjZu3MimTZuOfH/22WcZMGAAO3bsYOXKlQC88cYbHDx4kA996EP87Gc/A+CFF15g27Zt9O/f/6h1jho1ih/+8Ifs3bsXgO3bt/OnP/2JHTt20LVrV6655hpuvvlmnn667c4eZ3Mp3BJJnwR+Fc39OmJmZtZO7N27l8mTJ/P6669TUFBAcXExd999N9deey2TJ0/mrbfe4tRTT+WRRx7hpptu4sYbb2Tw4MEUFBQwb968Ixew1TVy5Eg2bNjA8OHDgcwQ/Pz583nxxRe55ZZbOOmkk+jUqRPf//7322y/1FIOS3oD6AYcAt7inceYnt5mVVnqDBs2LFatWpXvMsysHdiwYQMDBgzIdxntSmPHRNLqiBh2LOtpsaed3PZlZmZmeZbVneKSRgMfSr4+FhEPtl1JZmZm1pgWH2MqaSbwBWB98vOFZJqZmZnlUDY97QqgNLmSHEk/Bp4BvtqWhZmZWXpFhF8akmjNa7izeWEIwJl1Pp/Rals3M7MOp0uXLrz66qutGlZpVfs+7S5durTK+rLpac8AnpH0KJkrxz8E+NnjZmbWqKKiIqqrq1vtHdJp16VLF4qKilplXdlcPV4p6TGgnExofyUi/qtVtm5mZh1Op06dOP/88/NdRofUYmjXeVlIdfJnoaRuwNaIONhmlZmZmVk92QyP3wW8H3ieTE/7wuTz2ZJujIglbVifmZmZJbK5EG0LUBYRwyJiKFAGrAUuB/65DWszMzOzOrLpaV8QEetqv0TEekllEfGSL+e3WjtWr+br/vtgZu3Y1A5wNXs2ob1R0veBBcn3scALkk4BDrRZZWZmZlZPNsPjE4AXgS8CXwJeSqYdAEa0VWFmZmZWXza3fL0FzEp+Gtrb6hWZmZlZo5oMbUlrgKZOAERElLRNSWZmZtaY5nraVzYyTUAR8LW2KcfMzMya0uQ57YjYWvsDvAf4HPAY8E9AVW7KMzMza12LFy+mf//+FBcXM3Pm0S+t3LZtGyNGjKCsrIwhQ4ZQVZWJvBUrVlBaWkppaSklJSXcf//9uS4dNfVAd0n9gHHA1cCrwELg5og4L3flWVoUSnFDvoswM2vG1AgOHTpEv379ePjhhykqKqK8vJzKykoGDhx4pN3EiRMpKytj0qRJrF+/noqKCrZs2cK+ffvo3LkzBQUF7Ny5k5KSEnbs2EFBQTY3Yh1N0uqIGHYsyzR39fgfgMuAj0XE/4qI2cCh46rMzMysHVixYgXFxcX07duXzp07M27cOBYtWlSvjST27NkDwO7duyksLASga9euRwK6pqYmL68ebS60Pwn8F/CopHskXUbmnLaZmVkqbd++nV69eh35XlRUxPbt2+u1mTZtGvPnz6eoqIiKigpmz559ZN7y5csZNGgQgwcPZu7cucfdyz5ezZ3Tvj8ixgIXkDmX/SXgLyR9X9LIHNVnx0nSbZLWSXpe0rOSfiNpRoM2pZI2JJ+3SHq8wfxnJa3NZd1mZm2psVPCDXvMlZWVTJgwgerqaqqqqhg/fjyHDx8G4OKLL2bdunWsXLmSGTNmUFNTk5O6a7X4cJWIeDMifhYRV5K5cvxZ4KttXpkdN0nDyVz9//6IGELmOfEzyTzNrq5xwM/rfD9NUq9kHQNyUauZWS4VFRXx8ssvH/leXV19ZPi71r333suYMWMAGD58ODU1NezatatemwEDBtCtWzfWrs1tvyabJ6IdERGvRcS/RsRftVVB1irOBXZFxNsAEbErIn4LvC7p4jrtxvDO42kBfsE7wX41UJmLYs3McqW8vJxNmzaxefNm9u/fz4IFCxg9enS9Nr1792bp0qUAbNiwgZqaGnr27MnmzZs5eDDzRuqtW7eyceNG+vTpk9P6jym0LTWWAL0kvSDpLkkfTqZXkuldI+kDwKsRsanOcr8E/jr5/DHgP5rbiKSJklZJWrWvdes3M2sTBQUFzJkzh1GjRjFgwADGjBnDoEGDmDJlCg888AAAs2bN4p577qGkpISrr76aefPmIYlly5ZRUlJCaWkpV111FXfddRc9evTIaf1N3vJl6SbpZOASMs+Hv4HMKY2lwJPAecB3gG0R8Z2k/RZgGPBj4KfAaDIP0XkwIi5saXu+5cvM2rv29pav47nlK7eXvVnORMQhMhcQPpY8kvYzETEvCecPk7k7YHgjiy4E7iTzUhgzM2tHHNodkKT+wOE6Q9+lwNbkcyVwB/DHiKhuZPH7yZwTfwgobGS+mZnliUO7Y+oOzJZ0JnCQzKtVJybz7gO+C0xubMGIeAP4Fhx9G4SZmeWXQ7sDiojVwAebmPcK0KmR6X0ambYFaPF8tpmZ5YavHjczM0sJh7aZmVlKOLTNzMxSwqFtZmaWEg5tMzOzlHBom5mZpYRD28zMLCUc2mZmZinhh6tYqygcOpSpq1bluwwzsw7NPW0zM7OUcGibmZmlhEPbzMwsJRzaZmZmKeHQNjMzSwmHtpmZWUo4tM3MzFLCoW1mZpYSDm0zM7OUcGibmZmlhEPbzMwsJRzaZmZmKeHQNjMzSwmHtpmZWUo4tM3MzFLCoW1mZpYSDm0zM7OUcGibmZmlhEPbzMwsJRzaZmZmKeHQNjMzSwmHtpmZWUo4tM3MzFLCoW1mZpYSDm0zM7OUcGibmZmlhCIi3zVYB1AoxQ35LsLMrBlT21neSVodEcOOZRn3tM3MzFLCoW1mZpYSDm0zM7OUcGibmZmlhEPbzMwsJRzaZmZ2Qlm8eDH9+/enuLiYmTNnHjV/27ZtjBgxgrKyMoYMGUJVVRUAK1asoLS0lNLSUkpKSrj//vtzXbpv+bLW4Vu+zKy9mxrBoUOH6NevHw8//DBFRUWUl5dTWVnJwIEDj7SbOHEiZWVlTJo0ifXr11NRUcGWLVvYt28fnTt3pqCggJ07d1JSUsKOHTsoKCg4rnp8y5eZmVkzVqxYQXFxMX379qVz586MGzeORYsW1WsjiT179gCwe/duCgsLAejateuRgK6pqUFSbovHoW1mZieQ7du306tXryPfi4qK2L59e70206ZNY/78+RQVFVFRUcHs2bOPzFu+fDmDBg1i8ODBzJ0797h72ccrVaEtaW8rrKNQ0i+bmX+mpJuybd/I8vMkbZb0rKTnJF32bmtuTZJulPS3+a7DzCwfGjsl3LDHXFlZyYQJE6iurqaqqorx48dz+PBhAC6++GLWrVvHypUrmTFjBjU1NTmpu1aqQrs1RMSOiPhUM03OBG46hvaNuSUiSoEvAnOPo8yjSGqVX+ciYm5E/KQ11mVmljZFRUW8/PLLR75XV1cfGf6ude+99zJmzBgAhg8fTk1NDbt27arXZsCAAXTr1o21a9e2fdF1pD60JZ0naamk55M/eyfT/1LS7yWtlDS9tpcuqY+ktcnnQZJWJL3i5yW9D5gJ/GUy7fYG7U+W9G1Ja5L2k1so7yngvXVqHSrpt5JWS3pI0rnJ9PJkfU8l26zd3gRJ90n6D2BJMu2WZJ+el/T1ZFo3Sb9OevZrJY1Nps+UtD5p++1k2jRJNyefS5Nj9Lyk+yW9J5n+mKRvJcfmBUmXtMJ/KjOzvCsvL2fTpk1s3ryZ/fv3s2DBAkaPHl2vTe/evVm6dCkAGzZsoKamhp49e7J582YOHjwIwNatW9m4cSN9+vTJaf25HYxvG3OAn0TEjyVdB3wP+ATwXeC7EVEp6cYmlr0xafMzSZ2Bk4GvAhcmPWUk9anTfiJwPlAWEQclndVCbR8F/j1ZTydgNvDxiHglCdZvAtcBPwImRsSTkhrefzAcGBIRr0kaCbwPuAgQ8ICkDwE9gR0RcUWyrTOS2q4CLoiIkHRmI/X9BJgcEb+VNB2YSmZ0AKAgIi6SVJFMv7yFfTUza/cKCgqYM2cOo0aN4tChQ1x33XUMGjSIKVOmMGzYMEaPHs2sWbO4/vrrueOOO5DEvHnzkMSyZcuYOXMmnTp14qSTTuKuu+6iR48eOa0/Vbd8SdobEd0bTNsFnBsRB5Jg3BkRPSS9CvxFEq6nkwm17kkIPxgRF0r6P8BtZMLrVxGxqe78ZP112/8bMDciHm6mxnnAh4EAzgE+EBFrJV0IPAm8lDQ9GdgJjAGei4jzkuWHAD9PtjcB+HBEXJvM+zbwKeD1ZB3dgRnA48BDwC+SWh9PhtNXA6uAXyfT90uaBuwF7gHWRMSRkQngvoh4v6THgNsi4glJfwE8ERHFjezrRDK/yHAGDP1SUwfFzKwd8Fu+2qes/6tExM+B0cBbwEOS/qqFRZTl+m8BioF/AH5cZ9l1EVGa/AyOiJHJ9Oa82WD7M+qsozgi7o2IF4ChwBpghqQpEXGQTI/838iMPCzOou663k7+PEQTIzIRcXdEDIuIYV2PceVmZnbsOkJoPwmMSz5/GliWfP498Mnk87iGCwFI6gu8FBHfAx4AhgBvAKc1sa0lwI21F4U1NzweEYfJDNGfJGkUsBHoKWl4smwnSYMi4s/AG5I+0FytiYeA6yR1T9bxXknnSCoE9kXEfODbwPuTNmdERBWZIe/SBvXtBv5c53z1eOC3zWzbzMzyLG3ntLtKqq7z/TvA/wV+KOkW4BXg2mTeF4H5kv6OzPDw7kbWNxa4RtIB4L+A6cm54yeSi8F+A9xZp/0PgH7A88ky95A5p96o5FzyN4C/j4iHJH0K+J6kM8gc+38B1gGfBe6R9CbwWBO1EhFLJA0AnlLmFoW9wDVkevW3SzoMHAAmkfnFY5GkLmR66I2NXn8GmCupK5lh+2sbaWNmZu1Eqs5pH4skiN5KgnMccHVEfDzfdTVGUveIqL26/atkztF/Ic9lHRM/xtTM2ruOcE47bT3tYzEUmKNMl/R1Mldpt1dXSLqVzH+PrcCE/JZjZmbtUYcN7Yh4HCjJdx3ZiIiFwMJ812FmZu1bR7gQzczM7ITg0DYzM0sJh7aZmVlKOLTNzMxSwqFtZmaWEg5tMzOzlOiwt3xZbhUOHcrUVavyXYaZWYfmnraZmVlKOLTNzMxSwqFtZmaWEg5tMzOzlHBom5mZpYRD28zMLCUc2mZmZinh0DYzM0sJh7aZmVlKOLTNzMxSwqFtZmaWEg5tMzOzlHBom5mZpYQiIt81WAcg6Q1gY77raKd6ALvyXUQ75uPTPB+f5qX5+JwXET2PZQG/mtNay8aIGJbvItojSat8bJrm49M8H5/mnWjHx8PjZmZmKeHQNjMzSwmHtrWWu/NdQDvmY9M8H5/m+fg074Q6Pr4QzczMLCXc0zYzM0sJh7ZlTdJHJW2U9KKkrzYy/xRJC5P5yyX1yX2V+ZPF8fmypPWSnpe0VNJ5+agzX1o6PnXafUpSSDphrgjO5thIGpP8/Vkn6ee5rjGfsvi31VvSo5KeSf59VeSjzpyICP/4p8Uf4GTgj0BfoDPwHDCwQZtjyo74AAAEtElEQVSbgLnJ53HAwnzX3c6Ozwiga/J5ko9P/eOTtDsN+B3we2BYvutuL8cGeB/wDPCe5Ps5+a67nR2fu4FJyeeBwJZ8191WP+5pW7YuAl6MiJciYj+wAPh4gzYfB36cfP4lcJkk5bDGfGrx+ETEoxGxL/n6e6AoxzXmUzZ/fwD+CfhnoCaXxeVZNsfmeuDOiPgzQET8Kcc15lM2xyeA05PPZwA7clhfTjm0LVvvBV6u8706mdZom4g4COwGzs5JdfmXzfGp67PAb9q0ovalxeMjqQzoFREP5rKwdiCbvzv9gH6SnpD0e0kfzVl1+ZfN8ZkGXCOpGqgCJuemtNzzE9EsW431mBveepBNm44q632XdA0wDPhwm1bUvjR7fCSdBNwBTMhVQe1INn93CsgMkV9KZoTmcUkXRsTrbVxbe5DN8bkamBcRsyQNB36aHJ/DbV9ebrmnbdmqBnrV+V7E0UNQR9pIKiAzTPVaTqrLv2yOD5IuB24DRkfE2zmqrT1o6ficBlwIPCZpC/AB4IET5GK0bP9tLYqIAxGxmcxz/t+Xo/ryLZvj81ngFwAR8RTQhcwzyTsch7ZlayXwPknnS+pM5kKzBxq0eQD4TPL5U8B/RnJlyAmgxeOTDP/+K5nAPpHOSUILxycidkdEj4joExF9yJzzHx0Rq/JTbk5l82/r38lcyIikHmSGy1/KaZX5k83x2QZcBiBpAJnQfiWnVeaIQ9uykpyj/jzwELAB+EVErJM0XdLopNm9wNmSXgS+DDR5W09Hk+XxuR3oDtwn6VlJDf/H02FleXxOSFkem4eAVyWtBx4FbomIV/NTcW5leXz+Drhe0nNAJTCho3YY/EQ0MzOzlHBP28zMLCUc2mZmZinh0DYzM0sJh7aZmVlKOLTNzMxSwqFtZu2epNuSt1s9n9wud3G+azLLBz/G1MzateSxlFcC74+It5OHi3R+F+srSO79NUsd97TNrL07F9hV+9jXiNgVETsklUt6UtJzklZIOk1SF0k/krQmebdy7VPEJki6T9J/AEuSabdIWpn03r+eTOsm6dfJOtdKGpuvnTZrjHvaZtbeLQGmSHoBeARYCDyV/Dk2IlZKOh14C/gCQEQMlnQBsERSv2Q9w4EhEfGapJFknt19EZkXUjwg6UNAT2BHRFwBIOmMnO2lWRbc0zazdi0i9gJDgYlknie9ELgB2BkRK5M2e5Ih7/8F/DSZ9gdgK5nndAM8HBG1L7AZmfw8AzwNXEAmxNcAl0v6lqRLImJ3DnbRLGvuaZtZuxcRh4DHyLwFbA3wORp/9Wljr3Gs9WaDdjMi4l+PWoE0FKgAZkhaEhHTj7tws1bmnraZtWuS+kuq+xrKUjIvjiiUVJ60OS15HezvgE8n0/oBvcm8xrKhh4DrJHVP2r5X0jmSCoF9ETEf+Dbw/rbaL7Pj4Z62mbV33YHZks4EDgIvkhkq/1Ey/VQy57MvB+4C5ia98YNk3vb0tlS/Ax4RS5JXOD6VzNsLXAMUA7dLOgwcACblYP/Msua3fJmZmaWEh8fNzMxSwqFtZmaWEg5tMzOzlHBom5mZpYRD28zMLCUc2mZmZinh0DYzM0sJh7aZmVlK/H804cvO5qKj3AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = {'Algorithm':['Logistic Regression','SVM','Decision Tree Classifier','KNN'],'Scores':[log_score,svm_score,tree_score,knn_score]}\n",
    "score_df = pd.DataFrame(d)\n",
    "score_df.set_index('Algorithm',inplace=True)\n",
    "plt.figure(figsize=(12,6))\n",
    "score_df.plot(kind='barh',color='maroon')\n",
    "plt.xlabel('Scores')\n",
    "plt.ylabel('Algorithm')\n",
    "for i,row in score_df.reset_index().iterrows():\n",
    "    text = f'{row[\"Scores\"]:.2f}'\n",
    "    plt.annotate(text,(row[\"Scores\"],i))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Authors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\">Joseph Santarcangelo</a> has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change Log\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Date (YYYY-MM-DD) | Version | Changed By    | Change Description      |\n",
    "| ----------------- | ------- | ------------- | ----------------------- |\n",
    "| 2021-08-31        | 1.1     | Lakshmi Holla | Modified markdown       |\n",
    "| 2020-09-20        | 1.0     | Joseph        | Modified Multiple Areas |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright © 2020 IBM Corporation. All rights reserved.\n"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}