SpaceX_Machine_Learning_Prediction_Part_5.jupyterlite.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "41092b48-3d26-4826-9516-ccaea45f5040",
   "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\">\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",
   "id": "a7b195fb-7957-4e70-9739-a26fc8eb95bb",
   "metadata": {},
   "source": [
    "# **Space X  Falcon 9 First Stage Landing Prediction**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b088492-0aa6-4e07-a816-0b74dbc58203",
   "metadata": {},
   "source": [
    "## Assignment:  Machine Learning Prediction\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19c6c99b-e968-4d6e-a07a-38307d36ed2b",
   "metadata": {},
   "source": [
    "Estimated time needed: **60** minutes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4bec0cd-b2fe-48ff-8a60-44d57af5473c",
   "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",
   "id": "9b9e79e6-8497-49ce-b2e1-e914bf87726c",
   "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",
   "id": "0fc39343-e511-4f02-aaaa-930eb60428d7",
   "metadata": {},
   "source": [
    "Several examples of an unsuccessful landing are shown here:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8f06dd0-f33f-4d53-9a20-580a42011408",
   "metadata": {},
   "source": [
    "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef614108-fd8d-4344-948a-39d131d671df",
   "metadata": {},
   "source": [
    "Most unsuccessful landings are planed. Space X; performs a controlled landing in the oceans.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21c1b9fb-9eed-461b-8c77-5c0950a00e6f",
   "metadata": {},
   "source": [
    "## Objectives\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25fe0855-ff49-4adb-8954-6c46fab06877",
   "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",
   "id": "28d653b1-3b9a-429e-80e0-02bd16de722e",
   "metadata": {},
   "source": [
    "## Import Libraries and Define Auxiliary Functions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4818736e-9044-4cbe-9367-8b21b0246077",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import piplite\n",
    "# await piplite.install(['numpy'])\n",
    "# await piplite.install(['pandas'])\n",
    "# await piplite.install(['seaborn'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3bd986c-7b04-48d7-bdbe-1e3067039e5f",
   "metadata": {},
   "source": [
    "We will import the following libraries for the lab\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8513bd32-b465-4ded-8bd1-75315990d233",
   "metadata": {},
   "outputs": [],
   "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",
   "id": "2a2bf13b-19b1-43d6-a2cd-d2f20bd69fb5",
   "metadata": {},
   "source": [
    "This function is to plot the confusion matrix.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1f30279e-9d5e-4df5-a814-8053d099c511",
   "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']) \n",
    "    plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0abf51b-e8f8-4fa5-8275-1205554ba883",
   "metadata": {},
   "source": [
    "## Load the dataframe\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6b453e1-b094-4508-a4e1-5159ee52d46e",
   "metadata": {},
   "source": [
    "Load the data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "303c7783-faac-4ca0-98a5-df6c89c65df4",
   "metadata": {},
   "outputs": [
    {
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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",
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       "      <th>ReusedCount</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-06-04</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>6104.959412</td>\n",
       "      <td>LEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
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       "      <th>1</th>\n",
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       "      <td>2012-05-22</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>LEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2013-03-01</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>ISS</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
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       "      <td>B0007</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\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",
       "      <th>4</th>\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",
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       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
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       "      <th>85</th>\n",
       "      <td>86</td>\n",
       "      <td>2020-09-03</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>15400.000000</td>\n",
       "      <td>VLEO</td>\n",
       "      <td>KSC LC 39A</td>\n",
       "      <td>True ASDS</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>87</td>\n",
       "      <td>2020-10-06</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>15400.000000</td>\n",
       "      <td>VLEO</td>\n",
       "      <td>KSC LC 39A</td>\n",
       "      <td>True ASDS</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>88</td>\n",
       "      <td>2020-10-18</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>15400.000000</td>\n",
       "      <td>VLEO</td>\n",
       "      <td>KSC LC 39A</td>\n",
       "      <td>True ASDS</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>5e9e3032383ecb6bb234e7ca</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>89</td>\n",
       "      <td>2020-10-24</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>15400.000000</td>\n",
       "      <td>VLEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>True ASDS</td>\n",
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       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>90</td>\n",
       "      <td>2020-11-05</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>3681.000000</td>\n",
       "      <td>MEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>True ASDS</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B1062</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>1</td>\n",
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       "<p>90 rows × 18 columns</p>\n",
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      ],
      "text/plain": [
       "    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",
       "85            86  2020-09-03       Falcon 9  15400.000000  VLEO    KSC LC 39A   \n",
       "86            87  2020-10-06       Falcon 9  15400.000000  VLEO    KSC LC 39A   \n",
       "87            88  2020-10-18       Falcon 9  15400.000000  VLEO    KSC LC 39A   \n",
       "88            89  2020-10-24       Falcon 9  15400.000000  VLEO  CCAFS SLC 40   \n",
       "89            90  2020-11-05       Falcon 9   3681.000000   MEO  CCAFS SLC 40   \n",
       "\n",
       "        Outcome  Flights  GridFins  Reused   Legs                LandingPad  \\\n",
       "0     None None        1     False   False  False                       NaN   \n",
       "1     None None        1     False   False  False                       NaN   \n",
       "2     None None        1     False   False  False                       NaN   \n",
       "3   False Ocean        1     False   False  False                       NaN   \n",
       "4     None None        1     False   False  False                       NaN   \n",
       "..          ...      ...       ...     ...    ...                       ...   \n",
       "85    True ASDS        2      True    True   True  5e9e3032383ecb6bb234e7ca   \n",
       "86    True ASDS        3      True    True   True  5e9e3032383ecb6bb234e7ca   \n",
       "87    True ASDS        6      True    True   True  5e9e3032383ecb6bb234e7ca   \n",
       "88    True ASDS        3      True    True   True  5e9e3033383ecbb9e534e7cc   \n",
       "89    True ASDS        1      True   False   True  5e9e3032383ecb6bb234e7ca   \n",
       "\n",
       "    Block  ReusedCount Serial   Longitude   Latitude  Class  \n",
       "0     1.0            0  B0003  -80.577366  28.561857      0  \n",
       "1     1.0            0  B0005  -80.577366  28.561857      0  \n",
       "2     1.0            0  B0007  -80.577366  28.561857      0  \n",
       "3     1.0            0  B1003 -120.610829  34.632093      0  \n",
       "4     1.0            0  B1004  -80.577366  28.561857      0  \n",
       "..    ...          ...    ...         ...        ...    ...  \n",
       "85    5.0            2  B1060  -80.603956  28.608058      1  \n",
       "86    5.0            2  B1058  -80.603956  28.608058      1  \n",
       "87    5.0            5  B1051  -80.603956  28.608058      1  \n",
       "88    5.0            2  B1060  -80.577366  28.561857      1  \n",
       "89    5.0            0  B1062  -80.577366  28.561857      1  \n",
       "\n",
       "[90 rows x 18 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "URL = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv'\n",
    "data=pd.read_csv(URL)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2bbe74d1-22a0-4164-a154-7b61fd30db7f",
   "metadata": {},
   "outputs": [
    {
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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",
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       "3            0  B1003 -120.610829  34.632093      0  \n",
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0ca7bbd2-ed4e-4eef-8f27-be9309caed78",
   "metadata": {},
   "outputs": [],
   "source": [
    "URL2 = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_3.csv'\n",
    "X = pd.read_csv(URL2)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "02af7a31-bf3d-431a-ae95-fd146cd09329",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "    FlightNumber   PayloadMass  Flights  Block  ReusedCount  Orbit_ES-L1  \\\n",
       "0            1.0   6104.959412      1.0    1.0          0.0          0.0   \n",
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       "..           ...           ...      ...    ...          ...          ...   \n",
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       "86          87.0  15400.000000      3.0    5.0          2.0          0.0   \n",
       "87          88.0  15400.000000      6.0    5.0          5.0          0.0   \n",
       "88          89.0  15400.000000      3.0    5.0          2.0          0.0   \n",
       "89          90.0   3681.000000      1.0    5.0          0.0          0.0   \n",
       "\n",
       "    Orbit_GEO  Orbit_GTO  Orbit_HEO  Orbit_ISS  ...  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        1.0  ...           0.0   \n",
       "3         0.0        0.0        0.0        0.0  ...           0.0   \n",
       "4         0.0        1.0        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        0.0        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  GridFins_False  GridFins_True  \\\n",
       "0            0.0           0.0           0.0             1.0            0.0   \n",
       "1            0.0           0.0           0.0             1.0            0.0   \n",
       "2            0.0           0.0           0.0             1.0            0.0   \n",
       "3            0.0           0.0           0.0             1.0            0.0   \n",
       "4            0.0           0.0           0.0             1.0            0.0   \n",
       "..           ...           ...           ...             ...            ...   \n",
       "85           0.0           1.0           0.0             0.0            1.0   \n",
       "86           0.0           0.0           0.0             0.0            1.0   \n",
       "87           0.0           0.0           0.0             0.0            1.0   \n",
       "88           0.0           1.0           0.0             0.0            1.0   \n",
       "89           0.0           0.0           1.0             0.0            1.0   \n",
       "\n",
       "    Reused_False  Reused_True  Legs_False  Legs_True  \n",
       "0            1.0          0.0         1.0        0.0  \n",
       "1            1.0          0.0         1.0        0.0  \n",
       "2            1.0          0.0         1.0        0.0  \n",
       "3            1.0          0.0         1.0        0.0  \n",
       "4            1.0          0.0         1.0        0.0  \n",
       "..           ...          ...         ...        ...  \n",
       "85           0.0          1.0         0.0        1.0  \n",
       "86           0.0          1.0         0.0        1.0  \n",
       "87           0.0          1.0         0.0        1.0  \n",
       "88           0.0          1.0         0.0        1.0  \n",
       "89           1.0          0.0         0.0        1.0  \n",
       "\n",
       "[90 rows x 83 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fc5aab8-54d4-49d9-aff6-0dd579baa44f",
   "metadata": {},
   "source": [
    "## TASK  1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "013d35ed-ebae-4e84-8c60-935e3a699346",
   "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": 8,
   "id": "bc72dffc-1032-4a3a-b84f-d2968b3faffc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1,\n",
       "       1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1,\n",
       "       1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1], dtype=int64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = data[\"Class\"].to_numpy()\n",
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d79f1ca-2636-4221-895a-4afc091c73b0",
   "metadata": {},
   "source": [
    "## TASK  2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "324ed6d1-626f-4c23-bf84-efd98f797280",
   "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": 9,
   "id": "9067e374-f502-44c6-a628-29186009fd45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.71291154e+00, -1.94814463e-16, -6.53912840e-01, ...,\n",
       "        -8.35531692e-01,  1.93309133e+00, -1.93309133e+00],\n",
       "       [-1.67441914e+00, -1.19523159e+00, -6.53912840e-01, ...,\n",
       "        -8.35531692e-01,  1.93309133e+00, -1.93309133e+00],\n",
       "       [-1.63592675e+00, -1.16267307e+00, -6.53912840e-01, ...,\n",
       "        -8.35531692e-01,  1.93309133e+00, -1.93309133e+00],\n",
       "       ...,\n",
       "       [ 1.63592675e+00,  1.99100483e+00,  3.49060516e+00, ...,\n",
       "         1.19684269e+00, -5.17306132e-01,  5.17306132e-01],\n",
       "       [ 1.67441914e+00,  1.99100483e+00,  1.00389436e+00, ...,\n",
       "         1.19684269e+00, -5.17306132e-01,  5.17306132e-01],\n",
       "       [ 1.71291154e+00, -5.19213966e-01, -6.53912840e-01, ...,\n",
       "        -8.35531692e-01, -5.17306132e-01,  5.17306132e-01]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# students get this \n",
    "transform = preprocessing.StandardScaler()\n",
    "X = transform.fit(X).transform(X)\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6f007c0-65fe-408a-9af6-febef40b1e1b",
   "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",
   "id": "c4a2cfde-e37a-475f-95ad-95246535ff18",
   "metadata": {},
   "source": [
    "## TASK  3\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b2ff7f9-f969-4ba2-a2bd-17f0b7d6464e",
   "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",
   "id": "5da81a62-3998-436c-b96e-9e7a1db0825f",
   "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",
   "id": "e81b772b-0a2e-4f30-a774-8bff2b8fe8b0",
   "metadata": {},
   "source": [
    "we can see we only have 18 test samples.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "77d6496a-d933-420f-9352-1c95a832b84d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18,)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e778d63-eec7-4bf8-a83e-3397997026fa",
   "metadata": {},
   "source": [
    "## TASK  4\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06a7589e-a488-431b-b329-c78ea814f020",
   "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,
   "id": "93fd1432-7426-4005-8dfe-04380700d42d",
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters ={'C':[0.01,0.1,1],\n",
    "             'penalty':['l2'],\n",
    "             'solver':['lbfgs']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "de49af42-d4af-4f3b-bbbb-3b7cc6793c21",
   "metadata": {},
   "outputs": [],
   "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 = logreg_cv.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a75d5bed-04e0-447a-a14a-eb0229e8da9e",
   "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,
   "id": "8dfcfcaf-4c12-44f5-b7e4-3d00e56313dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tuned hpyerparameters :(best parameters)  {'C': 0.01, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "accuracy : 0.8464285714285713\n"
     ]
    }
   ],
   "source": [
    "print(\"tuned hpyerparameters :(best parameters) \",logreg_cv.best_params_)\n",
    "print(\"accuracy :\",logreg_cv.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5df3f620-3e95-406d-b01d-667801f1d09d",
   "metadata": {},
   "source": [
    "## TASK  5\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8890f2b5-9f2a-478b-8772-078b2e55483d",
   "metadata": {},
   "source": [
    "Calculate the accuracy on the test data using the method <code>score</code>:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ecd83d0e-92bc-4c73-b6e7-a355725cc4ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8333333333333334"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy = logreg_cv.score(X_test, Y_test)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef7d820b-6494-448b-99da-ddee09fac04b",
   "metadata": {},
   "source": [
    "Lets look at the confusion matrix:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d12d8520-4f14-4cbf-af23-23893d3d8854",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yhat=logreg_cv.predict(X_test)\n",
    "plot_confusion_matrix(Y_test,yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c4f7b4d-7cfb-4ff3-bf60-7b71da3a88c0",
   "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",
   "id": "93e8f80b-f9e9-43cc-8703-446e4032ce1a",
   "metadata": {},
   "source": [
    "## TASK  6\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6f9fa64-44d6-4472-8d1b-e859ad3308a4",
   "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": 17,
   "id": "642cfb6b-2c24-4d0d-96ad-e01809716b19",
   "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": 18,
   "id": "4ac8b9eb-801e-4596-bce7-fa7a980dd5ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_cv = GridSearchCV(svm, parameters, cv=10)\n",
    "svm_cv = svm_cv.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a017bdfd-1c68-4eec-9b88-6900d8971ced",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tuned hpyerparameters :(best parameters)  {'C': 1.0, 'gamma': 0.03162277660168379, 'kernel': 'sigmoid'}\n",
      "accuracy : 0.8482142857142856\n"
     ]
    }
   ],
   "source": [
    "print(\"tuned hpyerparameters :(best parameters) \",svm_cv.best_params_)\n",
    "print(\"accuracy :\",svm_cv.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41b16eb3-b7ec-401d-a44a-1d2ab4638af0",
   "metadata": {},
   "source": [
    "## TASK  7\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97ce1130-daf0-4059-abcf-bdfcc15b5f0b",
   "metadata": {},
   "source": [
    "Calculate the accuracy on the test data using the method <code>score</code>:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7aedad55-850c-4383-b8d1-c2dc61895fe8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8333333333333334"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_cv.score(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d52749c-ace1-4457-b304-f0e459a2d057",
   "metadata": {},
   "source": [
    "We can plot the confusion matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2f45fbab-8fcb-4973-9aef-d1980e5283c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yhat=svm_cv.predict(X_test)\n",
    "plot_confusion_matrix(Y_test,yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76e0b51a-3ccd-49d4-a214-6ef189aedc2f",
   "metadata": {},
   "source": [
    "## TASK  8\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a136130-54e9-40d3-b775-758f69b30bf1",
   "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": 22,
   "id": "d85a7713-82bc-4857-9a18-56f2f425abcc",
   "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": 23,
   "id": "966e2a3f-d15a-4940-99cf-e9b3bc1b7df4",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\model_selection\\_validation.py:425: FitFailedWarning: \n",
      "3240 fits failed out of a total of 6480.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "3240 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\user\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\model_selection\\_validation.py\", line 729, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"C:\\Users\\user\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\base.py\", line 1145, in wrapper\n",
      "    estimator._validate_params()\n",
      "  File \"C:\\Users\\user\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\base.py\", line 638, in _validate_params\n",
      "    validate_parameter_constraints(\n",
      "  File \"C:\\Users\\user\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\utils\\_param_validation.py\", line 96, in validate_parameter_constraints\n",
      "    raise InvalidParameterError(\n",
      "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of DecisionTreeClassifier must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'sqrt', 'log2'} or None. Got 'auto' instead.\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "C:\\Users\\user\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\model_selection\\_search.py:979: UserWarning: One or more of the test scores are non-finite: [       nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.7875     0.69642857 0.77857143 0.80535714 0.76785714 0.76071429\n",
      " 0.81785714 0.73928571 0.76071429 0.76607143 0.81785714 0.72142857\n",
      " 0.72321429 0.77678571 0.72321429 0.725      0.7875     0.83214286\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.76428571 0.78928571 0.84642857 0.83392857 0.82142857 0.7375\n",
      " 0.83035714 0.77678571 0.80535714 0.78928571 0.775      0.775\n",
      " 0.76071429 0.80535714 0.79107143 0.83392857 0.79107143 0.84821429\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.8625     0.77678571 0.775      0.79107143 0.82142857 0.80535714\n",
      " 0.81964286 0.79107143 0.79285714 0.79107143 0.73928571 0.77857143\n",
      " 0.75178571 0.76607143 0.76071429 0.775      0.76607143 0.78214286\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.83392857 0.80714286 0.77678571 0.84642857 0.77857143 0.70714286\n",
      " 0.79464286 0.81964286 0.82857143 0.69464286 0.75       0.74821429\n",
      " 0.73571429 0.76785714 0.77678571 0.83392857 0.73214286 0.76428571\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.79107143 0.69285714 0.71964286 0.79107143 0.7625     0.84642857\n",
      " 0.72321429 0.74821429 0.80357143 0.7375     0.72321429 0.81785714\n",
      " 0.76428571 0.75357143 0.71964286 0.81964286 0.81964286 0.81964286\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.75178571 0.80535714 0.78928571 0.75       0.78035714 0.80714286\n",
      " 0.72321429 0.72142857 0.67857143 0.83392857 0.77857143 0.83214286\n",
      " 0.78928571 0.80535714 0.80535714 0.81964286 0.71964286 0.84642857\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.83214286 0.81785714 0.70714286 0.77321429 0.80714286 0.83392857\n",
      " 0.80178571 0.80535714 0.83214286 0.76785714 0.79107143 0.69642857\n",
      " 0.81964286 0.84642857 0.77678571 0.80357143 0.725      0.82142857\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.69821429 0.75       0.77857143 0.7625     0.80535714 0.86071429\n",
      " 0.79285714 0.7625     0.73214286 0.82142857 0.76071429 0.83214286\n",
      " 0.79107143 0.76607143 0.84464286 0.66785714 0.78928571 0.80357143\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.78035714 0.75       0.74821429 0.74464286 0.84642857 0.7625\n",
      " 0.81428571 0.7875     0.76071429 0.76071429 0.76428571 0.79464286\n",
      " 0.76607143 0.69464286 0.75178571 0.73392857 0.80357143 0.80357143\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.75178571 0.80357143 0.75178571 0.80357143 0.79107143 0.76071429\n",
      " 0.73571429 0.78928571 0.73392857 0.78928571 0.77678571 0.78035714\n",
      " 0.76071429 0.725      0.76785714 0.80535714 0.76607143 0.7375\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.66785714 0.84821429 0.77678571 0.80357143 0.80714286 0.81785714\n",
      " 0.80535714 0.78928571 0.84821429 0.83214286 0.7625     0.77857143\n",
      " 0.7625     0.77678571 0.84821429 0.73392857 0.725      0.79464286\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.77857143 0.80714286 0.76071429 0.83571429 0.7375     0.83392857\n",
      " 0.79107143 0.79107143 0.79107143 0.85892857 0.7625     0.78928571\n",
      " 0.69642857 0.80357143 0.67678571 0.76607143 0.79107143 0.79107143\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.73392857 0.73571429 0.77857143 0.83392857 0.80535714 0.81964286\n",
      " 0.81964286 0.775      0.78928571 0.73392857 0.79107143 0.86071429\n",
      " 0.71785714 0.79285714 0.87321429 0.79107143 0.73571429 0.7375\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.7625     0.7375     0.80535714 0.76428571 0.8625     0.77678571\n",
      " 0.7625     0.81785714 0.75       0.74821429 0.7625     0.77678571\n",
      " 0.68035714 0.77857143 0.82142857 0.78928571 0.79107143 0.83214286\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.84642857 0.82142857 0.77857143 0.775      0.76428571 0.80535714\n",
      " 0.7375     0.80357143 0.65178571 0.79107143 0.71071429 0.83392857\n",
      " 0.78928571 0.83214286 0.80357143 0.73928571 0.84642857 0.76607143\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.84642857 0.72142857 0.79285714 0.77857143 0.87321429 0.77857143\n",
      " 0.77321429 0.70892857 0.76428571 0.77857143 0.81785714 0.84821429\n",
      " 0.77678571 0.74821429 0.84821429 0.75178571 0.775      0.74821429\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.70714286 0.77857143 0.74821429 0.81964286 0.79285714 0.83214286\n",
      " 0.81785714 0.76607143 0.80535714 0.73392857 0.76428571 0.7625\n",
      " 0.70535714 0.75       0.76607143 0.77678571 0.71964286 0.79107143\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.76607143 0.73571429 0.79285714 0.83214286 0.7625     0.79285714\n",
      " 0.70535714 0.80535714 0.77857143 0.74821429 0.72142857 0.79107143\n",
      " 0.77857143 0.84642857 0.77857143 0.79107143 0.71964286 0.87142857]\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "tree_cv = GridSearchCV(tree, parameters, cv=10)\n",
    "tree_cv = tree_cv.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4c26b867-0f93-4713-bd7c-9bdef8dea18f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tuned hpyerparameters :(best parameters)  {'criterion': 'entropy', 'max_depth': 8, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 5, 'splitter': 'best'}\n",
      "accuracy : 0.8732142857142857\n"
     ]
    }
   ],
   "source": [
    "print(\"tuned hpyerparameters :(best parameters) \",tree_cv.best_params_)\n",
    "print(\"accuracy :\",tree_cv.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a845437-17f0-4bd4-8e9a-06f30ef827bf",
   "metadata": {},
   "source": [
    "## TASK  9\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60d92686-3013-45bf-9983-9bd33aa530f2",
   "metadata": {},
   "source": [
    "Calculate the accuracy of tree_cv on the test data using the method <code>score</code>:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "55003514-94f0-459a-a6eb-093f1c5f9f05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8888888888888888"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_cv.score(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78fb6d64-6239-4783-8974-23606d5754c0",
   "metadata": {},
   "source": [
    "We can plot the confusion matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c15ce013-6b4c-4723-9b04-7e60f23f6448",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yhat = tree_cv.predict(X_test)\n",
    "plot_confusion_matrix(Y_test,yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "612dbb58-fb2f-47ee-a7bb-d77a00e6dbd3",
   "metadata": {},
   "source": [
    "## TASK  10\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a748cd07-5a39-4cda-809d-3cea906bd148",
   "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": 27,
   "id": "0cee22b2-bcf7-471e-96cf-e63302a47b52",
   "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": 28,
   "id": "ffbaf94b-0b03-4094-84b6-f5233194434c",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_cv = GridSearchCV(KNN, parameters, cv=10)\n",
    "knn_cv = knn_cv.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6309852d-7fb4-400f-b44a-1a3a04f03fba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tuned hpyerparameters :(best parameters)  {'algorithm': 'auto', 'n_neighbors': 10, 'p': 1}\n",
      "accuracy : 0.8482142857142858\n"
     ]
    }
   ],
   "source": [
    "print(\"tuned hpyerparameters :(best parameters) \",knn_cv.best_params_)\n",
    "print(\"accuracy :\",knn_cv.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98e9e899-25da-4c9b-99eb-b680c559ebda",
   "metadata": {},
   "source": [
    "## TASK  11\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a20e83b-5c1b-440d-bba3-57c44a8605a6",
   "metadata": {},
   "source": [
    "Calculate the accuracy of knn_cv on the test data using the method <code>score</code>:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "89196934-71d7-408d-9ff2-e99a934fb7de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8333333333333334"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_cv.score(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6cafe12-0a65-4cdc-8e44-67892989248e",
   "metadata": {},
   "source": [
    "We can plot the confusion matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7b021ac7-870a-445b-9459-021c9e78f745",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yhat = knn_cv.predict(X_test)\n",
    "plot_confusion_matrix(Y_test,yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a98ab95-6763-4bcc-ae98-d76fc1c0812b",
   "metadata": {},
   "source": [
    "## TASK  12\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "965a2d1d-7ac3-458c-ba11-10f97d22e927",
   "metadata": {},
   "source": [
    "Find the method performs best:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best method is DecisionTreeClassifier with best score 0.8732142857142857\n"
     ]
    }
   ],
   "source": [
    "mx_score = 0\n",
    "best_method = \"\"\n",
    "\n",
    "for method in [logreg_cv, svm_cv, tree_cv, knn_cv]:\n",
    "    bst = method.best_score_\n",
    "    if bst > mx_score:\n",
    "        mx_score = bst\n",
    "        best_method = method\n",
    "print(f\"Best method is {best_method.__repr__().split('(')[1].split('=')[2]} with best score {mx_score}\")    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2388a641-817f-4e15-815a-f66278c656b0",
   "metadata": {},
   "source": [
    "## Authors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8845aea5-d412-4eac-9b51-3457f0be2c0f",
   "metadata": {},
   "source": [
    "[Pratiksha Verma](https://www.linkedin.com/in/pratiksha-verma-6487561b1/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork865-2023-01-01)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02bdba00-e939-4425-b819-417baeb84f8c",
   "metadata": {},
   "source": [
    "## Change Log\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91fce393-48e3-493a-a228-9d58989c4766",
   "metadata": {},
   "source": [
    "| Date (YYYY-MM-DD) | Version | Changed By      | Change Description      |\n",
    "| ----------------- | ------- | -------------   | ----------------------- |\n",
    "| 2022-11-09        | 1.0     | Pratiksha Verma | Converted initial version to Jupyterlite|\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73d1479f-e16f-449f-a5fe-ef1d4e367a1a",
   "metadata": {},
   "source": [
    "### <h3 align=\"center\"> IBM Corporation 2022. All rights reserved. <h3/>\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}