SpaceX_Machine_Learning_Prediction_Part_5.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/FRavasco/IBMDataCapstone/blob/main/SpaceX_Machine_Learning_Prediction_Part_5.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SwJBMuyP3fTS"
      },
      "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": {
        "id": "DDMfyAOQ3fTV"
      },
      "source": [
        "# **Space X  Falcon 9 First Stage Landing Prediction**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X09J3W1c3fTW"
      },
      "source": [
        "## Assignment:  Machine Learning Prediction\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ow2IN1JT3fTW"
      },
      "source": [
        "Estimated time needed: **60** minutes\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YDc8UjB_3fTX"
      },
      "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": {
        "id": "8c1KHBiB3fTX"
      },
      "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": {
        "id": "3AUVXHgy3fTY"
      },
      "source": [
        "Several examples of an unsuccessful landing are shown here:\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4aXHHJSk3fTY"
      },
      "source": [
        "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_iZ2Lf-03fTY"
      },
      "source": [
        "Most unsuccessful landings are planed. Space X; performs a controlled landing in the oceans.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Po1--3fW3fTZ"
      },
      "source": [
        "## Objectives\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Tk8ekL33fTZ"
      },
      "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": {
        "id": "0flgpy963fTZ"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h4fgLj3O3fTa"
      },
      "source": [
        "***\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7E8xq_aV3fTa"
      },
      "source": [
        "## Import Libraries and Define Auxiliary Functions\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WdMFZJn73fTa"
      },
      "source": [
        "We will import the following libraries for the lab\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "r2a-T7Ee3fTb"
      },
      "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",
      "metadata": {
        "id": "ymYB8NJC3fTc"
      },
      "source": [
        "This function is to plot the confusion matrix.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "IqMeSgWQ3fTd"
      },
      "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": {
        "id": "Nwtf7tYN3fTe"
      },
      "source": [
        "## Load the dataframe\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bwEk5Yi_3fTe"
      },
      "source": [
        "Load the data\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
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          "base_uri": "https://localhost:8080/",
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        "id": "fvpeieTy3fTf",
        "outputId": "7de30db3-2e95-439c-984c-c0dc2dd3dba6"
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      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "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",
              "       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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              "      <th></th>\n",
              "      <th>FlightNumber</th>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2010-06-04</td>\n",
              "      <td>Falcon 9</td>\n",
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              "      <td>LEO</td>\n",
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              "      <td>2012-05-22</td>\n",
              "      <td>Falcon 9</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",
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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",
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              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>B1003</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",
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              "                                                     [key], {});\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "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('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/dataset_part_2.csv')\n",
        "\n",
        "data.head()"
      ]
    },
    {
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      "execution_count": 4,
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          "height": 500
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        "id": "vhqq0Jqa3fTg",
        "outputId": "ad9edcbb-cedf-4149-e261-724ff4af5571"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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",
              "1            2.0    525.000000      1.0    1.0          0.0          0.0   \n",
              "2            3.0    677.000000      1.0    1.0          0.0          0.0   \n",
              "3            4.0    500.000000      1.0    1.0          0.0          0.0   \n",
              "4            5.0   3170.000000      1.0    1.0          0.0          0.0   \n",
              "..           ...           ...      ...    ...          ...          ...   \n",
              "85          86.0  15400.000000      2.0    5.0          2.0          0.0   \n",
              "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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "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('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/dataset_part_3.csv')\n",
        "\n",
        "X.head(100)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "goNr-cOx3fTg"
      },
      "source": [
        "## TASK  1\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NhuufSVY3fTg"
      },
      "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": {
        "id": "aCW2qI853fTh"
      },
      "outputs": [],
      "source": [
        "Y = data['Class'].to_numpy()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BhdSOLc53fTh"
      },
      "source": [
        "## TASK  2\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7S48TQx-3fTi"
      },
      "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": 6,
      "metadata": {
        "id": "oUOB_Nh23fTi"
      },
      "outputs": [],
      "source": [
        "# students get this \n",
        "transform = preprocessing.StandardScaler()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "LjZpOXia3fTi"
      },
      "outputs": [],
      "source": [
        "std = transform.fit_transform(X)\n",
        "X = std"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cKM8qdPs3fTj"
      },
      "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": {
        "id": "YQriVKsR3fTj"
      },
      "source": [
        "## TASK  3\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gwdyzUDF3fTj"
      },
      "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": {
        "id": "Y-XNLQE33fTj"
      },
      "source": [
        "<code>X_train, X_test, Y_train, Y_test</code>\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "QGPvStqz3fTk"
      },
      "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": {
        "id": "aS6uoG043fTk"
      },
      "source": [
        "we can see we only have 18 test samples.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V7Z0oSI63fTk",
        "outputId": "a861b73e-233d-45ce-e959-e8001f82ff3e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(18,)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "Y_test.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3NX4vFAW3fTk"
      },
      "source": [
        "## TASK  4\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U7Bg63os3fTk"
      },
      "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": 10,
      "metadata": {
        "id": "AA7ezPrd3fTk"
      },
      "outputs": [],
      "source": [
        "parameters ={'C':[0.01,0.1,1],\n",
        "             'penalty':['l2'],\n",
        "             'solver':['lbfgs']}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EVDrG3wS3fTl",
        "outputId": "e1f49933-417f-43d1-bdad-0297c67c0040"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=10, estimator=LogisticRegression(),\n",
              "             param_grid={'C': [0.01, 0.1, 1], 'penalty': ['l2'],\n",
              "                         'solver': ['lbfgs']})"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "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)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BD0377hn3fTl"
      },
      "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": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yhDgQ4CG3fTl",
        "outputId": "cd2b61df-f904-454a-b271-34b69c2616b7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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",
      "metadata": {
        "id": "Lxp-kWmd3fTl"
      },
      "source": [
        "## TASK  5\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L1UvkDoV3fTl"
      },
      "source": [
        "Calculate the accuracy on the test data using the method <code>score</code>:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "_taCdiU_3fTl"
      },
      "outputs": [],
      "source": [
        "logregscore = logreg_cv.score(X_test,Y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g4zpQ06z3fTl"
      },
      "source": [
        "Lets look at the confusion matrix:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "E0RLBYrI3fTm",
        "outputId": "68b225a1-cada-4214-928c-4572c107d8b2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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EfQxwDjAcmEV20cqxRQZlZtZoLTmCjojngcMaEIuZWdOUcZpdNbM41pZ0naTnJM2RdE263NvMbJHR0aGqH41SzSyOS4ErgNWA1cku+76syKDMzBotQlU/GqWaBL1kRIyNiAXpcTGwRNGBmZk1UktNs5O0fHp6o6STgD+Qrc1xMHBDA2IzM2uYVpvFMZksIXf9uji64lgAJxcVlJlZo7XULI6IGNnIQMzMmqmjs5qKb2NVdSWhpA2BDaioPUfERUUFZWbWaK1W4gBA0mhgJ7IEfQOwB3AH2V27zcwWCZ05zs6QtCxwHrAhWUn4yIi4q9Z2qhnTfwrYFXg2Io4ANgaG1dqRmVmZ5TzN7hzgpohYnyxn1rU8RjUljjciolPSAknLAHOANevpzMysrPIqcUgaRrbi5xeyduMt4K162qomQd+Xhuu/JZvZ8SpQ81C9VhfNKrwLa0FvzPpbs0OwRVQtJQ5Jo4BRFbvGRMSY9Hwk8Bzwe0kbk+XNEyLitVpjUtTwa0PSCGCZiHiw1o5qNWix4SUs2VuzOUFbTwavuHa/C8j3rH5g1Tln61lXLbQ/SVsAdwPbR8Q9ks4BXomI/1drTL1dqLJZb8ciYkqtnZmZlVWOI8KZwMyIuCdt/xE4qZ6Geitx/LSXYwHsUk+HZmZllNcsjoh4VtLTktaLiH+QTbKYVk9bvV2osnO9AZqZtZqcF0H6KnCJpMWA6cAR9TRS1YUqZmaLujxv6h0RU4Et+tuOE7SZGRC00FocZmYDyYIWvaOKJB0u6bS0vZakrYoPzcyscQJV/WiUai71/iWwLXBI2p4LnFtYRGZmTdBZw6NRqilxbB0Rm0m6HyAiXkxnJs3MFhmtWoOeL6mdNI9b0ko09peImVnhypjUqknQvwDGAStL+gHZ6nanFhqVmVmDdbTiCDoiLpE0mexqGAH7R0RdS+eZmZVVCe94VdWC/WsBrwPXVe6LiH8VGZiZWSN1tuIIGhjPOzePXYJsKb1/AB8qMC4zs4Yq4/KZ1ZQ4NqrcTqvcfaWwiMzMmqBVTxK+S0RMkbR1EcGYmTVLp1qwxCHp6xWbbcBmwKzCIjIza4KOZgfQg2pG0EMrni8gq0n/qZhwzMyao+VmcaQLVIZGxDcbFI+ZWVO01CwOSYMiYoGk7RsZkJlZM7TaLI57yerNUyVdC1wJvH1X2oi4quDYzMwapuVKHMkSwAtk9yDsmg8dgBO0mS0yWm2a3cppBsfDvJOYu5TxrwEzs7p1tNgIuh1YGnqsnDtBm9kipdVG0LMj4oyGRWJm1kStlqBLOOA3MytGCW9J2GuC3rVhUZiZNVlLjaAj4j+NDMTMrJla9VJvM7NFXqvOgzYzW+S1VInDzGwgKWOCbmt2AGZmZRA1PKohqV3S/ZKurzcmj6DNzCikBn0C8CiwTL0NeARtZkY2i6PaR18krQHsBZzXn5icoM3MgE6i6oekUZLuq3iM6tbcz4ET6Wdp2yUOMzNqy6QRMQYY09MxSXsDcyJisqSd+hOTE7SZGbmuALc9sK+kPcmWa15G0sURcXitDbnEYWZGNoKu9tGbiDg5ItaIiBHAZ4Db60nOUNAIWtKBvR333VjMrGwWqHyrKBdV4tgn/bsysB1we9reGfg7vhuLmZVMEek5IiYAE+p9fyEJOiKOAJB0C7BBRMxO26sBFxTRp5lZf5TxSsKiTxKu2ZWck38DaxXcp5lZzTpLeKOoohP0bZJuBi5L2wcDtxbcp5lZzcqXngtO0BFxnKQDgB3SrjERMa7IPs3M6jEQSxwAU4C5EXGrpCUlDY2IuQ3o18ysah0lHEMXOg9a0peAPwK/SbuGA1cX2aeZWT3ymgedp6IvVDmW7KqaVwAi4gmyqXdmZqUSNfzXKEWXOOZFxFtSto6fpEGUsxZvZgNcGWvQRY+g/yrpO8AQSbsBVwLXFdznIuUTH9+JRx6eyGPT7uDEbx3b7HCsSU794dnssNdn2P/wY97e95P/PY99DvkSB3zuyxx/8hm8MvfVJkbY+mpZza5Rik7QJwHPAQ8BRwM3AKcW3Ocio62tjV+c8wP23udwNtp4Zw4+eH8++MF1mh2WNcH+e+7Gr8/+/rv2bbvlpowb+2vGXfQrRqw5nPPGXt6k6BYNed9RJQ+FJuiI6IyI30bEpyPiU+m5SxxV2mrLTfnnP59kxox/MX/+fK644hr23ecTzQ7LmmCLTTZi2DJD37Vv+603Z9CgdgA+/KH1+fec55sR2iJjAVH1o1GKWizpIXr5RRMRHy6i30XN6sNX5emZs97envnMbLbactMmRmRlNW78Ley+647NDqOlNfLkX7WKOkm4d/q3q2g6Nv17OL0k7nRXglEAah9GW9tSBYVntuj4zYWX0d7ezt4f37nZobS0Mp4kLGqxpKcAJO0WEZVDvm9LmkJWm+7pfW/fpWDQYsPL9+uswWY98yxrrrH629trDF+NWbOebWJEVjZXj/8zE++8l/N+cSZds6WsPmUcQRd9klCStq/Y2K4BfS4yJt03lQ98YCQjRqzJ4MGDOeig/bju+luaHZaVxB1338f5l17J/5w1miFLLNHscFpeGS9UKXoe9FHA+ZKGAQJeBI4suM9FRkdHByf816ncMP5S2tvauODCy5k27fFmh2VN8K3RP2LS/Q/y0kuvsOv+h/OVoz7LeWMv56358/nSf50CZCcKR5/41SZH2ro6Sjh/QY2YVJESNBHxcrXvcYnDevLGrL81OwQrocErrt3v+s6h7zug6pxz6VPjGlJPKnQELWlx4JPACGBQV40sIs4osl8zs1qVsQZddInjGuBlYDIwr+C+zMzqNmBmcVRYIyJ2L7gPM7N+K+MdVYqeUfF3SRsV3IeZWb8NxNXsPgJ8QdIMshKHgPCVhGZWNmWcxVF0gt6j4PbNzHJRxhJH0fck7LqicGXAM+nNrLTKeJKw6Fte7SvpCWAG8FfgSeDGIvs0M6tHGWvQRZ8k/B6wDfB4RIwEdgXuLrhPM7OaDcQF++dHxAtAm6S2iPgLsEXBfZqZ1Swiqn40StEnCV+StDQwEbhE0hzgtYL7NDOrWUdOI2NJawIXAauQLa88JiLOqaetokfQ+wFvAF8DbgL+CexTcJ9mZjXLscSxAPhGRGxAVuI9VtIG9cRU9CyOytHyhUX2ZWbWH3mVLiJiNjA7PZ8r6VFgODCt1raKuuXVXHq+c0rXhSrLFNGvmVm9ijj5J2kEsClwTz3vL+qOKkP7fpWZWXnUMn2u8vZ8yZh0R6jK1ywN/An4r4h4pZ6Yij5JaGbWEmq51Lvy9nw9kTSYLDlfEhFX1RuTE7SZGfmVOJQtfP874NGIOLs/bfn+gGZm5DqLY3vgs8Aukqamx571xOQRtJkZuc7iuINsQkS/OUGbmTEAV7MzM2sVA/GehGZmLaEjyrfgqBO0mRn51aDz5ARtZoZr0GZmpeUatJlZSXW6xGFmVk4eQZuZlZRncZiZlZRLHGZmJeUSh5lZSXkEbWZWUh5Bm5mVVEd0NDuE93CCNjPDl3qbmZWWL/U2Myspj6DNzErKszjMzErKszjMzErKl3qbmZWUa9BmZiXlGrSZWUl5BG1mVlKeB21mVlIeQZuZlZRncZiZlZRPEpqZlVQZSxxtzQ7AzKwMoob/+iJpd0n/kPR/kk6qNyaPoM3MyG8ELakdOBfYDZgJTJJ0bURMq7UtJ2gzM3KtQW8F/F9ETAeQ9AdgP2DRSdAL3npGzY6hLCSNiogxzY7DysWfi3zVknMkjQJGVewaU/GzGA48XXFsJrB1PTG5Bt0aRvX9EhuA/LlokogYExFbVDwK+UXpBG1mlq9ngDUrttdI+2rmBG1mlq9JwDqSRkpaDPgMcG09DZW2Bm3v4jqj9cSfixKKiAWSjgNuBtqB8yPikXraUhknZ5uZmUscZmal5QRtZlZSTtD9IOm7kr6Znp8h6WM9vGYnSdfn1N93ejn2pKQVc+rn1Tzasfrk9f2XNELSw3m0Zc3hBJ2TiDgtIm4tuJuFJmgzW/Q4QddI0imSHpd0B7Bexf4LJH0qPd9d0mOSpgAHLqSdL0i6StJNkp6Q9N8Vxw6R9JCkhyWdlfb9CBgiaaqkS/qI8WpJkyU9kq546tr/qqQfSHpA0t2SVkn7R0q6K/X5/X58eyxHkpaWdJukKelns1/aP0LSo5J+m37Gt0gako5tnn6+DwDHNvULsH5zgq6BpM3J5jRuAuwJbNnDa5YAfgvsA2wOrNpLk5sABwMbAQdLWlPS6sBZwC7p+JaS9o+Ik4A3ImKTiDisj1CPjIjNgS2A4yWtkPYvBdwdERsDE4Evpf3nAL+KiI2A2X20bY3zJnBARGwG7Az8VFLX5cjrAOdGxIeAl4BPpv2/B76afsbW4pyga/NRYFxEvB4Rr9Dz5PP1gRkR8URkcxgv7qW92yLi5Yh4k2whlfeRJf0JEfFcRCwALgF2qDHO49MI6m6yK5rWSfvfArrq4ZOBEen59sBl6fnYGvuy4gj4oaQHgVvJ1nhYJR2bERFT0/PJwAhJywLLRsTEtN8/yxbnC1Waa17F8w5y+HlI2gn4GLBtRLwuaQKwRDo8P96Z+N69P0+IL5/DgJWAzSNivqQneedn2f2zM6TBsVkDeARdm4nA/pKGSBpKVsbo7jGy0cz70/YhNfZxL7CjpBXTurKHAH9Nx+ZLGtzH+4cBL6bkvD6wTRV93klWuoEsKVg5DAPmpOS8M9lfWAsVES8BL0n6SNrln2WLc4KuQURMAS4HHgBuJLvmvvtr3iRbZWx8Okk4p8Y+ZgMnAX9J/UyOiGvS4THAg32cJLwJGCTpUeBHZGWOvpwAHCvpIbI/o60cLgG2SD+Xz5H98u/LEcC5kqaSlUishflSbzOzkvII2syspJygzcxKygnazKyknKDNzErKCdrMrKScoO09JHWkNT8elnSlpCX70VblGiXnSdqgl9fuJGm7OvrocSW/alb4q3XluMoVDM2K5gRtPela82NDssvDj6k8KKmuKx4j4osRMa2Xl+wE1JygzRZVTtDWl78BH0ij279JuhaYJqld0o8lTZL0oKSjAZT5X0n/kHQrsHJXQ5ImSNoiPd89rdL2QFqxbQTZL4KvpdH7RyWtJOlPqY9JkrZP710hreD2iKTzqOKCjIWt8JeO/Sztv03SSmnf+9NKg5PT171+D20eL2la+vr/UN+312zhvBaHLVQaKe9BdnUiwGbAhhExIyW5lyNiS0mLA3dKugXYlGwZ1g3IFvaZBpzfrd2VyFb82yG1tXxE/EfSr4FXI+In6XWXAj+LiDskrUV2E84PAqOBOyLiDEl7AUdV8eUcmfoYAkyS9KeIeIFshb/7IuJrkk5LbR9HdtXmMRHxhKStgV+SrTBY6SRgZETMSwsVmeXKCdp6MiRdKgzZCPp3ZKWHeyNiRtr/ceDDXfVlsnUj1iFbee+yiOgAZkm6vYf2twEmdrUVEf9ZSBwfAzZ4Z4VNlpG0dOrjwPTe8ZJerOJrOl7SAel51wp/LwCdZJfvQ7by4FWpj+2AKyv6XryHNh8ELpF0NXB1FTGY1cQJ2nryRkRsUrkjJarXKneRrTt8c7fX7ZljHG3ANml9k+6xVK2PFf66i9TvS92/Bz3Yi+yXxT7AKZI2SkvEmuXCNWir183Al7tW15O0rqSlyFb8OzjVqFcjW2i+u7uBHSSNTO9dPu2fCwyteN0twFe7NiR1JcyJwKFp3x7Acn3E2tsKf21A118Bh5KVTl4BZkj6dOpDkt61AL6kNmDNiPgL8O3Ux9J9xGFWEydoq9d5ZPXlKcpuTPobsr/IxgFPpGMXAXd1f2NEPEe24t9Vym4s0FViuA44oOskIXA82WpuD0qaxjuzSU4nS/CPkJU6/tVHrL2t8PcasFX6GnYBzkj7DwOOSvE9AuzXrc124GJlK83dD/wiLfdplhuvZmdmVlIeQZuZlZQTtJlZSTlBm5mVlBO0mVlJOUGbmZWUE7SZWUk5QZuZldT/B2ofo+RjXXByAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "yhat=logreg_cv.predict(X_test)\n",
        "plot_confusion_matrix(Y_test,yhat)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5NAm4IQx3fTm"
      },
      "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": {
        "id": "Qsk-wavp3fTm"
      },
      "source": [
        "## TASK  6\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0hE3ZKr13fTm"
      },
      "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": 15,
      "metadata": {
        "id": "5LwdH_oj3fTm"
      },
      "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": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6W5SHjj93fTm",
        "outputId": "80f7e906-e2b0-4e6d-98d0-539600b9ddb7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "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')})"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "svm_cv = GridSearchCV(svm,parameters,cv=10)\n",
        "svm_cv.fit(X_train,Y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zk_lim9E3fTm",
        "outputId": "87240c21-d970-4f6d-d46e-f5f7772a9c59"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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",
      "metadata": {
        "id": "k5Ji3gEE3fTm"
      },
      "source": [
        "## TASK  7\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cr2VYFV43fTn"
      },
      "source": [
        "Calculate the accuracy on the test data using the method <code>score</code>:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "bUIEfJ3e3fTn"
      },
      "outputs": [],
      "source": [
        "svmscore = svm_cv.score(X_test,Y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SJhQi2zM3fTn"
      },
      "source": [
        "We can plot the confusion matrix\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "fe8CNd3D3fTn",
        "outputId": "1c64f6d5-6770-490e-9f13-14e02a2ebea9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "yhat=svm_cv.predict(X_test)\n",
        "plot_confusion_matrix(Y_test,yhat)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4PNjhW563fTn"
      },
      "source": [
        "## TASK  8\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n72epGQX3fTn"
      },
      "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": 54,
      "metadata": {
        "id": "PLcLGiI33fTn"
      },
      "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": 55,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dSvMXHSY3fTo",
        "outputId": "f07f3b96-3beb-49a3-cf12-d7fd994f3c56"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "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']})"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ],
      "source": [
        "tree_cv = GridSearchCV(tree,parameters,cv=10)\n",
        "tree_cv.fit(X_train,Y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Eykj4nbe3fTo",
        "outputId": "e6d0f226-70ee-4025-9a3d-901af80e199e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tuned hpyerparameters :(best parameters)  {'criterion': 'entropy', 'max_depth': 16, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 5, 'splitter': 'random'}\n",
            "accuracy : 0.8767857142857143\n"
          ]
        }
      ],
      "source": [
        "print(\"tuned hpyerparameters :(best parameters) \",tree_cv.best_params_)\n",
        "print(\"accuracy :\",tree_cv.best_score_)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VZRsJAwt3fTo"
      },
      "source": [
        "## TASK  9\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dL0oNX403fTo"
      },
      "source": [
        "Calculate the accuracy of tree_cv on the test data using the method <code>score</code>:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "QQomicD63fTo"
      },
      "outputs": [],
      "source": [
        "treescore = tree_cv.score(X_test, Y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P_F4JppA3fTo"
      },
      "source": [
        "We can plot the confusion matrix\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "_Mbc5UKy3fTo",
        "outputId": "ee6b1e42-953b-4feb-dc5a-52aa47212a6d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "yhat = tree_cv.predict(X_test)\n",
        "plot_confusion_matrix(Y_test,yhat)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2OghblRs3fTo"
      },
      "source": [
        "## TASK  10\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XKHJTHoD3fTo"
      },
      "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": 25,
      "metadata": {
        "id": "ir5pVwQn3fTp"
      },
      "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": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pSEKxHZr3fTp",
        "outputId": "564855e7-5617-494f-83a3-7b3d3cf0deac"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "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]})"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "knn_cv = GridSearchCV(KNN,parameters,cv=10)\n",
        "knn_cv.fit(X_train,Y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F8hX1mZW3fTp",
        "outputId": "fc3c73f4-08f7-4088-fa2c-315b0398819c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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",
      "metadata": {
        "id": "8_XcqBlU3fTp"
      },
      "source": [
        "## TASK  11\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YaQ85Wz83fTp"
      },
      "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": {
        "id": "zbMjXLTf3fTp"
      },
      "outputs": [],
      "source": [
        "knnscore = knn_cv.score(X_test,Y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ABUPyLIY3fTp"
      },
      "source": [
        "We can plot the confusion matrix\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "lSnkNdZe3fTp",
        "outputId": "493f281a-7b67-4114-c4bf-613ab3abc1ed"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "yhat = knn_cv.predict(X_test)\n",
        "plot_confusion_matrix(Y_test,yhat)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_pUVGEHW3fTq"
      },
      "source": [
        "## TASK  12\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b5zfqsQR3fTq"
      },
      "source": [
        "Find the method performs best:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "UX3j0c773fTq",
        "outputId": "c0e45bb2-7d68-42f5-fe13-c52e0c44c220",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        }
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-46-e69dc63c687a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0msplt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbarplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscores\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscores\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpalette\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Blues_d\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0msplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Model Type'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mylabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Score'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0msplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m: 'AxesSubplot' object has no attribute 'bar_label'"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#The method that performs the best is the decision tree with 0,888 accuracy\n",
        "scores = ['LogReg', 'SVM', 'Tree', 'KNN'],[logregscore*100, svmscore*100, treescore*100, knnscore*100]\n",
        "splt = sns.barplot(x = scores[0], y = scores[1], palette = \"Blues_d\")\n",
        "splt.set(xlabel = 'Model Type', ylabel = 'Score')\n",
        "splt.bar_label(scores[1])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6bIgMWZY3fTq"
      },
      "source": [
        "## Authors\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ANon5Qz23fTq"
      },
      "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": {
        "id": "5WFbFhFJ3fTq"
      },
      "source": [
        "## Change Log\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-SAO2n0M3fTq"
      },
      "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": {
        "id": "1AkHMWWF3fTq"
      },
      "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": "3.8.8"
    },
    "colab": {
      "name": "SpaceX_Machine Learning Prediction_Part_5.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}