IBM_DataScience_Spacex_Machine_Learning_Prediction_Part_5.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":[{"ename":"ModuleNotFoundError","evalue":"No module named 'piplite'","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)","Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mpiplite\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[39mawait\u001b[39;00m piplite\u001b[39m.\u001b[39minstall([\u001b[39m'\u001b[39m\u001b[39mnumpy\u001b[39m\u001b[39m'\u001b[39m])\n\u001b[1;32m      3\u001b[0m \u001b[39mawait\u001b[39;00m piplite\u001b[39m.\u001b[39minstall([\u001b[39m'\u001b[39m\u001b[39mpandas\u001b[39m\u001b[39m'\u001b[39m])\n","\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'piplite'"]}],"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":10,"id":"303c7783-faac-4ca0-98a5-df6c89c65df4","metadata":{},"outputs":[],"source":["import requests\n","#from js import fetch\n","import io\n","\n","URL1 = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv\"\n","resp1 = requests.get(URL1)\n","text1 = io.BytesIO(resp1.content)\n","data = pd.read_csv(text1)"]},{"cell_type":"code","execution_count":11,"id":"2bbe74d1-22a0-4164-a154-7b61fd30db7f","metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>FlightNumber</th>\n","      <th>Date</th>\n","      <th>BoosterVersion</th>\n","      <th>PayloadMass</th>\n","      <th>Orbit</th>\n","      <th>LaunchSite</th>\n","      <th>Outcome</th>\n","      <th>Flights</th>\n","      <th>GridFins</th>\n","      <th>Reused</th>\n","      <th>Legs</th>\n","      <th>LandingPad</th>\n","      <th>Block</th>\n","      <th>ReusedCount</th>\n","      <th>Serial</th>\n","      <th>Longitude</th>\n","      <th>Latitude</th>\n","      <th>Class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <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","      <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>B0003</td>\n","      <td>-80.577366</td>\n","      <td>28.561857</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>2012-05-22</td>\n","      <td>Falcon 9</td>\n","      <td>525.000000</td>\n","      <td>LEO</td>\n","      <td>CCAFS SLC 40</td>\n","      <td>None None</td>\n","      <td>1</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>B0005</td>\n","      <td>-80.577366</td>\n","      <td>28.561857</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <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","      <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>B0007</td>\n","      <td>-80.577366</td>\n","      <td>28.561857</td>\n","      <td>0</td>\n","    </tr>\n","    <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","    </tr>\n","    <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","      <td>False</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>B1004</td>\n","      <td>-80.577366</td>\n","      <td>28.561857</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"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  "]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["data.head()"]},{"cell_type":"code","execution_count":12,"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","resp2 = requests.get(URL2)\n","text2 = io.BytesIO(resp2.content)\n","X = pd.read_csv(text2)"]},{"cell_type":"code","execution_count":13,"id":"02af7a31-bf3d-431a-ae95-fd146cd09329","metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>FlightNumber</th>\n","      <th>PayloadMass</th>\n","      <th>Flights</th>\n","      <th>Block</th>\n","      <th>ReusedCount</th>\n","      <th>Orbit_ES-L1</th>\n","      <th>Orbit_GEO</th>\n","      <th>Orbit_GTO</th>\n","      <th>Orbit_HEO</th>\n","      <th>Orbit_ISS</th>\n","      <th>...</th>\n","      <th>Serial_B1058</th>\n","      <th>Serial_B1059</th>\n","      <th>Serial_B1060</th>\n","      <th>Serial_B1062</th>\n","      <th>GridFins_False</th>\n","      <th>GridFins_True</th>\n","      <th>Reused_False</th>\n","      <th>Reused_True</th>\n","      <th>Legs_False</th>\n","      <th>Legs_True</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1.0</td>\n","      <td>6104.959412</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2.0</td>\n","      <td>525.000000</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3.0</td>\n","      <td>677.000000</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4.0</td>\n","      <td>500.000000</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5.0</td>\n","      <td>3170.000000</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>85</th>\n","      <td>86.0</td>\n","      <td>15400.000000</td>\n","      <td>2.0</td>\n","      <td>5.0</td>\n","      <td>2.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>86</th>\n","      <td>87.0</td>\n","      <td>15400.000000</td>\n","      <td>3.0</td>\n","      <td>5.0</td>\n","      <td>2.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>87</th>\n","      <td>88.0</td>\n","      <td>15400.000000</td>\n","      <td>6.0</td>\n","      <td>5.0</td>\n","      <td>5.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>88</th>\n","      <td>89.0</td>\n","      <td>15400.000000</td>\n","      <td>3.0</td>\n","      <td>5.0</td>\n","      <td>2.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>89</th>\n","      <td>90.0</td>\n","      <td>3681.000000</td>\n","      <td>1.0</td>\n","      <td>5.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>90 rows × 83 columns</p>\n","</div>"],"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","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":13,"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":15,"id":"bc72dffc-1032-4a3a-b84f-d2968b3faffc","metadata":{},"outputs":[],"source":["y = data['Class']"]},{"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":17,"id":"9067e374-f502-44c6-a628-29186009fd45","metadata":{},"outputs":[],"source":["# students get this \n","transform = preprocessing.StandardScaler()\n","X = transform.fit_transform(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":20,"id":"bef9d582-2c05-4a6f-ac6a-125766d093c7","metadata":{},"outputs":[{"data":{"text/plain":["((72, 83), (72,), (18, 83), (18,))"]},"execution_count":20,"metadata":{},"output_type":"execute_result"}],"source":["X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=4)\n","X_train.shape, y_train.shape, X_test.shape, y_test.shape\n"]},{"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":21,"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":26,"id":"de49af42-d4af-4f3b-bbbb-3b7cc6793c21","metadata":{},"outputs":[{"data":{"text/html":["<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=LogisticRegression(),\n","             param_grid={&#x27;C&#x27;: [0.01, 0.1, 1], &#x27;penalty&#x27;: [&#x27;l2&#x27;],\n","                         &#x27;solver&#x27;: [&#x27;lbfgs&#x27;]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=LogisticRegression(),\n","             param_grid={&#x27;C&#x27;: [0.01, 0.1, 1], &#x27;penalty&#x27;: [&#x27;l2&#x27;],\n","                         &#x27;solver&#x27;: [&#x27;lbfgs&#x27;]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div></div></div></div>"],"text/plain":["GridSearchCV(cv=5, estimator=LogisticRegression(),\n","             param_grid={'C': [0.01, 0.1, 1], 'penalty': ['l2'],\n","                         'solver': ['lbfgs']})"]},"execution_count":26,"metadata":{},"output_type":"execute_result"}],"source":["parameters ={\"C\":[0.01,0.1,1],'penalty':['l2'], 'solver':['lbfgs']}# l1 lasso l2 ridge\n","lr=LogisticRegression()\n","logreg_cv = GridSearchCV(lr, parameters, cv=5)\n","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":27,"id":"8dfcfcaf-4c12-44f5-b7e4-3d00e56313dc","metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["tuned hpyerparameters :(best parameters)  {'C': 0.1, 'penalty': 'l2', 'solver': 'lbfgs'}\n","accuracy : 0.8495238095238096\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":28,"id":"ecd83d0e-92bc-4c73-b6e7-a355725cc4ac","metadata":{},"outputs":[{"data":{"text/html":["<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=0.1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=0.1)</pre></div></div></div></div></div>"],"text/plain":["LogisticRegression(C=0.1)"]},"execution_count":28,"metadata":{},"output_type":"execute_result"}],"source":["logreg_cv.best_estimator_.fit(X_train,y_train)"]},{"cell_type":"markdown","id":"ef7d820b-6494-448b-99da-ddee09fac04b","metadata":{},"source":["Lets look at the confusion matrix:\n"]},{"cell_type":"code","execution_count":30,"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":31,"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":33,"id":"4ac8b9eb-801e-4596-bce7-fa7a980dd5ba","metadata":{},"outputs":[{"data":{"text/html":["<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=SVC(),\n","             param_grid={&#x27;C&#x27;: array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n","       1.00000000e+03]),\n","                         &#x27;gamma&#x27;: array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n","       1.00000000e+03]),\n","                         &#x27;kernel&#x27;: (&#x27;linear&#x27;, &#x27;rbf&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;, &#x27;sigmoid&#x27;)})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=SVC(),\n","             param_grid={&#x27;C&#x27;: array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n","       1.00000000e+03]),\n","                         &#x27;gamma&#x27;: array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n","       1.00000000e+03]),\n","                         &#x27;kernel&#x27;: (&#x27;linear&#x27;, &#x27;rbf&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;, &#x27;sigmoid&#x27;)})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div></div></div></div></div></div>"],"text/plain":["GridSearchCV(cv=5, estimator=SVC(),\n","             param_grid={'C': array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n","       1.00000000e+03]),\n","                         'gamma': array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n","       1.00000000e+03]),\n","                         'kernel': ('linear', 'rbf', 'poly', 'rbf', 'sigmoid')})"]},"execution_count":33,"metadata":{},"output_type":"execute_result"}],"source":["svm_cv = GridSearchCV(svm, parameters, cv=5)\n","svm_cv.fit(X_train,y_train)"]},{"cell_type":"code","execution_count":34,"id":"a017bdfd-1c68-4eec-9b88-6900d8971ced","metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["tuned hpyerparameters :(best parameters)  {'C': 0.03162277660168379, 'gamma': 0.001, 'kernel': 'linear'}\n","accuracy : 0.8638095238095238\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":35,"id":"7aedad55-850c-4383-b8d1-c2dc61895fe8","metadata":{},"outputs":[{"data":{"text/html":["<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=0.03162277660168379, gamma=0.001, kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=0.03162277660168379, gamma=0.001, kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"],"text/plain":["SVC(C=0.03162277660168379, gamma=0.001, kernel='linear')"]},"execution_count":35,"metadata":{},"output_type":"execute_result"}],"source":["svm_cv.best_estimator_.fit(X_train,y_train)"]},{"cell_type":"markdown","id":"7d52749c-ace1-4457-b304-f0e459a2d057","metadata":{},"source":["We can plot the confusion matrix\n"]},{"cell_type":"code","execution_count":37,"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":38,"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":39,"id":"966e2a3f-d15a-4940-99cf-e9b3bc1b7df4","metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["/home/themanyface/.local/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:425: FitFailedWarning: \n","1620 fits failed out of a total of 3240.\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","1620 fits failed with the following error:\n","Traceback (most recent call last):\n","  File \"/home/themanyface/.local/lib/python3.11/site-packages/sklearn/model_selection/_validation.py\", line 732, in _fit_and_score\n","    estimator.fit(X_train, y_train, **fit_params)\n","  File \"/home/themanyface/.local/lib/python3.11/site-packages/sklearn/base.py\", line 1144, in wrapper\n","    estimator._validate_params()\n","  File \"/home/themanyface/.local/lib/python3.11/site-packages/sklearn/base.py\", line 637, in _validate_params\n","    validate_parameter_constraints(\n","  File \"/home/themanyface/.local/lib/python3.11/site-packages/sklearn/utils/_param_validation.py\", line 95, 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 {'log2', 'sqrt'} or None. Got 'auto' instead.\n","\n","  warnings.warn(some_fits_failed_message, FitFailedWarning)\n","/home/themanyface/.local/lib/python3.11/site-packages/sklearn/model_selection/_search.py:976: 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"," 0.83619048 0.88952381 0.86285714 0.79333333 0.87714286 0.83714286\n"," 0.87714286 0.75238095 0.73809524 0.79238095 0.82285714 0.82095238\n"," 0.80761905 0.83428571 0.80857143 0.86190476 0.84952381 0.9047619\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n"," 0.86285714 0.82095238 0.87714286 0.76571429 0.87714286 0.82\n"," 0.89142857 0.83333333 0.82       0.7952381  0.7952381  0.78\n"," 0.86380952 0.81904762 0.7352381  0.84952381 0.84952381 0.86380952\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n"," 0.84952381 0.88952381 0.87714286 0.80571429 0.77714286 0.87619048\n"," 0.86190476 0.86285714 0.84952381 0.77904762 0.79333333 0.87809524\n"," 0.79333333 0.87809524 0.89047619 0.78095238 0.78       0.9047619\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n"," 0.82190476 0.83428571 0.80666667 0.84857143 0.86380952 0.83428571\n"," 0.85047619 0.84761905 0.83428571 0.87619048 0.90380952 0.8352381\n"," 0.79238095 0.79333333 0.87714286 0.73619048 0.86380952 0.80761905\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n"," 0.86095238 0.91809524 0.85047619 0.90380952 0.8752381  0.9047619\n"," 0.84952381 0.82285714 0.79428571 0.87714286 0.8352381  0.87619048\n"," 0.79619048 0.90380952 0.86380952 0.87619048 0.77904762 0.79047619\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n"," 0.77904762 0.87619048 0.9047619  0.86285714 0.7952381  0.87619048\n"," 0.84857143 0.85142857 0.79238095 0.77904762 0.86285714 0.83428571\n"," 0.84761905 0.8352381  0.86285714 0.84857143 0.73809524 0.87619048\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n"," 0.79333333 0.80666667 0.86285714 0.84857143 0.87619048 0.77904762\n"," 0.73714286 0.87619048 0.89142857 0.80761905 0.79428571 0.87809524\n"," 0.84857143 0.80666667 0.80285714 0.79428571 0.83333333 0.84952381\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n"," 0.80761905 0.82190476 0.89238095 0.79238095 0.87714286 0.86380952\n"," 0.80666667 0.84761905 0.80857143 0.83619048 0.76666667 0.83619048\n"," 0.82095238 0.9047619  0.89142857 0.81904762 0.85047619 0.84761905\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n"," 0.87714286 0.86190476 0.8647619  0.80761905 0.82190476 0.82095238\n"," 0.87714286 0.75428571 0.86285714 0.86285714 0.8352381  0.84857143\n"," 0.83619048 0.82285714 0.77904762 0.82190476 0.87619048 0.80761905\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n"," 0.83333333 0.84761905 0.89047619 0.71142857 0.84857143 0.76666667\n"," 0.79238095 0.84857143 0.86380952 0.82095238 0.82095238 0.63714286\n"," 0.9047619  0.76571429 0.7647619  0.75238095 0.80285714 0.83619048\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n"," 0.77904762 0.89142857 0.81047619 0.82       0.83714286 0.89142857\n"," 0.80857143 0.87809524 0.82190476 0.82095238 0.87714286 0.82190476\n"," 0.84952381 0.87809524 0.87809524 0.82190476 0.7647619  0.84952381\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n"," 0.88952381 0.79428571 0.87714286 0.86095238 0.82       0.8647619\n"," 0.81904762 0.80761905 0.82190476 0.85142857 0.86285714 0.84761905\n"," 0.78       0.82095238 0.83809524 0.75142857 0.85047619 0.8352381\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n"," 0.86380952 0.90380952 0.83619048 0.84857143 0.86380952 0.82095238\n"," 0.84857143 0.82380952 0.80666667 0.83428571 0.80761905 0.86190476\n"," 0.80761905 0.76285714 0.70857143 0.80761905 0.79142857 0.72285714\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n"," 0.83809524 0.82095238 0.84952381 0.83619048 0.82190476 0.82\n"," 0.85047619 0.76571429 0.87619048 0.85047619 0.83619048 0.80666667\n"," 0.80666667 0.77809524 0.81904762 0.82095238 0.80761905 0.86285714\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n"," 0.79428571 0.82       0.84952381 0.79238095 0.86285714 0.80666667\n"," 0.84952381 0.83333333 0.79142857 0.91809524 0.85142857 0.8647619\n"," 0.81904762 0.88952381 0.86285714 0.78095238 0.87619048 0.7952381\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n"," 0.85047619 0.76666667 0.8752381  0.78       0.8352381  0.89142857\n"," 0.79333333 0.83428571 0.83619048 0.79047619 0.89142857 0.8047619\n"," 0.80666667 0.83333333 0.79047619 0.83333333 0.83428571 0.86285714\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n"," 0.86285714 0.80761905 0.78095238 0.8352381  0.83428571 0.84761905\n"," 0.82095238 0.8352381  0.82285714 0.82095238 0.81904762 0.84952381\n"," 0.82095238 0.86285714 0.80857143 0.7952381  0.78190476 0.71904762\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n"," 0.84952381 0.8752381  0.87619048 0.82095238 0.87714286 0.84857143\n"," 0.8352381  0.80857143 0.83428571 0.83428571 0.76380952 0.81904762\n"," 0.86190476 0.8352381  0.84952381 0.83619048 0.8047619  0.84761905]\n","  warnings.warn(\n"]},{"data":{"text/html":["<style>#sk-container-id-6 {color: black;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=DecisionTreeClassifier(),\n","             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n","                         &#x27;max_depth&#x27;: [2, 4, 6, 8, 10, 12, 14, 16, 18],\n","                         &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n","                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n","                         &#x27;min_samples_split&#x27;: [2, 5, 10],\n","                         &#x27;splitter&#x27;: [&#x27;best&#x27;, &#x27;random&#x27;]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=DecisionTreeClassifier(),\n","             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n","                         &#x27;max_depth&#x27;: [2, 4, 6, 8, 10, 12, 14, 16, 18],\n","                         &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n","                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n","                         &#x27;min_samples_split&#x27;: [2, 5, 10],\n","                         &#x27;splitter&#x27;: [&#x27;best&#x27;, &#x27;random&#x27;]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"],"text/plain":["GridSearchCV(cv=5, estimator=DecisionTreeClassifier(),\n","             param_grid={'criterion': ['gini', 'entropy'],\n","                         'max_depth': [2, 4, 6, 8, 10, 12, 14, 16, 18],\n","                         'max_features': ['auto', 'sqrt'],\n","                         'min_samples_leaf': [1, 2, 4],\n","                         'min_samples_split': [2, 5, 10],\n","                         'splitter': ['best', 'random']})"]},"execution_count":39,"metadata":{},"output_type":"execute_result"}],"source":["tree_cv = GridSearchCV(tree, parameters, cv=5)\n","tree_cv.fit(X_train,y_train)"]},{"cell_type":"code","execution_count":40,"id":"4c26b867-0f93-4713-bd7c-9bdef8dea18f","metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["tuned hpyerparameters :(best parameters)  {'criterion': 'gini', 'max_depth': 10, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'splitter': 'random'}\n","accuracy : 0.9180952380952382\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":41,"id":"55003514-94f0-459a-a6eb-093f1c5f9f05","metadata":{},"outputs":[{"data":{"text/html":["<style>#sk-container-id-7 {color: black;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=10, max_features=&#x27;sqrt&#x27;, splitter=&#x27;random&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" checked><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=10, max_features=&#x27;sqrt&#x27;, splitter=&#x27;random&#x27;)</pre></div></div></div></div></div>"],"text/plain":["DecisionTreeClassifier(max_depth=10, max_features='sqrt', splitter='random')"]},"execution_count":41,"metadata":{},"output_type":"execute_result"}],"source":["tree_cv.best_estimator_.fit(X_train,y_train)"]},{"cell_type":"markdown","id":"78fb6d64-6239-4783-8974-23606d5754c0","metadata":{},"source":["We can plot the confusion matrix\n"]},{"cell_type":"code","execution_count":42,"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":43,"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":44,"id":"ffbaf94b-0b03-4094-84b6-f5233194434c","metadata":{},"outputs":[{"data":{"text/html":["<style>#sk-container-id-8 {color: black;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n","             param_grid={&#x27;algorithm&#x27;: [&#x27;auto&#x27;, &#x27;ball_tree&#x27;, &#x27;kd_tree&#x27;, &#x27;brute&#x27;],\n","                         &#x27;n_neighbors&#x27;: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n","                         &#x27;p&#x27;: [1, 2]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n","             param_grid={&#x27;algorithm&#x27;: [&#x27;auto&#x27;, &#x27;ball_tree&#x27;, &#x27;kd_tree&#x27;, &#x27;brute&#x27;],\n","                         &#x27;n_neighbors&#x27;: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n","                         &#x27;p&#x27;: [1, 2]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"],"text/plain":["GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n","             param_grid={'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n","                         'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n","                         'p': [1, 2]})"]},"execution_count":44,"metadata":{},"output_type":"execute_result"}],"source":["knn_cv = GridSearchCV(KNN, parameters, cv=5)\n","knn_cv.fit(X_train,y_train)"]},{"cell_type":"code","execution_count":45,"id":"6309852d-7fb4-400f-b44a-1a3a04f03fba","metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["tuned hpyerparameters :(best parameters)  {'algorithm': 'auto', 'n_neighbors': 1, 'p': 1}\n","accuracy : 0.8638095238095238\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":46,"id":"89196934-71d7-408d-9ff2-e99a934fb7de","metadata":{},"outputs":[{"data":{"text/html":["<style>#sk-container-id-9 {color: black;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=1, p=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" checked><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=1, p=1)</pre></div></div></div></div></div>"],"text/plain":["KNeighborsClassifier(n_neighbors=1, p=1)"]},"execution_count":46,"metadata":{},"output_type":"execute_result"}],"source":["knn_cv.best_estimator_.fit(X_train,y_train)"]},{"cell_type":"markdown","id":"a6cafe12-0a65-4cdc-8e44-67892989248e","metadata":{},"source":["We can plot the confusion matrix\n"]},{"cell_type":"code","execution_count":48,"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":49,"id":"7e2da33c-70ca-46ae-98a7-9ed8cdb4dabe","metadata":{},"outputs":[{"data":{"text/html":["<style>#sk-container-id-10 {color: black;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=1, p=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" checked><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=1, p=1)</pre></div></div></div></div></div>"],"text/plain":["KNeighborsClassifier(n_neighbors=1, p=1)"]},"execution_count":49,"metadata":{},"output_type":"execute_result"}],"source":["knn_cv.best_estimator_.fit(X,y)"]},{"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":"data","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":"3.11.4"}},"nbformat":4,"nbformat_minor":4}