4. Machine_Learning_Prediction.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":51,"id":"4818736e-9044-4cbe-9367-8b21b0246077","metadata":{},"outputs":[],"source":["# import piplite\n","# await piplite.install(['numpy'])\n","# await piplite.install(['pandas'])\n","# await piplite.install(['seaborn'])"]},{"cell_type":"markdown","id":"e3bd986c-7b04-48d7-bdbe-1e3067039e5f","metadata":{},"source":["We will import the following libraries for the lab\n"]},{"cell_type":"code","execution_count":52,"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":53,"id":"1f30279e-9d5e-4df5-a814-8053d099c511","metadata":{},"outputs":[],"source":["def plot_confusion_matrix(y,y_predict, title=\"\"):\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'.format(title)); \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":54,"id":"303c7783-faac-4ca0-98a5-df6c89c65df4","metadata":{},"outputs":[],"source":["# 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 = await fetch(URL1)\n","# text1 = io.BytesIO((await resp1.arrayBuffer()).to_py())\n","data = pd.read_csv(URL1)"]},{"cell_type":"code","execution_count":55,"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":55,"metadata":{},"output_type":"execute_result"}],"source":["data.head()"]},{"cell_type":"code","execution_count":56,"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 = await fetch(URL2)\n","# text2 = io.BytesIO((await resp2.arrayBuffer()).to_py())\n","X = pd.read_csv(URL2)"]},{"cell_type":"code","execution_count":57,"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","  </tbody>\n","</table>\n","<p>5 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","   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","   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","   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","[5 rows x 83 columns]"]},"execution_count":57,"metadata":{},"output_type":"execute_result"}],"source":["X.head()"]},{"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":58,"id":"bc72dffc-1032-4a3a-b84f-d2968b3faffc","metadata":{},"outputs":[{"data":{"text/plain":["array([0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1,\n","       1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","       1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1,\n","       1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","       1, 1], dtype=int64)"]},"execution_count":58,"metadata":{},"output_type":"execute_result"}],"source":["Y = data['Class'].to_numpy()\n","Y"]},{"cell_type":"markdown","id":"4d79f1ca-2636-4221-895a-4afc091c73b0","metadata":{},"source":["## TASK  2\n"]},{"cell_type":"markdown","id":"324ed6d1-626f-4c23-bf84-efd98f797280","metadata":{},"source":["Standardize the data in <code>X</code> then reassign it to the variable  <code>X</code> using the transform provided below.\n"]},{"cell_type":"code","execution_count":59,"id":"9067e374-f502-44c6-a628-29186009fd45","metadata":{},"outputs":[{"data":{"text/plain":["array([[-1.71291154e+00, -1.94814463e-16, -6.53912840e-01, ...,\n","        -8.35531692e-01,  1.93309133e+00, -1.93309133e+00],\n","       [-1.67441914e+00, -1.19523159e+00, -6.53912840e-01, ...,\n","        -8.35531692e-01,  1.93309133e+00, -1.93309133e+00],\n","       [-1.63592675e+00, -1.16267307e+00, -6.53912840e-01, ...,\n","        -8.35531692e-01,  1.93309133e+00, -1.93309133e+00],\n","       ...,\n","       [ 1.63592675e+00,  1.99100483e+00,  3.49060516e+00, ...,\n","         1.19684269e+00, -5.17306132e-01,  5.17306132e-01],\n","       [ 1.67441914e+00,  1.99100483e+00,  1.00389436e+00, ...,\n","         1.19684269e+00, -5.17306132e-01,  5.17306132e-01],\n","       [ 1.71291154e+00, -5.19213966e-01, -6.53912840e-01, ...,\n","        -8.35531692e-01, -5.17306132e-01,  5.17306132e-01]])"]},"execution_count":59,"metadata":{},"output_type":"execute_result"}],"source":["# students get this \n","transform = preprocessing.StandardScaler()\n","X = transform.fit_transform(X)\n","X"]},{"cell_type":"markdown","id":"a6f007c0-65fe-408a-9af6-febef40b1e1b","metadata":{},"source":["We split the data into training and testing data using the  function  <code>train_test_split</code>.   The training data is divided into validation data, a second set used for training  data; then the models are trained and hyperparameters are selected using the function <code>GridSearchCV</code>.\n"]},{"cell_type":"markdown","id":"c4a2cfde-e37a-475f-95ad-95246535ff18","metadata":{},"source":["## TASK  3\n"]},{"cell_type":"markdown","id":"3b2ff7f9-f969-4ba2-a2bd-17f0b7d6464e","metadata":{},"source":["Use the function train_test_split to split the data X and Y into training and test data. Set the parameter test_size to  0.2 and random_state to 2. The training data and test data should be assigned to the following labels.\n"]},{"cell_type":"markdown","id":"5da81a62-3998-436c-b96e-9e7a1db0825f","metadata":{},"source":["<code>X_train, X_test, Y_train, Y_test</code>\n"]},{"cell_type":"code","execution_count":60,"id":"bef9d582-2c05-4a6f-ac6a-125766d093c7","metadata":{},"outputs":[],"source":["X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size=0.2,random_state=2)"]},{"cell_type":"markdown","id":"e81b772b-0a2e-4f30-a774-8bff2b8fe8b0","metadata":{},"source":["we can see we only have 18 test samples.\n"]},{"cell_type":"code","execution_count":61,"id":"77d6496a-d933-420f-9352-1c95a832b84d","metadata":{},"outputs":[{"data":{"text/plain":["(18,)"]},"execution_count":61,"metadata":{},"output_type":"execute_result"}],"source":["Y_test.shape"]},{"cell_type":"markdown","id":"1e778d63-eec7-4bf8-a83e-3397997026fa","metadata":{},"source":["## TASK  4\n"]},{"cell_type":"markdown","id":"06a7589e-a488-431b-b329-c78ea814f020","metadata":{},"source":["Create a logistic regression object  then create a  GridSearchCV object  <code>logreg_cv</code> with cv = 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n"]},{"cell_type":"code","execution_count":62,"id":"de49af42-d4af-4f3b-bbbb-3b7cc6793c21","metadata":{},"outputs":[],"source":["parameters ={\"C\":[0.01,0.1,1],'penalty':['l2'], 'solver':['lbfgs']}# l1 lasso l2 ridge\n","lr=LogisticRegression()\n","logreg_cv = GridSearchCV(lr, parameters, cv = 10)"]},{"cell_type":"code","execution_count":63,"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>GridSearchCV(cv=10, 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-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, 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-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" 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-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" 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=10, estimator=LogisticRegression(),\n","             param_grid={'C': [0.01, 0.1, 1], 'penalty': ['l2'],\n","                         'solver': ['lbfgs']})"]},"execution_count":63,"metadata":{},"output_type":"execute_result"}],"source":["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":64,"id":"8dfcfcaf-4c12-44f5-b7e4-3d00e56313dc","metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["tuned hpyerparameters :(best parameters)  {'C': 0.01, 'penalty': 'l2', 'solver': 'lbfgs'}\n","accuracy : 0.8464285714285713\n"]}],"source":["print(\"tuned hpyerparameters :(best parameters) \", logreg_cv.best_params_)\n","print(\"accuracy :\", logreg_cv.best_score_)"]},{"cell_type":"markdown","id":"5df3f620-3e95-406d-b01d-667801f1d09d","metadata":{},"source":["## TASK  5\n"]},{"cell_type":"markdown","id":"8890f2b5-9f2a-478b-8772-078b2e55483d","metadata":{},"source":["Calculate the accuracy on the test data using the method <code>score</code>:\n"]},{"cell_type":"code","execution_count":65,"id":"ecd83d0e-92bc-4c73-b6e7-a355725cc4ac","metadata":{},"outputs":[{"data":{"text/plain":["0.8333333333333334"]},"execution_count":65,"metadata":{},"output_type":"execute_result"}],"source":["logregScore = logreg_cv.best_estimator_.score(X_test,Y_test)  #Accuracy check with best parameters\n","logregScore"]},{"cell_type":"markdown","id":"ef7d820b-6494-448b-99da-ddee09fac04b","metadata":{},"source":["Lets look at the confusion matrix:\n"]},{"cell_type":"code","execution_count":85,"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, \"Logistic Regression\")"]},{"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":67,"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":68,"id":"4ac8b9eb-801e-4596-bce7-fa7a980dd5ba","metadata":{},"outputs":[{"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=10, 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-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=10, 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-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" 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-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" 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=10, estimator=SVC(),\n","             param_grid={'C': array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n","       1.00000000e+03]),\n","                         'gamma': array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n","       1.00000000e+03]),\n","                         'kernel': ('linear', 'rbf', 'poly', 'rbf', 'sigmoid')})"]},"execution_count":68,"metadata":{},"output_type":"execute_result"}],"source":["svm_cv = GridSearchCV(svm, parameters, cv = 10)\n","svm_cv.fit(X_train, Y_train)"]},{"cell_type":"code","execution_count":69,"id":"a017bdfd-1c68-4eec-9b88-6900d8971ced","metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["tuned hpyerparameters :(best parameters)  {'C': 1.0, 'gamma': 0.03162277660168379, 'kernel': 'sigmoid'}\n","accuracy : 0.8482142857142856\n"]}],"source":["print(\"tuned hpyerparameters :(best parameters) \",svm_cv.best_params_)\n","print(\"accuracy :\",svm_cv.best_score_)"]},{"cell_type":"markdown","id":"41b16eb3-b7ec-401d-a44a-1d2ab4638af0","metadata":{},"source":["## TASK  7\n"]},{"cell_type":"markdown","id":"97ce1130-daf0-4059-abcf-bdfcc15b5f0b","metadata":{},"source":["Calculate the accuracy on the test data using the method <code>score</code>:\n"]},{"cell_type":"code","execution_count":70,"id":"7aedad55-850c-4383-b8d1-c2dc61895fe8","metadata":{},"outputs":[{"data":{"text/plain":["0.8333333333333334"]},"execution_count":70,"metadata":{},"output_type":"execute_result"}],"source":["svmScore = svm_cv.best_estimator_.score(X_test, Y_test)\n","svmScore"]},{"cell_type":"markdown","id":"7d52749c-ace1-4457-b304-f0e459a2d057","metadata":{},"source":["We can plot the confusion matrix\n"]},{"cell_type":"code","execution_count":86,"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, \"SVM\")"]},{"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":72,"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":73,"id":"966e2a3f-d15a-4940-99cf-e9b3bc1b7df4","metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:425: FitFailedWarning: \n","3240 fits failed out of a total of 6480.\n","The score on these train-test partitions for these parameters will be set to nan.\n","If these failures are not expected, you can try to debug them by setting error_score='raise'.\n","\n","Below are more details about the failures:\n","--------------------------------------------------------------------------------\n","3240 fits failed with the following error:\n","Traceback (most recent call last):\n","  File \"c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 729, in _fit_and_score\n","    estimator.fit(X_train, y_train, **fit_params)\n","  File \"c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py\", line 1145, in wrapper\n","    estimator._validate_params()\n","  File \"c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py\", line 638, in _validate_params\n","    validate_parameter_constraints(\n","  File \"c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 96, in validate_parameter_constraints\n","    raise InvalidParameterError(\n","sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of DecisionTreeClassifier must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'sqrt', 'log2'} or None. Got 'auto' instead.\n","\n","  warnings.warn(some_fits_failed_message, FitFailedWarning)\n","c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:979: UserWarning: One or more of the test scores are non-finite: [       nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n"," 0.79464286 0.70892857 0.79107143 0.76607143 0.73392857 0.75\n"," 0.7625     0.725      0.79285714 0.69107143 0.81964286 0.7625\n"," 0.76071429 0.70892857 0.74642857 0.73392857 0.7375     0.71964286\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n"," 0.81964286 0.78035714 0.69285714 0.80535714 0.78928571 0.81785714\n"," 0.76071429 0.83392857 0.77678571 0.76428571 0.70892857 0.79107143\n"," 0.77678571 0.81964286 0.77678571 0.8625     0.77678571 0.83392857\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n"," 0.80535714 0.83571429 0.80357143 0.79464286 0.7625     0.81964286\n"," 0.85       0.88928571 0.77678571 0.74821429 0.77678571 0.80357143\n"," 0.81785714 0.76428571 0.7625     0.81964286 0.77857143 0.73214286\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n"," 0.77678571 0.79107143 0.8625     0.79464286 0.83035714 0.80535714\n"," 0.81964286 0.79107143 0.77857143 0.79107143 0.69642857 0.81785714\n"," 0.74642857 0.77678571 0.73392857 0.78928571 0.76428571 0.75357143\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n"," 0.70714286 0.80535714 0.80357143 0.77678571 0.79107143 0.81785714\n"," 0.77857143 0.80892857 0.70714286 0.80357143 0.7625     0.73928571\n"," 0.74821429 0.81964286 0.74821429 0.83214286 0.775      0.81964286\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n"," 0.75357143 0.84642857 0.74821429 0.80535714 0.7375     0.71964286\n"," 0.79107143 0.7625     0.77857143 0.76428571 0.78928571 0.78928571\n"," 0.75178571 0.80357143 0.76071429 0.71071429 0.79107143 0.76785714\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n"," 0.80357143 0.81964286 0.82142857 0.77678571 0.76785714 0.77857143\n"," 0.78928571 0.70892857 0.81785714 0.77678571 0.67678571 0.77857143\n"," 0.775      0.80357143 0.78035714 0.80357143 0.80178571 0.79107143\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n"," 0.73214286 0.81964286 0.73214286 0.77857143 0.74642857 0.80535714\n"," 0.77857143 0.80535714 0.70714286 0.75178571 0.72321429 0.775\n"," 0.81785714 0.7375     0.77678571 0.73392857 0.71964286 0.79107143\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n"," 0.77857143 0.75       0.7375     0.81964286 0.66428571 0.8625\n"," 0.78035714 0.775      0.79107143 0.81964286 0.83214286 0.73392857\n"," 0.71071429 0.76607143 0.80535714 0.74642857 0.79107143 0.77678571\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n"," 0.74642857 0.78928571 0.85892857 0.79107143 0.83214286 0.83392857\n"," 0.80357143 0.7625     0.79285714 0.80357143 0.75       0.7625\n"," 0.76071429 0.69642857 0.8375     0.78928571 0.83035714 0.81964286\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n"," 0.80535714 0.76428571 0.775      0.83392857 0.81964286 0.80714286\n"," 0.875      0.86071429 0.83571429 0.83571429 0.75357143 0.83392857\n"," 0.81785714 0.80357143 0.77678571 0.71964286 0.625      0.775\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n"," 0.80357143 0.8625     0.79107143 0.75178571 0.81964286 0.84642857\n"," 0.775      0.7625     0.71964286 0.83214286 0.76071429 0.81785714\n"," 0.75357143 0.80357143 0.77857143 0.86071429 0.80357143 0.76428571\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n"," 0.76428571 0.73571429 0.79285714 0.78928571 0.74642857 0.81785714\n"," 0.81964286 0.83392857 0.74821429 0.74821429 0.7375     0.80535714\n"," 0.79285714 0.84821429 0.74821429 0.84464286 0.79285714 0.83214286\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n"," 0.81964286 0.79285714 0.77678571 0.80714286 0.79285714 0.80357143\n"," 0.7625     0.7625     0.74642857 0.7625     0.80178571 0.79107143\n"," 0.73928571 0.80892857 0.76428571 0.76071429 0.80714286 0.79285714\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n"," 0.74642857 0.80535714 0.80535714 0.8625     0.77857143 0.80714286\n"," 0.69107143 0.79107143 0.76071429 0.72142857 0.775      0.77678571\n"," 0.73392857 0.80357143 0.83035714 0.80892857 0.66785714 0.7625\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n"," 0.85892857 0.78214286 0.78214286 0.73392857 0.70714286 0.77678571\n"," 0.76071429 0.67678571 0.76071429 0.77678571 0.77678571 0.80535714\n"," 0.83214286 0.75       0.78928571 0.81964286 0.80357143 0.83392857\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n"," 0.76428571 0.74821429 0.81785714 0.69107143 0.7625     0.78035714\n"," 0.76071429 0.73214286 0.7625     0.73571429 0.79464286 0.80714286\n"," 0.81607143 0.82142857 0.74821429 0.76428571 0.81964286 0.775\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n","        nan        nan        nan        nan        nan        nan\n"," 0.80535714 0.76607143 0.77678571 0.76428571 0.78928571 0.86071429\n"," 0.79107143 0.79107143 0.77678571 0.76428571 0.80357143 0.84821429\n"," 0.73928571 0.77678571 0.81785714 0.80535714 0.80357143 0.83392857]\n","  warnings.warn(\n"]},{"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>GridSearchCV(cv=10, 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-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, 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-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" 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-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" 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=10, estimator=DecisionTreeClassifier(),\n","             param_grid={'criterion': ['gini', 'entropy'],\n","                         'max_depth': [2, 4, 6, 8, 10, 12, 14, 16, 18],\n","                         'max_features': ['auto', 'sqrt'],\n","                         'min_samples_leaf': [1, 2, 4],\n","                         'min_samples_split': [2, 5, 10],\n","                         'splitter': ['best', 'random']})"]},"execution_count":73,"metadata":{},"output_type":"execute_result"}],"source":["tree_cv = GridSearchCV(tree, parameters, cv = 10)\n","tree_cv.fit(X_train, Y_train)"]},{"cell_type":"code","execution_count":74,"id":"4c26b867-0f93-4713-bd7c-9bdef8dea18f","metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["tuned hpyerparameters :(best parameters)  {'criterion': 'gini', 'max_depth': 6, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'splitter': 'random'}\n","accuracy : 0.8892857142857142\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":75,"id":"55003514-94f0-459a-a6eb-093f1c5f9f05","metadata":{},"outputs":[{"data":{"text/plain":["0.8333333333333334"]},"execution_count":75,"metadata":{},"output_type":"execute_result"}],"source":["treeScore = tree_cv.best_estimator_.score(X_test, Y_test)\n","treeScore"]},{"cell_type":"markdown","id":"78fb6d64-6239-4783-8974-23606d5754c0","metadata":{},"source":["We can plot the confusion matrix\n"]},{"cell_type":"code","execution_count":87,"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, \"Decision Tree\")"]},{"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":77,"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":78,"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=10, 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-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, 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-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" 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-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" 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=10, estimator=KNeighborsClassifier(),\n","             param_grid={'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n","                         'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n","                         'p': [1, 2]})"]},"execution_count":78,"metadata":{},"output_type":"execute_result"}],"source":["knn_cv = GridSearchCV(KNN, parameters, cv = 10)\n","knn_cv.fit(X_train, Y_train)"]},{"cell_type":"code","execution_count":79,"id":"6309852d-7fb4-400f-b44a-1a3a04f03fba","metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["tuned hpyerparameters :(best parameters)  {'algorithm': 'auto', 'n_neighbors': 10, 'p': 1}\n","accuracy : 0.8482142857142858\n"]}],"source":["print(\"tuned hpyerparameters :(best parameters) \",knn_cv.best_params_)\n","print(\"accuracy :\",knn_cv.best_score_)"]},{"cell_type":"markdown","id":"98e9e899-25da-4c9b-99eb-b680c559ebda","metadata":{},"source":["## TASK  11\n"]},{"cell_type":"markdown","id":"9a20e83b-5c1b-440d-bba3-57c44a8605a6","metadata":{},"source":["Calculate the accuracy of knn_cv on the test data using the method <code>score</code>:\n"]},{"cell_type":"code","execution_count":80,"id":"89196934-71d7-408d-9ff2-e99a934fb7de","metadata":{},"outputs":[{"data":{"text/plain":["0.8333333333333334"]},"execution_count":80,"metadata":{},"output_type":"execute_result"}],"source":["knnScore = knn_cv.best_estimator_.score(X_test, Y_test)\n","knnScore"]},{"cell_type":"markdown","id":"a6cafe12-0a65-4cdc-8e44-67892989248e","metadata":{},"source":["We can plot the confusion matrix\n"]},{"cell_type":"code","execution_count":88,"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, \"KNN\")"]},{"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":82,"metadata":{},"outputs":[],"source":["ml_performance = {\n","  \"Model\": ['Logistic Regression', 'SVM', 'Decision Tree', 'K Near Neighbor'],\n","  \"Test Performance\": [logregScore, svmScore, treeScore, knnScore]\n","} "]},{"cell_type":"code","execution_count":83,"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>Model</th>\n","      <th>Test Performance</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Logistic Regression</td>\n","      <td>0.833333</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>SVM</td>\n","      <td>0.833333</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Decision Tree</td>\n","      <td>0.833333</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>K Near Neighbor</td>\n","      <td>0.833333</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                 Model  Test Performance\n","0  Logistic Regression          0.833333\n","1                  SVM          0.833333\n","2        Decision Tree          0.833333\n","3      K Near Neighbor          0.833333"]},"execution_count":83,"metadata":{},"output_type":"execute_result"}],"source":["ml_df = pd.DataFrame(ml_performance)\n","ml_df"]},{"cell_type":"code","execution_count":84,"metadata":{},"outputs":[{"data":{"text/plain":["Text(0.5, 1.0, \"Comparison of Prediction Model's Test Performance\")"]},"execution_count":84,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["sns.barplot(x='Model', y='Test Performance', data=ml_df)\n","plt.title(\"Comparison of Prediction Model's Test Performance\")"]},{"cell_type":"markdown","id":"7e2da33c-70ca-46ae-98a7-9ed8cdb4dabe","metadata":{},"source":["Besides the Decision Tree classifier which scored low due to it being unable to  fit all training data, all other prediction models performed the same in training and testing accuracy. Even the confusion matrix are the same."]},{"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","| 2023-12-20 | 1.1 | Nalin Malla | Completed the lab |\n","| 2022-11-09        | 1.0     | Pratiksha Verma | Converted initial version to Jupyterlite|\n"]},{"cell_type":"markdown","id":"73d1479f-e16f-449f-a5fe-ef1d4e367a1a","metadata":{},"source":["### <h3 align=\"center\"> IBM Corporation 2022. All rights reserved. <h3/>\n"]}],"metadata":{"kernelspec":{"display_name":"Python 3","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":"3.11.3"}},"nbformat":4,"nbformat_minor":4}