From 558ce51ed71f2b41f2827246af2e4f1828f1e7ba Mon Sep 17 00:00:00 2001 From: Mrdecent08 <60995685+Mrdecent08@users.noreply.github.com> Date: Sat, 2 Oct 2021 21:50:35 +0530 Subject: [PATCH] Add files via upload --- .../KNN(Supervised-Classification).ipynb | 1 + 1 file changed, 1 insertion(+) create mode 100644 Python/Algorithms/Machine Learning Algorithms/KNN(Supervised-Classification).ipynb diff --git a/Python/Algorithms/Machine Learning Algorithms/KNN(Supervised-Classification).ipynb b/Python/Algorithms/Machine Learning Algorithms/KNN(Supervised-Classification).ipynb new file mode 100644 index 00000000..837e263a --- /dev/null +++ b/Python/Algorithms/Machine Learning Algorithms/KNN(Supervised-Classification).ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"KNN(Supervised-Classification).ipynb","provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyOoTGyECblS/y6s4fHuRML5"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","metadata":{"id":"2NmnADCtmHbe"},"source":["It is also called as lazy learner.\n","\n","It has no training phase.\n","![image.png](data:image/png;base64,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)"]},{"cell_type":"markdown","metadata":{"id":"sBqLmRiZnlG1"},"source":["Used for large datasets."]},{"cell_type":"code","metadata":{"id":"3N7qlA3ElNoA","executionInfo":{"status":"ok","timestamp":1623861272159,"user_tz":-330,"elapsed":925,"user":{"displayName":"Mr decent","photoUrl":"","userId":"12594074046933137286"}}},"source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import sklearn\n","from sklearn.model_selection import train_test_split\n","from sklearn.datasets import load_iris\n","from sklearn.neighbors import KNeighborsClassifier\n","from sklearn.metrics import accuracy_score"],"execution_count":19,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SDxIOGarpYyH","executionInfo":{"status":"ok","timestamp":1623860829015,"user_tz":-330,"elapsed":656,"user":{"displayName":"Mr decent","photoUrl":"","userId":"12594074046933137286"}},"outputId":"f5ae96de-27e7-4249-f641-195d3329b160"},"source":["data = load_iris()\n","data\n"],"execution_count":3,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'DESCR': '.. _iris_dataset:\\n\\nIris plants dataset\\n--------------------\\n\\n**Data Set Characteristics:**\\n\\n :Number of Instances: 150 (50 in each of three classes)\\n :Number of Attributes: 4 numeric, predictive attributes and the class\\n :Attribute Information:\\n - sepal length in cm\\n - sepal width in cm\\n - petal length in cm\\n - petal width in cm\\n - class:\\n - Iris-Setosa\\n - Iris-Versicolour\\n - Iris-Virginica\\n \\n :Summary Statistics:\\n\\n ============== ==== ==== ======= ===== ====================\\n Min Max Mean SD Class Correlation\\n ============== ==== ==== ======= ===== ====================\\n sepal length: 4.3 7.9 5.84 0.83 0.7826\\n sepal width: 2.0 4.4 3.05 0.43 -0.4194\\n petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)\\n petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)\\n ============== ==== ==== ======= ===== ====================\\n\\n :Missing Attribute Values: None\\n :Class Distribution: 33.3% for each of 3 classes.\\n :Creator: R.A. Fisher\\n :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n :Date: July, 1988\\n\\nThe famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\\nfrom Fisher\\'s paper. Note that it\\'s the same as in R, but not as in the UCI\\nMachine Learning Repository, which has two wrong data points.\\n\\nThis is perhaps the best known database to be found in the\\npattern recognition literature. Fisher\\'s paper is a classic in the field and\\nis referenced frequently to this day. (See Duda & Hart, for example.) The\\ndata set contains 3 classes of 50 instances each, where each class refers to a\\ntype of iris plant. One class is linearly separable from the other 2; the\\nlatter are NOT linearly separable from each other.\\n\\n.. topic:: References\\n\\n - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\\n Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\\n Mathematical Statistics\" (John Wiley, NY, 1950).\\n - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\\n (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.\\n - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\\n Structure and Classification Rule for Recognition in Partially Exposed\\n Environments\". IEEE Transactions on Pattern Analysis and Machine\\n Intelligence, Vol. PAMI-2, No. 1, 67-71.\\n - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\". IEEE Transactions\\n on Information Theory, May 1972, 431-433.\\n - See also: 1988 MLC Proceedings, 54-64. Cheeseman et al\"s AUTOCLASS II\\n conceptual clustering system finds 3 classes in the data.\\n - Many, many more ...',\n"," 'data': array([[5.1, 3.5, 1.4, 0.2],\n"," [4.9, 3. , 1.4, 0.2],\n"," [4.7, 3.2, 1.3, 0.2],\n"," [4.6, 3.1, 1.5, 0.2],\n"," [5. , 3.6, 1.4, 0.2],\n"," [5.4, 3.9, 1.7, 0.4],\n"," [4.6, 3.4, 1.4, 0.3],\n"," [5. , 3.4, 1.5, 0.2],\n"," [4.4, 2.9, 1.4, 0.2],\n"," [4.9, 3.1, 1.5, 0.1],\n"," [5.4, 3.7, 1.5, 0.2],\n"," [4.8, 3.4, 1.6, 0.2],\n"," [4.8, 3. , 1.4, 0.1],\n"," [4.3, 3. , 1.1, 0.1],\n"," [5.8, 4. , 1.2, 0.2],\n"," [5.7, 4.4, 1.5, 0.4],\n"," [5.4, 3.9, 1.3, 0.4],\n"," [5.1, 3.5, 1.4, 0.3],\n"," [5.7, 3.8, 1.7, 0.3],\n"," [5.1, 3.8, 1.5, 0.3],\n"," [5.4, 3.4, 1.7, 0.2],\n"," [5.1, 3.7, 1.5, 0.4],\n"," [4.6, 3.6, 1. , 0.2],\n"," [5.1, 3.3, 1.7, 0.5],\n"," [4.8, 3.4, 1.9, 0.2],\n"," [5. , 3. , 1.6, 0.2],\n"," [5. , 3.4, 1.6, 0.4],\n"," [5.2, 3.5, 1.5, 0.2],\n"," [5.2, 3.4, 1.4, 0.2],\n"," [4.7, 3.2, 1.6, 0.2],\n"," [4.8, 3.1, 1.6, 0.2],\n"," [5.4, 3.4, 1.5, 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2.1],\n"," [4.9, 2.5, 4.5, 1.7],\n"," [7.3, 2.9, 6.3, 1.8],\n"," [6.7, 2.5, 5.8, 1.8],\n"," [7.2, 3.6, 6.1, 2.5],\n"," [6.5, 3.2, 5.1, 2. ],\n"," [6.4, 2.7, 5.3, 1.9],\n"," [6.8, 3. , 5.5, 2.1],\n"," [5.7, 2.5, 5. , 2. ],\n"," [5.8, 2.8, 5.1, 2.4],\n"," [6.4, 3.2, 5.3, 2.3],\n"," [6.5, 3. , 5.5, 1.8],\n"," [7.7, 3.8, 6.7, 2.2],\n"," [7.7, 2.6, 6.9, 2.3],\n"," [6. , 2.2, 5. , 1.5],\n"," [6.9, 3.2, 5.7, 2.3],\n"," [5.6, 2.8, 4.9, 2. ],\n"," [7.7, 2.8, 6.7, 2. ],\n"," [6.3, 2.7, 4.9, 1.8],\n"," [6.7, 3.3, 5.7, 2.1],\n"," [7.2, 3.2, 6. , 1.8],\n"," [6.2, 2.8, 4.8, 1.8],\n"," [6.1, 3. , 4.9, 1.8],\n"," [6.4, 2.8, 5.6, 2.1],\n"," [7.2, 3. , 5.8, 1.6],\n"," [7.4, 2.8, 6.1, 1.9],\n"," [7.9, 3.8, 6.4, 2. ],\n"," [6.4, 2.8, 5.6, 2.2],\n"," [6.3, 2.8, 5.1, 1.5],\n"," [6.1, 2.6, 5.6, 1.4],\n"," [7.7, 3. , 6.1, 2.3],\n"," [6.3, 3.4, 5.6, 2.4],\n"," [6.4, 3.1, 5.5, 1.8],\n"," [6. , 3. , 4.8, 1.8],\n"," [6.9, 3.1, 5.4, 2.1],\n"," [6.7, 3.1, 5.6, 2.4],\n"," [6.9, 3.1, 5.1, 2.3],\n"," [5.8, 2.7, 5.1, 1.9],\n"," [6.8, 3.2, 5.9, 2.3],\n"," [6.7, 3.3, 5.7, 2.5],\n"," [6.7, 3. , 5.2, 2.3],\n"," [6.3, 2.5, 5. , 1.9],\n"," [6.5, 3. , 5.2, 2. ],\n"," [6.2, 3.4, 5.4, 2.3],\n"," [5.9, 3. , 5.1, 1.8]]),\n"," 'feature_names': ['sepal length (cm)',\n"," 'sepal width (cm)',\n"," 'petal length (cm)',\n"," 'petal width (cm)'],\n"," 'filename': '/usr/local/lib/python3.7/dist-packages/sklearn/datasets/data/iris.csv',\n"," 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n"," 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n"," 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n"," 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n"," 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n"," 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n"," 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]),\n"," 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='\n","\n","\n"," 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sepal length (cm)sepal width (cm)petal length (cm)petal width (cm)
05.13.51.40.2
14.93.01.40.2
24.73.21.30.2
34.63.11.50.2
45.03.61.40.2
\n",""],"text/plain":[" sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n","0 5.1 3.5 1.4 0.2\n","1 4.9 3.0 1.4 0.2\n","2 4.7 3.2 1.3 0.2\n","3 4.6 3.1 1.5 0.2\n","4 5.0 3.6 1.4 0.2"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"code","metadata":{"id":"v7-_PVzepnqd","executionInfo":{"status":"ok","timestamp":1623860906576,"user_tz":-330,"elapsed":658,"user":{"displayName":"Mr decent","photoUrl":"","userId":"12594074046933137286"}}},"source":["x = df\n","y = data['target']"],"execution_count":6,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":417},"id":"iFXL4bzIpwR_","executionInfo":{"status":"ok","timestamp":1623860911930,"user_tz":-330,"elapsed":936,"user":{"displayName":"Mr decent","photoUrl":"","userId":"12594074046933137286"}},"outputId":"3398ed25-e777-4f35-de5b-b7dce53fb5bb"},"source":["x"],"execution_count":7,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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sepal length (cm)sepal width (cm)petal length (cm)petal width (cm)
05.13.51.40.2
14.93.01.40.2
24.73.21.30.2
34.63.11.50.2
45.03.61.40.2
...............
1456.73.05.22.3
1466.32.55.01.9
1476.53.05.22.0
1486.23.45.42.3
1495.93.05.11.8
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150 rows × 4 columns

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"],"text/plain":[" sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n","0 5.1 3.5 1.4 0.2\n","1 4.9 3.0 1.4 0.2\n","2 4.7 3.2 1.3 0.2\n","3 4.6 3.1 1.5 0.2\n","4 5.0 3.6 1.4 0.2\n",".. ... ... ... ...\n","145 6.7 3.0 5.2 2.3\n","146 6.3 2.5 5.0 1.9\n","147 6.5 3.0 5.2 2.0\n","148 6.2 3.4 5.4 2.3\n","149 5.9 3.0 5.1 1.8\n","\n","[150 rows x 4 columns]"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LKwCGA2npxDQ","executionInfo":{"status":"ok","timestamp":1623860916373,"user_tz":-330,"elapsed":638,"user":{"displayName":"Mr decent","photoUrl":"","userId":"12594074046933137286"}},"outputId":"c2ec18a3-9d51-418d-f4b2-af9ef0be68fc"},"source":["y"],"execution_count":8,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n"," 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n"," 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n"," 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n"," 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n"," 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n"," 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"]},"metadata":{"tags":[]},"execution_count":8}]},{"cell_type":"code","metadata":{"id":"2n90Iio4pyZ-","executionInfo":{"status":"ok","timestamp":1623861073729,"user_tz":-330,"elapsed":1547,"user":{"displayName":"Mr decent","photoUrl":"","userId":"12594074046933137286"}}},"source":["x_train,x_test,y_train,y_test = train_test_split(x,y,random_state=0,test_size=0.25,stratify=y)"],"execution_count":13,"outputs":[]},{"cell_type":"code","metadata":{"id":"oxjEqXHPp_m8","executionInfo":{"status":"ok","timestamp":1623861133116,"user_tz":-330,"elapsed":610,"user":{"displayName":"Mr decent","photoUrl":"","userId":"12594074046933137286"}}},"source":["model = KNeighborsClassifier(n_neighbors=3)"],"execution_count":15,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SOO2Ddleqnmc","executionInfo":{"status":"ok","timestamp":1623861178954,"user_tz":-330,"elapsed":1277,"user":{"displayName":"Mr decent","photoUrl":"","userId":"12594074046933137286"}},"outputId":"d1987fc6-0698-47ad-daae-ffca1930bca2"},"source":["model.fit(x_train,y_train)"],"execution_count":16,"outputs":[{"output_type":"execute_result","data":{"text/plain":["KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n"," metric_params=None, n_jobs=None, n_neighbors=3, p=2,\n"," weights='uniform')"]},"metadata":{"tags":[]},"execution_count":16}]},{"cell_type":"code","metadata":{"id":"P7acnC7oqynH","executionInfo":{"status":"ok","timestamp":1623861238271,"user_tz":-330,"elapsed":725,"user":{"displayName":"Mr decent","photoUrl":"","userId":"12594074046933137286"}}},"source":["y_test_predict = model.predict(x_test)"],"execution_count":17,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1QmO8D3orBDN","executionInfo":{"status":"ok","timestamp":1623861245376,"user_tz":-330,"elapsed":754,"user":{"displayName":"Mr decent","photoUrl":"","userId":"12594074046933137286"}},"outputId":"907b4b85-e372-4717-b60d-813c5f441d3f"},"source":["y_test_predict"],"execution_count":18,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0, 0, 0, 0, 1, 1, 1, 0, 1, 2, 2, 2, 1, 2, 1, 0, 0, 2, 0, 1, 2, 1,\n"," 1, 0, 2, 0, 0, 1, 2, 1, 0, 1, 2, 2, 0, 1, 2, 2])"]},"metadata":{"tags":[]},"execution_count":18}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Fbd1d1dLrC9m","executionInfo":{"status":"ok","timestamp":1623861379458,"user_tz":-330,"elapsed":633,"user":{"displayName":"Mr decent","photoUrl":"","userId":"12594074046933137286"}},"outputId":"c394d108-a581-47bd-e00c-891070dd6a56"},"source":["score1 = accuracy_score(y_test,y_test_predict)\n","score1"],"execution_count":22,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1.0"]},"metadata":{"tags":[]},"execution_count":22}]},{"cell_type":"code","metadata":{"id":"1GDu9GQJrVGi","executionInfo":{"status":"ok","timestamp":1623861380057,"user_tz":-330,"elapsed":5,"user":{"displayName":"Mr decent","photoUrl":"","userId":"12594074046933137286"}}},"source":["y_train_predict = model.predict(x_train)"],"execution_count":23,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"t5ciW_6yrbYq","executionInfo":{"status":"ok","timestamp":1623861380846,"user_tz":-330,"elapsed":7,"user":{"displayName":"Mr decent","photoUrl":"","userId":"12594074046933137286"}},"outputId":"caac3f56-425e-46b0-81ff-5d90f936f6ab"},"source":["score2 = accuracy_score(y_train,y_train_predict)\n","score2"],"execution_count":24,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9464285714285714"]},"metadata":{"tags":[]},"execution_count":24}]},{"cell_type":"code","metadata":{"id":"VZnyoPy8rj8x","executionInfo":{"status":"ok","timestamp":1623861412888,"user_tz":-330,"elapsed":583,"user":{"displayName":"Mr decent","photoUrl":"","userId":"12594074046933137286"}}},"source":["from sklearn.metrics import confusion_matrix,classification_report"],"execution_count":25,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-iyCKtDprr6L","executionInfo":{"status":"ok","timestamp":1623861436144,"user_tz":-330,"elapsed":968,"user":{"displayName":"Mr decent","photoUrl":"","userId":"12594074046933137286"}},"outputId":"39a1a871-ecf3-470b-b59b-ee441b45e2da"},"source":["cm = confusion_matrix(y_test,y_test_predict)\n","cm"],"execution_count":26,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[13, 0, 0],\n"," [ 0, 13, 0],\n"," [ 0, 0, 12]])"]},"metadata":{"tags":[]},"execution_count":26}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"CxBt2glqrxSY","executionInfo":{"status":"ok","timestamp":1623861478458,"user_tz":-330,"elapsed":571,"user":{"displayName":"Mr decent","photoUrl":"","userId":"12594074046933137286"}},"outputId":"11580502-0b75-4608-c2f5-507a6887059a"},"source":["cm = confusion_matrix(y_train,y_train_predict)\n","cm"],"execution_count":27,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[37, 0, 0],\n"," [ 0, 34, 3],\n"," [ 0, 3, 35]])"]},"metadata":{"tags":[]},"execution_count":27}]},{"cell_type":"code","metadata":{"id":"04OYxT93r77C"},"source":[""],"execution_count":null,"outputs":[]}]} \ No newline at end of file