{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceType":"competition","sourceId":3004,"databundleVersionId":861823}],"dockerImageVersionId":30096,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2026-03-18T15:57:18.177597Z","iopub.execute_input":"2026-03-18T15:57:18.177941Z","iopub.status.idle":"2026-03-18T15:57:18.187086Z","shell.execute_reply.started":"2026-03-18T15:57:18.177911Z","shell.execute_reply":"2026-03-18T15:57:18.185713Z"}},"outputs":[{"name":"stdout","text":"/kaggle/input/digit-recognizer/sample_submission.csv\n/kaggle/input/digit-recognizer/train.csv\n/kaggle/input/digit-recognizer/test.csv\n","output_type":"stream"}],"execution_count":3},{"cell_type":"code","source":"df = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-18T15:57:18.53934Z","iopub.execute_input":"2026-03-18T15:57:18.539793Z","iopub.status.idle":"2026-03-18T15:57:22.079618Z","shell.execute_reply.started":"2026-03-18T15:57:18.53975Z","shell.execute_reply":"2026-03-18T15:57:22.078677Z"}},"outputs":[],"execution_count":4},{"cell_type":"code","source":"df.shape\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-18T15:57:22.080926Z","iopub.execute_input":"2026-03-18T15:57:22.081173Z","iopub.status.idle":"2026-03-18T15:57:22.090273Z","shell.execute_reply.started":"2026-03-18T15:57:22.081149Z","shell.execute_reply":"2026-03-18T15:57:22.088635Z"}},"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"(42000, 785)"},"metadata":{}}],"execution_count":5},{"cell_type":"code","source":"df.sample()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-18T15:57:22.092206Z","iopub.execute_input":"2026-03-18T15:57:22.092623Z","iopub.status.idle":"2026-03-18T15:57:22.135518Z","shell.execute_reply.started":"2026-03-18T15:57:22.092571Z","shell.execute_reply":"2026-03-18T15:57:22.133608Z"}},"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":" label pixel0 pixel1 pixel2 pixel3 pixel4 pixel5 pixel6 pixel7 \\\n24681 3 0 0 0 0 0 0 0 0 \n\n pixel8 ... pixel774 pixel775 pixel776 pixel777 pixel778 \\\n24681 0 ... 0 0 0 0 0 \n\n pixel779 pixel780 pixel781 pixel782 pixel783 \n24681 0 0 0 0 0 \n\n[1 rows x 785 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
labelpixel0pixel1pixel2pixel3pixel4pixel5pixel6pixel7pixel8...pixel774pixel775pixel776pixel777pixel778pixel779pixel780pixel781pixel782pixel783
246813000000000...0000000000
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1 rows × 785 columns

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"},"metadata":{}}],"execution_count":6},{"cell_type":"code","source":"import matplotlib.pyplot as plt","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-18T15:57:22.137272Z","iopub.execute_input":"2026-03-18T15:57:22.137707Z","iopub.status.idle":"2026-03-18T15:57:22.143525Z","shell.execute_reply.started":"2026-03-18T15:57:22.13767Z","shell.execute_reply":"2026-03-18T15:57:22.141506Z"}},"outputs":[],"execution_count":7},{"cell_type":"code","source":"plt.imshow(df.iloc[13081,1:].values.reshape(28,28))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-18T15:57:22.145633Z","iopub.execute_input":"2026-03-18T15:57:22.146158Z","iopub.status.idle":"2026-03-18T15:57:22.343991Z","shell.execute_reply.started":"2026-03-18T15:57:22.146101Z","shell.execute_reply":"2026-03-18T15:57:22.342203Z"}},"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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= df.iloc[:,1:]\ny = df.iloc[:,0]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-18T15:57:22.419305Z","iopub.execute_input":"2026-03-18T15:57:22.41982Z","iopub.status.idle":"2026-03-18T15:57:22.426541Z","shell.execute_reply.started":"2026-03-18T15:57:22.419772Z","shell.execute_reply":"2026-03-18T15:57:22.424583Z"}},"outputs":[],"execution_count":9},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nX_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=42)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-18T15:57:25.069021Z","iopub.execute_input":"2026-03-18T15:57:25.069485Z","iopub.status.idle":"2026-03-18T15:57:25.417223Z","shell.execute_reply.started":"2026-03-18T15:57:25.069446Z","shell.execute_reply":"2026-03-18T15:57:25.415778Z"}},"outputs":[],"execution_count":10},{"cell_type":"code","source":"X_train.info()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-18T15:57:25.479246Z","iopub.execute_input":"2026-03-18T15:57:25.479567Z","iopub.status.idle":"2026-03-18T15:57:25.537484Z","shell.execute_reply.started":"2026-03-18T15:57:25.479539Z","shell.execute_reply":"2026-03-18T15:57:25.5357Z"}},"outputs":[{"name":"stdout","text":"\nInt64Index: 33600 entries, 34941 to 15795\nColumns: 784 entries, pixel0 to pixel783\ndtypes: int64(784)\nmemory usage: 201.2 MB\n","output_type":"stream"}],"execution_count":11},{"cell_type":"code","source":"X_train.shape","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-18T15:57:26.847092Z","iopub.execute_input":"2026-03-18T15:57:26.847531Z","iopub.status.idle":"2026-03-18T15:57:26.855173Z","shell.execute_reply.started":"2026-03-18T15:57:26.847492Z","shell.execute_reply":"2026-03-18T15:57:26.852989Z"}},"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"(33600, 784)"},"metadata":{}}],"execution_count":12},{"cell_type":"code","source":"from sklearn.neighbors import KNeighborsClassifier","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-18T15:57:28.8244Z","iopub.execute_input":"2026-03-18T15:57:28.82484Z","iopub.status.idle":"2026-03-18T15:57:29.017635Z","shell.execute_reply.started":"2026-03-18T15:57:28.824801Z","shell.execute_reply":"2026-03-18T15:57:29.016131Z"}},"outputs":[],"execution_count":13},{"cell_type":"code","source":"knn = KNeighborsClassifier()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-18T15:57:29.422562Z","iopub.execute_input":"2026-03-18T15:57:29.422964Z","iopub.status.idle":"2026-03-18T15:57:29.428676Z","shell.execute_reply.started":"2026-03-18T15:57:29.422925Z","shell.execute_reply":"2026-03-18T15:57:29.427329Z"}},"outputs":[],"execution_count":14},{"cell_type":"code","source":"knn.fit(X_train,y_train)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-18T15:57:29.748884Z","iopub.execute_input":"2026-03-18T15:57:29.749523Z","iopub.status.idle":"2026-03-18T15:57:29.774908Z","shell.execute_reply.started":"2026-03-18T15:57:29.749472Z","shell.execute_reply":"2026-03-18T15:57:29.773288Z"}},"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"KNeighborsClassifier()"},"metadata":{}}],"execution_count":15},{"cell_type":"code","source":"import time\nstart = time.time()\ny_pred = knn.predict(X_test)\nprint(time.time() - start)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-18T15:57:32.316256Z","iopub.execute_input":"2026-03-18T15:57:32.316705Z","iopub.status.idle":"2026-03-18T15:57:45.64218Z","shell.execute_reply.started":"2026-03-18T15:57:32.316667Z","shell.execute_reply":"2026-03-18T15:57:45.640575Z"}},"outputs":[{"name":"stdout","text":"13.319295644760132\n","output_type":"stream"}],"execution_count":16},{"cell_type":"code","source":"from sklearn.metrics import accuracy_score\naccuracy_score(y_test,y_pred)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-18T15:57:45.644269Z","iopub.execute_input":"2026-03-18T15:57:45.644933Z","iopub.status.idle":"2026-03-18T15:57:45.653662Z","shell.execute_reply.started":"2026-03-18T15:57:45.644843Z","shell.execute_reply":"2026-03-18T15:57:45.651868Z"}},"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"0.9648809523809524"},"metadata":{}}],"execution_count":17},{"cell_type":"code","source":"from sklearn.preprocessing import StandardScaler\nscaler = StandardScaler()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-18T15:57:45.656335Z","iopub.execute_input":"2026-03-18T15:57:45.656884Z","iopub.status.idle":"2026-03-18T15:57:45.665636Z","shell.execute_reply.started":"2026-03-18T15:57:45.656831Z","shell.execute_reply":"2026-03-18T15:57:45.664468Z"}},"outputs":[],"execution_count":18},{"cell_type":"code","source":"X_train = scaler.fit_transform(X_train)\nX_test = scaler.transform(X_test)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-18T15:57:45.667154Z","iopub.execute_input":"2026-03-18T15:57:45.667505Z","iopub.status.idle":"2026-03-18T15:57:46.500713Z","shell.execute_reply.started":"2026-03-18T15:57:45.667471Z","shell.execute_reply":"2026-03-18T15:57:46.498987Z"}},"outputs":[],"execution_count":19},{"cell_type":"code","source":"# PCA \nfrom sklearn.decomposition import PCA\npca = PCA(n_components=200)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-18T15:57:46.502281Z","iopub.execute_input":"2026-03-18T15:57:46.502641Z","iopub.status.idle":"2026-03-18T15:57:46.509635Z","shell.execute_reply.started":"2026-03-18T15:57:46.50261Z","shell.execute_reply":"2026-03-18T15:57:46.506923Z"}},"outputs":[],"execution_count":20},{"cell_type":"code","source":"X_train[1]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-18T15:57:58.76891Z","iopub.execute_input":"2026-03-18T15:57:58.76964Z","iopub.status.idle":"2026-03-18T15:57:58.786972Z","shell.execute_reply.started":"2026-03-18T15:57:58.769593Z","shell.execute_reply":"2026-03-18T15:57:58.785316Z"}},"outputs":[{"execution_count":22,"output_type":"execute_result","data":{"text/plain":"array([ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n -5.90395033e-03, -7.69030123e-03, -5.45552844e-03, -5.45552844e-03,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, -5.45552844e-03, -1.04208431e-02, -1.30479198e-02,\n -1.64226616e-02, -2.02464092e-02, -2.36347357e-02, -2.75343230e-02,\n -3.10564920e-02, -3.15227543e-02, -3.18771916e-02, -2.90165470e-02,\n -2.83993876e-02, -2.75863977e-02, -2.45314036e-02, -1.95599047e-02,\n -1.70076952e-02, -1.04011821e-02, -5.45552844e-03, -5.45552844e-03,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, -5.45552844e-03, -5.45552844e-03,\n -7.99616188e-03, -1.00525477e-02, -1.91579956e-02, -2.81976445e-02,\n -3.91352474e-02, -5.33705657e-02, -6.67140241e-02, -8.45971629e-02,\n -1.02382226e-01, -1.19424078e-01, 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-5.28022129e-01,\n -5.19149944e-01, -4.80359038e-01, -4.22074861e-01, -3.58084264e-01,\n -2.86664021e-01, -2.18010041e-01, -1.56918244e-01, -1.03286619e-01,\n -6.70799114e-02, -3.60641967e-02, -1.60962440e-02, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, -9.92566311e-03, -2.10459848e-02,\n -5.26679004e-02, -8.94451844e-02, -1.38334682e-01, -1.95324072e-01,\n -2.62805716e-01, -3.42039957e-01, -4.26492653e-01, -5.20224612e-01,\n -6.20596691e-01, -7.20886731e-01, -7.99989479e-01, -8.21945567e-01,\n -8.08199901e-01, -7.04669670e-01, -1.70215125e-01, 8.93069275e-01,\n 2.65451297e+00, 3.73620912e+00, 4.00636267e+00, -4.30601921e-03,\n -1.27586500e-01, -7.09998991e-02, -2.93422347e-02, 0.00000000e+00,\n 0.00000000e+00, -5.45552844e-03, -1.39487782e-02, -3.55046180e-02,\n -7.93701440e-02, -1.31255430e-01, -1.98150015e-01, -2.74126990e-01,\n -3.63514190e-01, -4.66791050e-01, -5.78987399e-01, -7.02600917e-01,\n -8.05538187e-01, -8.47531423e-01, -9.49704756e-01, 1.17408808e-01,\n 3.57694256e-01, 1.06718036e+00, 1.43536604e+00, 1.72221402e+00,\n 2.13539465e+00, 2.79607081e+00, 3.77330001e+00, 1.63802021e+00,\n 5.66504016e-02, -1.02092938e-01, -4.65801899e-02, -1.31161894e-02,\n 0.00000000e+00, -1.62233537e-02, -2.79482056e-02, -6.19017414e-02,\n -1.16492109e-01, -1.78203478e-01, -2.57251859e-01, -3.50475833e-01,\n -4.62632208e-01, -5.89698745e-01, -7.32299390e-01, -8.83886741e-01,\n -4.88970768e-01, 1.14582649e+00, 1.07868136e+00, 1.06713980e+00,\n 1.09191681e+00, 1.14865443e+00, 1.26779165e+00, 1.47873333e+00,\n 1.82817155e+00, 2.36354262e+00, 2.95870639e+00, 4.51722564e+00,\n 3.45242885e+00, -1.19086414e-01, -5.53950119e-02, -1.26526148e-02,\n -5.45552844e-03, -2.03930462e-02, -4.67232466e-02, -8.66118541e-02,\n -1.44426061e-01, -2.13240195e-01, -2.99887284e-01, -4.08375012e-01,\n -5.36427076e-01, -6.87575311e-01, -8.14880041e-01, -1.83879558e-02,\n 8.92894781e-01, 1.15296760e+00, 1.16254399e+00, 1.18133345e+00,\n 1.07083363e+00, 1.10885649e+00, 1.00323939e+00, 1.15995381e+00,\n 4.14696363e-01, 5.75609187e-01, -3.11032048e-01, 1.54767118e+00,\n 1.31366541e+00, -1.20368941e-01, -5.68785761e-02, -1.16654023e-02,\n -7.13875132e-03, -2.37054614e-02, -5.53218778e-02, -9.54199367e-02,\n -1.54217005e-01, -2.26238022e-01, -3.23612642e-01, -4.42518623e-01,\n -5.89155776e-01, -7.58847647e-01, -5.67316451e-01, 1.26386865e+00,\n 1.27081524e+00, 1.34811042e+00, 9.26899731e-01, -7.47236413e-04,\n -4.85677922e-01, -3.95988982e-01, -9.98763963e-01, -8.99493513e-01,\n -7.34665158e-01, -5.64694140e-01, -4.15998757e-01, -2.86391158e-01,\n -1.80652111e-01, -1.05866342e-01, -4.78706284e-02, -1.47973371e-02,\n -7.69509751e-03, -2.20626116e-02, -5.45009196e-02, -9.38939428e-02,\n -1.48056356e-01, -2.25740719e-01, -3.30857650e-01, -4.63760672e-01,\n -6.27132836e-01, -7.93826332e-01, -4.12523928e-01, 1.34881778e+00,\n 1.48081189e+00, 1.11765236e+00, -5.29315669e-01, -8.01780663e-01,\n -8.74975970e-01, -9.51287890e-01, -9.62262657e-01, 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-6.14278465e-01, -4.70076200e-01, -3.55735938e-01, -2.57277686e-01,\n -1.54555428e-01, -5.41582125e-02, -2.03835899e-02, -1.00478276e-02,\n 0.00000000e+00, -9.16792242e-03, -2.61302601e-02, -5.64325553e-02,\n -1.22989694e-01, -2.38931893e-01, -3.72777835e-01, -5.33213503e-01,\n -7.02587113e-01, -8.38542702e-01, -8.83102756e-01, 4.49233269e-01,\n 1.56104894e+00, 1.43630388e+00, -3.82842658e-01, -1.11412835e+00,\n -1.18557475e+00, -1.13310869e+00, -9.79100834e-01, -7.75427798e-01,\n -5.95383627e-01, -4.62205783e-01, -3.58362958e-01, -2.64999499e-01,\n -1.66383052e-01, -5.87436524e-02, -1.67928760e-02, -5.45552844e-03,\n 0.00000000e+00, -5.45552844e-03, -1.71288570e-02, -4.89998601e-02,\n -1.27079897e-01, -2.57403857e-01, -3.93156649e-01, -5.50200157e-01,\n -7.07373029e-01, -8.23908905e-01, -8.67085989e-01, -7.48146841e-01,\n 6.18104511e-01, 1.23680871e+00, 2.44872710e-01, -1.27508481e+00,\n -1.24983050e+00, -1.14141291e+00, -9.65982505e-01, -7.64484308e-01,\n -5.98333123e-01, -4.74893383e-01, -3.69888246e-01, -2.73803136e-01,\n -1.74809256e-01, -6.80302334e-02, -2.29458528e-02, -5.76648396e-03,\n 0.00000000e+00, 0.00000000e+00, -1.80475517e-02, -5.18161961e-02,\n -1.37998510e-01, -2.75732197e-01, -4.09616435e-01, -5.51667249e-01,\n -6.90112866e-01, -7.91480313e-01, -8.41087955e-01, -8.67215080e-01,\n -5.00117368e-01, 1.15122154e+00, 9.06351281e-01, -5.38646951e-01,\n -1.18020361e+00, -1.07790771e+00, -9.26204550e-01, -7.55937323e-01,\n -6.13695170e-01, -4.90602483e-01, -3.81385019e-01, -2.78069709e-01,\n -1.75165168e-01, -7.52264300e-02, -2.88970582e-02, -1.07974377e-02,\n 0.00000000e+00, -5.45552844e-03, -2.15298780e-02, -6.18690607e-02,\n -1.56198622e-01, -2.94215357e-01, -4.17908970e-01, -5.44675772e-01,\n -6.59051077e-01, -7.40876424e-01, -7.88955126e-01, -8.31991119e-01,\n -8.16390348e-01, -2.88543169e-01, 9.46650410e-01, 5.18789874e-01,\n -1.07544478e+00, -9.97149415e-01, -8.84056051e-01, -7.49739777e-01,\n -6.21669826e-01, -4.96418407e-01, -3.79803387e-01, -2.71958470e-01,\n -1.70506389e-01, -8.05697280e-02, -3.41688935e-02, -1.23847838e-02,\n 0.00000000e+00, -7.70504551e-03, -2.54675979e-02, -7.58701520e-02,\n -1.76302631e-01, -3.07763192e-01, 8.85495313e-02, -5.34387781e-01,\n -6.23236968e-01, -6.85682589e-01, -7.23317260e-01, -7.61778788e-01,\n -8.20581088e-01, -9.14505079e-01, -4.03541910e-01, 1.05662350e-01,\n -1.01053682e+00, -9.54463046e-01, -8.64668197e-01, -7.48852868e-01,\n -6.20620038e-01, -4.88543002e-01, -3.69151797e-01, -2.58613560e-01,\n -1.64820774e-01, -8.68412234e-02, -3.74919669e-02, -1.52141629e-02,\n -7.69015894e-03, -8.90953516e-03, -3.36981320e-02, -9.36210730e-02,\n -1.96889630e-01, 4.56826966e-01, 2.49916769e+00, 1.70565178e+00,\n -2.35725035e-01, -6.59899030e-01, -6.96218324e-01, -7.27456815e-01,\n -7.77124905e-01, -8.64473566e-01, -9.54686633e-01, -4.90854188e-01,\n -2.09286709e-01, -9.63066101e-01, -8.66976168e-01, -7.42873658e-01,\n -6.03282171e-01, -4.67235108e-01, -3.48434354e-01, -2.43325150e-01,\n -1.54411738e-01, -8.36411098e-02, -3.14992944e-02, -9.08945554e-03,\n 0.00000000e+00, -7.96134663e-03, -4.04687313e-02, -1.04764556e-01,\n -2.10459495e-01, 9.94539026e-01, 2.65922339e+00, 2.18559497e+00,\n 1.71126011e+00, 5.66563166e-01, -7.38474505e-01, -7.69895940e-01,\n -8.22629864e-01, -9.12386438e-01, -1.00943231e+00, 5.05574347e-01,\n 1.13368616e+00, -6.01958285e-01, -8.55892265e-01, -7.09640668e-01,\n -5.62015874e-01, -4.30472993e-01, -3.17187290e-01, -2.20721733e-01,\n -1.39513138e-01, -7.81414237e-02, -2.88564614e-02, -5.45552844e-03,\n 0.00000000e+00, -9.21767279e-03, -4.42097982e-02, -1.09630763e-01,\n -2.09931844e-01, -2.64726839e-01, 2.09975722e+00, 2.11065429e+00,\n 1.79160755e+00, 1.57304319e+00, 1.04534182e+00, -5.80032661e-01,\n -9.58078665e-01, -1.04952501e+00, -1.12832857e+00, 1.53598586e-01,\n 1.19599608e+00, -5.09129780e-01, -7.95936913e-01, -6.35310069e-01,\n -4.91515197e-01, -3.70571849e-01, -2.70458715e-01, -1.86369847e-01,\n -1.20743430e-01, -6.86757258e-02, -2.66999331e-02, 0.00000000e+00,\n -5.45552844e-03, -1.06512755e-02, -4.13792608e-02, -1.01310357e-01,\n -1.89564262e-01, -2.98620928e-01, -1.76228928e-01, 1.11966496e+00,\n 1.76539177e+00, 1.51325507e+00, 1.36473103e+00, 1.12462567e+00,\n 7.41754623e-01, -4.31525413e-01, -4.98402116e-01, 7.16477700e-01,\n 1.27601278e+00, 9.30700180e-02, -6.74164080e-01, -5.25130070e-01,\n -3.99962213e-01, -2.96507965e-01, -2.12217759e-01, -1.49806500e-01,\n -9.76559366e-02, -5.21330783e-02, -2.18183094e-02, -5.45552844e-03,\n -5.45552844e-03, -7.09153617e-03, -3.24066035e-02, -8.02857989e-02,\n -1.47180133e-01, -2.41635751e-01, -3.57906884e-01, -4.61366152e-01,\n 3.06077350e-01, 1.20223063e+00, 1.37902453e+00, 1.24140689e+00,\n 1.16339752e+00, 1.09955319e+00, 1.13273899e+00, 1.30038351e+00,\n 1.52825210e+00, 1.05972264e+00, -5.25750874e-01, -4.02394553e-01,\n -3.00998048e-01, -2.18025374e-01, -1.56537623e-01, -1.08481401e-01,\n -6.95431461e-02, -3.99683813e-02, -1.40170989e-02, -5.45552844e-03,\n 0.00000000e+00, 0.00000000e+00, -2.21361623e-02, -5.32067004e-02,\n -9.88927976e-02, -1.65695461e-01, -2.59439656e-01, -3.68996786e-01,\n -4.88753718e-01, -3.72919804e-01, 7.20590779e-01, 1.48125771e+00,\n 1.38568492e+00, 1.39001203e+00, 1.49557961e+00, 1.71729598e+00,\n 2.07596721e+00, 9.10937070e-01, -3.80505278e-01, -2.87125674e-01,\n -2.10995701e-01, -1.49966881e-01, -1.04580284e-01, -7.02952385e-02,\n -4.09461236e-02, -2.31064746e-02, -9.36494480e-03, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, -9.97027553e-03, -2.93887318e-02,\n -5.65463900e-02, -9.57419095e-02, -1.51939260e-01, -2.20638651e-01,\n -2.99843912e-01, -3.84152008e-01, -4.51926061e-01, -4.70869038e-01,\n 7.02050644e-01, 2.22255333e+00, 2.04907698e+00, 1.17070267e+00,\n 9.33144022e-01, -3.10020781e-01, -2.56270128e-01, -1.95224870e-01,\n -1.44343175e-01, -1.00169011e-01, -6.81853053e-02, -4.61628831e-02,\n -2.29805256e-02, -1.25329382e-02, -6.35135263e-03, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, -7.69266997e-03, -8.15231548e-03,\n -2.74867439e-02, -5.18267708e-02, -8.55445180e-02, -1.25252126e-01,\n -1.71483095e-01, -2.18436761e-01, -2.62057075e-01, -2.92711668e-01,\n -3.07948987e-01, -3.07214468e-01, -2.92487529e-01, -2.67563929e-01,\n -2.38330964e-01, -2.06451743e-01, -1.67124615e-01, -1.30981728e-01,\n -9.48973385e-02, -6.63490128e-02, -4.70156166e-02, -2.93951437e-02,\n -1.32121276e-02, -7.58404282e-03, -5.45552844e-03, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n -1.45321194e-02, -2.97899280e-02, -5.36585967e-02, -7.72441916e-02,\n -1.03630105e-01, -1.27523415e-01, -1.52753324e-01, -1.73899962e-01,\n -1.84527816e-01, -1.83864249e-01, -1.72794905e-01, -1.56728253e-01,\n -1.39114135e-01, -1.19945048e-01, -9.82046652e-02, -7.90011674e-02,\n -5.61767763e-02, -3.92412430e-02, -2.55668925e-02, -1.50407547e-02,\n -6.86699055e-03, -5.45552844e-03, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, -7.23101260e-03, -1.62716813e-02, -2.10458804e-02,\n -2.40297404e-02, -2.93397190e-02, -4.02427417e-02, -4.50935109e-02,\n -5.08151877e-02, -5.43929287e-02, -5.98430796e-02, -5.62578176e-02,\n -5.13761636e-02, -4.21122838e-02, -3.47693412e-02, -2.67851641e-02,\n -1.87571742e-02, -1.28272425e-02, -1.01732533e-02, -7.71111411e-03,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00])"},"metadata":{}}],"execution_count":22},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"X_train_trf = pca.fit_transform(X_train)\nX_test_trf = pca.transform(X_test)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-18T15:58:17.655992Z","iopub.execute_input":"2026-03-18T15:58:17.656464Z","iopub.status.idle":"2026-03-18T15:58:22.911477Z","shell.execute_reply.started":"2026-03-18T15:58:17.656391Z","shell.execute_reply":"2026-03-18T15:58:22.909662Z"}},"outputs":[],"execution_count":23},{"cell_type":"code","source":"X_train_trf.shape","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-18T15:58:52.5477Z","iopub.execute_input":"2026-03-18T15:58:52.548074Z","iopub.status.idle":"2026-03-18T15:58:52.554786Z","shell.execute_reply.started":"2026-03-18T15:58:52.548042Z","shell.execute_reply":"2026-03-18T15:58:52.553577Z"}},"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"(33600, 200)"},"metadata":{}}],"execution_count":24},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"X_train_trf[1] ","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-18T15:59:37.751498Z","iopub.execute_input":"2026-03-18T15:59:37.751918Z","iopub.status.idle":"2026-03-18T15:59:37.761083Z","shell.execute_reply.started":"2026-03-18T15:59:37.751868Z","shell.execute_reply":"2026-03-18T15:59:37.759695Z"}},"outputs":[{"execution_count":25,"output_type":"execute_result","data":{"text/plain":"array([-6.76980207e-01, -6.75362496e+00, -2.33590112e+00, -7.16966972e+00,\n 2.10908082e+00, -2.89246786e+00, 4.51783703e-01, -1.14506930e+00,\n 3.47578537e+00, 4.65032191e-01, 2.70891774e+00, -1.78356183e+00,\n 6.58951439e+00, -6.65523600e-02, 1.11382285e+00, 1.35941595e+00,\n 4.99548341e-01, 4.65163638e-01, -6.19709166e-01, 3.27497453e+00,\n 2.54491052e-01, -2.47446078e-01, 2.22633896e-01, -1.42705883e+00,\n 1.54580832e+00, -5.06807076e-01, -2.23294715e+00, -3.27182561e-01,\n -1.79216264e+00, 7.58304440e-01, 1.35359333e+00, -8.59813247e-01,\n 1.48427279e+00, -1.05903101e+00, -1.01351772e-01, -2.94391412e-01,\n -2.64745570e-01, 1.52241437e+00, 1.01491991e-01, 3.46857543e-01,\n -1.63219575e-01, 5.51400196e-01, 9.14931859e-01, 2.34801880e+00,\n -7.70350484e-01, 2.54209676e-01, -3.58624205e+00, -3.46033393e-01,\n 2.47507120e-02, 4.77380837e-01, -8.53839992e-01, 5.55812800e-01,\n -3.75598799e-01, 1.63130873e+00, -1.54772063e+00, -8.33062098e-01,\n 1.05916819e+00, -3.79874829e-01, -1.41368984e-01, 2.09353551e-01,\n 2.85611910e-01, -7.69569563e-01, 1.40060100e+00, -1.03958361e+00,\n 2.02821948e+00, 8.49914829e-01, -7.90495088e-01, 9.98418622e-01,\n 2.26630750e+00, 6.51634783e-01, -4.97080268e-01, -1.57351939e+00,\n 1.02767672e-01, -1.45012188e+00, -2.18844017e-01, -2.31809186e+00,\n 4.07967733e-01, -1.27549224e-01, -2.55794354e+00, 4.33889465e-01,\n 1.34056859e+00, -1.55202653e-01, 5.53833728e-01, -6.46448123e-01,\n -5.14014548e-01, 1.78970867e+00, 6.59769697e-01, 9.75354157e-01,\n 3.51228791e-01, -6.82255172e-01, -1.57605032e+00, 3.56878517e-01,\n -5.56366355e-01, -1.33401740e+00, -9.60895360e-01, 3.48070817e-01,\n 4.40849094e-01, 1.64965329e-01, 7.58399461e-01, -9.52717955e-01,\n 6.14216169e-01, -1.97412876e-01, 1.45389202e+00, -6.96218383e-01,\n 2.99261514e-02, -7.71237427e-01, 4.91308740e-01, 1.83207392e-01,\n 7.65910755e-01, 4.96715508e-01, -2.35293369e-02, 5.71956795e-01,\n -8.42742524e-01, -6.05269825e-01, 8.59407338e-01, 3.32664185e-01,\n 3.90055044e-01, -8.64061098e-01, -2.66551391e-01, 1.21468375e+00,\n -6.88035587e-01, -3.54913209e-01, -4.26560727e-02, 6.59146672e-02,\n 5.69597298e-01, 4.88719279e-01, -4.75981102e-01, 2.38555246e-02,\n 8.26510701e-02, -5.63874594e-01, -6.94375928e-01, -6.86057295e-01,\n -6.53632263e-01, 1.02215672e+00, 7.57113107e-01, 3.23722034e-01,\n 2.19453404e-01, 3.47104769e-01, 9.77802539e-01, -5.51912045e-01,\n 4.03720324e-02, 7.23320223e-01, 1.44434216e+00, -7.46390658e-02,\n 1.83360847e-01, 6.90829917e-01, 7.47424633e-01, -4.52138281e-02,\n -7.43412502e-01, 7.35780293e-01, -6.43980755e-01, -3.07677711e-01,\n 4.73443152e-01, -1.37748779e-01, 4.30977977e-01, -1.05937872e-01,\n -1.59764767e-01, 2.17644147e-01, 2.32951066e-01, 1.57316780e-02,\n -6.94573183e-02, 4.58551892e-01, -2.80055113e-01, -5.19088971e-02,\n -1.25824471e+00, -3.29123930e-01, -5.00177307e-01, 2.74169092e-01,\n -2.39977410e-01, -1.06645360e+00, 3.66932180e-01, -6.26503692e-01,\n -1.53080734e-01, -1.42043390e+00, 5.00397900e-01, -3.03213761e-01,\n -2.46269035e-01, 7.26730677e-01, -7.71300311e-03, -2.18708269e-01,\n 1.03055089e+00, 9.49931498e-01, 6.41108199e-01, 1.69366309e-01,\n -1.35979637e-01, -6.30524028e-02, -2.90263587e-01, 4.23082732e-01,\n -2.02198870e-02, 1.65564428e-01, -6.97304742e-03, -3.82534171e-02,\n 2.76769552e-01, -1.63532248e-01, -2.25652073e-01, -8.73007369e-02,\n 2.30471343e-01, 6.19688049e-02, -7.78979351e-01, -6.80292965e-02])"},"metadata":{}}],"execution_count":25},{"cell_type":"raw","source":"knn = KNeighborsClassifier()","metadata":{"execution":{"iopub.status.busy":"2025-09-29T19:34:10.61035Z","iopub.execute_input":"2025-09-29T19:34:10.610727Z","iopub.status.idle":"2025-09-29T19:34:10.61515Z","shell.execute_reply.started":"2025-09-29T19:34:10.610692Z","shell.execute_reply":"2025-09-29T19:34:10.613779Z"}}},{"cell_type":"code","source":"knn.fit(X_train_trf,y_train)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-29T19:34:11.136451Z","iopub.execute_input":"2025-09-29T19:34:11.136795Z","iopub.status.idle":"2025-09-29T19:34:11.157394Z","shell.execute_reply.started":"2025-09-29T19:34:11.136764Z","shell.execute_reply":"2025-09-29T19:34:11.156061Z"}},"outputs":[{"execution_count":27,"output_type":"execute_result","data":{"text/plain":"KNeighborsClassifier()"},"metadata":{}}],"execution_count":27},{"cell_type":"code","source":"start = time.time()\ny_pred = knn.predict(X_test_trf)\nprint(time.time() - start)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-29T19:34:18.101225Z","iopub.execute_input":"2025-09-29T19:34:18.101584Z","iopub.status.idle":"2025-09-29T19:34:25.735041Z","shell.execute_reply.started":"2025-09-29T19:34:18.101554Z","shell.execute_reply":"2025-09-29T19:34:25.733404Z"}},"outputs":[{"name":"stdout","text":"7.6283605098724365\n","output_type":"stream"}],"execution_count":28},{"cell_type":"code","source":"accuracy_score(y_test,y_pred)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-29T19:34:54.967368Z","iopub.execute_input":"2025-09-29T19:34:54.967742Z","iopub.status.idle":"2025-09-29T19:34:54.976296Z","shell.execute_reply.started":"2025-09-29T19:34:54.967705Z","shell.execute_reply":"2025-09-29T19:34:54.975095Z"}},"outputs":[{"execution_count":29,"output_type":"execute_result","data":{"text/plain":"0.950595238095238"},"metadata":{}}],"execution_count":29},{"cell_type":"code","source":"for i in range(1,785):\n pca = PCA(n_components=i)\n X_train_trf = pca.fit_transform(X_train)\n X_test_trf = pca.transform(X_test)\n \n knn = KNeighborsClassifier()\n \n knn.fit(X_train_trf,y_train)\n \n y_pred = knn.predict(X_test_trf)\n \n print(accuracy_score(y_test,y_pred))\n ","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-29T19:35:24.977131Z","iopub.execute_input":"2025-09-29T19:35:24.977552Z","iopub.status.idle":"2025-09-29T19:36:36.616618Z","shell.execute_reply.started":"2025-09-29T19:35:24.977515Z","shell.execute_reply":"2025-09-29T19:36:36.614696Z"}},"outputs":[{"name":"stdout","text":"0.2580952380952381\n0.32404761904761903\n0.5101190476190476\n0.6661904761904762\n0.7378571428571429\n0.8227380952380953\n0.8436904761904762\n0.8726190476190476\n0.8872619047619048\n0.9060714285714285\n0.9108333333333334\n0.9180952380952381\n0.9270238095238095\n0.9341666666666667\n0.9385714285714286\n0.9384523809523809\n","output_type":"stream"},{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 8\u001b[0m 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\u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maccuracy_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/sklearn/neighbors/_classification.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 195\u001b[0m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'csr'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 196\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 197\u001b[0;31m \u001b[0mneigh_dist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mneigh_ind\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkneighbors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 198\u001b[0m \u001b[0mclasses_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 199\u001b[0m \u001b[0m_y\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_y\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/sklearn/neighbors/_base.py\u001b[0m in \u001b[0;36mkneighbors\u001b[0;34m(self, X, n_neighbors, return_distance)\u001b[0m\n\u001b[1;32m 706\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit_X\u001b[0m\u001b[0;34m,\u001b[0m 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Y, Y_norm_squared, squared, X_norm_squared)\u001b[0m\n\u001b[1;32m 314\u001b[0m \u001b[0mdistances\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mXX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 315\u001b[0m \u001b[0mdistances\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mYY\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 316\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaximum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdistances\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdistances\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 317\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 318\u001b[0m \u001b[0;31m# Ensure that distances between vectors and themselves are set to 0.0.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "],"ename":"KeyboardInterrupt","evalue":"","output_type":"error"}],"execution_count":30},{"cell_type":"code","source":"# transforming to a 2D coordinate system\npca = PCA(n_components=2)\nX_train_trf = pca.fit_transform(X_train)\nX_test_trf = pca.transform(X_test)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-29T19:37:34.271142Z","iopub.execute_input":"2025-09-29T19:37:34.271541Z","iopub.status.idle":"2025-09-29T19:37:36.815659Z","shell.execute_reply.started":"2025-09-29T19:37:34.271508Z","shell.execute_reply":"2025-09-29T19:37:36.814378Z"}},"outputs":[],"execution_count":32},{"cell_type":"code","source":"X_train_trf","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-29T19:37:37.292932Z","iopub.execute_input":"2025-09-29T19:37:37.29332Z","iopub.status.idle":"2025-09-29T19:37:37.299853Z","shell.execute_reply.started":"2025-09-29T19:37:37.293285Z","shell.execute_reply":"2025-09-29T19:37:37.29878Z"}},"outputs":[{"execution_count":33,"output_type":"execute_result","data":{"text/plain":"array([[-2.7186608 , -0.49001395],\n [-0.67689657, -6.75354605],\n [-3.03290614, 6.51062215],\n ...,\n [ 2.14882989, 0.78102877],\n [ 1.05954436, 0.94759599],\n [17.7025697 , 1.96169283]])"},"metadata":{}}],"execution_count":33},{"cell_type":"code","source":"import plotly.express as px\ny_train_trf = y_train.astype(str)\nfig = px.scatter(x=X_train_trf[:,0],\n y=X_train_trf[:,1],\n color=y_train_trf,\n color_discrete_sequence=px.colors.qualitative.G10\n )\nfig.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-29T19:37:39.824655Z","iopub.execute_input":"2025-09-29T19:37:39.825034Z","iopub.status.idle":"2025-09-29T19:37:43.124808Z","shell.execute_reply.started":"2025-09-29T19:37:39.824997Z","shell.execute_reply":"2025-09-29T19:37:43.122992Z"}},"outputs":[{"output_type":"display_data","data":{"text/html":" \n "},"metadata":{}},{"output_type":"display_data","data":{"text/html":"
"},"metadata":{}}],"execution_count":34},{"cell_type":"code","source":"# transforming in 3D\npca = PCA(n_components=3)\nX_train_trf = pca.fit_transform(X_train)\nX_test_trf = pca.transform(X_test)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-29T19:38:06.506034Z","iopub.execute_input":"2025-09-29T19:38:06.506517Z","iopub.status.idle":"2025-09-29T19:38:08.9929Z","shell.execute_reply.started":"2025-09-29T19:38:06.50648Z","shell.execute_reply":"2025-09-29T19:38:08.991565Z"}},"outputs":[],"execution_count":35},{"cell_type":"code","source":"X_train_trf","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-29T19:38:08.994723Z","iopub.execute_input":"2025-09-29T19:38:08.995205Z","iopub.status.idle":"2025-09-29T19:38:09.005504Z","shell.execute_reply.started":"2025-09-29T19:38:08.995155Z","shell.execute_reply":"2025-09-29T19:38:09.004305Z"}},"outputs":[{"execution_count":36,"output_type":"execute_result","data":{"text/plain":"array([[-2.71864805, -0.4898199 , 1.13581691],\n [-0.67696867, -6.75373597, -2.33656363],\n [-3.03321172, 6.5096887 , 7.49195782],\n ...,\n [ 2.14885183, 0.78083602, -0.74748829],\n [ 1.05955176, 0.94769263, 3.95029296],\n [17.70259007, 1.96176146, -4.94315711]])"},"metadata":{}}],"execution_count":36},{"cell_type":"code","source":"import plotly.express as px\ny_train_trf = y_train.astype(str)\nfig = px.scatter_3d(df, x=X_train_trf[:,0], y=X_train_trf[:,1], z=X_train_trf[:,2],\n color=y_train_trf)\nfig.update_layout(\n margin=dict(l=20, r=20, t=20, b=20),\n paper_bgcolor=\"LightSteelBlue\",\n)\nfig.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-29T19:38:09.232788Z","iopub.execute_input":"2025-09-29T19:38:09.233397Z","iopub.status.idle":"2025-09-29T19:38:09.605045Z","shell.execute_reply.started":"2025-09-29T19:38:09.233351Z","shell.execute_reply":"2025-09-29T19:38:09.603831Z"}},"outputs":[{"output_type":"display_data","data":{"text/html":"
"},"metadata":{}}],"execution_count":37},{"cell_type":"code","source":"pca.explained_variance_\n# Eigen values","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-31T18:44:51.074555Z","iopub.execute_input":"2024-12-31T18:44:51.074935Z","iopub.status.idle":"2024-12-31T18:44:51.081063Z","shell.execute_reply.started":"2024-12-31T18:44:51.074899Z","shell.execute_reply":"2024-12-31T18:44:51.079985Z"}},"outputs":[{"execution_count":23,"output_type":"execute_result","data":{"text/plain":"array([40.67111198, 29.17023348, 26.7445903 ])"},"metadata":{}}],"execution_count":23},{"cell_type":"code","source":"pca.components_.shape\n# Eigen vectors","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-31T18:45:25.663857Z","iopub.execute_input":"2024-12-31T18:45:25.664225Z","iopub.status.idle":"2024-12-31T18:45:25.669866Z","shell.execute_reply.started":"2024-12-31T18:45:25.664195Z","shell.execute_reply":"2024-12-31T18:45:25.668911Z"}},"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"(3, 784)"},"metadata":{}}],"execution_count":24},{"cell_type":"code","source":"pca.explained_variance_ratio_","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-31T18:46:37.367098Z","iopub.execute_input":"2024-12-31T18:46:37.367579Z","iopub.status.idle":"2024-12-31T18:46:37.376816Z","shell.execute_reply.started":"2024-12-31T18:46:37.367534Z","shell.execute_reply":"2024-12-31T18:46:37.375678Z"}},"outputs":[{"execution_count":25,"output_type":"execute_result","data":{"text/plain":"array([0.05785192, 0.0414927 , 0.03804238])"},"metadata":{}}],"execution_count":25},{"cell_type":"code","source":"pca = PCA(n_components=None)\nX_train_trf = pca.fit_transform(X_train)\nX_test_trf = pca.transform(X_test)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-31T18:47:41.553666Z","iopub.execute_input":"2024-12-31T18:47:41.554037Z","iopub.status.idle":"2024-12-31T18:47:45.838136Z","shell.execute_reply.started":"2024-12-31T18:47:41.554008Z","shell.execute_reply":"2024-12-31T18:47:45.836771Z"}},"outputs":[],"execution_count":26},{"cell_type":"code","source":"pca.explained_variance_.shape","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-31T18:47:48.937253Z","iopub.execute_input":"2024-12-31T18:47:48.937586Z","iopub.status.idle":"2024-12-31T18:47:48.94335Z","shell.execute_reply.started":"2024-12-31T18:47:48.937557Z","shell.execute_reply":"2024-12-31T18:47:48.94236Z"}},"outputs":[{"execution_count":27,"output_type":"execute_result","data":{"text/plain":"(784,)"},"metadata":{}}],"execution_count":27},{"cell_type":"code","source":"pca.components_.shape","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-31T18:47:50.355742Z","iopub.execute_input":"2024-12-31T18:47:50.356094Z","iopub.status.idle":"2024-12-31T18:47:50.361675Z","shell.execute_reply.started":"2024-12-31T18:47:50.356065Z","shell.execute_reply":"2024-12-31T18:47:50.360531Z"}},"outputs":[{"execution_count":28,"output_type":"execute_result","data":{"text/plain":"(784, 784)"},"metadata":{}}],"execution_count":28},{"cell_type":"code","source":"np.cumsum(pca.explained_variance_ratio_)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-31T18:48:01.334783Z","iopub.execute_input":"2024-12-31T18:48:01.335202Z","iopub.status.idle":"2024-12-31T18:48:01.349681Z","shell.execute_reply.started":"2024-12-31T18:48:01.335167Z","shell.execute_reply":"2024-12-31T18:48:01.348713Z"}},"outputs":[{"execution_count":29,"output_type":"execute_result","data":{"text/plain":"array([0.05785192, 0.09934462, 0.13738701, 0.16704964, 0.19286525,\n 0.21541506, 0.23514574, 0.25289854, 0.26858504, 0.28294568,\n 0.29664822, 0.30892077, 0.32038989, 0.33162017, 0.34220546,\n 0.35240477, 0.36198153, 0.37140862, 0.38053351, 0.38943521,\n 0.39783462, 0.40603525, 0.41388828, 0.42145568, 0.42882829,\n 0.43584714, 0.44282633, 0.44953738, 0.45593583, 0.4622392 ,\n 0.46839271, 0.4744166 , 0.48025334, 0.48603881, 0.49176214,\n 0.49722614, 0.50265822, 0.50793994, 0.51307376, 0.51801392,\n 0.52289063, 0.52771041, 0.53239246, 0.53698588, 0.54156249,\n 0.54605468, 0.5505015 , 0.55491691, 0.55926803, 0.56358836,\n 0.56780645, 0.57198888, 0.57606882, 0.58008887, 0.58407223,\n 0.58801822, 0.59188694, 0.59571889, 0.59948441, 0.60322635,\n 0.60687487, 0.6104829 , 0.61402176, 0.61750117, 0.62094441,\n 0.62432378, 0.62767368, 0.63099377, 0.63426257, 0.63746765,\n 0.64065542, 0.64376931, 0.64686564, 0.64992395, 0.65296104,\n 0.6559748 , 0.65895992, 0.66192105, 0.66483654, 0.66772536,\n 0.67060106, 0.6734651 , 0.67629295, 0.67910917, 0.68191711,\n 0.68468646, 0.68743018, 0.69013786, 0.69282727, 0.69548652,\n 0.6981275 , 0.70075516, 0.70335672, 0.7059274 , 0.70848658,\n 0.71103639, 0.71354984, 0.71605747, 0.71852606, 0.72096777,\n 0.72336938, 0.72575703, 0.72811046, 0.73043803, 0.73274819,\n 0.73504902, 0.73730548, 0.73955195, 0.74179168, 0.7440142 ,\n 0.74620053, 0.7483705 , 0.75051238, 0.7526383 , 0.75471786,\n 0.75677534, 0.758826 , 0.76083529, 0.76283446, 0.76482579,\n 0.76678392, 0.76873756, 0.77066405, 0.77258 , 0.77447592,\n 0.77635207, 0.77822334, 0.78007113, 0.78191268, 0.78373974,\n 0.78553564, 0.78731499, 0.78907371, 0.79080624, 0.79253247,\n 0.79425224, 0.79594416, 0.79762874, 0.79928778, 0.80093672,\n 0.80256446, 0.80416702, 0.80576592, 0.80735478, 0.80893549,\n 0.81050042, 0.81203815, 0.81356986, 0.81508446, 0.81658796,\n 0.81807414, 0.819548 , 0.82101567, 0.82247554, 0.82393208,\n 0.82538281, 0.8268107 , 0.82823494, 0.82965372, 0.83107178,\n 0.83248637, 0.83389408, 0.83528722, 0.83667705, 0.83806218,\n 0.83942504, 0.84078165, 0.84212383, 0.84346439, 0.84479466,\n 0.84611832, 0.84742054, 0.84871121, 0.84999906, 0.85127623,\n 0.85253753, 0.85378389, 0.85502004, 0.85624547, 0.85745736,\n 0.85866188, 0.85984637, 0.86101778, 0.86217663, 0.86332437,\n 0.86445325, 0.86558088, 0.8666952 , 0.86779646, 0.86888392,\n 0.86996579, 0.87104442, 0.87211364, 0.87317315, 0.87423006,\n 0.87527947, 0.87631514, 0.87734044, 0.87835265, 0.87936295,\n 0.88035936, 0.88134553, 0.88232488, 0.8832977 , 0.88427021,\n 0.88523831, 0.88619619, 0.88714161, 0.88807599, 0.88898991,\n 0.88989857, 0.89080345, 0.8916966 , 0.89258607, 0.89346635,\n 0.89433434, 0.89518395, 0.89603076, 0.89686706, 0.89768939,\n 0.89850803, 0.89932265, 0.90013248, 0.90093596, 0.90173482,\n 0.90251607, 0.90329546, 0.90406669, 0.90482606, 0.90557771,\n 0.90632501, 0.90706619, 0.90780234, 0.90853143, 0.90925438,\n 0.90996846, 0.91067525, 0.91137687, 0.9120734 , 0.91276691,\n 0.9134538 , 0.91412926, 0.91480048, 0.9154622 , 0.91612113,\n 0.91677252, 0.9174119 , 0.91804297, 0.91866944, 0.91929542,\n 0.91991348, 0.92052815, 0.9211373 , 0.92173916, 0.92233446,\n 0.92292101, 0.92350484, 0.92408706, 0.92466241, 0.92523367,\n 0.92579612, 0.926357 , 0.92691164, 0.92746163, 0.92800811,\n 0.92854974, 0.92908623, 0.92962044, 0.93015238, 0.93068315,\n 0.93120494, 0.93172158, 0.93223752, 0.93274696, 0.93325128,\n 0.93375434, 0.93425382, 0.93475251, 0.93524534, 0.93572834,\n 0.93620936, 0.93668669, 0.93716108, 0.93763137, 0.93809362,\n 0.93855416, 0.93901132, 0.93946516, 0.93991563, 0.94035889,\n 0.94080031, 0.94123623, 0.94166634, 0.94209054, 0.94251334,\n 0.9429314 , 0.94334561, 0.94375684, 0.94416693, 0.94457215,\n 0.94497536, 0.94537442, 0.94577212, 0.94616716, 0.94656128,\n 0.94694734, 0.94733105, 0.94771196, 0.94809051, 0.94846724,\n 0.94883789, 0.94920803, 0.94957156, 0.94992961, 0.95028537,\n 0.9506398 , 0.95099343, 0.9513436 , 0.95168786, 0.95202923,\n 0.9523691 , 0.95270547, 0.95303995, 0.95337297, 0.95370415,\n 0.95403451, 0.95436355, 0.95468948, 0.95500992, 0.95532964,\n 0.95564747, 0.95596177, 0.9562738 , 0.9565845 , 0.95689448,\n 0.95720249, 0.95750637, 0.95780805, 0.95810729, 0.95840523,\n 0.95870234, 0.9589963 , 0.95928521, 0.9595721 , 0.95985691,\n 0.9601397 , 0.9604215 , 0.96070051, 0.96097721, 0.96125114,\n 0.9615244 , 0.96179631, 0.96206536, 0.9623333 , 0.96259692,\n 0.96285944, 0.9631192 , 0.96337626, 0.96363152, 0.9638845 ,\n 0.96413674, 0.96438723, 0.96463661, 0.96488486, 0.96513187,\n 0.96537698, 0.96562037, 0.96586205, 0.96610289, 0.96634084,\n 0.96657869, 0.96681272, 0.96704657, 0.96727896, 0.96750876,\n 0.96773538, 0.96796067, 0.96818505, 0.968409 , 0.96863013,\n 0.96885103, 0.96907065, 0.96928885, 0.96950343, 0.96971703,\n 0.96992842, 0.97013804, 0.9703469 , 0.97055541, 0.97076129,\n 0.97096617, 0.9711703 , 0.97137304, 0.97157367, 0.97177272,\n 0.97196898, 0.97216465, 0.97235955, 0.97255284, 0.97274564,\n 0.97293686, 0.97312619, 0.97331534, 0.97350238, 0.97368816,\n 0.97387195, 0.9740554 , 0.9742372 , 0.97441869, 0.9745998 ,\n 0.97477959, 0.97495709, 0.97513385, 0.9753098 , 0.97548507,\n 0.97565906, 0.97583276, 0.97600475, 0.97617632, 0.97634726,\n 0.97651789, 0.97668656, 0.97685386, 0.9770202 , 0.97718552,\n 0.97735024, 0.97751419, 0.97767708, 0.97783857, 0.97799968,\n 0.97816011, 0.97831997, 0.97847803, 0.97863471, 0.97879125,\n 0.97894717, 0.97910129, 0.97925534, 0.9794075 , 0.97955873,\n 0.97970896, 0.97985833, 0.98000708, 0.9801544 , 0.98030149,\n 0.98044778, 0.98059235, 0.98073638, 0.98088019, 0.98102317,\n 0.98116567, 0.98130745, 0.98144769, 0.98158734, 0.98172653,\n 0.98186487, 0.9820027 , 0.98213961, 0.98227604, 0.98241149,\n 0.98254653, 0.98268118, 0.98281498, 0.98294806, 0.98307997,\n 0.98321127, 0.98334209, 0.98347147, 0.98360011, 0.98372841,\n 0.98385617, 0.983983 , 0.98410924, 0.98423434, 0.98435884,\n 0.98448278, 0.98460617, 0.98472912, 0.984852 , 0.98497324,\n 0.98509422, 0.98521456, 0.98533478, 0.98545394, 0.9855726 ,\n 0.98569101, 0.98580854, 0.98592469, 0.9860402 , 0.98615511,\n 0.98626975, 0.98638343, 0.9864965 , 0.98660857, 0.98672004,\n 0.98683101, 0.98694155, 0.98705146, 0.9871611 , 0.98727017,\n 0.98737845, 0.98748622, 0.98759382, 0.98770049, 0.98780695,\n 0.98791282, 0.98801809, 0.98812308, 0.98822752, 0.98833112,\n 0.98843461, 0.98853712, 0.98863913, 0.98874026, 0.98884103,\n 0.98894147, 0.98904132, 0.98914006, 0.98923872, 0.98933657,\n 0.98943426, 0.9895316 , 0.98962858, 0.98972495, 0.98982042,\n 0.98991553, 0.99001034, 0.99010474, 0.99019869, 0.99029232,\n 0.99038547, 0.99047802, 0.99056952, 0.99066046, 0.99075123,\n 0.9908412 , 0.99093095, 0.99102027, 0.99110878, 0.99119686,\n 0.991284 , 0.99137064, 0.99145684, 0.99154301, 0.9916287 ,\n 0.99171383, 0.99179822, 0.99188227, 0.99196602, 0.99204914,\n 0.99213215, 0.99221454, 0.99229625, 0.9923773 , 0.99245801,\n 0.99253818, 0.99261801, 0.99269711, 0.99277575, 0.99285437,\n 0.99293242, 0.99301037, 0.99308714, 0.99316362, 0.99323997,\n 0.99331588, 0.99339135, 0.99346645, 0.99354112, 0.99361543,\n 0.99368914, 0.99376232, 0.9938352 , 0.99390791, 0.99398013,\n 0.99405219, 0.99412331, 0.99419377, 0.994264 , 0.99433386,\n 0.99440353, 0.99447278, 0.99454165, 0.99461022, 0.99467846,\n 0.99474631, 0.99481384, 0.99488084, 0.99494741, 0.99501349,\n 0.99507947, 0.99514485, 0.99520937, 0.99527369, 0.99533783,\n 0.99540182, 0.99546534, 0.99552812, 0.99559085, 0.99565334,\n 0.99571532, 0.99577699, 0.99583833, 0.99589931, 0.9959597 ,\n 0.99601994, 0.99608004, 0.9961398 , 0.99619902, 0.99625806,\n 0.99631668, 0.99637498, 0.99643246, 0.99648964, 0.99654658,\n 0.99660348, 0.99666008, 0.99671624, 0.99677226, 0.99682789,\n 0.99688332, 0.99693851, 0.99699341, 0.99704791, 0.99710219,\n 0.99715597, 0.99720952, 0.99726274, 0.99731563, 0.99736803,\n 0.99742014, 0.99747165, 0.99752302, 0.99757402, 0.99762466,\n 0.99767508, 0.99772515, 0.99777484, 0.99782431, 0.99787324,\n 0.99792198, 0.99797022, 0.99801832, 0.99806623, 0.99811294,\n 0.99815934, 0.99820553, 0.9982516 , 0.99829736, 0.99834261,\n 0.99838751, 0.99843215, 0.99847669, 0.99852092, 0.99856455,\n 0.99860796, 0.9986511 , 0.99869369, 0.99873568, 0.99877733,\n 0.9988188 , 0.99886 , 0.99890061, 0.99894074, 0.99898074,\n 0.99902045, 0.99905956, 0.99909851, 0.99913737, 0.99917557,\n 0.99921352, 0.99925108, 0.99928825, 0.99932528, 0.99936201,\n 0.99939802, 0.99943366, 0.99946908, 0.99950427, 0.99953896,\n 0.99957313, 0.99960703, 0.99964055, 0.99967369, 0.99970635,\n 0.9997389 , 0.99977105, 0.99980226, 0.9998326 , 0.99986238,\n 0.99989173, 0.99992013, 0.99994621, 0.99997163, 0.99998665,\n 0.99999752, 1. , 1. , 1. , 1. ,\n 1. , 1. , 1. , 1. , 1. ,\n 1. , 1. , 1. , 1. , 1. ,\n 1. , 1. , 1. , 1. , 1. ,\n 1. , 1. , 1. , 1. , 1. ,\n 1. , 1. , 1. , 1. , 1. ,\n 1. , 1. , 1. , 1. , 1. ,\n 1. , 1. , 1. , 1. , 1. ,\n 1. , 1. , 1. , 1. , 1. ,\n 1. , 1. , 1. , 1. , 1. ,\n 1. , 1. , 1. , 1. , 1. ,\n 1. , 1. , 1. , 1. , 1. ,\n 1. , 1. , 1. , 1. , 1. ,\n 1. , 1. , 1. , 1. , 1. ,\n 1. , 1. , 1. , 1. , 1. ,\n 1. , 1. , 1. , 1. , 1. ,\n 1. , 1. , 1. , 1. , 1. ,\n 1. , 1. , 1. , 1. ])"},"metadata":{}}],"execution_count":29},{"cell_type":"code","source":"plt.plot(np.cumsum(pca.explained_variance_ratio_))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-31T18:48:45.185734Z","iopub.execute_input":"2024-12-31T18:48:45.18611Z","iopub.status.idle":"2024-12-31T18:48:45.316034Z","shell.execute_reply.started":"2024-12-31T18:48:45.18608Z","shell.execute_reply":"2024-12-31T18:48:45.315094Z"}},"outputs":[{"execution_count":30,"output_type":"execute_result","data":{"text/plain":"[]"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}],"execution_count":30},{"cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null}]}