MNIST_Classifier

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":3004,"databundleVersionId":861823,"sourceType":"competition"}],"dockerImageVersionId":30761,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"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)\nimport matplotlib.pyplot as plt\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","execution":{"iopub.status.busy":"2024-09-16T18:14:49.281994Z","iopub.execute_input":"2024-09-16T18:14:49.282561Z","iopub.status.idle":"2024-09-16T18:14:49.290333Z","shell.execute_reply.started":"2024-09-16T18:14:49.282516Z","shell.execute_reply":"2024-09-16T18:14:49.288902Z"},"trusted":true},"execution_count":31,"outputs":[]},{"cell_type":"code","source":"df = pd.read_csv(\"/kaggle/input/digit-recognizer/train.csv\")\nX = df.drop(\"label\", axis = 1)\ny = df.label","metadata":{"execution":{"iopub.status.busy":"2024-09-16T18:18:04.284013Z","iopub.execute_input":"2024-09-16T18:18:04.284461Z","iopub.status.idle":"2024-09-16T18:18:07.855892Z","shell.execute_reply.started":"2024-09-16T18:18:04.284426Z","shell.execute_reply":"2024-09-16T18:18:07.854803Z"},"trusted":true},"execution_count":38,"outputs":[]},{"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, stratify = y)","metadata":{"execution":{"iopub.status.busy":"2024-09-16T18:19:26.833628Z","iopub.execute_input":"2024-09-16T18:19:26.834055Z","iopub.status.idle":"2024-09-16T18:19:27.115031Z","shell.execute_reply.started":"2024-09-16T18:19:26.834019Z","shell.execute_reply":"2024-09-16T18:19:27.113719Z"},"trusted":true},"execution_count":39,"outputs":[]},{"cell_type":"code","source":"ex = X_train.iloc[0, :]\nprint(y_train[0])\nex_ex = ex.to_numpy().reshape(28, 28)\nplt.imshow(ex_ex , cmap = 'binary')\nplt.show()\ntype(y)","metadata":{"execution":{"iopub.status.busy":"2024-09-16T18:20:17.715675Z","iopub.execute_input":"2024-09-16T18:20:17.716087Z","iopub.status.idle":"2024-09-16T18:20:17.937154Z","shell.execute_reply.started":"2024-09-16T18:20:17.716053Z","shell.execute_reply":"2024-09-16T18:20:17.936036Z"},"trusted":true},"execution_count":40,"outputs":[{"name":"stdout","text":"1\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}},{"execution_count":40,"output_type":"execute_result","data":{"text/plain":"pandas.core.series.Series"},"metadata":{}}]},{"cell_type":"code","source":"# Binary Classification\ny_train_5 = (y_train == 5)\ny_test_5 = (y_test == 5)\nfrom sklearn.linear_model import SGDClassifier\nsgd_classifier = SGDClassifier(random_state = 42)\nsgd_classifier.fit(X_train, y_train_5)","metadata":{"execution":{"iopub.status.busy":"2024-09-16T18:20:33.845378Z","iopub.execute_input":"2024-09-16T18:20:33.846018Z","iopub.status.idle":"2024-09-16T18:20:43.802628Z","shell.execute_reply.started":"2024-09-16T18:20:33.845972Z","shell.execute_reply":"2024-09-16T18:20:43.801110Z"},"trusted":true},"execution_count":41,"outputs":[{"execution_count":41,"output_type":"execute_result","data":{"text/plain":"SGDClassifier(random_state=42)","text/html":"<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SGDClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SGDClassifier</label><div class=\"sk-toggleable__content\"><pre>SGDClassifier(random_state=42)</pre></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"# y_predict = sgd_classifier.predict(X_test)","metadata":{"execution":{"iopub.status.busy":"2024-09-16T18:21:16.025727Z","iopub.execute_input":"2024-09-16T18:21:16.026232Z","iopub.status.idle":"2024-09-16T18:21:16.075096Z","shell.execute_reply.started":"2024-09-16T18:21:16.026187Z","shell.execute_reply":"2024-09-16T18:21:16.073690Z"},"trusted":true},"execution_count":43,"outputs":[]},{"cell_type":"markdown","source":"# Performance Measures ","metadata":{}},{"cell_type":"code","source":"from sklearn.model_selection import cross_val_score\ncross_val_score(sgd_classifier, X_train, y_train_5, cv = 3, scoring = \"accuracy\")","metadata":{"execution":{"iopub.status.busy":"2024-09-16T18:25:15.287577Z","iopub.execute_input":"2024-09-16T18:25:15.288078Z","iopub.status.idle":"2024-09-16T18:25:32.395139Z","shell.execute_reply.started":"2024-09-16T18:25:15.288038Z","shell.execute_reply":"2024-09-16T18:25:32.393807Z"},"trusted":true},"execution_count":46,"outputs":[{"execution_count":46,"output_type":"execute_result","data":{"text/plain":"array([0.95830357, 0.96      , 0.94696429])"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.model_selection import cross_val_predict\ny_train_predict = cross_val_predict(sgd_classifier, X_train, y_train_5, cv = 3)","metadata":{"execution":{"iopub.status.busy":"2024-09-16T18:30:19.067203Z","iopub.execute_input":"2024-09-16T18:30:19.067732Z","iopub.status.idle":"2024-09-16T18:30:35.911934Z","shell.execute_reply.started":"2024-09-16T18:30:19.067693Z","shell.execute_reply":"2024-09-16T18:30:35.910290Z"},"trusted":true},"execution_count":47,"outputs":[]},{"cell_type":"code","source":"from sklearn.metrics import confusion_matrix\nconfusion_matrix(y_train_5, y_train_predict)","metadata":{"execution":{"iopub.status.busy":"2024-09-16T18:32:27.414759Z","iopub.execute_input":"2024-09-16T18:32:27.415833Z","iopub.status.idle":"2024-09-16T18:32:27.433642Z","shell.execute_reply.started":"2024-09-16T18:32:27.415781Z","shell.execute_reply":"2024-09-16T18:32:27.432284Z"},"trusted":true},"execution_count":50,"outputs":[{"execution_count":50,"output_type":"execute_result","data":{"text/plain":"array([[29861,   703],\n       [  806,  2230]])"},"metadata":{}}]},{"cell_type":"code","source":"y_predict_joke = y_train_5\nconfusion_matrix(y_train_5, y_predict_joke)","metadata":{"execution":{"iopub.status.busy":"2024-09-16T18:33:07.189375Z","iopub.execute_input":"2024-09-16T18:33:07.189831Z","iopub.status.idle":"2024-09-16T18:33:07.205898Z","shell.execute_reply.started":"2024-09-16T18:33:07.189788Z","shell.execute_reply":"2024-09-16T18:33:07.204750Z"},"trusted":true},"execution_count":51,"outputs":[{"execution_count":51,"output_type":"execute_result","data":{"text/plain":"array([[30564,     0],\n       [    0,  3036]])"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.metrics import precision_score, recall_score\nprint(precision_score(y_train_5, y_train_predict))\nprint(recall_score(y_train_5, y_train_predict))","metadata":{"execution":{"iopub.status.busy":"2024-09-16T18:37:34.423490Z","iopub.execute_input":"2024-09-16T18:37:34.424632Z","iopub.status.idle":"2024-09-16T18:37:34.462996Z","shell.execute_reply.started":"2024-09-16T18:37:34.424582Z","shell.execute_reply":"2024-09-16T18:37:34.461008Z"},"trusted":true},"execution_count":53,"outputs":[{"name":"stdout","text":"0.7603136720081828\n0.7345191040843215\n","output_type":"stream"}]},{"cell_type":"code","source":"from sklearn.metrics import f1_score\nf1_score(y_train_5, y_train_predict)","metadata":{"execution":{"iopub.status.busy":"2024-09-16T18:42:22.501815Z","iopub.execute_input":"2024-09-16T18:42:22.502554Z","iopub.status.idle":"2024-09-16T18:42:22.526744Z","shell.execute_reply.started":"2024-09-16T18:42:22.502513Z","shell.execute_reply":"2024-09-16T18:42:22.525434Z"},"trusted":true},"execution_count":55,"outputs":[{"execution_count":55,"output_type":"execute_result","data":{"text/plain":"0.7471938348132017"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}