{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","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":30626,"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)\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":"2024-01-08T14:59:45.274963Z","iopub.execute_input":"2024-01-08T14:59:45.275386Z","iopub.status.idle":"2024-01-08T14:59:45.285930Z","shell.execute_reply.started":"2024-01-08T14:59:45.275353Z","shell.execute_reply":"2024-01-08T14:59:45.284188Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nimport pandas as pd\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense\n\n\ndf = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')\n\ntest = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')\ntest = test/255\n\nX = df.drop('label', axis=1)/255\ny = df['label']\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)\n\nmodel = Sequential([\n    Dense(200, activation='relu', input_dim=784),\n    Dense(10, activation='softmax')\n])\n\nmodel.compile(optimizer='adam',\n              loss='sparse_categorical_crossentropy',\n              metrics=['accuracy'])\n\nmodel.fit(X_train, y_train, epochs=100, batch_size=32, validation_split=0.2)\n\ntest_loss, test_acc = model.evaluate(X_test, y_test)\nprint(f'Test accuracy: {test_acc}')\n\npredictions = model.predict(test)\npredictions = [np.argmax(i) for i in predictions]\nlabel = np.array(predictions)\n\noutput= pd.DataFrame({'label': label})\noutput['ImageId'] = range(1, len(label)+1)\noutput = output[['ImageId', 'label']]\noutput.to_csv('submission.csv', index=False)\nprint(\"Your submission was successfully saved!\")","metadata":{"execution":{"iopub.status.busy":"2024-01-08T15:05:05.792182Z","iopub.execute_input":"2024-01-08T15:05:05.792560Z","iopub.status.idle":"2024-01-08T15:08:38.953660Z","shell.execute_reply.started":"2024-01-08T15:05:05.792526Z","shell.execute_reply":"2024-01-08T15:08:38.952651Z"},"trusted":true},"execution_count":null,"outputs":[]}]}