{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"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 in \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 \"../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# Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"from sklearn.linear_model import LogisticRegression, LinearRegression\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\nimport pandas as pd","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"Dig_MNIST = pd.read_csv(\"../input/Kannada-MNIST/Dig-MNIST.csv\")\nsample_submission = pd.read_csv(\"../input/Kannada-MNIST/sample_submission.csv\")\ntest_data = pd.read_csv(\"../input/Kannada-MNIST/test.csv\")\ntrain_data = pd.read_csv(\"../input/Kannada-MNIST/train.csv\")","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"","_uuid":"","trusted":true},"cell_type":"code","source":"X = train_data.drop('label', axis=1)\nY = train_data['label']\nx_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.20)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"%%time\nlinear = LogisticRegression(C=20, solver='lbfgs', multi_class='multinomial')\nlinear.fit(x_train, y_train)\ny_hat = linear.predict(x_test)\nscore = accuracy_score(y_test.values, y_hat.round())\nprint (\"score\", score)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"%%time\nlinear = LogisticRegression(multi_class='multinomial', solver='lbfgs')\nlinear.fit(x_train, y_train)\ny_hat = linear.predict(x_test)\nscore = accuracy_score(y_test.values, y_hat.round())\nprint (\"score\", score)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"%%time\nlinear = LogisticRegression(multi_class='multinomial', solver='newton-cg')\nlinear.fit(x_train, y_train)\ny_hat = linear.predict(x_test)\nscore = accuracy_score(y_test.values, y_hat.round())\nprint (\"score\", score)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"%%time\nlinear = LogisticRegression(multi_class='multinomial', solver='sag')\nlinear.fit(x_train, y_train)\ny_hat = linear.predict(x_test)\nscore = accuracy_score(y_test.values, y_hat.round())\nprint (\"score\", score)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"%%time\nlinear = LogisticRegression(multi_class='multinomial', solver='saga')\nlinear.fit(x_train, y_train)\ny_hat = linear.predict(x_test)\nscore = accuracy_score(y_test.values, y_hat.round())\nprint (\"score\", score)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"%%time\nlinear = LogisticRegression(multi_class='multinomial', solver='saga', penalty='elasticnet', l1_ratio=0)\nlinear.fit(x_train, y_train)\ny_hat = linear.predict(x_test)\nscore = accuracy_score(y_test.values, y_hat.round())\nprint (\"score\", score)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"%%time\nlinear = LogisticRegression()\nlinear.fit(x_train, y_train)\ny_hat = linear.predict(x_test)\nscore = accuracy_score(y_test.values, y_hat.round())\nprint (\"score\", score)","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":1}