{"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":"import pandas as pd\ntrain = pd.read_csv('/kaggle/input/Kannada-MNIST/train.csv')\ntest = pd.read_csv('/kaggle/input/Kannada-MNIST/test.csv')\n\n#y_train = train.values[:,0]\n#y_test = train.values[0:5000,0]\n#x_test = test.values[:,1:]\n#x_train = train.values[: , 1:]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.tree import DecisionTreeClassifier\nfrom sklearn.model_selection import train_test_split\nfrom sklearn import metrics\nfrom sklearn.metrics import confusion_matrix\n\nx = train.values[:,1:]\ny = train.values[:,0]\nx_train,x_test,y_train,y_test = train_test_split(x, y,test_size=0.2)\n\nclf = DecisionTreeClassifier()\n# Train Decision Tree Classifer\nclf = clf.fit(x_train,y_train)\n#Predict the response for test dataset\ny_pred = clf.predict(x_test)\nacc1 = metrics.accuracy_score(y_test, y_pred)\nmatrix=confusion_matrix(y_test,y_pred)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"acc1","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.ensemble import RandomForestClassifier\n#Create a Gaussian Classifier\nclf=RandomForestClassifier()\n#Train the model using the training sets y_pred=clf.predict(X_test)\nclf.fit(x_train,y_train)\ny_pred2=clf.predict(x_test)\nacc2 = metrics.accuracy_score(y_test, y_pred2)\nmatrix2=confusion_matrix(y_test,y_pred2)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"acc2"},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","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}