LAB1.ipynb

{"metadata":{"language_info":{"name":"python","version":"3.7.8","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"from sklearn.datasets import load_iris\ndt = load_iris()\nfrom sklearn.model_selection import train_test_split\nx_train,x_test,y_train,y_test = train_test_split(dt['data'], dt['target'], test_size=0.25, random_state=0)\nfrom sklearn.neighbors import KNeighborsClassifier\nmodel = KNeighborsClassifier(n_neighbors=1)\nmodel.fit(x_train,y_train)\ny_pred = model.predict(x_test)\nprint(\"Predictions:\\n\", y_pred)","metadata":{"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"Predictions:\n [2 1 0 2 0 2 0 1 1 1 2 1 1 1 1 0 1 1 0 0 2 1 0 0 2 0 0 1 1 0 2 1 0 2 2 1 0\n 2]\n","output_type":"stream"}]}]}