diff --git a/practise/KNN+Practise.ipynb b/practise/KNN+Practise.ipynb index 07a0698..d5f3515 100644 --- a/practise/KNN+Practise.ipynb +++ b/practise/KNN+Practise.ipynb @@ -19,13 +19,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2017-03-09T17:11:00.462641Z", "start_time": "2017-03-09T12:11:00.457060-05:00" - }, - "collapsed": true + } }, "outputs": [], "source": [ @@ -48,46 +47,187 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2017-03-09T17:11:00.913456Z", "start_time": "2017-03-09T12:11:00.883452-05:00" - }, - "collapsed": true + } }, "outputs": [], "source": [ "import pandas as pd\n", "\n", "# Import the data using the file path\n", - "filepath = os.sep.join(data_path + ['Orange_Telecom_Churn_Data.csv'])\n", + "filepath = 'Orange_Telecom_Churn_Data.csv'\n", "data = pd.read_csv(filepath)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2017-03-09T17:11:01.087485Z", "start_time": "2017-03-09T12:11:01.075442-05:00" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
0
stateKS
account_length128
area_code415
phone_number382-4657
intl_planno
voice_mail_planyes
number_vmail_messages25
total_day_minutes265.1
total_day_calls110
total_day_charge45.07
total_eve_minutes197.4
total_eve_calls99
total_eve_charge16.78
total_night_minutes244.7
total_night_calls91
total_night_charge11.01
total_intl_minutes10
total_intl_calls3
total_intl_charge2.7
number_customer_service_calls1
churnedFalse
\n", + "
" + ], + "text/plain": [ + " 0\n", + "state KS\n", + "account_length 128\n", + "area_code 415\n", + "phone_number 382-4657\n", + "intl_plan no\n", + "voice_mail_plan yes\n", + "number_vmail_messages 25\n", + "total_day_minutes 265.1\n", + "total_day_calls 110\n", + "total_day_charge 45.07\n", + "total_eve_minutes 197.4\n", + "total_eve_calls 99\n", + "total_eve_charge 16.78\n", + "total_night_minutes 244.7\n", + "total_night_calls 91\n", + "total_night_charge 11.01\n", + "total_intl_minutes 10\n", + "total_intl_calls 3\n", + "total_intl_charge 2.7\n", + "number_customer_service_calls 1\n", + "churned False" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data.head(1).T" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2017-03-09T17:11:01.564122Z", "start_time": "2017-03-09T12:11:01.557967-05:00" - }, - "collapsed": true + } }, "outputs": [], "source": [ @@ -97,14 +237,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2017-03-09T17:11:02.585712Z", "start_time": "2017-03-09T12:11:02.579981-05:00" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['account_length', 'intl_plan', 'voice_mail_plan',\n", + " 'number_vmail_messages', 'total_day_minutes', 'total_day_calls',\n", + " 'total_day_charge', 'total_eve_minutes', 'total_eve_calls',\n", + " 'total_eve_charge', 'total_night_minutes', 'total_night_calls',\n", + " 'total_night_charge', 'total_intl_minutes', 'total_intl_calls',\n", + " 'total_intl_charge', 'number_customer_service_calls', 'churned'],\n", + " dtype='object')" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data.columns" ] @@ -121,13 +278,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2017-03-09T17:11:04.545751Z", "start_time": "2017-03-09T12:11:04.509105-05:00" - }, - "collapsed": true + } }, "outputs": [], "source": [ @@ -141,13 +297,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2017-03-09T17:11:04.736451Z", "start_time": "2017-03-09T12:11:04.718049-05:00" - }, - "collapsed": true + } }, "outputs": [], "source": [ @@ -175,13 +330,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2017-03-09T17:11:50.280188Z", "start_time": "2017-03-09T12:11:50.269326-05:00" - }, - "collapsed": true + } }, "outputs": [], "source": [ @@ -199,13 +353,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2017-03-09T17:11:50.989446Z", "start_time": "2017-03-09T12:11:50.498708-05:00" - }, - "collapsed": true + } }, "outputs": [], "source": [ @@ -232,32 +385,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2017-03-09T17:11:50.997204Z", "start_time": "2017-03-09T12:11:50.991392-05:00" - }, - "collapsed": true + } }, "outputs": [], "source": [ "# Function to calculate the % of values that were correctly predicted\n", "\n", "def accuracy(real, predict):\n", - " return sum(y_data == y_pred) / float(real.shape[0])" + " return sum(real == predict) / float(real.shape[0])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2017-03-09T17:11:51.128466Z", "start_time": "2017-03-09T12:11:51.115874-05:00" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.9422\n" + ] + } + ], "source": [ "print(accuracy(y_data, y_pred))" ] @@ -276,31 +436,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2017-03-09T17:11:52.047123Z", "start_time": "2017-03-09T12:11:51.538212-05:00" - }, - "collapsed": true + } }, "outputs": [], "source": [ - "#Student writes code here" + "\n", + "\n", + "knn = KNeighborsClassifier(n_neighbors=3, weights='distance')\n", + "\n", + "knn = knn.fit(X_data, y_data)\n", + "\n", + "y_pred = knn.predict(X_data)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2017-03-09T17:11:52.755941Z", "start_time": "2017-03-09T12:11:52.049816-05:00" - }, - "collapsed": true + } }, "outputs": [], - "source": [] + "source": [ + "\n", + "\n", + "knn = KNeighborsClassifier(n_neighbors=3,p=1)\n", + "\n", + "knn = knn.fit(X_data, y_data)\n", + "\n", + "y_pred = knn.predict(X_data)" + ] }, { "cell_type": "markdown", @@ -314,31 +486,67 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2017-03-09T17:12:01.329053Z", "start_time": "2017-03-09T12:11:52.759302-05:00" - }, - "collapsed": true + } }, "outputs": [], "source": [ - "#Student writes code here" + "acc=[]\n", + "a=[]\n", + "for i in range(1,21):\n", + " a.append(i)\n", + "for i in range(1,21):\n", + " knn = KNeighborsClassifier(n_neighbors=i,p=1)\n", + " knn = knn.fit(X_data, y_data)\n", + " y_pred = knn.predict(X_data)\n", + " b=accuracy(y_data,y_pred)\n", + " acc.append(b)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2017-03-09T17:12:01.829160Z", "start_time": "2017-03-09T12:12:01.331021-05:00" - }, - "collapsed": true + } }, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5,1,'accuracies vs k')" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "plt.plot(a,acc,'r+')\n", + "plt.xticks(range(1,21))\n", + "plt.xlabel('k')\n", + "plt.ylabel('accuracies')\n", + "plt.title('accuracies vs k')" + ] }, { "cell_type": "code", @@ -347,8 +555,7 @@ "ExecuteTime": { "end_time": "2017-03-09T17:12:02.238935Z", "start_time": "2017-03-09T12:12:01.831094-05:00" - }, - "collapsed": true + } }, "outputs": [], "source": [] @@ -356,9 +563,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [] } @@ -380,7 +585,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.3" + "version": "3.5.2" }, "name": "Linear_Regression_and_K_Nearest_Neighbors_Exercises-ANSWERS", "notebookId": 2125319687183902