From db8dd429a2918d48582beb7d6696549b69823e0a Mon Sep 17 00:00:00 2001 From: Juntao <41448486+juntaoduan@users.noreply.github.com> Date: Tue, 15 Mar 2022 22:55:44 -0400 Subject: [PATCH 1/3] Add files via upload --- Datasets/UniversalBank.csv | 5001 ++++++++++++++++++++++++++++++++++++ 1 file changed, 5001 insertions(+) create mode 100644 Datasets/UniversalBank.csv diff --git a/Datasets/UniversalBank.csv b/Datasets/UniversalBank.csv new file mode 100644 index 0000000..deaad7a --- /dev/null +++ b/Datasets/UniversalBank.csv @@ -0,0 +1,5001 @@ +ID,Age,Experience,Income,ZIP Code,Family,CCAvg,Education,Mortgage,Personal Loan,Securities Account,CD Account,Online,CreditCard +1,25,1,49,91107,4,1.6,1,0,0,1,0,0,0 +2,45,19,34,90089,3,1.5,1,0,0,1,0,0,0 +3,39,15,11,94720,1,1,1,0,0,0,0,0,0 +4,35,9,100,94112,1,2.7,2,0,0,0,0,0,0 +5,35,8,45,91330,4,1,2,0,0,0,0,0,1 +6,37,13,29,92121,4,0.4,2,155,0,0,0,1,0 +7,53,27,72,91711,2,1.5,2,0,0,0,0,1,0 +8,50,24,22,93943,1,0.3,3,0,0,0,0,0,1 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"version": 3 + }, + "pygments_lexer": "ipython3", + "nbconvert_exporter": "python", + "file_extension": ".py" + }, + "colab": { + "name": "gradient-boosting.ipynb", + "provenance": [], + "include_colab_link": true + } + }, + "nbformat_minor": 0, + "nbformat": 4, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Libraries and Data**" + ], + "metadata": { + "id": "020mBJTsnebN" + } + }, + { + "cell_type": "code", + "source": [ + "!wget https://raw.githubusercontent.com/juntaoduan/Machine-Learning-with-Python/master/Datasets/UniversalBank.csv" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "v2J1e6a3quCn", + "outputId": "ec045dc3-a5f7-48bf-dea2-f6cbf01b777f" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2022-03-17 15:43:55-- https://raw.githubusercontent.com/juntaoduan/Machine-Learning-with-Python/master/Datasets/UniversalBank.csv\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 205669 (201K) [text/plain]\n", + "Saving to: ‘UniversalBank.csv’\n", + "\n", + "UniversalBank.csv 100%[===================>] 200.85K --.-KB/s in 0.05s \n", + "\n", + "2022-03-17 15:43:55 (4.11 MB/s) - ‘UniversalBank.csv’ saved [205669/205669]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "pip install scikit-plot" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IFZ4Fs_nq6Jj", + "outputId": "b046da77-1fad-4da8-9e70-cfe68e25ee14" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting scikit-plot\n", + " Downloading scikit_plot-0.3.7-py3-none-any.whl (33 kB)\n", + "Requirement already satisfied: scikit-learn>=0.18 in /usr/local/lib/python3.7/dist-packages (from scikit-plot) (1.0.2)\n", + "Requirement already satisfied: joblib>=0.10 in /usr/local/lib/python3.7/dist-packages (from scikit-plot) (1.1.0)\n", + "Requirement already satisfied: scipy>=0.9 in /usr/local/lib/python3.7/dist-packages (from scikit-plot) (1.4.1)\n", + "Requirement already satisfied: matplotlib>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from scikit-plot) (3.2.2)\n", + "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.4.0->scikit-plot) (2.8.2)\n", + "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.4.0->scikit-plot) (1.21.5)\n", + "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.4.0->scikit-plot) (3.0.7)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.4.0->scikit-plot) (1.3.2)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.4.0->scikit-plot) (0.11.0)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib>=1.4.0->scikit-plot) (1.15.0)\n", + "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.18->scikit-plot) (3.1.0)\n", + "Installing collected packages: scikit-plot\n", + "Successfully installed scikit-plot-0.3.7\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from matplotlib import pyplot as plt\n", + "\n", + "import sklearn\n", + "from sklearn.model_selection import train_test_split, cross_val_score, KFold, GridSearchCV\n", + "from sklearn.metrics import mean_squared_error, r2_score,\\\n", + " classification_report, confusion_matrix, accuracy_score\n", + "import scikitplot as skplt\n", + "\n", + "data = pd.read_csv(\"UniversalBank.csv\")\n", + "\n", + "display(data)\n", + "\n", + "# train = pd.read_csv(\"../input/titanic/train.csv\")\n", + "# test = pd.read_csv('../input/titanic/test.csv')\n", + "# train" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2022-03-16T02:42:51.621587Z", + "iopub.execute_input": "2022-03-16T02:42:51.622088Z", + "iopub.status.idle": "2022-03-16T02:42:51.673351Z", + "shell.execute_reply.started": "2022-03-16T02:42:51.622038Z", + "shell.execute_reply": "2022-03-16T02:42:51.672328Z" + }, + "trusted": true, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 467 + }, + "id": "DKNzRYfRnebV", + "outputId": "da66cab3-c308-44fc-d26f-19d887fbeb6e" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + " ID Age Experience Income ZIP Code Family CCAvg Education \\\n", + "0 1 25 1 49 91107 4 1.6 1 \n", + "1 2 45 19 34 90089 3 1.5 1 \n", + "2 3 39 15 11 94720 1 1.0 1 \n", + "3 4 35 9 100 94112 1 2.7 2 \n", + "4 5 35 8 45 91330 4 1.0 2 \n", + "... ... ... ... ... ... ... ... ... \n", + "4995 4996 29 3 40 92697 1 1.9 3 \n", + "4996 4997 30 4 15 92037 4 0.4 1 \n", + "4997 4998 63 39 24 93023 2 0.3 3 \n", + "4998 4999 65 40 49 90034 3 0.5 2 \n", + "4999 5000 28 4 83 92612 3 0.8 1 \n", + "\n", + " Mortgage Personal Loan Securities Account CD Account Online \\\n", + "0 0 0 1 0 0 \n", + "1 0 0 1 0 0 \n", + "2 0 0 0 0 0 \n", + "3 0 0 0 0 0 \n", + "4 0 0 0 0 0 \n", + "... ... ... ... ... ... \n", + "4995 0 0 0 0 1 \n", + "4996 85 0 0 0 1 \n", + "4997 0 0 0 0 0 \n", + "4998 0 0 0 0 1 \n", + "4999 0 0 0 0 1 \n", + "\n", + " CreditCard \n", + "0 0 \n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "4 1 \n", + "... ... \n", + "4995 0 \n", + "4996 0 \n", + "4997 0 \n", + "4998 0 \n", + "4999 1 \n", + "\n", + "[5000 rows x 14 columns]" + ], + "text/html": [ + "\n", + "
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IDAgeExperienceIncomeZIP CodeFamilyCCAvgEducationMortgagePersonal LoanSecurities AccountCD AccountOnlineCreditCard
01251499110741.61001000
124519349008931.51001000
233915119472011.01000000
343591009411212.72000000
45358459133041.02000001
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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Support Vector Machine \n", + "**Support Vector Machines is a classier method that performs classification tasks by constructing hyperplanes in a multidimensional space that separates cases of different class labels. SVM supports both regression and classification tasks and can handle multiple continuous and categorical variables \n", + "**" + ], + "metadata": { + "id": "jHeRy7Vznebc" + } + }, + { + "cell_type": "markdown", + "source": [ + "**Libraries**" + ], + "metadata": { + "id": "xzlxpzVLnebd" + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn.svm import SVC" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2022-03-16T02:42:51.929394Z", + "iopub.execute_input": "2022-03-16T02:42:51.929983Z", + "iopub.status.idle": "2022-03-16T02:42:51.935562Z", + "shell.execute_reply.started": "2022-03-16T02:42:51.929934Z", + "shell.execute_reply": "2022-03-16T02:42:51.934398Z" + }, + "trusted": true, + "id": "jQTP4xTinebe" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**Model and Accuracy**" + ], + "metadata": { + "id": "A8jPwc9Pnebf" + } + }, + { + "cell_type": "code", + "source": [ + "svmc = SVC(kernel = 'rbf', random_state = 0)\n", + "svmc.fit(X_train, y_train)\n", + "\n", + "# print(classification_report(y_test,y_pred))\n", + "print( svmc.score(X_test, y_test) )\n", + "y_test_pred = svmc.predict(X_test)\n", + "skplt.metrics.plot_confusion_matrix(y_test, y_test_pred)" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2022-03-16T02:42:51.939592Z", + "iopub.execute_input": "2022-03-16T02:42:51.940258Z", + "iopub.status.idle": "2022-03-16T02:42:52.894591Z", + "shell.execute_reply.started": "2022-03-16T02:42:51.940206Z", + "shell.execute_reply": "2022-03-16T02:42:52.893712Z" + }, + "trusted": true, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 330 + }, + "id": "g6QWlTCinebg", + "outputId": "b97bb18a-f9f3-4c07-f1f9-9c56ece4da77" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "0.7008\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 7 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "SVM fails to identify all 374 potential customers " + ], + "metadata": { + "id": "n2Foy6pBiuCa" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Sklearn Gradient Boosting\n", + "**Gradient boosting is an alogithm under supervised machine learning, boosting means converting weak into strong.**" + ], + "metadata": { + "id": "TdULnRJlnebh" + } + }, + { + "cell_type": "markdown", + "source": [ + "**Libraries**" + ], + "metadata": { + "id": "ad2YeAFfnebi" + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn.ensemble import GradientBoostingClassifier\n", + "# https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#sklearn.ensemble.GradientBoostingClassifier" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2022-03-16T02:42:52.896519Z", + "iopub.execute_input": "2022-03-16T02:42:52.896798Z", + "iopub.status.idle": "2022-03-16T02:42:53.043388Z", + "shell.execute_reply.started": "2022-03-16T02:42:52.896770Z", + "shell.execute_reply": "2022-03-16T02:42:53.042397Z" + }, + "trusted": true, + "id": "eLYzQ7oqnebi" + }, + "execution_count": 8, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**Model and Accuracy**" + ], + "metadata": { + "id": "Sj5OoOJcnebj" + } + }, + { + "cell_type": "code", + "source": [ + "\n", + "# sc_X = StandardScaler()\n", + "# X_train = sc_X.fit_transform(X_train)\n", + "# X_test = sc_X.transform(X_test)\n", + "gbk = GradientBoostingClassifier(n_estimators=200, learning_rate=0.05, max_depth=7, random_state=0)\n", + "gbk.fit(X_train, y_train)\n", + "gbk.score(X_test, y_test)" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2022-03-16T02:42:53.044698Z", + "iopub.execute_input": "2022-03-16T02:42:53.045240Z", + "iopub.status.idle": "2022-03-16T02:42:55.942099Z", + "shell.execute_reply.started": "2022-03-16T02:42:53.045191Z", + "shell.execute_reply": "2022-03-16T02:42:55.941328Z" + }, + "trusted": true, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "uaj_aSW6nebj", + "outputId": "a10ea9bb-d801-47d9-9b59-531a8bfc8983" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.7096" + ] + }, + "metadata": {}, + "execution_count": 9 + } + ] + }, + { + "cell_type": "code", + "source": [ + "y_test_pred = gbk.predict(X_test)\n", + "skplt.metrics.plot_confusion_matrix(y_test, y_test_pred)" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2022-03-16T02:42:55.943363Z", + "iopub.execute_input": "2022-03-16T02:42:55.943972Z", + "iopub.status.idle": "2022-03-16T02:42:56.153768Z", + "shell.execute_reply.started": "2022-03-16T02:42:55.943930Z", + "shell.execute_reply": "2022-03-16T02:42:56.152331Z" + }, + "trusted": true, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 313 + }, + "id": "GcysysQenebk", + "outputId": "206fd483-6663-498d-abc3-0ac22d491bf4" + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 10 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Sklearn-GBM identified 61 potential customers " + ], + "metadata": { + "id": "jJkvFt0pmDPt" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Light GBM" + ], + "metadata": { + "id": "6MBk4-eJnebk" + } + }, + { + "cell_type": "markdown", + "source": [ + "**LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages:**\n", + "\n", + "1. Faster training speed and higher efficiency.\n", + "2. Lower memory usage.\n", + "3. Better accuracy.\n", + "4. Support of parallel and GPU learning.\n", + "5. Capable of handling large-scale data." + ], + "metadata": { + "id": "2agW2jS1nebl" + } + }, + { + "cell_type": "markdown", + "source": [ + "**Library**" + ], + "metadata": { + "id": "dxqryAM9nebl" + } + }, + { + "cell_type": "code", + "source": [ + "import lightgbm as lgbm\n", + "# https://lightgbm.readthedocs.io/en/latest/Python-Intro.html\n", + "# https://github.com/microsoft/LightGBM\n" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2022-03-16T02:42:56.155398Z", + "iopub.execute_input": "2022-03-16T02:42:56.155720Z", + "iopub.status.idle": "2022-03-16T02:42:56.283506Z", + "shell.execute_reply.started": "2022-03-16T02:42:56.155689Z", + "shell.execute_reply": "2022-03-16T02:42:56.282413Z" + }, + "trusted": true, + "id": "k7mgk5ygnebm" + }, + "execution_count": 11, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**Model and Accuracy**" + ], + "metadata": { + "id": "wQKmGPR2nebm" + } + }, + { + "cell_type": "code", + "source": [ + "m_lgb = lgbm.LGBMClassifier(objective='binary', \n", + " n_estimators=200, \n", + " learning_rate=0.05, \n", + " max_depth=7,\n", + " max_bin = 55, \n", + " bagging_fraction = 0.8,\n", + " bagging_freq = 5, \n", + " feature_fraction = 0.8,\n", + " min_data_in_leaf =6, \n", + " silent= -1)\n", + "# https://github.com/microsoft/LightGBM/blob/master/docs/Parameters.rst\n", + "eval_set = [(X_train, y_train), (X_test, y_test)]\n", + "m_lgb.fit(X_train, y_train, eval_set=eval_set, verbose=0)\n", + "print(m_lgb.score(X_test, y_test))\n", + "lgbm.plot_metric(m_lgb)\n", + "y_test_pred = m_lgb.predict(X_test)\n", + "skplt.metrics.plot_confusion_matrix(y_test, y_test_pred)\n", + "plt.show()" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2022-03-16T02:42:56.284723Z", + "iopub.execute_input": "2022-03-16T02:42:56.284991Z", + "iopub.status.idle": "2022-03-16T02:42:57.038261Z", + "shell.execute_reply.started": "2022-03-16T02:42:56.284964Z", + "shell.execute_reply": "2022-03-16T02:42:57.036897Z" + }, + "trusted": true, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 608 + }, + "id": "8kwODQvJnebm", + "outputId": "969e58c4-df44-4915-a2c5-7e36fc71f706" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "0.7312\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 12 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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aiHSu7Maq+jLwMkB48ww9ePlbhO2aDxHx0DgOIpMIikmBF/vTPfdfcOE3EFxzD3w5mZycHLKzswMaQ2VYnL5lcfpO2RiXL19eo08ErKwDBw7UurjCw8Pp3r27T/fp70SSB6R6vU9x53nLBWapaiGwXkRW4SSWPJzk4r1tTkUHU+DVdU2497xbf7zwwr/D25fC5EdhmLWjGGPqhr59+3L06NET5r311lt07lzp79t+5+9EMgfIEJF0nMRwJXB1mXU+Aq4CXheReKAdsA5YCzzh1cA+FKdRvlwRwcJbMzZya3ZbIsPKfLSMIdDnFzDzeYhrDW3OgagkCGlUzY9ojKkNVLXcezTqslmzZvlsX/5qyvBrY7uqFgFjgEnAcmC8qi4VkcdEZIS72iRgl4gsA6YA96rqLlXdDTyOk4zmAI+588oVEybsP1LEuNmbTr7CeU9Am7Phs7vhmW7wXG84sM0XH9UYE0Dh4eHs2rXLbxfK+kBV2bVrF+Hh4T7ft9/bSFR1IjCxzLyHvF4r8Gt3Krvta8BrlT1WmAd6tI7jjxOXszb/IPcMbUdcpFd7iCcYRr0JSz+EoqPw1cPw7lUw/E8QGgmJgW+IN8ZUXUpKCrm5ueTn5wc6lBMcOXLELxfu0xUeHk5KSorP91sbGtt96p8/7ck/Jq/mje828OmiLfxqSDtuODONoCC3yBsWBT1+6ryObgHjroFXz3Xej3gOelwXmMCNMactJCSE9PT0QIfxIzk5OT5v2K6N6t3ov9HhITx4YUe+uGsg3Vs25bFPl3HzW/M4cKTwxyu3vwB+MQ2ueR/Sz3KqvFZNgk2zoLDiAdOMMcY46l0iKdW2WRRv3NibR0dkMWXlDn7+r7knrz9t3gUyzoXLXofIZvDOKHhtKLxyDuzLrfnAjTGmjqm3iQRARLj+zDQeGZHF7A27mbZ6Z/krN46Dn3/lJJSRL8C+zU4y2TK/5gI2xpg6qF4nklJX9EoluUkj/m/yqop7dUQ3h06XQPdr4OdfgicUXj8fVkwsfxtjjGngGkQiCQ0O4tbsNszftLfiUom3Zh1g9GRIaA/vXQML3vVvkMYYU0c1iEQCcHmvFFKaNuLJicspLqlkX/OoRLj+E0gbCB/dAtP/DiUl/g3UGGPqmHrX/bc8YcEe7h/egdvf+Z735mzm6r4tK7lhJFw9Hj78BUx+BNZNhbbnQKNYp+qr+Bgc3Q97N0F4EzhzDIQ29utnMcaY2qTBJBKA8zsn0Sctlr98uZLuLZvQoXl05TYMCYfL/wVzxsL/HoN1U06yTgQUHoJF45zG+lZn+DR2Y4w5peIiCPKACBQXwtEDzpfdoqPul94DoCUQHgMSBCVFEJN66v2eQoNKJCLCYxdnce3YWYx4bjq/v6Aj15+ZVtmNoc9N0Hu0UwI5tNv5JXhCIDQKImJhw3T4+DZ4fTj0uxXOecjG8jKmIVN1LuLicUbWKKuk2Lm4HyuAowXOT+/XZZcdLYBjB9yfB3+8TrF7rJAIZ73K+Nmkan/MBpVIANonRfPlr87i1+MX8Niny+jfNp62zarw7AURJ5uHx/x4WfpAuHUGTH4YZr4Aa7+GS8dCUu0ZpdMYU0WqsD/Pqb4+mO9cqIPDITjUGatv+1LYvwUObHXeH90PRUeg6CjZRUdgqrufkAgIi3ZqOI4ddC78RZW88VmCnC+soY2d6vbQSOdnRKsT34dGOomr8JBT1R4e7VTBB4c5MYc2BsSJUdUpvcRV/zkuDS6RAMQ2DuUvl3dl8NM5PPX5csZe39t3Ow+LhAv+CpnD4aPb4J9nQc/rIbY17F4H3a713bGMMaevpMS5+O/ZAHs3Oj/3uD+P7ndqHIoL4fBuOLKv/P14QiGquTMldnS+ZAY3gpBwNuRuI61NO7fksd+ZCo+cmBC8k0BY1MnfhzRyvsTWUg0ykQDER4Zx2+C2/OmLFXy8II+R3ZJ9e4C2Q5zSydSnYO5rzh9lcDjMfY3MpCHQKwsiE3x7TGPqs5JiKNgBBdtpuvt7WLjN+WYv4lTvHNrtXPT3boJti532gvBo52LsCYGiY07VT5E7Hd3vtBscJxCTAk1aOV/8PCEQFOyUIhI7OvMaJ/xQXVV02HmAXny7k1dbARtyckgblF0jpyeQGmwiAbixfxqfLtrCneMW8OmirTx7VXfCQ3z4WMzGcXD+0zDwHud9aARM/TOJM56HZ3vCuY9Czxtq9TcNY3yqtAE4pNGJ7YfFRVCw3Skh7N/irAOwfQnkzYN9ec4yLQagK8CiMvsOCnZ6U0Y3h8zznW/9Rw+4VU3HnKooT9gP1VJhUdA0zUkcTdOcRufgUP+fg3qoQSeS8BAPH97Wn7HT1/HnL1by6vT13D7YD899jkr84fXQx5lbmEmfne/Bp3fB8k+cb0GeEOhzMyRk+v74xlREFQ7vcerVg0IILjzgXICDQuDgDti1xqkG8gQ7F2stcapnDmyBvZudEsCBrU77QUmx22vIA0FBzs/CQ07V0JH9J7YJBIc7F34tcY6jJ7lHKzgcWvSA9EHOaN3RLSAyke9XbaZH/3OdKiDVH6qC7EtZQDToRALOXe+3Zbdl/qa9vJizlit6pxIf6d9nuh9qnALDP4ZZL8GUP8K2RU7D25xXoetVzqOAGzU99Y6MOZWjB5yL/YGtzkX22CHYsRz2bnC++e/f4nzb9+rhMwDg20ruXzwQkwzRyRCf4SSakmInKZQUOyWIkAi3iinaaQAOi3RG1z6820lgiJMgopIgqoVTogiLBhSiU05aSti/PQfi/fClz5yWBp9ISt03vD1D/z6NJyYu58+XdiHY4+eb/oOC4IzbnG7CInBwJ3z3DHz3HKz+EmLTf/hHjM+ErldC62zn256pn1Rhz3rn4l9S7HTO2LsJjuwt803f41ywxf1bOJjvVAsV7HDvGzjq9Bo6st/Z9mQiE52Lf1xb5++qSUvnG31JIatXLicjvZXTfhAR67YBhEFJoVM1JUFOSSEqyWlgLqd9wDQc9hfgapMQyeiB6fxz6jrW7ijgmau60yquBu5QLy2KN46Hcx+DrEtg2tNOI2KQBxBYPQkWj3e+rWVdDEldoGU/J9mY2qukxBlFumAHFB8jav8qyG/h1M0fPQBbF8LWBZC/0rnw717ndDMtyxPqVDNpsdNpo6QY8BrmJyzGeQRCZKKTEIJD3a6ekU61aUyKkzRK95XQzomhHHkHc8jon+3TU2HqN0skXu4b1p7OyTE88OESbnpzLh/fPoBGoTVcAmjRDa58+8R5hUdg1efOwJFzxv7Q06R5N2e4lmYdnSqCRk2h5RlOVYOpOcWFsHs95K+AnSshf5Xzc+dqp33A1RPg+zLbesLcC3s0pPaBtAEQmeQsi23tNAKHRvz4mCUlTmJRtQZiE3CWSLyICBd2aUF0eAg/fW02j326jCcvqQU3E4aEQ9ZPnKm4yGn8XP0lLJ8A0//veE+W42JaQnJ3t51FnIvZsYPOt9nwJk59dEImRMQ530xjUp0qiqBaOoZn4RHnIWOHdjlTab9+d2q3aT3se/+Hm65CIpxzFhLhvC8pcr7t71ztVBVFxDklwMN7nJKAeJxzFR7jlBSO7HX2fXiv0zgc2ti50IdFOduFxzjLDu50qpX2bnSOUSom1akO6tnf+el2plg0fy5d2qU5+w4Oh+Zdnd+DJ6Tq5yQoiAY05qqp5SyRnMSgdgncclYbXpq6lp6tmnJZz5RAh/QDTzA0a+9M/e9wGun3bnLqsg9sg00zYdN3zt22R/YD6lxQQxs7VWXblzm9bbwvfOBUecSkOt+Ao5pD8TEy83dD0Fy3i2RL52Ia2tidIqv2Tbi40G3YzXUu1oWHfujpU3TUvUgnOK8LtjnVPQd3OhfdPRt+nCxLhUUTpx4oCHf79h85oRRwXEgExLVxGoQP7Xb2HxHrtgsUOVVQ25c6jcLhMU6X0KQuThfVYwd/6Eaav8qJKSLWSUjNu0DHkU5CiG/nTGEnHylh9yagc3blz5kxdYQlknLcPbQdi3L38rv/LiY9vjE9W9XSXlRhkc7NUuA0frboBv1uqXibomPOxbn0W/3eje6dve5dvTuWQ3AocQV74OvJ5e8nKMTr7tzGP5QIPKE//Dy0y0keB7acvHtnULCz7rGDHK/394Q5F+Qot86/0yVOo3DjeOfi3SjWudiHRUGQhxk5OWRnZ/+wz9IbxgoPOb2DJMhpP6itJS5j6jhLJOUI8QTxwjU9GPn8t9zy73l89ssBNIsOD3RYvhHsNriewnc5OWSf2dtJMPtynS6ixw7+MFjc8dfuN/bSEkHxMSeBFB11qozSB7qNvqnOz/AmTtVTVHNneelIpYd2O/NDI6vXO03ErdqqJ78vY2o5vycSERkG/APwAGNV9akyy28AngZKu6s8p6pj3WXFwGJ3/iZVHeHveL01iQjl5et6cfHz3zLm3fm8M7qv/7sF1zahjZ0ST2mpx188ISfeuGmMqTP8elUUEQ/wPDAc6AhcJSInuyK9p6rd3Gms1/zDXvNrNImUykyK4slLOjN7/W6enrQyECEYY0yt5u+v132ANaq6TlWPAeOAkX4+ps9d3D2Z6/q14p/T1vHFkq2BDscYY2oVUa3k88tPZ+cilwHDVHW0+/46oK+qjvFa5wbgSSAfWAX8SlU3u8uKgAVAEfCUqn50kmPcDNwMkJCQ0HP8+PF++SyFJcqTs46wpaCEW7uF0TXh9GsFCwoKiIyswjNQAsTi9C2L03fqQoxQd+IcPHjwPFXtddo7UFW/TcBlOO0ipe+vw2kD8V4nDghzX/8C+NprWbL7szWwAWhT0fHatWun/pS355AO/dtUbfXbT/XX7y3QbfsOn9Z+pkyZ4tvA/MTi9C2L03fqQoyqdSdOYK5W41rv76qtPMD7gcAp/NCoDoCq7lLVo+7bsbg3ALvL8tyf64AcoLs/gz2VFk0aMeGX/bl9cBsmLMwj++kcnvx8OTv2HwlkWMYYE1D+TiRzgAwRSReRUOBKYIL3CiLS3OvtCGC5O7+piIS5r+OB/sAyP8d7SmHBHu49rz2Tf30W53ZM5JVp6xjw5yk8MXE5+w4VBjo8Y4ypcX5NJKpaBIwBJuEkiPGqulREHhOR0l5Yd4jIUhFZCNwB3ODO7wDMdedPwWkjCXgiKdUqrjHPXNWdr+/O5qIuLXjlm3UM+8c0tu2z0okxpmHx+30kqjoRmFhm3kNer+8H7j/Jdt8BtWCgq4qlxTfmr6O6cm2/llw7dhY/f2MO439xBo3D7F5PY0zD0MDurvOf7i2b8tzVPVi+dT+3/HseRwrLGRvKGGPqGUskPjS4fTOeurQL09fs5OdvzOHwMUsmxpj6zxKJj43qlcrTl3Xlu7W7uP2d7yksPslAhcYYU49YIvGDy3qm8PjITny9Ygf3/mchJSX+u+nTGGMCzVqE/eTafq3Yd7iQpyetpElEKA9f1BEpfayuMcbUI5UukYjI5SIS5b7+vYj8V0R6+C+0uu+27DaMHpDOv77bwG8/WMTug8cCHZIxxvhcVaq2HlTVAyIyABgCvAq86J+w6gcR4YELOvCLs1rzwfd5ZD89hWmr8gMdljHG+FRVEklpF6QLgJdV9TOgCs9abZhEhPuHd+CLOweS3DSC0W/MZe62olNvaIwxdURVEkmeiPwTuAKY6A5fYo31lZSRGMW4m/rRKTma5xcc5f15uYEOyRhjfKIqiWAUzlAn56nqXiAWuNcvUdVTMREhvPXzvnSMC+Ke/yxk7DfrSkc5NsaYOqsqiaQ58JmqrhaRbOByYLZfoqrHGocFc2ePcIZ3SuIPny3ngY+WcLTIblw0xtRdVUkkHwDFItIWeBlnePh3/BJVPRfqEZ6/uge3nNWGd2Zt4vx/fMOMtbsCHZYxxpyWqiSSEnc030uAZ1X1XpxSijkNQUHCfcPb8/qNvSksVq4eO5OXp621qi5jTJ1TlURSKCJXAT8FPnXnhfg+pIZlcGYzJt01iPM7NeeJiSv47QeLOFZkw6oYY+qOqiSSG4EzgD+q6noRSQfe8k9YDUujUA/PXtWdO85uy/i5uVz36iw27z4U6LCMMaZSKp1I3IdK3QMsFpFOQK6q/slvkTUwQUHCr4dm8o8ru7Fg817O/msOD360hPSO9PYAACAASURBVO32GF9jTC1X6bG23J5abwAbAAFSReR6VZ3mn9AappHdkumdFstzU9bw7uxNjJ+7mTGD23L74LYEBdlYXcaY2qcqVVt/BYaq6lmqOgg4D/i7f8Jq2Fo0acQTP+nM13dnM6RDIn/9ahU3vTmXvYdsrC5jTO1TlUQSoqorS9+o6iqssd2vWsZF8NzV3XlsZBZTV+Uz5G9T+WThFuvZZYypVaqSSOaKyFgRyXanV4C5/grMOESEn56RxoQxA2ge04hfvjufC5+dzpQVOwIdmjHGAFVLJLcCy4A73GmZO8/UgI4tovnwtjP56+VdKThaxI3/msONr89mXX5BoEMzxjRwlW5sV9WjwN/cyQRAsCeIS3umcFHXFrw5YwP/mLya8/5vGlf1aUn/tvH0ax1HTCOrbTTG1KxTJhIRWQyUWymvql18GpE5pdDgIEYPbM3Ibsk8PWkF78zaxJszNhIdHsydQ9pxXb9WhAbbwMzGmJpRmavNhcBFFUwVEpFhIrJSRNaIyH0nWX6DiOSLyAJ3Gu217HoRWe1O11f2QzUUCVFh/Pmyrix59Dzeu7kfXVOb8Pinyzjv/6Yxedl2a5Q3xtSIU5ZIVHXj6e5cRDzA88C5QC4wR0QmuDc3entPVceU2TYWeBjohVMimuduu+d046mvwkM89G0dR5/0WHJW5vP4Z8sY/eZcBrSN5/cXdqB9UnSgQzTG1GNVeWb7ARHZX2baLCIfikjrcjbrA6xR1XWqegwYB4ys5CHPA75S1d1u8vgKGFbZeBsiEWFwe2fsrkcu6sjivH2c/49v+N2Hi9lZcDTQ4Rlj6impbPWHiDyOU6p4B+fO9iuBNsD3wK2qmn2SbS4DhqnqaPf9dUBf79KHiNwAPAnkA6uAX6nqZhG5BwhX1T+46z0IHFbVv5Q5xs3AzQAJCQk9x48fX+kPHygFBQVERkb6/zjHlI/XHuPrTUWEeuCiNiGc2yqEkEreIV9TcVaXxelbdSHOuhAj1J04Bw8ePE9Ve532DlS1UhOw8CTzFpS3zJ1/GTDW6/11wHNl1okDwtzXvwC+dl/fA/zea70HgXsqirFdu3ZaF0yZMqVGj7d6+wG98fXZ2uq3n+rAP32tH83P1cKi4lNuV9Nxni6L07fqQpx1IUbVuhMnMFcrmQtONlWla88hERklIkHuNAooHVGwvGJNHs4DsEqluPO8E9kudboWA4wFelZ2W1M5bZtF8toNvXnzZ30IDwniznELGPTnKfxz6lr2HSoMdHjGmDquKonkGpwSxQ53ug64VkQaAWPK2WYOkCEi6SISilMdNsF7BRHxfjjWCGC5+3oSMFREmopIU2CoO8+cpkHtEvj8zkGM/Wkv0uIb8+TnK+j35P/47fuLWJS7N9DhGWPqqKrckLiO8rv7Ti9nmyIRGYOTADzAa6q6VEQewylKTQDuEJERQBGwG7jB3Xa32y4zx93dY6q6u7LxmpPzBAlDOiYypGMiy7bs580ZG/h4wRbem7uZTsnR3HJWG87v1NxGGjbGVFpVhpFPAZ4F+ruzvgHuVNXcirZT1YnAxDLzHvJ6fT9wfznbvga8VtkYTdV0bBHNU5d24XcXdODj+Xm8MWMjY96ZT1aLtfxmWHu7D8UYUylVqdp6HadaqoU7feLOM3VcdHgI152RxqS7BvG3UV3Zd7iQ61+bzVOzjzBvo922Y4ypWFUSSYKqvq6qRe70LyDBT3GZAPAECZf0SOHru7N5dEQWWw8ql774HaNemsEH83IpKrZnyRtjfqwqiWSXiFwrIh53uhbY5a/ATOCEBgdx/ZlpPD2oEb87vz07C45y938WcuGz05m+eqdVeRljTlCVRPIzYBSwDdiKc4/Ijf4IytQOYcHCzYPa8L+7z+Kla3tw4EgR1746iwufnc4XS7ZZQjHGAFXrtbURp3uuaWBEhGGdmpOd2YyP5ufx8jfruOXf8+id1pQHLuhIt9QmgQ7RGBNAlRlG/lkqHkb+Dp9GZGqt8BAPV/ZpyWU9U3hv7mb+/tUqLn7+Wy7q2oLfnJdJamxEoEM0xgRAZUok9jhdc4JgTxDX9G3FyG7J/HPqWl75Zh2TlmxjRLcWXNojhb7psXYfijENSGWGkX+jMjsSkWdV9ZfVD8nUFZFhwdw9NJOr+7bk2a/XMGHBFt6fl0tyk0Zc3L0FI7omk5kUFegwjTF+Vuk2kkrof+pVTH3UPKYRT/ykMw9e0JEvl23jg+/zeDFnLc9PWUu31Cb8fEA6wzslEeyxpzYaUx/5MpGYBq5RqIeR3ZIZ2S2Z/ANH+WThFt6csYFfvjuf5CaNuP7MVlzRu6U9V96Yesa+Ihq/SIgK42cD0vn67mzG/rQXLWMjeGLiCs548n88MmEpG3cdDHSIxhgf8WWJxFpXzY8EeQ0SuXTLPl6dvp63Z23kjRkbGNIhkdED0umTHouI/fkYU1dV5VG7nU+xyj+qGYup57JaxPC3Ud349rdnM2ZwW+Zu2M0VL8/kouemM3HxVrvB0Zg6qipVWy+IyGwRuU1EYsoudMfeMuaUmkWHc/fQTGbcfw5P/KQzh48Vc9vb3zPy+W95e9ZGdtnz5Y2pUyqdSFR1IM7DrVKBeSLyjoic67fITL0XHuLh6r4t+fJXZ/H0ZV0oOFLEAx8u4Ywnv+be/yxkxbb9gQ7RGFMJVWojUdXVIvJ7nJsUnwG6i1O5/TtV/a8/AjT1nydIuLxXKpf1TGH51gO8O3sT/5m3mf/My2VgRjw3D2rNgLbx1o5iTC1VlTaSLiLyd5xH4Z4NXKSqHdzXf/dTfKYBERE6tojm8Ys7MeO+c7j3vExWbDvAda/O5oJnpvPxgjwbyt6YWqgqbSTPAt8DXVX1dlX9HkBVtwC/90dwpuFq2jiU2we3ZfpvB/PnS7twtKiYO8ctYMCfpvDM/1azY/+RQIdojHFVqmpLRDxAnqq+dbLl5c03prrCgj2M6u1Ue329YgdvzNjA375axTP/W815nZK4rl8r6+1lTIBVKpGoarGIpIpIqKoe83dQxpTlfT/K+p0HeXvmRv4zL5fPFm2lRaTwi7ANnJeVRFJMeKBDNabBqUpj+3rgWxGZABy/LVlV/+bzqIypQHp8Y35/YUfuHprJJ4u28OKXS3h4wlIenrCUzMQoRnRrwcXdk0lu0ijQoRrTIFQlkax1pyDAhnQ1Adco1MOoXqk0K1hLUvsefLNqJ5OWbuPpSSv5y5cr6Zcex096JDO8UxJR4Ta+lzH+UpUnJD7qz0CMqY72SdG0T4rmpkGt2bTrEB/Oz+PD+bn85v1FPPTxEoZ2TOIn3ZMZkBFPiI1CbIxPVTqRiEgC8BsgCzheEa2qZ59iu2E4w6d4gLGq+lQ5610KvA/0VtW5IpKG09V4pbvKTFW9pbLxmoarZVwEdw7J4I5z2vL9pr18OD+XTxZuZcLCLcQ2DmVgRjwD2sYztGMSMRFWUjGmuqpStfU28B5wIXALcD2QX9EGbm+v54FzgVxgjohMUNVlZdaLAu4EZpXZxVpV7VaFGI05TkTo2aopPVs15aELs5i2Kp9PF21h+ppdfLxgCw94lnBBl+b8+tx29phgY6qhKokkTlVfFZE7VXUqMFVE5pximz7AGlVdByAi44CRwLIy6z0O/Am4twrxGFNpocFBx3t9qSqLcvfx4fw8xs3ZxKeLtnBeVhKX9khhYEa8PYDLmCqSyvbBF5GZqtpPRCbhDI+yBXhfVdtUsM1lwDBVHe2+vw7oq6pjvNbpATygqpeKSA5wj1fV1lJgFbAf+L2qfnOSY9wM3AyQkJDQc/z48ZX6PIFUUFBAZGRkoMM4pYYQ554jJUxcX8iMLUUUFEJ0qNA7yUOXBA8d4zyE+PDZ8w3hfNaUuhAj1J04Bw8ePE9Ve53u9lUpkfzBHfX3bpy73KOBX53ugQFEJAj4G3DDSRZvBVqq6i4R6Ql8JCJZqnrCSH6q+jLwMkBmZqZmZ2dXJ6QakZOTg8XpO9WN8yfAsaISclbu4L/f5zF1VT7/23SUmEYhXNClOT/pnkzPlk0JqmZSaSjnsybUhRih7sRZXVXptfWp+3IfMLiSm+XhjBZcKsWdVyoK6ATkuAPyJQETRGSEqs4FjrrHnicia4F2OANGGuNTocFBDM1KYmhWEkcKi5mxdhcfL8jjw+/zeGfWJlKaNuIn3ZO5uHsybRJq/zdMY2pSVXtt3QSkeW+nqj+rYLM5QIaIpOMkkCuBq7223QfEex0jhx+qthKA3e5d9a2BDGBdZeM15nSFh3gY3L4Zg9s34+DRIiYt3caH8/N4fsoanv16DV1SYrigc3PO79zcGumNoWpVWx8D3wCTgeLKbKCqRSIyBpiE0/33NVVdKiKPAXNVdUIFmw8CHhORQqAEuEVVd1chXmOqrXFYMJf0SOGSHins2H+ECQu38PGCLTz5+Qqe/HwFXVJiuLpPS37SI5mwYE+gwzUmIKqSSCJU9bdVPYCqTgQmlpn3UDnrZnu9/gD4oKrHM8ZfmkWHM3pga0YPdG56nLhkKx8v2MJ9/13MX79axWU9U7isZ4pVfZkGpyqJ5FMROd9NDMY0aC3jIrjlrDb8YlBrvl2zi9e/Xc/L09bxYs5aerRswuW9UrmwS3MbmsU0CFVJJHcCvxORo0AhIICqarRfIjOmDhARBmTEMyAjnh37j/Dh/Dz+My+X+/+7mEc/WcrwTs25um9LeqfFBjpUY/ymKr22bKBGYyrQLDqcX5zVhpsHtWbB5r38Z14unyzcwofz8+iTHkt2fDFnqdojg029c8pEIiLtVXWFe+Pgj5Q+KdEY4xARurdsSveWTXnwgo68N2cTL05dy5/XH2Xy9u+4oX86w7KSCA22O+hN/VCZEsmvce4c/yvgfRu8uO8rHLTRmIasUaiHG/qnc2Wfljzx7tfkbDvGHe/OJyo8mIEZ8WS3a8ZZmQkkRtsDuUzddcpEoqo3uy/PB24DBuAkkG+AF/0XmjH1R3iIh7NbhvDItWcxbXU+ny/eRs6qHUxcvA0ROKd9M27sn86ZbeKs6svUOVVpbH8DZ8yrZ9z3VwNvAqN8HZQx9VVQkJCd2YzszGaoKiu2HeCzRVt5d/YmJi+fRWZiFKN6pzK8UxIt7AmPpo6oSiLppKodvd5PEZGyo/gaYypJROjQPJoOzaMZc3ZbJizcwpszNvD4p8t4/NNlDGgbz6jeqQztmEh4iN3saGqvqiSS70Wkn6rOBBCRvti4V8b4RHiI89jgUb1SWZdfwCcLtzJ+7mbueHc+TSJCGN6pOUOzEjmzTZzdQW9qncr02lqM0yYSAnwnIpvc962AFf4Nz5iGp3VCJHcOyeCXZ7fl27U7GT83lwkL8nh39iYah3rIzmzG0KxEsjObEdPIbng0gVeZEsmFfo/CGPMjQUHCwIwEBmYkOCMSr9vFl0u389Wy7Xy2eCvBQcIZbeIY1imJoR2TSIgKC3TIpoGqTK+tjTURiDGmfOEhHgZnNmNwZjP+eHEn5m/ey5fLtvHl0u088OESHvxoCb3TYrmkRzIjuibTKNSqv0zNqUobiTGmFggK+uFZ9PcNa8/K7QeYuHgbExdv5bcfLOaJiSs4p0MzhnRIZFC7BCLD7N/c+Jf9hRlTh4kI7ZOiaZ8Uza+GZDBr/W7Gzd7E/5Y7T3sM8Qj9WsdxTvtm9GwVS2ZSlN1Rb3zOEokx9YSIkzT6tY6jqLiEeRv38L8VO5i8fDuPfOL01G8c6mFYp+ac3zmJM9rEERFqlwBTffZXZEw9FOwJom/rOPq2juN353dg8+5DLMzdy7RVzl31H3yfS6gnyBlMMjOBczokkh7fONBhmzrKEokxDUBqbASpsRFc2KUFj43sxNwNe5i6agdTV+Xzh8+W84fPltM6oTFDOiQSd6SYAcUlBHusCsxUjiUSYxqY8BDP8WeoPHABbN59iK/dKrDXv11PYbHywuLJDM5M4NyOSWRnJtDYGuxNBeyvw5gGLjU2guvPTOP6M9M4cKSQlz6aytageKas2MFHC7YQFhxEr7SmnNkmnp90T7YxwMyPWCIxxhwXFR5C76RgsrO7UVyizF6/my+XbWPmut08PWklf/1yJWe1S+CK3i05p0MzQqz6y2CJxBhTDo975/wZbeIApwps/NzNjJ+7mVv+PY/4yDAu65nClb1TSbOG+gbNEokxplJSYyO4e2gmd56TwdRV+bw7ezOvfLOOl6aupV/rWK7s3ZIz2sTRLCrMnqnSwPg9kYjIMOAfgAcYq6pPlbPepcD7QG9VnevOux/4OVAM3KGqk/wdrzGmYsGeIM7pkMg5HRLZvv8I78/LZdycTdz13gIAosKDSW7SiOQmjWjhTi1jI+iTHmvjgdVTfk0kIuIBngfOBXKBOSIyQVWXlVkvCrgTmOU1ryNwJZAFtAAmi0g7VS32Z8zGmMpLjA7n9sFtufWsNszbtIeleftYm3+QrfsOk7f3CHM37mHf4cLj67eOb0yn5Bg6JUeT1SKGrBbRNIkIDeAnML7g7xJJH2CNqq4DEJFxwEig7AOxHgf+BNzrNW8kME5VjwLrRWSNu78Zfo7ZGFNFQUFC77RYeqfF/mhZwdEi1u4o4Lu1u5i/aQ/zNu5hwsItx5enxTmllb7pcfRJjyU1NqImQzc+4O9Ekgxs9nqfC/T1XkFEegCpqvqZiNxbZtuZZbZN9legxhj/iAwLpmtqE7qmNjk+b/fBYyzdso/Fefv4fuNeJi3dzvi5uQAkN2lE3/RY+qTHcmabeFrGWWKp7QLa2C4iQcDfgBuqsY+bgZsBEhISyMnJ8Uls/lRQUGBx+pDF6Vs1GWdHoGMruLplCHkFwazYXczK3cf4amke/52fB0BCIyErzkNWvIcOsR4iQ8XOZS3j70SSB6R6vU9x55WKAjoBOW4vjyRggoiMqMS2AKjqy8DLAJmZmZqdne3D8P0jJycHi9N3LE7fqg1xqipr8wv4ds0upq/Zycy1u8jJPYoI9EmLpVPjQn517oBaP0R+bTiXNcHfv4U5QIaIpOMkgSuBq0sXquo+IL70vYjkAPeo6lwROQy8IyJ/w2lszwBm+zleY0wtICK0bRZF22ZRXH9mGkXFJSzM3ce0Vfl8vCCPWeuP8c7KyZzbMZHzspI4K9OeuxJIfj3zqlokImOASTjdf19T1aUi8hgwV1UnVLDtUhEZj9MwXwTcbj22jGmYgj1Bxx/mddeQDMZ+9DXrtBlfLNnKhIVbCPUE0b9tHOdlJXFOh0TrZlzD/J7CVXUiMLHMvIfKWTe7zPs/An/0W3DGmDpHRMho6uGm7M48PjKLeRv38OWy7Uxauo0pKxcjspieLZsyNCuRoR2T7K77GmBlQWNMneX93JXfX9CB5VsPHH+W/RMTV/DExBW0S4xkaMckhmYl0jk5xu669wNLJMaYekFE6Ngimo4torlrSDs27z7EV8u28+WybbyQs4bnpqyhTUJjbjgzjfOykmgWHR7okOsNSyTGmHopNTaCnw1I52cD0tl98BhfLdvGv2du4sGPl/Lgx0tplxjJgLYJDMyIp096rD1zpRrszBlj6r3YxqFc0bslo3qlsmzrfr5ZvZPpq3fy71kbee3b9YR4hF6tYjkrM4Gz2iXQPinKqsCqwBKJMabBEBF3jK8YbjmrDUcKi5mzYTffrN7JtFX5PPX5Cp76fAWtExpz/RlpDO+cRLMoqwI7FUskxpgGKzzEw8CMBAZmJPC78zuwbd8Rclbu4N3Zm3h4wlIenrCUrBbRZGcmkJ3ZjO6pTexZ9idhicQYY1xJMeFc2aclV/ZpybIt+5mycgdTV+bz0tR1PD9lLdHhwQzMSDheBZZoDfaAJRJjjDmp0h5gtw9uy77DhXy7Zic5K3eQszKfzxZvBaBDc7e00i6BHq2aNthHD1siMcaYU4hpFML5nZtzfufmqCrLtx4gZ5WTVF6eto4Xc9YSFRZM/7bxDGqXwKB28aQ0bTijFlsiMcaYKvC+X+W27LbsP1LIt6t3MnVVPtNW5fPF0m2A8xCvtIijHInfyhmt44mJCAlw5P5jicQYY6ohOjyE4Z2bM9wtrazNLyBnZT7frd3Ft6sP8vW/vydIoFNyDP1ax9E7LZZerZrStHH9eTKkJRJjjPER71GLRw9szeSvpxDTuivTV+/ku7U7+de3G3h52joA2idFcWabeAZkxNEnPa5Oj15cdyM3xphaLtjrEcS/OrcdRwqLWbh5L3M27GbGul3Hb4gMDhK6pTahf9t4BmTE0y21SZ1quLdEYowxNSQ8xHN8kMkxZ2dwpLCYeRv3MH3NTr5bs5Nnvl7NP/63mohQD73SYslqEU2nFjH0Smtaq7saWyIxxpgACQ/x0L9tPP3bOs/323voGDPXOU+FnLN+D9+t2UlRiQKQGtuI3q2cZ9n3ax1Hq7iIWjOMiyUSY4ypJZpEhDKsU3OGdWoOwLGiEpZt3c/cDbuZu2EPU1flH3+WfWJ0GH3T4+jXOo6BGfGkxgauu7ElEmOMqaVCg4PoltqEbqlNGD3wh2fZz1y3m5nrdjFj3S4mLNwCQHp8YwZmxDMoI4F+bWq28d4SiTHG1BHevcKu7dfqeGL5ZvVOvlm9k//MzeXNGRsJDhI6p8TQq1VTd5DKaNLjG/ttnDBLJMYYU0d5J5Yb+6dztMhtvF+9k5nrdvHGdxs5VlwCQFhwEO2TopybKZtH07FFDB2bR9Mo1FPtOCyRGGNMPREW7OHMNvGc2cZpvC8sLmFtfgHLtux3pq37+XzJNt6dvRmAIIF/j+5b7eNaIjHGmHoqxBNE+6Ro2idFc0kPZ56qsnXfEZZu2c/ivH20S4yq9nEskRhjTAMiIrRo0ogWTRpxbsdEn+yz7tw6aYwxplbyeyIRkWEislJE1ojIfSdZfouILBaRBSIyXUQ6uvPTROSwO3+BiLzk71iNMcZUnV+rtkTEAzwPnAvkAnNEZIKqLvNa7R1VfcldfwTwN2CYu2ytqnbzZ4zGGGOqx98lkj7AGlVdp6rHgHHASO8VVHW/19vGgPo5JmOMMT7k70SSDGz2ep/rzjuBiNwuImuBPwN3eC1KF5H5IjJVRAb6N1RjjDGnQ1T9VwAQkcuAYao62n1/HdBXVceUs/7VwHmqer2IhAGRqrpLRHoCHwFZZUowiMjNwM0ACQkJPcePH++3z+MrBQUFREZGBjqMU7I4fcvi9J26ECPUnTgHDx48T1V7nfYOVNVvE3AGMMnr/f3A/RWsHwTsK2dZDtCrouO1a9dO64IpU6YEOoRKsTh9y+L0nboQo2rdiROYq9W41vu7amsOkCEi6SISClwJTPBeQUQyvN5eAKx25ye4jfWISGsgA1jn53iNMcZUkV97balqkYiMASYBHuA1VV0qIo/hZMAJwBgRGQIUAnuA693NBwGPiUghUALcoqq7/RmvMcaYqvP7ne2qOhGYWGbeQ16v7yxnuw+AD/wbnTHGmOqyO9uNMcZUiyUSY4wx1WKJxBhjTLVYIjHGGFMtlkiMMcZUiyUSY4wx1WKJxBhjTLVYIjHGGFMtlkiMMcZUiyUSY4wx1WKJxBhjTLVYIjHGGFMtlkiMMcZUiyUSY4wx1WKJxBhjTLVYIjHGGFMtlkiMMcZUiyUSY4wx1WKJxBhjTLWIqgY6Bp8RkQPAykDHUQnxwM5AB1EJFqdvWZy+UxdihLoTZ6aqRp3uxsG+jKQWWKmqvQIdxKmIyFyL03csTt+qC3HWhRihbsVZne2tassYY0y1WCIxxhhTLfUtkbwc6AAqyeL0LYvTt+pCnHUhRmggcdarxnZjjDE1r76VSIwxxtQwSyTGGGOqpd4kEhEZJiIrRWSNiNwX6HhKiUiqiEwRkWUislRE7nTnPyIieSKywJ3OrwWxbhCRxW48c915sSLylYisdn82DWB8mV7na4GI7BeRu2rDuRSR10Rkh4gs8Zp30nMnjmfcv9VFItIjwHE+LSIr3Fg+FJEm7vw0ETnsdV5fCnCc5f6eReR+93yuFJHzAhzne14xbhCRBe78gJzPCq5Bvvv7VNU6PwEeYC3QGggFFgIdAx2XG1tzoIf7OgpYBXQEHgHuCXR8ZWLdAMSXmfdn4D739X3AnwIdp9fvfBvQqjacS2AQ0ANYcqpzB5wPfA4I0A+YFeA4hwLB7us/ecWZ5r1eLTifJ/09u/9PC4EwIN29FngCFWeZ5X8FHgrk+azgGuSzv8/6UiLpA6xR1XWqegwYB4wMcEwAqOpWVf3efX0AWA4kBzaqKhkJvOG+fgO4OICxeDsHWKuqGwMdCICqTgN2l5ld3rkbCbypjplAExFpHqg4VfVLVS1y384EUmoiloqUcz7LMxIYp6pHVXU9sAbnmuB3FcUpIgKMAt6tiVjKU8E1yGd/n/UlkSQDm73e51ILL9YikgZ0B2a5s8a4RcfXAlll5EWBL0Vknojc7M5LVNWt7uttQGJgQvuRKznxH7S2nUso/9zV5r/Xn+F8Gy2VLiLzRWSqiAwMVFBeTvZ7rq3ncyCwXVVXe80L6Pkscw3y2d9nfUkktZ6IRAIfAHep6n7gRaAN0A3YilMEDrQBqtoDGA7cLiKDvBeqU+4NeH9xEQkFRgD/cWfVxnN5gtpy7ioiIg8ARcDb7qytQEtV7Q78GnhHRKIDFR914PdcxlWc+GUnoOfzJNeg46r791lfEkkekOr1PsWdVyuISAjOL/BtVf0vgKpuV9ViVS0BXqGGiuIVUdU89+cO4EOcmLaXFmvdnzsCF+Fxw4HvVXU71M5z6Srv3NW6v1cRuQG4ELjGvaj8f3v39yJVGcdx/P3BQEVkIfOii8AVFKFAi4QsAy8iKipou1AKEuoigwr0QiT/gb2IoIsgAkEIicAo5yIsCiqxC5e2dV0zK7qSfhAWS7UUKl8vvs+Rs8PMpp1pzmKfFww7++yZZ5595jDfOc858/1SlorOl/tfkOce1rc1xgVe58U4nzcAY8DbgQrSzwAAA2lJREFUVVub89nrPYgB7p/XSyCZANZJGi2fVncAnZbHBFxZJz0AnImIV2rt9TXHx4CZ7scOk6QVklZW98kTsDPkPO4sm+0EjrQzwnnmfdJbbHNZ02/uOsBT5eqYu4DZ2hLD0El6ANgLPBoRc7X21ZKWlPtrgXXA9+2McsHXuQPskLRU0ig5zhPDHl+X+4CvI+Jc1dDWfPZ7D2KQ++ewryD4r27klQbfkFF+f9vjqY1rK3nIOA1MldtDwJvAqdLeAW5ueZxryStfTgKnqzkEVgEfA98CHwE3tjzOFcB5YKTW1vpckoHtR+ACuab8TL+5I6+Gea3sq6eAO1se53fkmni1f75etn287AtTwCTwSMvj7Ps6A/vLfJ4FHmxznKX9ILCra9tW5nOB96CB7Z9OkWJmZo1cL0tbZmbWEgcSMzNrxIHEzMwacSAxM7NGHEjMzKwRBxKzGkl/lJ9rJD0x4L5f6vr980H2b9YWBxKz3tYA1xRIyreZFzIvkETE3dc4JrNFyYHErLdx4N5SN2K3pCXKuh0TJWngswCStkk6JqkDfFXa3iuJL09XyS8ljQPLS3+HSlt19KPS94yyHsz2Wt+fSDqsrBdyqHxLGUnjyvoS05JeHvrsmNX80ycos/+rfWTti4cBSkCYjYjNkpYCxyV9WLa9A7gtMoU5wNMR8auk5cCEpHciYp+k5yNiU4/nGiMTEW4EbiqP+az87XbgVuAH4Dhwj6QzZIqQDRERKoWozNriIxKzq3M/mX9oikzBvYrMlQRwohZEAF6UdJKs7XFLbbt+tgJvRSYk/Bn4FNhc6/tcZKLCKXLJbRb4CzggaQyY69Gn2dA4kJhdHQEvRMSmchuNiOqI5M8rG0nbyIR9WyJiI/AlsKzB8/5du3+JrGR4kcx8e5jM2Hu0Qf9mjTmQmPX2O1mWtPIB8FxJx42k9SVLcrcR4LeImJO0gSxVWrlQPb7LMWB7OQ+zmizf2jd7bakrMRIR7wO7ySUxs9b4HIlZb9PApbJEdRB4lVxWmiwnvH+hd9nho8Cuch7jLLm8VXkDmJY0GRFP1trfBbaQmZcD2BsRP5VA1MtK4IikZeSR0p5/9y+aDYaz/5qZWSNe2jIzs0YcSMzMrBEHEjMza8SBxMzMGnEgMTOzRhxIzMysEQcSMzNr5DLV5zTZsWlZMQAAAABJRU5ErkJggg==\n" 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**LGBM identified 66 potential customers** 👍" + ], + "metadata": { + "id": "M0C4mDVSmQSO" + } + }, + { + "cell_type": "markdown", + "source": [ + "# **XGBoost**" + ], + "metadata": { + "id": "K-DeYbY-nebn" + } + }, + { + "cell_type": "markdown", + "source": [ + "**XGBoost is a decision-tree-based ensemble method that uses gradient boosting framework. It is a perfect combination of software and hardware optimization techniques to yield superior results.**" + ], + "metadata": { + "id": "15184-cQnebo" + } + }, + { + "cell_type": "markdown", + "source": [ + "**Library and Data**" + ], + "metadata": { + "id": "PYZDNKVdnebo" + } + }, + { + "cell_type": "code", + "source": [ + "import xgboost as xgb\n", + "# https://xgboost.readthedocs.io/en/stable/python/index.html" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2022-03-16T02:42:57.039970Z", + "iopub.execute_input": "2022-03-16T02:42:57.040282Z", + "iopub.status.idle": "2022-03-16T02:42:58.156938Z", + "shell.execute_reply.started": "2022-03-16T02:42:57.040250Z", + "shell.execute_reply": "2022-03-16T02:42:58.155155Z" + }, + "trusted": true, + "id": "GEOOElnFnebo" + }, + "execution_count": 13, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**Model and Accuracy**" + ], + "metadata": { + "id": "8rkdle4onebp" + } + }, + { + "cell_type": "code", + "source": [ + "m_xgb = xgb.XGBClassifier(objective='binary:logistic', \n", + " n_estimators=200, \n", + " learning_rate=0.05, \n", + " max_depth=7, \n", + " colsample_bytree=0.8,\n", + " random_state =7, \n", + " nthread = -1)\n", + "eval_set = [(X_train, y_train), (X_test, y_test)]\n", + "m_xgb.fit(X_train, y_train, eval_set=eval_set, eval_metric='logloss', verbose=0)\n", + "print(m_xgb.score(X_test, y_test))\n", + "res = m_xgb.evals_result()\n", + "plt.plot(res['validation_0']['logloss'], label='Train')\n", + "plt.plot(res['validation_1']['logloss'], label='valid_1')\n", + "plt.legend()\n", + "plt.grid()\n", + "y_test_pred = m_xgb.predict(X_test)\n", + "skplt.metrics.plot_confusion_matrix(y_test, y_test_pred)" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2022-03-16T02:43:40.911673Z", + "iopub.execute_input": "2022-03-16T02:43:40.911992Z", + "iopub.status.idle": "2022-03-16T02:43:42.383821Z", + "shell.execute_reply.started": "2022-03-16T02:43:40.911965Z", + "shell.execute_reply": "2022-03-16T02:43:42.382802Z" + }, + "trusted": true, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 578 + }, + "id": "yjKo-tI5nebp", + "outputId": "57a7a0af-4e47-461a-a175-50de961564b0" + }, + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "0.7288\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 14 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Catboost" + ], + "metadata": { + "id": "cdm_I3j8nebp" + } + }, + { + "cell_type": "markdown", + "source": [ + "**Catboost is a type of gradient boosting algorithms which can automatically deal with categorical variables without showing the type conversion error, which helps you to focus on tuning your model better rather than sorting out trivial errors.Make sure you handle missing data well before you proceed with the implementation.\n", + "**" + ], + "metadata": { + "id": "wTfoZauonebq" + } + }, + { + "cell_type": "code", + "source": [ + "pip install catboost" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Iun4-p_TraqE", + "outputId": "c47d4048-76df-47c5-8de2-e8829d540009" + }, + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting catboost\n", + " Downloading catboost-1.0.4-cp37-none-manylinux1_x86_64.whl (76.1 MB)\n", + "\u001b[K |████████████████████████████████| 76.1 MB 54 kB/s \n", + "\u001b[?25hRequirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from catboost) (1.4.1)\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from catboost) (3.2.2)\n", + "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.7/dist-packages (from catboost) (1.3.5)\n", + "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from catboost) (1.21.5)\n", + "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from catboost) (1.15.0)\n", + "Requirement already satisfied: graphviz in /usr/local/lib/python3.7/dist-packages (from catboost) (0.10.1)\n", + "Requirement already satisfied: plotly in /usr/local/lib/python3.7/dist-packages (from catboost) (5.5.0)\n", + "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->catboost) (2018.9)\n", + "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->catboost) (2.8.2)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (1.3.2)\n", + "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (3.0.7)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (0.11.0)\n", + "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.7/dist-packages (from plotly->catboost) (8.0.1)\n", + "Installing collected packages: catboost\n", + "Successfully installed catboost-1.0.4\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import catboost as cgb\n", + "# https://catboost.ai/en/docs/concepts/python-quickstart" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2022-03-16T02:42:59.635194Z", + "iopub.execute_input": "2022-03-16T02:42:59.635556Z", + "iopub.status.idle": "2022-03-16T02:42:59.779086Z", + "shell.execute_reply.started": "2022-03-16T02:42:59.635525Z", + "shell.execute_reply": "2022-03-16T02:42:59.777848Z" + }, + "trusted": true, + "id": "IQ7BMnr4nebq" + }, + "execution_count": 16, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "m_cgb = cgb.CatBoostClassifier(n_estimators=200,\n", + " learning_rate=0.05,\n", + " max_depth=7,\n", + " min_data_in_leaf= 2, \n", + " colsample_bylevel=0.8,\n", + " random_state =7)\n", + "eval_set = [(X_test, y_test)]\n", + "m_cgb.fit(X_train, y_train, eval_set=eval_set, verbose=0)\n", + "\n", + "print(m_cgb.score(X_test, y_test))\n" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2022-03-16T02:43:57.815598Z", + "iopub.execute_input": "2022-03-16T02:43:57.815904Z", + "iopub.status.idle": "2022-03-16T02:43:58.775253Z", + "shell.execute_reply.started": "2022-03-16T02:43:57.815876Z", + "shell.execute_reply": "2022-03-16T02:43:58.774410Z" + }, + "trusted": true, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "mCKdmJMYnebq", + "outputId": "40a84ea0-c6f0-485d-de69-629cfd03ce30" + }, + "execution_count": 17, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "0.7416\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "%%capture cap --no-stderr\n", + "m_cgb.fit(X_train, y_train, eval_set=eval_set, verbose=1)\n" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2022-03-16T02:44:04.498671Z", + "iopub.execute_input": "2022-03-16T02:44:04.499173Z", + "iopub.status.idle": "2022-03-16T02:44:05.505082Z", + "shell.execute_reply.started": "2022-03-16T02:44:04.499124Z", + "shell.execute_reply": "2022-03-16T02:44:05.504193Z" + }, + "trusted": true, + "id": "Dly2M_Q5nebq" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "epoc = cap.stdout.split('\\n')\n", + "import re\n", + "loss = np.zeros((200,2))\n", + "for i in range(200):\n", + " temp = re.split('\\t| ', epoc[i])\n", + " loss[i,0]= temp[2] # train loss\n", + " loss[i,1]= temp[4] # test loss\n", + "plt.plot(loss[:,0], label='Train')\n", + "plt.plot(loss[:,1], label='valid_1')\n", + "plt.legend()\n", + "plt.grid()\n", + "y_test_pred = m_cgb.predict(X_test)\n", + "skplt.metrics.plot_confusion_matrix(y_test, y_test_pred)" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2022-03-16T02:44:06.975778Z", + "iopub.execute_input": "2022-03-16T02:44:06.976375Z", + "iopub.status.idle": "2022-03-16T02:44:07.389245Z", + "shell.execute_reply.started": "2022-03-16T02:44:06.976315Z", + "shell.execute_reply": "2022-03-16T02:44:07.387890Z" + }, + "trusted": true, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 235 + }, + "id": "CxIXygm6nebr", + "outputId": "3e17909f-69cc-45be-8645-203030b680cf" + }, + "execution_count": 19, + "outputs": [ + { + "output_type": "error", + "ename": "IndexError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m200\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mtemp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mre\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\t| '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mloss\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mtemp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m# train loss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mtemp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m# test loss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Train'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mIndexError\u001b[0m: list index out of range" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Bonus Question: Why GBM only identified around 60 out of 374 customers in the test data set? Can you help the bank to improve?" + ], + "metadata": { + "id": "AAUww25hmiEG" + } + }, + { + "cell_type": "markdown", + "source": [ + "Hint: Remember the classes are very imblanced 70% 0's, 30% 1's" + ], + "metadata": { + "id": "ch117ueMm57Z" + } + } + ] +} \ No newline at end of file From 06454b55d275c334b9c6328d9e9b6c9052216988 Mon Sep 17 00:00:00 2001 From: Juntao <41448486+juntaoduan@users.noreply.github.com> Date: Thu, 17 Mar 2022 13:48:14 -0400 Subject: [PATCH 3/3] Update Readme.md --- Classification/Readme.md | 1 + 1 file changed, 1 insertion(+) diff --git a/Classification/Readme.md b/Classification/Readme.md index ada5066..852ad8f 100644 --- a/Classification/Readme.md +++ b/Classification/Readme.md @@ -5,3 +5,4 @@ ### [Support vector machine with breast cancer data](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Classification/Support_Vector_Machine_Classification.ipynb) ### [Skewed logistic regression (imbalanced class divisions)](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Classification/Skewed_Logistic_Regression.ipynb) ### [k-Nearest neighbor classification](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Classification/KNN_Classification.ipynb) +### [Gradient Boosting](https://github.com/juntaoduan/Machine-Learning-with-Python/blob/master/Classification/gradient_boosting.ipynb)