{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "___\n", "\n", "\n", "___\n", "
Copyright by Pierian Data Inc.
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For more information, visit us at www.pieriandata.com
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Support Vector Machines \n", "## Exercise - Solutions\n", "\n", "## Fraud in Wine\n", "\n", "Wine fraud relates to the commercial aspects of wine. The most prevalent type of fraud is one where wines are adulterated, usually with the addition of cheaper products (e.g. juices) and sometimes with harmful chemicals and sweeteners (compensating for color or flavor).\n", "\n", "Counterfeiting and the relabelling of inferior and cheaper wines to more expensive brands is another common type of wine fraud.\n", "\n", "\n", "\n", "## Project Goals\n", "\n", "A distribution company that was recently a victim of fraud has completed an audit of various samples of wine through the use of chemical analysis on samples. The distribution company specializes in exporting extremely high quality, expensive wines, but was defrauded by a supplier who was attempting to pass off cheap, low quality wine as higher grade wine. The distribution company has hired you to attempt to create a machine learning model that can help detect low quality (a.k.a \"fraud\") wine samples. They want to know if it is even possible to detect such a difference.\n", "\n", "\n", "Data Source: *P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferences by data mining from physicochemical properties.\n", "In Decision Support Systems, Elsevier, 47(4):547-553, 2009.*\n", "\n", "---\n", "---\n", "\n", "**TASK: Your overall goal is to use the wine dataset shown below to develop a machine learning model that attempts to predict if a wine is \"Legit\" or \"Fraud\" based on various chemical features. Complete the tasks below to follow along with the project.**\n", "\n", "---\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Complete the Tasks in bold\n", "\n", "**TASK: Run the cells below to import the libraries and load the dataset.**" ] }, { "cell_type": "code", "execution_count": 96, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv(\"../DATA/wine_fraud.csv\")" ] }, { "cell_type": "code", "execution_count": 98, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholqualitytype
07.40.700.001.90.07611.034.00.99783.510.569.4Legitred
17.80.880.002.60.09825.067.00.99683.200.689.8Legitred
27.80.760.042.30.09215.054.00.99703.260.659.8Legitred
311.20.280.561.90.07517.060.00.99803.160.589.8Legitred
47.40.700.001.90.07611.034.00.99783.510.569.4Legitred
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" ], "text/plain": [ " fixed acidity volatile acidity citric acid residual sugar chlorides \\\n", "0 7.4 0.70 0.00 1.9 0.076 \n", "1 7.8 0.88 0.00 2.6 0.098 \n", "2 7.8 0.76 0.04 2.3 0.092 \n", "3 11.2 0.28 0.56 1.9 0.075 \n", "4 7.4 0.70 0.00 1.9 0.076 \n", "\n", " free sulfur dioxide total sulfur dioxide density pH sulphates \\\n", "0 11.0 34.0 0.9978 3.51 0.56 \n", "1 25.0 67.0 0.9968 3.20 0.68 \n", "2 15.0 54.0 0.9970 3.26 0.65 \n", "3 17.0 60.0 0.9980 3.16 0.58 \n", "4 11.0 34.0 0.9978 3.51 0.56 \n", "\n", " alcohol quality type \n", "0 9.4 Legit red \n", "1 9.8 Legit red \n", "2 9.8 Legit red \n", "3 9.8 Legit red \n", "4 9.4 Legit red " ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**TASK: What are the unique variables in the target column we are trying to predict (quality)?**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 99, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['Legit', 'Fraud'], dtype=object)" ] }, "execution_count": 99, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['quality'].unique()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**TASK: Create a countplot that displays the count per category of Legit vs Fraud. Is the label/target balanced or unbalanced?**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 100, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 100, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.countplot(x='quality',data=df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**TASK: Let's find out if there is a difference between red and white wine when it comes to fraud. Create a countplot that has the wine *type* on the x axis with the hue separating columns by Fraud vs Legit.**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 101, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 101, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.countplot(x='type',hue='quality',data=df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**TASK: What percentage of red wines are Fraud? What percentage of white wines are fraud?**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 109, "metadata": {}, "outputs": [], "source": [ "reds = df[df[\"type\"]=='red']" ] }, { "cell_type": "code", "execution_count": 110, "metadata": {}, "outputs": [], "source": [ "whites = df[df[\"type\"]=='white']" ] }, { "cell_type": "code", "execution_count": 114, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Percentage of fraud in Red Wines:\n", "3.9399624765478425\n" ] } ], "source": [ "print(\"Percentage of fraud in Red Wines:\")\n", "print(100* (len(reds[reds['quality']=='Fraud'])/len(reds)))" ] }, { "cell_type": "code", "execution_count": 115, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Percentage of fraud in White Wines:\n", "3.7362188648427925\n" ] } ], "source": [ "print(\"Percentage of fraud in White Wines:\")\n", "print(100* (len(whites[whites['quality']=='Fraud'])/len(whites)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**TASK: Calculate the correlation between the various features and the \"quality\" column. To do this you may need to map the column to 0 and 1 instead of a string.**" ] }, { "cell_type": "code", "execution_count": 116, "metadata": {}, "outputs": [], "source": [ "df['Fraud']= df['quality'].map({'Legit':0,'Fraud':1})" ] }, { "cell_type": "code", "execution_count": 118, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "fixed acidity 0.021794\n", "volatile acidity 0.151228\n", "citric acid -0.061789\n", "residual sugar -0.048756\n", "chlorides 0.034499\n", "free sulfur dioxide -0.085204\n", "total sulfur dioxide -0.035252\n", "density 0.016351\n", "pH 0.020107\n", "sulphates -0.034046\n", "alcohol -0.051141\n", "Fraud 1.000000\n", "Name: Fraud, dtype: float64" ] }, "execution_count": 118, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.corr()['Fraud']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**TASK: Create a bar plot of the correlation values to Fraudlent wine.**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# CODE HERE" ] }, { "cell_type": "code", "execution_count": 121, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 121, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df.corr()['Fraud'][:-1].sort_values().plot(kind='bar')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**TASK: Create a clustermap with seaborn to explore the relationships between variables.**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 123, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 123, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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UqyQbV9WK2XxN5yBLkiT1LMniJD9K8iHgbODuSV6f5Mwk5yd5a7vdlkm+nOS8JBcmObBdfmqSsfbxi5L8d5LTgId1XuOoJM/sPL+2/bxVklOSnJ3kgiRPWU3W6TJcnGS79vFYklPbx3dK8rX2+B9N8svOdl9MclaSHyZZ0s2W5G1Jvg88ZJ2/wTPkCLIkSdIw3Bt4UVW9PMljgZ2BBwIBjk+yL3An4JKqeiJAkm26B0iyPfBWYC/gauCbwDmred3lwNOq6o9tcf1ekuOrqqbZ/vGjMkzhcOAbVfUvSR4PLOmse3FVXZlkc+DMJJ+vqiuALYELq+otqzn2BmFBHmEAJyNuWAc9r+8EkiQtGO0IabccLquqZZ3nv6yq77WPH9t+TJTbrWgK87eAdyd5B3BCVX1r0ss8CDi1qv63fc3PALusLhrwz20BXwncDbgL8Ltptr9gNRkm2wd4GkBVnZjkqs66VyV5Wvv47u3XeAWwAvj8ao67wViQR+v1ZMQN7V7vfc/hfWeQJGmhaMvwshGbXNd5HOBfquqjkzdKshfwBOBfkpxcVW+b/FLTHP9m2um17TznzdrlB9GMTO9VVX9KcjEjBgir6r+nyXDL8Sftn6mOk2Q/YH/gIVV1fTslY2K/5bM977jLOciSJEnDcxLw4iRbASS5W5I7J9kBuL6qPgm8G3jApP2+D+yX5M+SbAo8q7PuYpqpFwBPATZtH28DXNaW40cC9xgVbESG7vGf0dnl28Cz230fC9yh87pXteV4V+DBo153NjmCLEmSNDBVdXKSvwS+2wz2ci3wfOAvgHclWQn8CXjZpP0uTbIU+C5wKc0Jfxu3q/8D+FKSHwCncOuI9aeA/0oyDpwL/Hg18e4/TYa3Av+Z5B9pijqd5ce2J/Od1ua6BjgReGmS84GfAN9jICzIkiRJPauqi4H7TVr2PuB9kzb9H5rR5cn779d5fCRw5BTb/J5VR2n/oV1+OdNcKWKqayBX1UnTZPgWU893vhp4XFXdnOQhwCOr6sZ23QFr+rqzyYIsSZKkDWlH4LNJNgJuAv625zyrZUGWJEnSBlNVPwX27DvHTHiSniRJktRhQZYkSZI6LMiSJElShwVZkiRJ6rAgS5IkSR0WZEmSJKnDgixJkiR1WJAlSZKkDguyJEmS1GFBliRJkjosyJIkSVKHBVmSJEnq2KTvAOpPqu8E09tz/MC+I4x0zthn+o4w0gf+sGPfEUb61I5P6DvCtH574r37jjDSDr+9se8II2229cZ9RxjpptsPO1+l7wSj3bDdcL9/G93cdwLNJ44gS5IkSR0WZEmSJKnDgixJkiR1WJAlSZKkDguyJEmS1GFBliRJkjosyJIkSVKHBVmSJEnqsCBLkiRJHRZkSZIkqWOu3mp6+djY2NJZeJ3Fs/AakiRJGpA5WZDHx8ePmI3XmaUSLkmSpAFxioUkSZLUYUGWJEmSOizIkiRJUocFWZIkSeqwIEuSJA1Aklcl+VGST/WcY2mSQ9vHuyY5N8k5SXZaT8e/OMl27ePvrOUxXprkBVMsX5zkwnXNOCevYiFJkjQPvRw4oKp+0V2YZJOqurmnTE8FvlRVh6/pDjPJW1UPXZtQVfWRtdlvTTmCLEmS1LMkHwHuBRyf5JB2FHdZkpOBTyS5U5LPJzmz/XhYu9+WST7eLjsnyVOmOPb2SU5vR4IvTPLwdvm1nW2emeSoSfs9AXgN8DdJvjl5dDbJoUmWto9PTfLPSU4DXj3pOH+W5OQ230eBdNZd235Okne1+S5IcmC7/P1J3tI+flz7dWw0aZR7ryTnJfku8IrOsTduj3lmkvOT/N2a/nk4gixJktSzqnppkscDj6yqy9viuRewT1XdkOT/Ae+tqm8n2RE4CfhL4E3AN6rqxUm2BX6Q5OtVdV3n8M8DTqqqtyfZGNhiDTN9pS3u11bVu5MsXs0u21bVI6ZYfjjw7ap6W5InAkum2ObpwB7A7sB2wJlJTgcOax9/C3g/8ISqWpmku++RwN9X1WlJ3tVZ/hLg6qraO8ntgDOSnDx5hH4qFuQBGxsbOwxYtMFe4HnP22CHliRJq0qyhFXL4bKqWjZil+Or6ob28f7AfTrF8PZJtgYeCzx5YjSVpjfsCPyoc5wzgY8n2RT4YlWdu25fybQ+M83yfWkKMFX15SRXTbHNPsCxVbUC+H07Er13VR2f5G+B04FDqup/ujsl2YammJ/WLjoGOKB9/FhgtyTPbJ9vA+wMWJDnuEXj4+NLN9TBd3rPe9Z4PpEkSVo3bRkeVYgn644CbwQ8pFOYgWZqAvCMqvrJiNc9Pcm+wBOBY5K8q6o+AVRnszUZkLuZVafnTt7nOqZXI9ZBZ9rFFO4PXAHsMM1+0x07NCPLJ63mtW/DOciSJEnDdzLwyoknSfZoH54E/H1blEmy5+Qdk9wDuKyq/gP4T+AB7arfJ/nLJBsBT1uDDL8H7tzOKb4d8KQ1zH46cFCb5QDgDtNsc2A7b/hONKPOP2izvw7YEzggyYO6O1XVH4Crk+zTLjqos/ok4GXtyDlJdkmy5ZoEdgRZkiRp+F4F/HuS82n62+nAS4F/Av4NOL8tyRdz2+K6H/D6JH8CrgUmLo92GHAC8GvgQmCrUQGq6k9J3gZ8n2aawo/XMPtbgWOTnA2cBvxqim2+ADwEOI9mRPgNNIX8a8ChVXVJkpcARyXZe9K+L6KZQnI9TSme8DFgMXB2+735X5qrcqyWBVmSJGkAqmpx5/HSSesuBw6cYp8bgJFXZ6iqo4Gjp1h+HHDcFMuXTvW4ff5+mpPlJu+z34jXv4JmPvCEQzrrtmo/F/D69qNr/862Z9FMtwBYOmn57p19lrbLVwL/2H7MiFMsJEmSpA4LsiRJktRhQZYkSZI6LMiSJElShwVZkiRJ6rAgS5IkSR0WZEmSJKnDgixJkiR1WJAlSZKkDguyJEmS1GFBliRJkjosyJIkSVKHBVmSJEnqsCBLkiRJHZv0HUD9WbFoZd8RpnXnra7tO8JIH/jDjn1HGOnvt/1V3xFG+twfh/t3j2zcd4KRNrn+T31HGGnlpuk7wkib9R1gdTLs79+KzYab78ZtHPPT+mNBHm352NjY0h5ff3GPry1JkrQgWZBHGB8fP6LP1++5nEuSJC1Ivh8hSZIkdViQJUmSpA4LsiRJktRhQZYkSZI6LMiSJElShwVZkiRJ6rAgS5IkSR0WZEmSJKnDgixJkiR1WJAlSZKkDguyJEmS1GFBliRJkjosyJIkSVKHBVmSJEnqsCBLkiT1LMm2SV6+BtstTvK8NdzuwvWQa2mSQ9vHuyY5N8k5SXZa12O3x7w4yXbt4++s5TFemuQFUyxf6++BBVmSJKl/2wKrLcjAYmC1BXkDeSrwparas6r+Z012SLLJmh68qh66NqGq6iNV9Ym12Xc6FmRJkqT+HQHs1I7QviuNdyW5MMkFSQ7sbPfwdrtD2lHSbyU5u/0YWTKTbJ/k9Hb/C5M8vF1+bWebZyY5atJ+TwBeA/xNkm9OHp1NcmiSpe3jU5P8c5LTgFdPOs6fJTm5HYX+KJDOumvbz1N+7Unen+Qt7ePHtV/HRpNGufdKcl6S7wKv6Bx74/aYZyY5P8nfjfo+rXGrVy+Wj42NLd1gR3/xczbYoSVJ0qqSLAGWdBYtq6pl7ePDgPtV1R7tts8A9gB2B7YDzkxyervdoVX1pHa7LYDHVNXyJDsDxwJjI2I8Dzipqt6eZGNgizXJXlVfSfIR4NqqeneSxavZZduqesQUyw8Hvl1Vb0vyRFb9fkx4OtN/7Wcm+RbwfuAJVbUySXffI4G/r6rTkryrs/wlwNVVtXeS2wFnJDm5qn4xVXgL8oCNj48fsSGPv/hD7z58Qx5fkiTdqi3Dy1a7YWMf4NiqWgH8vh2N3Rv446TtNgU+mGQPYAWwy2qOeybw8SSbAl+sqnPXMM9MfWaa5fvSFGCq6stJrppimym/9qo6PsnfAqcDh0ye5pFkG5piflq76BjggPbxY4Hdkjyzfb4NsDNgQZYkSZojsvpNADgE+D3NaOtGwPJRG1fV6Un2BZ4IHJPkXe383epstmgNXvdmVp2qO3mf60bFWM2xR33t9weuAHaYZr/pjh2akeWTVvPagHOQJUmShuAaYOvO89OBA9u5s3eiGXn9wRTbbQNcWlUrgb8GNh71IknuAVxWVf8B/CfwgHbV75P8ZZKNgKetQd7fA3du5xTfDnjSGuwz8XUd1GY5ALjDNNvc5mtvs78O2BM4IMmDujtV1R+Aq5Ps0y46qLP6JOBl7cg5SXZJsuV0IR1BliRJ6llVXZHkjPbEt68CbwAeApxHMyr6hqr6XZIrgJuTnAccBXwI+HySZwHfZPTILcB+wOuT/Am4Fpi4PNphwAnAr4ELga1Wk/dPSd4GfJ9mmsKP1/BLfStwbJKzgdOAX02xzReY9LXTFPKv0cy/viTJS4Cjkuw9ad8X0UwhuZ6mFE/4GM0VQM5OM2n5f2muyjGlVK1ulPu2xsbGlo6Pjy+d8Y4alMUfevfM//Bnyc73+W3fEUZ60l0v6DvCSH+/7VQ/b4Zj35dPdU7GMFy+28jBl97teNK1q9+oRzdts1nfEUZasWjgb5xmTd9V78eKzYab7487DvzPFrjwXYcM9xuoVQz/b5MkSZI0iyzIkiRJUocFWZIkSeqwIEuSJEkdFmRJkiSpw4IsSZIkdViQJUmSpA4LsiRJktRhQZYkSZI6LMiSJElShwVZkiRJ6rAgS5IkSR0WZEmSJKnDgixJkiR1bNJ3APVny19v3HeEaa087s59RxjpUzs+oe8II33ujyv7jjDS6R9a1neEaT1uh937jjDSJa97aN8RRtpked8JRtvy0hV9Rxjpql2G+3MZ4MY7Vt8RprXFpX0n0HziCLIkSZLUYUGWJEmSOizIkiRJUocFWZIkSeqwIEuSJEkdFmRJkiSpw4IsSZIkdViQJUmSpA4LsiRJktRhQZYkSZI6LMiSJElShwVZkiRJ6rAgS5IkSR0WZEmSJKnDgixJkiR1WJAlSZIGKsnFSbZbi/2OSvLMGWy/OMmFM32d+cqCLEmSJHVYkCVJkgYgyReTnJXkh0mWTLH+BUnOT3JekmPaZfdIckq7/JQkO3Z22TfJd5L8fGI0OY13JbkwyQVJDpylL29O2aTvAJIkSQLgxVV1ZZLNgTOTfH5iRZL7Am8CHlZVlye5Y7vqg8AnquroJC8G3g88tV23PbAPsCtwPHAc8HRgD2B3YLv2dU7f4F/ZHLNeC/LY2NhhwKL1eUxtQI85qO8EkiQtGO2ocHdkeFlVLes8f1WSp7WP7w7s3Fn3KOC4qrocoKqubJc/hKb0AhwDvLOzzxeraiVwUZK7tMv2AY6tqhXA75OcBuwNnL9uX938sr5HkBeNj48vXc/H1AZy33947+F9Z5AkaaFoy/CyqdYl2Q/YH3hIVV2f5FRWHXQMUGvyMp3HN07av/tZIzgHWZIkqX/bAFe15XhX4MGT1p8CPDvJnwF0plh8B3hO+/gg4NureZ3TgQOTbJzkTsC+wA/WxxcwnzgHWZIkqX8nAi9Ncj7wE+B73ZVV9cMkbwdOS7ICOAc4GHgV8PEkrwf+F3jRal7nCzTTMs6jGW1+Q1X9Lsni9fi1zHkWZEmSpJ5V1Y3AAVOsWtzZ5mjg6En7XUwzP3ny8Q6e9Hyr9nMBr28/Jh/nfmsRfV5yioUkSZLUYUGWJEmSOizIkiRJUocFWZIkSeqwIEuSJEkdFmRJkiSpw4IsSZIkdViQJUmSpA4LsiRJktRhQZYkSZI6LMiSJElShwVZkiRJ6rAgS5IkSR0WZEmSJKnDgixJkiR1bNJ3APXnpm36TjC97d7+y74jjPTbE+/dd4TRsnHfCUZ63A679x1hWiddcl7fEUa618ljfUcY6W7bX9l3hJGuuGbLviOMtPyPt+s7wkib/89mfUeY1s1b9J1A84kjyJIkSVKHBVmSJEnqsCBLkiRJHRZkSZIkqcOCLEmSJHVYkCVJkqQOC7IkSZLUYUGWJEmSOizIkiRJUocFWZIkSeqwIEuSJEkdFmRJkiSpw4IsSZIkdViQJUmSpA4LsiRJktRhQZYkSRqYJEuTHLoej/eVJNu2Hy9fX8edryzIkiRJ81xVPaGq/gBsC1iQV8OCLEmSNABJ3pTkJ0m+Dty7XbZTkhOTnJXkW0l2bZcfleT9Sb6T5OdJntku3z7J6UnOTXJhkoe3yy9Osh1wBLBTu/5dSY5J8pROhk8lefKsf/EDs0nfASRJkha6JHsBzwH2pOlnZwNnAcuAl1bVT5M8CPgQ8Kh2t+2BfYBdgeOB44DnASdV1duTbAxsMemlDgPuV1V7tK/7COAQ4EtJtgEeCrxwQ32dc8W8KchjY2OHAYv6zjGnPPOgvhNIkrRgJFkCLOksWlZVy9rHDwe+UFXXt9seT9NrHgp8LsnEPrfr7P/FqloJXJTkLu2yM4GPJ9m0XX/uqExVdVqSf09yZ+DpwOer6ua1/iLniXlTkIFF4+PjS/sOMZfsfMR7D+87gyRJC0VbhpeN2mTS842AP0yM9k7hxs7jtK9xepJ9gScCxyR5V1V9YjXRjgEOohnBfvFqtl0QnIMsSZLUv9OBpyXZPMnWwF8B1wO/SPIsgDR2H3WQJPcALquq/wD+E3jApE2uAbaetOwo4DUAVfXDdfw65gULsiRJUs+q6mzgM8C5wOeBb7WrDgJekuQ84IfAU6Y8wK32A85Ncg7wDOB9k17nCuCM9gS+d7XLfg/8CDhyvXwx88B8mmIhSZI0Z1XV24G3T7Hq8VNse/Ck51u1n48Gjp5i+8Wdx8/rrkuyBbAzcOxaxJ6XHEGWJElaoJLsD/wY+EBVXd13nqFwBFmSJGmBqqqvAzv2nWNoHEGWJEmSOizIkiRJUocFWZIkSeqwIEuSJEkdFmRJkiSpw4IsSZIkdViQJUmSpA4LsiRJktRhQZYkSZI6LMiSJElShwVZkiRJ6rAgS5IkSR2b9B1A/bnHV2/oO8L0HgEXf3iXvlNM756ww7dv7DvFtC55+O3Y8aRr+44xrQIufd1D+44xjfO418kv6TvESD9/7H/2HWFaD7/gafz2F9v1HWNai7a7gY02Wtl3jNH+NOyxqxt2uqnvCFPa/H8248btqu8Y07rd5ek7gmZg2P8KtWANuhwz7HIMDLocw5DLMZbjdTTkcgxYjtfRUMsxMOhyDMPPp1UN+1+iJEmSNMssyJIkSVKHBVmSJEnqsCBLkiRJHRZkSZIkqcOCLEmSJHVYkCVJkqQOC7IkSZLUYUGWJEmSOizIkiRJUocFWZIkSeqwIEuSJEkdFmRJkiSpw4IsSZIkdViQJUmSpA4LsiRJ0hyTZHGSC9dgm+d1no8leX/7+OAkH9yA+d6WZP8plu+X5IT28ZOTHNY+fmqS+2yoPDO1Sd8BVmdsbOwwYNEabLp4A0eRJEmaSxYDzwP+H0BVjQPjs/HCVfWWNdjmeOD49ulTgROAizZgrDU2+IIMLBofH1+6uo3GxsZWu40kSdIQJXkH8Muq+lD7fClwDfAe4J3AAUAB/7eqPjNp38XAMcCW7aJXVtV3gCOAv0xyLnA0cA5waFU9adL+dwI+AuzYLnpNVZ2xhq9BkjcAfw2sBL5aVYclOQo4oaqOS/J44N+Ay4GzO8c8GBijKfBPBh6R5M3AM4DPVdUD2u12Bj5dVXut/ju5fsyFgixJkjTffZqmRH6off5s4PHA04E9gN2B7YAzk5w+ad/LgMdU1fK2TB5LUzwPo1OIk+w3zWu/D3hvVX07yY7AScBfrslrJDmAZvT3QVV1fZI7dndKsgj4D+BRwM+AzzBJVX0nyfG0hbrd7+oke1TVucCLgKOmyb5BLMiCPINpG/Patls+ve8IkiQtGEmWAEs6i5ZV1TKAqjonyZ2T7ADcCbiqqn6V5BDg2KpaAfw+yWnA3sD5neNsCnwwyR7ACmCXGUbbH7hPkonnt0+ydVVdswavsT9wZFVd334dV0469q7AL6rqp+334JOTvgfT+RjwoiSvBQ4EHjjDr2mdLMiCzBpO25jv9n/EPx/edwZJkhaKtgwvG7HJccAzgbvSjCgDZPrNb3EI8HuaUeaNgOUzjLYR8JCqumEtXiM0Uz9GWd36qXweOBz4BnBWVV2xFsdYa17FQpIkaRg+DTyHpiQf1y47HTgwycbtXOF9gR9M2m8b4NKqWkkzF3jjdvk1wNZr8LonA6+ceNKOEk823WucDLw4yRbtvnectN+PgXsm2al9/txpMqyStaqW00z1+DBw5Bp8DeuVBVmSJGkAquqHNCXxt1V1abv4CzTTKc6jGU19Q1X9btKuHwJemOR7NFMfrmuXnw/cnOS8dqrGdF5FM5/4/CQXAS+dYpspX6OqTqS5EsV4ezLgoZO+puU0Uyq+nOTbwC+nyfBp4PVJzumU6U/RjD6fPCL7BrG2UyyWT3PViMVrH0WSJGlhq6r7T3pewOvbj+7yi4H7tY9/CuzWWf0P7fI/AY+e9BKntuuOoj3xraoup5nnOyrXlK/RrjuC5ooZ3e0P7jw+kWYu8uRjdjOcAUy+DvI+wMfb+dezaq0K8vj4+BFTLfdSa5IkSVpXSb4A7ERz9YtZt1BP0pMkSdJAVdXT+nx95yBLkiRJHRZkSZIkqcOCLEmSJHVYkCVJkqQOC7IkSZLUYUGWJEmSOizIkiRJUocFWZIkSeqwIEuSJEkdFmRJkiSpw4IsSZIkdViQJUmSpI5N+g6g/vzi5dV3hGnd/vT0HWGkzbbeuO8II63cdNjfv02W951genfb/sq+I4x0r+P/tu8II/38yf/Rd4SRdjrlRX1HGClb3Nx3hJF2+MKmfUeY1pX3GfbPPc0tjiBLkiRJHRZkSZIkqcOCLEmSJHVYkCVJkqQOC7IkSZLUYUGWJEmSOizIkiRJUocFWZIkSeqwIEuSJEkdFmRJkiSpw4IsSZIkdViQJUmSpA4LsiRJktRhQZYkSZI6LMiSJElSxyZ9B1iPlo+NjS1dw20Xb8AckiRJmsPmTUEeHx8/Yk23nUGRliRJmhVJXgW8DDgb+Axwn6pa434zzTH3Aw6tqiet43HeBpxeVV+f7vhJnkybOclTgf+uqovW5XX7Mm8KsiRJ0hz3cuCAqvpF+/z4PsN0VdVb1mCb47k181OBE4A5WZCdgyxJktSzJB8B7gUcn+SQJAcn+WC77ktJXtA+/rskn2ofPzbJd5OcneRzSbZqlz8+yY+TfBt4+jSvtzjJt9p9z07y0M66NyS5IMl5SY5olx2V5Jmjjj+RuT3Wk4F3JTk3yU5Jzu5st3OSs9bn9299cwRZkiSpZ1X10iSPBx5ZVZcnObizeglwRpJfAK8DHpxkO+DNwP5VdV2SNwKvTfJO4D+ARwE/o5mqMZXLgMdU1fIkOwPHAmNJDqAZ/X1QVV2f5I7dnZIsWt3xq+o7SY4HTqiq49r9rk6yR1WdC7wIOGqG36JZZUHuGBsbOwxY1HeOWfP6p/WdQJKkBSPJEpqyO2FZVS1b3X5V9fskbwG+CTytqq5M8iTgPjTFGWAz4LvArsAvquqn7Wt+ctJrTtgU+GCSPYAVwC7t8v2BI6vq+va1r5y035oef7KPAS9K8lrgQOCBa7BPbyzIq1o0Pj6+tO8Qs2Wnz7z98L4zSJK0ULRleLWFeBr3B64AdmifB/haVT23u1FbeGsNjncI8Htgd5opt8s7x13d/mty/Mk+DxwOfAM4q6quWItjzBrnIEuSJA1YkgcCBwB7AocmuSfwPeBhSf6i3WaLJLsAPwbumWSndvfnTnVMYBvg0qpaCfw1sHG7/GTgxUm2aI97x0n7renxrwG2nnhSVcuBk4APA0eu/qvulwVZkiRpoJLcjmbO74ur6hKaOcgfBy4HDgaOTXI+TWHetS2iS4AvtyfR/XKaQ38IeGGS79FMr7gOoKpOpLkSxXiSc4FDuzvN4PifBl6f5JxOmf4UzejzyTP6JvTAKRaSJEkDUFWLO4+P4tYT2XbvLO9eSu0bwN5THOdEmrnCo17rp8BunUX/0Fl3BHDEpO0PXt3xu5mr6gyaOdJd+wAfr6oVo7INgQVZkiRJG1SSLwA70Vz9YvAsyJIkSdqgqmpOXTrLOciSJElShwVZkiRJ6rAgS5IkSR0WZEmSJKnDgixJkiR1WJAlSZKkDguyJEmS1GFBliRJkjosyJIkSVKHBVmSJEnqsCBLkiRJHRZkSZIkqWOTvgOoP5v+eIu+I0wrK/pOMNpNt9+47wgjbdZ3gNXY8tLh/gFfcc2WfUcYaYs7X9d3hJF2OuVFfUcY6X8efWTfEUba48zn9B1hpMt3W9R3hGllZd8JNJ84gixJkiR1WJAlSZKkDguyJEmS1GFBliRJkjosyJIkSVKHBVmSJEnqsCBLkiRJHRZkSZIkqcOCLEmSJHVYkCVJkqSOhXqr6eVjY2NLp1i+eJZzSJIkaWAWZEEeHx8/Yqrl05RmSZIkLSBOsZAkSZI6LMiSJElShwVZkiRJ6rAgS5IkSR0WZEmSpAFKclSSZ06xfHGSC2d4rB2SHDfNulOTjK1tzvloQV7FQpIkaaFIsklVXQLcpmxrao4gS5IkDUCSFyQ5P8l5SY5pF++b5DtJfj7NaPKiJEcmuSDJOUke2S4/OMnnkvwXcHJ31DnJ5kk+3b7WZ4DNO8d7bJLvJjm73X+rdvkRSS5q93n3Bv9m9MwRZEmSpJ4luS/wJuBhVXV5kjsC7wG2B/YBdgWOByZPk3gFQFXdP8muNGV4l3bdQ4DdqurKJIs7+7wMuL6qdkuyG3B2m2E74M3A/lV1XZI3Aq9N8kHgacCuVVVJtl3fX//QWJDX0tjY2GHAor5zrJO/OqjvBJIkLRhJlgBLOouWVdWy9vGjgOOq6nKAttQCfLGqVgIXJbnLFIfdB/hAu8+Pk/wSmCjIX6uqK6fYZ1/g/e0+5yc5v13+YOA+wBnta28GfBf4I7Ac+FiSLwMnzPiLn2MsyGtv0fj4+NK+Q6yLXd/63sP7ziBJ0kLRluFl06wOUFMsv3HSNlPtN53rRsWZ5lhfq6rn3mZF8kDg0cBzgFfSFPp5yznIkiRJ/TsFeHaSPwNop1isidOBg9p9dgF2BH4yg33uB+zWLv8e8LAkf9Gu2yLJLu085G2q6ivAa4A91jDbnOUIsiRJUs+q6odJ3g6clmQFcM4a7voh4CNJLgBuBg6uqhvbKRLT+TBwZDu14lzgB22G/01yMHBsktu1274ZuAb4UpJFNKPMh8zoi5uDLMiSJEkDUFVHA0ePWL9V+/li4H7t4+XAwVNsexRwVOd5d58baKZKTPUa3wD2nmLVA9fgS5g3nGIhSZIkdViQJUmSpA4LsiRJktSxvucgLx8bG1u6no+5eD0fT5IkSZrWei3I4+PjR6zP4wFsgMItSZIkTcspFpIkSVKHBVmSJEnqsCBLkiRJHRZkSZIkqcOCLEmSJHVYkCVJkqQOC7IkSZLUYUGWJEmSOizIkiRJUsf6vtX0XDeTW2Uv3oA5ZkUN+NejTZZX3xFGqvSdYDUy7IBX7bJx3xGmtfyPt+s7wmh/GvA/XCBb3Nx3hJH2OPM5fUcY6dy9P913hJHu9eslfUeY1lYXD/fniuYeC3LHTG6V7S2wJUmS5qdhD0VIkiRJs8yCLEmSJHVYkCVJkqQOC7IkSZLUYUGWJEmSOizIkiRJUocFWZIkSeqwIEuSJEkdFmRJkiSpw4IsSZIkdViQJUmSpA4LsiRJktRhQZYkSZI6LMiSJElShwVZkiRJ6rAgS5IkzSFJDk7ywXXdZop9XpNki3VLNz9YkCVJkgTwGsCCjAVZkiSpd0m2TPLlJOcluTDJgUkuTrJdu34syalT7HdUko8k+VaS/07ypM7qHZKcmOSnSd7Z2efDScaT/DDJW9tlrwJ2AL6Z5Jvtsscm+W6Ss5N8LslW7fIjklyU5Pwk795w35X+bNJ3gDls+djY2NK+Q6yTpxzUdwJJkhaMJEuAJZ1Fy6pqWfv48cAlVfXEdtttgHes4aEXA48AdqIpuH/RLt8D2BO4EfhJkg9U1a+BN1XVlUk2Bk5JsltVvT/Ja4FHVtXlbTF/M7B/VV2X5I3Aa9tpG08Ddq2qSrLt2nwvhs6CvJbGx8eP6DvDurr3P7338L4zSJK0ULRleNk0qy8A3p3kHcAJVfWtJGt66M9W1Urgp0l+DuzaLj+lqq4GSHIRcA/g18Cz27K+CbA9cB/g/EnHfHC7/Iw2x2bAd4E/AsuBjyX5MnDCmoacSyzIkiRJPauq/06yF/AE4F+SnAzczK3TYReN2n2a5zd2lq0ANklyT+BQYO+quirJUdMcO8DXquq5t1mRPBB4NPAc4JXAo0Z9bXORc5AlSZJ6lmQH4Pqq+iTwbuABwMXAXu0mzxix+7OSbJRkJ+BewE9GbHt74Drg6iR3AQ7orLsG2Lp9/D3gYRPTNZJskWSXdh7yNlX1FZqT+vZY4y9yDnEEWZIkqX/3B96VZCXwJ+BlwObAfyb5R+D7I/b9CXAacBfgpVW1fLrpGVV1XpJzgB8CPwfO6KxeBnw1yaVV9cgkBwPHJrldu/7NNCX6S0kW0YwyH7JWX+3AWZAlSZJ6VlUnASdNsWqXKbY9Cjiqs+iMqjpk1DZV9aTO44OnyfAB4AOd598A9p5i0wdOtf984hQLSZIkqcMRZEmSpDlqutFgrRtHkCVJkqQOC7IkSZLUYUGWJEmSOizIkiRJUocFWZIkSeqwIEuSJEkdFmRJkiSpw4IsSZIkdViQJUmSpA4LsiRJktRhQZYkSZI6LMiSJElSxyZ9B1B/tj/jpr4jTOvmLTbuO8JIN2w37HwrNkvfEUa68Y7Vd4Rpbf4/m/UdYaQbdhruv1uAHb6wad8RRrp8t0V9RxjpXr9e0neEkX7+9GV9R5jWfT/0sr4jaB5xBFmSJEnqsCBLkiRJHRZkSZIkqcOCLEmSJHVYkCVJkqQOC7IkSZLUYUGWJEmSOizIkiRJUocFWZIkSeqwIEuSJEkdFmRJkiSpw4IsSZIkdViQJUmSpA4LsiRJktRhQZYkSZI6LMiSJEkDluTUJGOd54uTXNhnpvnOgixJkiR1WJAlSZIGoB0Z/nGSo5Ocn+S4JFv0nWsh2qTvAJIkSbrFvYGXVNUZST4OvLxd/qkkN7SPNwNW9pJugZjTBXlsbOwwYFHfOeaqrbZ7Vt8RJElaMJIsAZZ0Fi2rqmWTNvt1VZ3RPv4k8Kr28UFVNd4eZzFwwobMutDN6YIMLBofH1/ad4i5ar/Hv+PwvjNIkrRQtGV4ciG+zWarea5Z4BxkSZKk4dgxyUPax88Fvt1nmIXKgixJkjQcPwJemOR84I7Ah3vOsyDN9SkWkiRJ88nKqnrppGX7dZ9U1cXA/WYr0ELkCLIkSZLU4QiyJEnSADgyPByOIEuSJEkdFmRJkiSpw4IsSZIkdViQJUmSpA4LsiRJktRhQZYkSZI6LMiSJElShwVZkiRJ6rAgS5IkSR0WZEmSJKnDgixJkiR1WJAlSZKkDguyJEmS1LFJ3wHWwPKxsbGl06xbPIs55p0r77NZ3xGmVek7wWgb3dx3gtFu3GbYv/tucWnfCaZ38xZ9Jxhto6s37TvCSFfeZ9j/eLOy7wSjbXXxxn1HGOm+H3pZ3xGm9cOXf7jvCGvgkL4DaA0NviCPj48fMd26EcVZkiRJWivDHmaSJEmSZpkFWZIkSeqwIEuSJEkdFmRJkiSpw4IsSZIkdViQJUmSpA4LsiRJktRhQZYkSZI6LMiSJElShwVZkiRJ6rAgS5IkSR0WZEmSJKnDgixJkiR1WJAlSZKkDguyJEnSACRZkeTczsfiDfAaFyfZbn0fd77ZpO8AkiRJAuCGqtpjqhVJAqSqVs5upIXJEWRJkqQBSrI4yY+SfAg4G7h7kg8nGU/ywyRv7Wx7y8hwkrEkp7aP/yzJyUnOSfJRIH18LXONBVmSJGkYNu9Mr/hCu+zewCeqas+q+iXwpqoaA3YDHpFkt9Uc83Dg21W1J3A8sOMGSz+PzPUpFsvHxsaW9h1iztr3oL4TSJK0YCRZAizpLFpWVcs6z1eZYtHOQf5lVX2vs82z2+NsAmwP3Ac4f8TL7gs8HaCqvpzkqnX6IhaIOV2Qx8fHj+g7w1y222vfe3jfGSRJWijaMrxstRuu6rqJB0nuCRwK7F1VVyU5CljUrr6ZW2cGLGJVNfO0C5tTLCRJkuaG29MU5quT3AU4oLPuYmCv9vEzOstPBw4CSHIAcIcNH3PusyBLkiTNAVV1HnAO8EPg48AZndVvBd6X5FvAiknL901yNvBY4FezFHdOm9NTLCRJkuaLqtpq0vOLgftNWnbwNPt+C9hliuVX0BTjCYesa86FwBFkSZIkqcOCLEmSJHVYkCVJkqQOC7IkSZLUYUGWJEmSOizIkiRJUocFWZIkSeqwIEuSJEkdFmRJkiSpw4IsSZIkdViQJUmSpA4LsiRJktRhQZYkSZI6LMiSJElShwVZkiRJ6khV9Z1B80SSJVW1rO8c0xlyviFnA/OtK/OtmyHnG3I2MN+6Gno+bTiOIGt9WtJ3gNUYcr4hZwPzrSvzrZsh5xtyNjDfuhp6Pm0gFmRJkiSpw4IsSZIkdViQtT4NfZ7WkPMNORuYb12Zb90MOd+Qs4H51tXQ82kD8SQ9SZIkqcMRZEmSJKnDgqx5KclGSR7adw5JkjT3WJA1L1XVSuBf+84xSpJ7JNm/fbx5kq37ztQ19Hxae0nGk7wiyR36zjKVoeeTNP9ZkDVjSS5Icv50H33n6zg5yTOSpO8gkyX5W+A44KPtoj8HvthboEmGnm/okjwpyZB/vj4H2AE4M8mnkzxuYP9Ohp5P81CSO4766DufZpcn6WnGktyjffiK9vMx7eeDgOur6m2zn+q2klwDbAncDCwHAlRV3b7XYECSc4EHAt+vqj3bZRdU1f17DdYacr4kFwBT/eCa+PPdbZYj3TZI8kngIcDngSOr6kc9R5pSW+KfBHwYWAl8HHhfVV3Za7DWUPMleSfwf4EbgBOB3YHXVNUn+8w1Yaj5hv5vN8kvaPIF2BG4qn28LfCrqrpnf+k02zbpO4Dmnqr6JUCSh1XVwzqrDktyBjCIglxVQ54ScGNV3TQxKJZkE6b+j6MvQ873pL4DrE5VPT/J7YHnAkcmKeBI4NiquqbfdI0kuwEvAp5AU+Q/BewDfAPYo79kjYHne2xVvSHJ04DfAM8CvgkMoiAz3HwT/3YDfJnmz3YwJgpwko8Ax1fVV9rnBwD795lNs8+CrHWxZZJ9qurbAO1JcVv2nGkV7RzGnYFFE8uq6vT+Et3itCT/CGye5DHAy4H/6jlT12DzTfyCBpDkLsDe7dMfVNVl/aS6rar6Y5LPA5sDrwGeBrw+yfur6gN9ZktyFvAH4D+Bw6rqxnbV95M8bNodZ8nQ8wGbtp+fQPNLz5UDmwEyyHyT/u3e2H0+MHtX1UsnnlTVV5P8U5+BNPucYqG1lmQvmrc8t2kX/QF4cVWd3VuojiR/A7yaZv7sucCDge9W1aP6zAW3vHX8EuCxNKMpJwEfq4H8gxx6PoAkzwbeBZxKk/HhwOur6rg+cwEkeTLN6OdONFOQjq6qy5JsAfyoqu4x8gAbPt+9qurnk5bds6p+0VemrjmQ7wjgqTRTGB5I8xb8CVX1oB5j3WLo+QCSnF1VD+g7x1SSnAR8i2bEvYDnA/tW1eN6DaZZZUHWOmvfSk5VXd13lq52vtvewPeqao8kuwJvraoDe46m9SDJecBjJkaNk9wJ+HpV7d5vMkjyCZpfKG7zbkWSR1fVKT3E6ma4TTlJclZV7dVXpq6h54Nb3p36Y1WtSLIlsHVV/a7vXBOGmC9J98/0UzTnrdxiQIMrdwQOB/ZtF51O83/HIObma3Y4xUIzluT5VfXJJK+dtByAqnpPL8Fua3lVLU9CkttV1Y+T3LvPQCNOUgFgACepDDrfJBtNmlJxBcO5Ms+lk8txkndU1Rv7LMftL4n3BbZJ8vTOqtvTmYbUl6Hnm9C+E/AKmhO5ltBccePewAl95pow4HzdS2/+Dnh3+zg0P3d6f3cPoC3Cr+47h/plQdbamJhnPOST4AB+k2RbmsuTfS3JVcAlvSa69SSVKa8AMvtxbmPo+bpObN8KPbZ9fiDwlR7zdD0GeOOkZQdMsWy23Zvmz3hb4K86y68B/raPQJMMPd+EI4GzgImbEf0G+Bz9F9AJg8xXVY+E5rrqNOc17ENTjL9Fc6WSQUjyTaYYKBjC9DzNHqdYaEFI8giaudInVtVNA8hzxqQrgEy5rC9DzzchyTOAh9GMQJ1eVV/oOc/LaP7j3wn4WWfV1sAZVfX8XoJNkuQhVfXdvnNMZw7kG6+qsSTndC6DeN4QpvfAnMj3WeCPNNMsoLnay7ZV9ez+Ut2qPb9mwiLgGcDNVfWGniKpB44ga60lORp4dVX9oX1+B+Bfq+rFvQZrTbqw+wXt56H8Rjj0K4AMPR8AVfV5mkuADcX/A74K/AtwWGf5NUOYv5jkDVX1TuB5SZ47eX1VvaqHWLcYer6Om9pR0AJIshNw4+hdZtXQ8917Uln/ZntOwSBU1VmTFp2R5LRewqg3FmSti90myjFAVV2VZM8e80x2NnB3Vr3Y+6VJLgP+doofgrPpJcDHk6xyBZD+4tzG0PPRzlF9B3Bnmj/fIdwIpqrq4iSvmLwiyR0HUJInblgy3muK6Q0934TDaW7Acfckn6J5F+PgXhOtaim3zfeiXhOt6pwkD66q7wEkeRBwRs+ZbjFpcGUjYC/grj3FUU+cYqG11v7Gv19VXdU+vyNwWg3gbmtwy8Xev1BVJ7XPHws8Hvgszd24er/k0VCvADJhyPmS/Az4qxrQXeqSnFBVT8qqd+SaUFV1r56iaT1L8mc0l44MzZVyLu850iqGnC/Jj2jmm/+qXbQjzS9HKxneHfVuBn4BvG3iHTUtDBZkrbUkLwD+AZi47uyzgLdX1THT7zV7JubhTbUsyblVtUcPmaa8AsiEvq8AMvR8XUOcEz10Sf6L0VcpefIsxrmNOZBv5HV7B3SZslOq6tGrW9aXJCOvAz7gG4hoAXGKhdZaVX0izR2vHknzm/bTq+qinmN1XZnkjcCn2+cHAlcl2ZhmpKIPQ78CyNDzTUytABhP8hmaq5TcMr+yqv6/PnJ1pbnb27lVdV2S5wMPAP6tqn61ml03tInLaj2d5i3jiVsPPxe4uI9Akww938RlyhYBY8B5ND/7dgO+T3NVht4kWQRsAWzXnhMy8Q7G7Wku9TYIc6EAJ7kfcB9WvQvrJ/pLpNnmCLLWWZI7s+oPkb5LAABJtqOZK7gPzX8U3wbeClwN7FhVPxux+6xLstkQrrAxnaHkS3LkiNU1hJNEk5wP7E5TnI6huWXy06vqEb0GayU5var2Xd2yvsyBfJ+mebfsgvb5/YBDq+rgnnO9mua25jsAv+XWgvxH4D+q6oM9RZtTkhwO7EdTkL9Cc4nGb1fVM/vMpdllQdZaS3M73X+l+WF8GXAPmtvo3rfXYHNAklOBg6vq4vb53jR3XhvKZZhOZcD5hi7tneCSvAX4bVX9ZwZ0a912DugTq72dc5J7Al+pqr/sN1ljDuS7zRStvqZtTSXJ31fVB/rOMVe1N0zaHTinqnZPchean39/tZpdNY84xULr4p9oTgL5elXtmeSRNG+FDsLAL/b+LzQ3ung/cDeaEYohnWU+9Hwk+XPgAzRn6BfNOwSvrqrf9BqscU2SfwCeD+zbTuvZtOdMXYcApyb5eft8MfB3/cW5jaHn+1GSj9FMASmaP+fBnCxaVR9wisA6uaGqVia5uT1R+TLAE2wXGEeQtdY6J7ydB+zZ/kD5QVU9sO9sMPyLvSfZD/gacDnN9+93vQaaZA7k+xrNdYcnTgp9PnBQVT2mv1SNJHcFngecWVXfSrIjzRVfBlNQktwO2LV9+uOqGtJ1cgedr53r+zJgYsrH6cCHq2p5f6lu5RSBdZPkQ8A/As8BXgdcS3NOwaAGCbRhWZC11pJ8HXgqzWjjdjS/Ze9dVQ8dtV+fkpw2hHmgSf4P8GxgCc081UOA11XVl3sN1hp6Phj+29xDlORRVfWNzomOq+j7BMeh55srnCKw9pIE+POq+nX7fDFw+6o6v9dgmnVOsdC6eApwA015OojmVs5v6zVRx8Av9r4d8MCqugH4bpITgY8BQymgQ88HcHl7hYhj2+fPBa7oMc8tBnoTE4BHAN8ApipKBfRdQAedL8lnq+rZbQGdavpWr9fv7XCKwFqqqkryRZr/L5g4D0MLjyPImreGfrH3dlRn7/bpD6rqsj7zTDYH8u0IfBB4CM2f83do5iD3fgmpId7EROsuyfZVdel01/Edwt89cIrAukry78BRVXVm31nUHwuy1IMkz6K55uupNAX+4cDrq+q4UfvNlqHnG7qh38QkyT8D76z2VvHtNXNfV1Vv7jVYa8j52hMuT6qq/fvOsiacIjBzSS6iudPfxcB13PoO0FDeIdAssCBr3mpL3olVdU2SN9PcrOH/DuFuV+2JjY+ZGJVNcieaq4EM4jJqQ88HkORomhHjP7TP7wD860Cug/w+muk8X2RgNzEBSHJOVe05admQLkM39HzHA39dA7wF+4Qkd6O59OYtUymr6vT+Eg1fkh2r6ldDf4dAs8M5yFprSbaknevWPt8IWFRV1/eb7Bb/p6o+l2Qf4HE0I6IfBh7UbywANpo0ZeEKmnnSQzH0fAC7TZRjgKq6KsmeI7afTbcHrgce21nW+xzajo2T3G7iyhBJNgdu13OmrqHnWw5c0F5J5bqJhVX1qv4i3SrJO2juHHoRsKJdXDRX29D0vgg8oKp+meTzVfWMvgOpPxZkrYtTgP1p5rdBc4vTk4GhXMVi4j+GJ9JcgulLSZb2mKfrxCQncesJZgfSXI5pKIaeD2CjJHeoqqvglpMyB/EzbQ7M9fwkcEqauxIW8GLg6H4jrWLo+b7MsE5YneypwL2HdGm8OSKdx57UuMA5xUJrbeiX2UpyAs3tVvenOSP5BpqTzQYxTSDJM2huchHg9Kr6Qs+RVjEH8r0A+AdgYl70s2hu/3vM9HvNjiS70LxbcZequl+S3YAnV9X/7TnaLZIcADya5s/35Ko6qedIq5gD+TYDdmmf/qSq/tRnnq4kXwWeVVXXrnZj3aI7jWdIU3rUDwuy1lqSM4C/n5jT296Y44NV9ZB+kzWSbAE8Hrigqn6aZHvg/lV1cs/RtJ4kuQ/wKJoSdUpVXdRzJKC53jbweuCjE3Npk1xYVffrN5nWh/YmOkfTnMQV4O7AC/ue45vkAzQj7nejuQ7yKaw6B34QU0CGKskKbj0pb3OaaVIwnMs0ahZZkLXWkuwNfBq4pF20PXBgVZ3VX6phS/LtqtonyTWseh3VQfwAHno+uM31rW+jqq6crSzTSXJmVe3dPdlsYO+uPJjmNt1/CWwGbAxcN4Q/X5gT+c4CnldVP2mf7wIcW1V7jd5zg+d64aj1VTWkaSrSoA1ivp7mpqo6M8muNJfDCc3tYAfzNuMQVdU+7eet+84ylaHna53Frde3hluLfNrHQ5g7eHmSnWizJXkmcGm/kVbxQZpr5H4OGANeAPxFr4lWNfR8m06UY4Cq+u8km/YZqM1xSwFup4DsSvN38CdVdVNvwaQ5yIKsGRtxO9idkwzmUlZDNPTRz6HnazPcc+Jxm3dnYFF/iab0CmAZsGuS39LcpOagfiOtqqp+lmTjqloBHJnkO31n6hp4vvEk/wlMzHc/iOYXt0FI8gTgo8D/0PzieM8kf1dVX+03mTR3WJC1NgZ9O9iB645+7ghc1T7eFvgVcM9p95wdQ893iyR/A7wa+HPgXODBNHfTe3SPmV7befoV4Js0l8e7DngG8J4+ck3h+naE8dwk76QZ3d6y50xdQ8/3Mppfgl5FexIr8KFeE63qPcAjq+pnAO27GV8GLMjSGnIOstZKe83jZ1bVZ/vOMtkU82dvWcVw5tF+BDi+qr7SPj8A2L+qXtdvssbQ8wEkuYDmVtjfq6o92uk+b62qA3vMdHj78N5tti/R/L37K5orgfxNX9m62hsh/J5mfu8hwDbAhyYKVd+Gnm/okpxeVft2ngc4rbtM0mgWZK21yT+EteaSnDX5hJ4k41U11lemrqHng1VOhDsXeFBV3TiUE+GSnAw8o6quaZ9vDXyuqh7fbzKti/aXsmn/06yB3Io4yYdp7qL3WZq8zwJ+ApwBw7mjozRkTrHQuvhakkOBz7Dq3aR6n6faleTOdOaoVtWveowz4fI0t7/+JM1/YM+nuVvdUAw9H8BvkmxLc/erryW5iluvqNK3HYHuSVE3AYv7iaL16El9B1hDi2hG4B/RPv9f4I4072Q4DU5aA44ga60l+cUUi6uqhnAVAZI8GfhXYAfgMpoRlR9V1X17DcYtJ5cdDuzLrbeAfdtQfrkYer7JkjyC5m34E4dwtn6SNwHPBr5A8/17GvCZqvqXXoNJktaIBVlrLcmiqlq+umV9SXIezU0kvl5VeyZ5JPDcqlrSczQtAEkeADy8fXp6VZ3TZ56pJNmyqq5b/Zbqaq/g8w7gzjRzzAdxfkPnRiFT8kYh0ppzioXWxXeAybfinGpZX/5UVVck2SjJRlX1zSTv6DuUFob2DpNn951jKkkeCnwM2ArYMcnuwN9V1ct7zvVfjC54T57FOKO8E/irqvpR30EmGe87gDRfWJA1Y0nuSnMr082T7MmtN2y4PbBFb8Fu6w9JtqKZHvCpJJcBN/ecSRqC9wKPA44HqKrzkgzhhNt3t5+fDtyVZg48wHNpbus8FL8fYDn2TnnSeuQUC81YezvTg2nucNUdsbgGOGooZ0gn2RJYTlPgD6KZo/qpqur1ZLMkGwOvqqr39pljOkPPp3WX5PtV9aBJt8I+r6p27zsbTH2FnCFcNadzc6RH0BT4LwI3Tqwf0M++bzLFSHxVPaqHONKc5AiyZqwdpTg6yTOq6vN955nOpLmVgxlZqaoVSZ5CM4o3OEPPp/Xi1+00i2pvyPEqYEgjondKcq+q+jlAknsCd+o5E9x6c6QCrgce21k3pKtDHNp5vIjmJjW+eybNgCPImrEkz6+qTyZ5HVOPUgzibmGTbhiyGbApcF3fJ9IAJHk7zYj25EvkDWLO6tDzad0k2Q54H7A/zTssJwOv7vvdlQlJHk9zq+6ft4sW08yRPqm3UB1Jjqb5fv2hfX4H4F+r6sW9BhshyWlV9YjVbykJHEHW2pm45etWvaZYjarauvs8yVOBB/aT5jYe2n5+W2dZ0Vx1YwiGnk/roKoup5l2NEhVdWKSnYFd20U/rqobR+0zy3abKMcAVXVVez7GILSXaZywEc10uLv2FEeakxxB1oKS5HtV9eC+c0h9mCuXAUuyBfBa4B5V9bdtWb53VZ3QczTglktI7ldVV7XP70hzK+f795us0V6jvmjeHfgTzQmOb6uqb/eZS5pLHEHWWhv624ydE2rg1lGUQfxGmOQtUy2vqrdNtXy2DT2f1tpcuQzYkcBZwEPa578BPgcMoiDT3IDoO0mOo/mZ8mzg7f1GWsUbaW6a88ck/4fm0pvX95xJmlMsyFoXg36bkVtPqIHmBJWLgaf0E+U2uicQLqK5he2QTpIaej6thTl0GbCdqurAJM8FqKobkmR1O82WqvpEknGaKUcBnl5VF/Ucq+vNVfXZJPsAj6Ep9B8GHtRvLGnusCBrXWyU5A6T3mYczN+pqnpR3xmmU1X/2n2e5N2016QdgqHn09pJ8m9V9ZrpbsgxoBtx3JRkc9qMSXaiczm1IWgL8ZBKcdeK9vMTgY9U1ZeSLO0xjzTnDKbMaE4a5NuMc2We5SRbAPfqO8QIQ8+nNXNM+/ndI7fq31LgRODuST4FPAwY7C+8A/TbJB+luUrJO5LcjmaamaQ1ZEHWWhvw24wT8ywfBtyH5lJlAM+imdfYuyQXcGuJ35jmGq+Dmd879HxaO1U18fd/j6p6X3ddklcDp81+qtuqqpOTnAU8mOZny6vbK29ozTwbeDzw7qr6Q5Ltgdf3nEmaU7yKheat9m5Sj62qP7XPNwVOrqpH9pjpnlX1iyT36Cy+mebWtb1fyH/o+bR+JDm7qh4wadktd9XrW5JjgFdW1dXt83sAH6+qR/ebTNJC4Qiy5rMdgK2BK9vnW7XL+nQcsBfD/c9+6Pm0DtqT3p4H3DNJd0751sAgbhLS+jbw/SSvBe5GM/r5un4jSVpILMiaz44AzmlHkgEeQTO3sU8bJTkc2KX9z38VA7gL4dDzad18B7gU2I7mHIIJ1wDn95JoClX10SQ/BL4JXA7sWVW/6zmWpAXEgqx5q6qOTPJVbr200WED+E/2OcBTaf7tbT16014MPZ/WQVX9Evglt15feJCS/DXwf4AXALsBX0nyoqo6r99kkhYK5yBr3kmya1X9OMkDplpfVWfPdqbJkhxQVV/tO8d0hp5P6ybJg4EPAH8JbEZzIuZ1VXX7XoO1knwRWFJVl7XPHwgsq6o9+swlaeGwIGveSbKsqpZ0plZ0VVU9atZDtaaattDV9xSGoefT+tFefeY5NHenG6MZqf2LqnpTr8FGSLJZVd3Udw5JC4NTLDTvVNWS9nNvV6sYYejTFoaeT+tJVf0sycZVtQI4Msl3+s6U5A1V9c4R1zIf4jXMJc1DFmTNW0meBZxYVdckeTPwAOCfquqcvjJV1Vv7eu01MfR8Wm+uT7IZcG6Sd9KcuLdlz5ng1jvTjY/cSpI2MKdYaN5Kcn5V7ZZkH+BfaO4e9o9V9aDV7LrBJTmSqW/1++Ie4tzG0PNp3bTXFb4M2BQ4BNgG+FBV/aznXMdU1V8nefXkG5lI0mxyBFnz2Yr28xOBD1fVl5Is7TFP1wmdx4uApwGX9JRlKkPPp3XQXs0C4AZgSO8a7NWW9xcn+QTNXfRuUVVXTr2bJK1fjiBr3kpyAvBbYH+am1/cAPygqnbvNdgUkmwEfL3PEwhHGXo+rZlJtxC/jarabRbj3EaSVwEvA+5F82+3W5Crqu7VSzBJC44FWfNWki2AxwMXVNVPk2wP3L+qTu452m0kuTfw5ar6i76zTGXo+bRmJt1C/DY6I8u9SvLhqnpZ3zkkLVxOsdC8VVXXJ7kM2Af4KXBz+7l3Sa5h1ZG83wFv7CnObQw9n9ZOtwAnuQuwd/v0BxPXHB4Cy7GkvjmCrHmrvWXyGHDvqtolyQ7A56rqYT1Hk3qV5NnAu4BTaaYxPBx4fVUd12cuSRqKjfoOIG1ATwOeDFwHUFWXMJDr/CZ5WJIt28fPT/Ke1b39PZuGnk/r7E3A3lX1wqp6AfBAmls7S5KwIGt+u6mat0gKYKLwDcSHaa5FuzvwBuCXwCf6jbSKoefTutlo0pSKK/D/A0m6hT8QNZ99NslHgW2T/C3wdeA/es404ea2vD8FeF97zddBjG63hp5P6+bEJCclOTjJwcCXga/0nEmSBsM5yJqXkgT4c2BX4LE08yxPqqqv9RqsleQ04ETgRcC+wP8C51bV/XsN1hp6Pq27JE+nOYE1wOlV9YWeI0nSYFiQNW8lOauq9uo7x1SS3BV4HnBmVX0ryY7AflU1iGkMQ8+ndZPkEJoTVn/TdxZJGiILsuatJP8OHFVVZ/adRRqS9govzwauBD4NHFdVv+83lSQNhwVZ81aSi4BdaE4wu47mreTq+25h0lAk2Q04EHgG8Juq2r/nSJI0CN4oRPPZAX0HkAbuMpqbwFwB3LnnLJI0GI4gSz1JsjmwY1X9pO8sWliSvIxm5PhOwHHAZ6rqon5TSdJwOIIs9SDJXwHvBjYD7plkD+BtVfXknnNdwKq3mL5lFU5PmU/uAbymqs7tO4gkDZEjyFIPkpwFPAo4tar2bJed33cBXd3d8qrql7OVRZKkvjiCLPXj5qq6urlc83BYgCVJ8k56Ul8uTPI8YOMkOyf5APCdvkNNSPLgJGcmuTbJTUlWJPlj37kkSZoNFmSpH38P3Be4Efh/wNXAa/oMNMkHgecCPwU2B/4G+ECviSRJmiXOQZZ6lGTLqrqu7xyTJRmvqrHuvOgk36mqh/adTZKkDc0RZKkHSR7a3sjkR+3z3ZN8qOdYXdcn2Qw4N8k721sTb9l3KEmSZoMFWerHe4HH0dyggao6D9i310Sr+muanw+vpLkL4d2Bp/eaSJKkWWJBlnpSVb+etGhFL0Gm9tSqWl5Vf6yqt1bVa4En9R1KkqTZYEGW+vHrJA8FKslmSQ6lnW4xEC+cYtnBsx1CkqQ+eB1kqR8vBd4H3A34DXAy8IpeEwFJngs8j+bufsd3Vt2edjqIJEnznQVZmmVJNgb+raoO6jvLFL4DXApsB/xrZ/k1wPm9JJIkaZZZkKVZVlUrktwpyWZVdVPfebraO+n9EnhIkrsAe7erflRVN/eXTJKk2WNBlvpxMXBGO43hlusgV9V7ekvUkeRZwLuBU4EAH0jy+qo6rtdgkiTNAguy1I9L2o+NgK17zjKVNwN7V9VlAEnuBHwdsCBLkuY9C7I0i5IcU1V/Dfyhqt7Xd54RNpoox60r8Ko3kqQFwoIsza69ktwDeHGST9BMX7hFVV3ZT6zbODHJScCx7fMDga/2mEeSpFmTquo7g7RgJHkV8DLgXsBvWbUgV1Xdq5dgU0jydGAfmoynV9UXeo4kSdKssCBLPUjy4ap6Wd85ppPkHVX1xtUtkyRpPrIgS7qNJGdX1QMmLTu/qnbrK5MkSbPFOciSbpHkZcDLgXsl6d4YZGvgjH5SSZI0uxxBlnSLJNsAdwD+BTiss+qaAZ1AKEnSBmVBliRJkjq8rqkkSZLUYUGWJEmSOizIkiRJUocFWZIkSeqwIEuSJEkd/z/l9KpE2AfsBAAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.clustermap(df.corr(),cmap='viridis')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----\n", "## Machine Learning Model\n", "\n", "**TASK: Convert the categorical column \"type\" from a string or \"red\" or \"white\" to dummy variables:**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# CODE HERE" ] }, { "cell_type": "code", "execution_count": 126, "metadata": {}, "outputs": [], "source": [ "df['type'] = pd.get_dummies(df['type'],drop_first=True)" ] }, { "cell_type": "code", "execution_count": 128, "metadata": {}, "outputs": [], "source": [ "df = df.drop('Fraud',axis=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**TASK: Separate out the data into X features and y target label (\"quality\" column)**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 129, "metadata": {}, "outputs": [], "source": [ "X = df.drop('quality',axis=1)\n", "y = df['quality']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**TASK: Perform a Train|Test split on the data, with a 10% test size. Note: The solution uses a random state of 101**" ] }, { "cell_type": "code", "execution_count": 130, "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split" ] }, { "cell_type": "code", "execution_count": 131, "metadata": {}, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=101)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**TASK: Scale the X train and X test data.**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 132, "metadata": {}, "outputs": [], "source": [ "from sklearn.preprocessing import StandardScaler" ] }, { "cell_type": "code", "execution_count": 133, "metadata": {}, "outputs": [], "source": [ "scaler = StandardScaler()" ] }, { "cell_type": "code", "execution_count": 134, "metadata": {}, "outputs": [], "source": [ "scaled_X_train = scaler.fit_transform(X_train)\n", "scaled_X_test = scaler.transform(X_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**TASK: Create an instance of a Support Vector Machine classifier. Previously we have left this model \"blank\", (e.g. with no parameters). However, we already know that the classes are unbalanced, in an attempt to help alleviate this issue, we can automatically adjust weights inversely proportional to class frequencies in the input data with a argument call in the SVC() call. Check out the [documentation for SVC] online and look up what the argument\\parameter is.**" ] }, { "cell_type": "code", "execution_count": 135, "metadata": {}, "outputs": [], "source": [ "# CODE HERE" ] }, { "cell_type": "code", "execution_count": 136, "metadata": {}, "outputs": [], "source": [ "from sklearn.svm import SVC" ] }, { "cell_type": "code", "execution_count": 138, "metadata": {}, "outputs": [], "source": [ "svc = SVC(class_weight='balanced')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**TASK: Use a GridSearchCV to run a grid search for the best C and gamma parameters.**" ] }, { "cell_type": "code", "execution_count": 139, "metadata": {}, "outputs": [], "source": [ "# CODE HERE" ] }, { "cell_type": "code", "execution_count": 140, "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import GridSearchCV" ] }, { "cell_type": "code", "execution_count": 141, "metadata": {}, "outputs": [], "source": [ "param_grid = {'C':[0.001,0.01,0.1,0.5,1],'gamma':['scale','auto']}\n", "grid = GridSearchCV(svc,param_grid)" ] }, { "cell_type": "code", "execution_count": 142, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "GridSearchCV(estimator=SVC(class_weight='balanced'),\n", " param_grid={'C': [0.001, 0.01, 0.1, 0.5, 1],\n", " 'gamma': ['scale', 'auto']})" ] }, "execution_count": 142, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid.fit(scaled_X_train,y_train)" ] }, { "cell_type": "code", "execution_count": 143, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'C': 1, 'gamma': 'auto'}" ] }, "execution_count": 143, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid.best_params_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**TASK: Display the confusion matrix and classification report for your model.**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 144, "metadata": {}, "outputs": [], "source": [ "from sklearn.metrics import confusion_matrix,classification_report" ] }, { "cell_type": "code", "execution_count": 145, "metadata": {}, "outputs": [], "source": [ "grid_pred = grid.predict(scaled_X_test)" ] }, { "cell_type": "code", "execution_count": 146, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 17, 10],\n", " [ 92, 531]], dtype=int64)" ] }, "execution_count": 146, "metadata": {}, "output_type": "execute_result" } ], "source": [ "confusion_matrix(y_test,grid_pred)" ] }, { "cell_type": "code", "execution_count": 147, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " Fraud 0.16 0.63 0.25 27\n", " Legit 0.98 0.85 0.91 623\n", "\n", " accuracy 0.84 650\n", " macro avg 0.57 0.74 0.58 650\n", "weighted avg 0.95 0.84 0.88 650\n", "\n" ] } ], "source": [ "print(classification_report(y_test,grid_pred))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**TASK: Finally, think about how well this model performed, would you suggest using it? Realistically will this work?**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# View video for full discussion on this." ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 1 }