├── ch.1
    ├── R
    │   └── SettingUpREnvironment.R
    └── python
    │   └── Setting Up Python Environment.ipynb
├── ch.10
    ├── R
    │   └── CustomerSegmentation.R
    └── python
    │   └── CustomerSegmentation.ipynb
├── ch.11
    ├── R
    │   └── CustomerRetention.R
    └── python
    │   └── CustomerRetention.ipynb
├── ch.12
    ├── R
    │   └── ABTesting.R
    └── python
    │   └── ABTesting.ipynb
├── ch.2
    ├── R
    │   └── ConversionRate.R
    └── python
    │   └── ConversionRate.ipynb
├── ch.3
    ├── R
    │   └── RegressionAnalysis.R
    └── python
    │   └── RegressionAnalysis.ipynb
├── ch.4
    ├── R
    │   └── FromEngagementToConversions.R
    └── python
    │   └── From Engagement to Conversions.ipynb
├── ch.5
    ├── R
    │   └── ProductAnalytics.R
    └── python
    │   └── Product Analytics.ipynb
├── ch.6
    ├── R
    │   └── ProductRecommendation.R
    └── python
    │   └── ProductRecommendation.ipynb
├── ch.7
    ├── R
    │   └── CustomerBehaviors.R
    └── python
    │   └── CustomerBehaviors.ipynb
├── ch.8
    ├── R
    │   └── PredictingEngagement.R
    └── python
    │   └── PredictingEngagement.ipynb
└── ch.9
    ├── R
        └── CustomerLifetimeValue.R
    └── python
        └── CustomerLifetimeValue.ipynb


/ch.1/R/SettingUpREnvironment.R:
--------------------------------------------------------------------------------
 1 | # Test Data
 2 | data <- data.frame(
 3 |   "X"=c(0, 0.25, 0.5, 1), 
 4 |   "Y"=c(0, 0.5, 0.5, 1), 
 5 |   "output"=c(0, 0, 1, 1)
 6 | )
 7 | 
 8 | # Train Logistic Regression
 9 | logit.fit <- glm(
10 |   output ~ X + Y, 
11 |   data = data, 
12 |   family = binomial
13 | )
14 | 
15 | # Show Fitted Results
16 | summary(logit.fit)
17 | 
18 | # Predict Class Probabilities
19 | logit.probs <- predict(
20 |   logit.fit, 
21 |   newdata=data, 
22 |   type="response"
23 | )
24 | 
25 | # Predict Classes
26 | logit.pred <- ifelse(logit.probs > 0.5, 1, 0)
27 | logit.pred
28 | 
29 | 
30 | # Plotting Library
31 | library(ggplot2)
32 | 
33 | # Simple Scatterplot
34 | ggplot(data, aes(x=X, y=Y, color=output)) +
35 |   geom_point(size=3, shape=19) +
36 |   ggtitle('Actual') +
37 |   theme(plot.title = element_text(hjust = 0.5))
38 | 
39 | ggplot(data, aes(x=X, y=Y, color=logit.pred)) + 
40 |   geom_point(size=3, shape=19) +
41 |   ggtitle('Predicted') +
42 |   theme(plot.title = element_text(hjust = 0.5))
43 | 


--------------------------------------------------------------------------------
/ch.1/python/Setting Up Python Environment.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import numpy as np\n",
 10 |     "from sklearn.linear_model import LogisticRegression"
 11 |    ]
 12 |   },
 13 |   {
 14 |    "cell_type": "code",
 15 |    "execution_count": 2,
 16 |    "metadata": {},
 17 |    "outputs": [],
 18 |    "source": [
 19 |     "input_data = np.array([\n",
 20 |     "    [0, 0],\n",
 21 |     "    [0.25, 0.25],\n",
 22 |     "    [0.5, 0.5],\n",
 23 |     "    [1, 1],\n",
 24 |     "])"
 25 |    ]
 26 |   },
 27 |   {
 28 |    "cell_type": "code",
 29 |    "execution_count": 3,
 30 |    "metadata": {},
 31 |    "outputs": [],
 32 |    "source": [
 33 |     "output_data = [\n",
 34 |     "    0,\n",
 35 |     "    0,\n",
 36 |     "    1,\n",
 37 |     "    1\n",
 38 |     "]"
 39 |    ]
 40 |   },
 41 |   {
 42 |    "cell_type": "code",
 43 |    "execution_count": 4,
 44 |    "metadata": {},
 45 |    "outputs": [],
 46 |    "source": [
 47 |     "logit_model = LogisticRegression()"
 48 |    ]
 49 |   },
 50 |   {
 51 |    "cell_type": "code",
 52 |    "execution_count": 5,
 53 |    "metadata": {},
 54 |    "outputs": [
 55 |     {
 56 |      "data": {
 57 |       "text/plain": [
 58 |        "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
 59 |        "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
 60 |        "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
 61 |        "          verbose=0, warm_start=False)"
 62 |       ]
 63 |      },
 64 |      "execution_count": 5,
 65 |      "metadata": {},
 66 |      "output_type": "execute_result"
 67 |     }
 68 |    ],
 69 |    "source": [
 70 |     "logit_model.fit(input_data, output_data)"
 71 |    ]
 72 |   },
 73 |   {
 74 |    "cell_type": "code",
 75 |    "execution_count": 6,
 76 |    "metadata": {},
 77 |    "outputs": [
 78 |     {
 79 |      "data": {
 80 |       "text/plain": [
 81 |        "array([[0.43001235, 0.43001235]])"
 82 |       ]
 83 |      },
 84 |      "execution_count": 6,
 85 |      "metadata": {},
 86 |      "output_type": "execute_result"
 87 |     }
 88 |    ],
 89 |    "source": [
 90 |     "logit_model.coef_"
 91 |    ]
 92 |   },
 93 |   {
 94 |    "cell_type": "code",
 95 |    "execution_count": 7,
 96 |    "metadata": {},
 97 |    "outputs": [
 98 |     {
 99 |      "data": {
100 |       "text/plain": [
101 |        "array([-0.18498028])"
102 |       ]
103 |      },
104 |      "execution_count": 7,
105 |      "metadata": {},
106 |      "output_type": "execute_result"
107 |     }
108 |    ],
109 |    "source": [
110 |     "logit_model.intercept_"
111 |    ]
112 |   },
113 |   {
114 |    "cell_type": "code",
115 |    "execution_count": 8,
116 |    "metadata": {},
117 |    "outputs": [],
118 |    "source": [
119 |     "predicted_output = logit_model.predict(input_data)"
120 |    ]
121 |   },
122 |   {
123 |    "cell_type": "code",
124 |    "execution_count": 9,
125 |    "metadata": {},
126 |    "outputs": [
127 |     {
128 |      "data": {
129 |       "text/plain": [
130 |        "array([0, 1, 1, 1])"
131 |       ]
132 |      },
133 |      "execution_count": 9,
134 |      "metadata": {},
135 |      "output_type": "execute_result"
136 |     }
137 |    ],
138 |    "source": [
139 |     "predicted_output"
140 |    ]
141 |   },
142 |   {
143 |    "cell_type": "code",
144 |    "execution_count": 10,
145 |    "metadata": {},
146 |    "outputs": [],
147 |    "source": [
148 |     "%matplotlib inline"
149 |    ]
150 |   },
151 |   {
152 |    "cell_type": "code",
153 |    "execution_count": 11,
154 |    "metadata": {},
155 |    "outputs": [],
156 |    "source": [
157 |     "import matplotlib.pyplot as plt"
158 |    ]
159 |   },
160 |   {
161 |    "cell_type": "code",
162 |    "execution_count": 12,
163 |    "metadata": {},
164 |    "outputs": [
165 |     {
166 |      "data": {
167 |       "image/png": 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\n",
168 |       "text/plain": [
169 |        "<Figure size 432x288 with 1 Axes>"
170 |       ]
171 |      },
172 |      "metadata": {},
173 |      "output_type": "display_data"
174 |     }
175 |    ],
176 |    "source": [
177 |     "plt.scatter(\n",
178 |     "    x=input_data[:,0], \n",
179 |     "    y=input_data[:,1], \n",
180 |     "    color=[('red' if x == 1 else 'blue') for x in output_data]\n",
181 |     ")\n",
182 |     "plt.xlabel('X')\n",
183 |     "plt.ylabel('Y')\n",
184 |     "plt.title('Actual')\n",
185 |     "plt.grid()\n",
186 |     "plt.show()"
187 |    ]
188 |   },
189 |   {
190 |    "cell_type": "code",
191 |    "execution_count": 13,
192 |    "metadata": {},
193 |    "outputs": [
194 |     {
195 |      "data": {
196 |       "image/png": 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BK6vq7O7ymcAJVbVuH+M/DPyoqt43xra1wFqA/v7+4zZs2DDl+oaHh+nr65vyPL3Cfueu+dQr2O/+WrFixbaqGphoXJtPSR3rPstjJlCS1wEDwEljba+q9cB6gIGBgRocHJxycUNDQ0zHPL3Cfueu+dQr2G/b2gyFXcCyEctLgdtHD0pyCvBO4KSqeqjFeiRJE2jzmsK1wJFJjkiyCDgd2DRyQJJjgEuB1VV1Z4u1SJImobVQqKpHgXXAVcBNwMaquiHJBUlWd4f9CdAHfDbJ/0uyaR/TSZJmQKu3uaiqzcDmUevOH/H4lDb3L0l6YnxFsySpYShIkhqGgiSpYShIkhqGgiSpYShIkhqGgiSpYShIkhqGgiSpYShIkhqGgiSpYShIkhqGgiSpYShIkhqGgiSpYShIkhqGgiSpYShIkhqGgiSpYShIkhqGgiSpYShIkhqGgiSpYShIkhqGgiSpYShIkhqGgiSpYShIkhoLZ7uAmfbYY3D99fDAA7BnDxxgLEpSo9VfiUlWJtmeZEeS88bY/pQkn+lu/1aS5W3Wc8018JznwEknwT/9Exx2GGzd2uYeJam3tBYKSRYAlwCnAkcBZyQ5atSwNwL3VNXzgQ8C/7Oteu66C1atgjvugOHhzlHCbbfBKad0liVJ7R4pHA/sqKqdVfUwsAFYM2rMGuDy7uPPAScnSRvFfPrTnVNHoz32GHz+823sUZJ6T6qqnYmT04CVVXV2d/lM4ISqWjdizHe7Y3Z1l7/fHXPXqLnWAmsB+vv7j9uwYcMTruf222H37p8vL106zK5dfSSdU0r9/U94yp4yPDxMX1/fbJcxY+ZTv/OpV7Df/bVixYptVTUw0bg2LzSP9Rf/6ASazBiqaj2wHmBgYKAGBwefcDFf+xr89m///FTRRRcNce65gyxeDFdfDS95yROesqcMDQ2xP5+3XjWf+p1PvYL9tq3N00e7gGUjlpcCt+9rTJKFwKHA3W0U8+u/DieeCE972s/XLV4Mr3jF3A8ESZqsNkPhWuDIJEckWQScDmwaNWYT8Ibu49OAr1dL57MS2LwZPvCBTjj09cGHPwwbN7axN0nqTa2FQlU9CqwDrgJuAjZW1Q1JLkiyujvsY8AvJtkBvBXY62mr0+nAA+F3fge++U345V+Gs86CBQva3KMk9ZZWX7xWVZuBzaPWnT/i8YPAq9usQZI0eb6eV5LUMBQkSQ1DQZLUMBQkSQ1DQZLUMBQkSQ1DQZLUaO2GeG1J8mPglmmYaglw14Sj5g77nbvmU69gv/vr8Kp65kSDei4UpkuSrZO5Y+BcYb9z13zqFey3bZ4+kiQ1DAVJUmM+h8L62S5ghtnv3DWfegX7bdW8vaYgSdrbfD5SkCSNYihIkhpzPhSSrEyyPcmOJHu9iU+SpyT5THf7t5Isn/kqp8cken1rkhuTfCfJ15IcPht1TpeJ+h0x7rQklaSnn8Y4mX6TvKb7Nb4hyadmusbpNInv58OSXJ3kuu739KrZqHM6JLksyZ1JvruP7Ulycfdz8Z0kx7ZWTFXN2Q9gAfB94LnAIuB64KhRY94EfLT7+HTgM7Ndd4u9rgCe1n38u73a62T77Y47GPgGsAUYmO26W/76HglcBzy9u/ys2a675X7XA7/bfXwUcPNs1z2Ffv89cCzw3X1sXwV8CQhwIvCttmqZ60cKxwM7qmpnVT0MbADWjBqzBri8+/hzwMlJMoM1TpcJe62qq6vq/u7iFmDpDNc4nSbztQV4L3Ah8OBMFteCyfR7DnBJVd0DUFV3znCN02ky/RZwSPfxocDtM1jftKqqbwB3jzNkDfCJ6tgC/EKSZ7dRy1wPhecAt45Y3tVdN+aY6ryv9L3AL85IddNrMr2O9EY6f3n0qgn7TXIMsKyqrpzJwloyma/vC4AXJPmHJFuSrJyx6qbfZPp9D/C6JLvovO3vf56Z0mbFE/353m+tvkfzk8BYf/GPfg7uZMb0gkn3keR1wABwUqsVtWvcfpMcAHwQOGumCmrZZL6+C+mcQhqkcxR4TZKjq+qnLdfWhsn0ewbw8ar6QJJfA67o9run/fJm3Iz9nprrRwq7gGUjlpey9yFmMybJQjqHoeMdxj1ZTaZXkpwCvBNYXVUPzVBtbZio34OBo4GhJDfTOQ+7qYcvNk/2e/mvq+qRqvoBsJ1OSPSiyfT7RmAjQFV9EziIzs3j5qJJ/XxPh7keCtcCRyY5IskiOheSN40aswl4Q/fxacDXq3tlp8dM2Gv3dMqldAKhl883wwT9VtW9VbWkqpZX1XI611BWV9XW2Sl3yibzvfwFOk8mIMkSOqeTds5oldNnMv3+EDgZIMmL6ITCj2e0ypmzCXh991lIJwL3VtXuNnY0p08fVdWjSdYBV9F5NsNlVXVDkguArVW1CfgYncPOHXSOEE6fvYr33yR7/ROgD/hs91r6D6tq9awVPQWT7HfOmGS/VwEvT3Ij8Bjwtqr6yexVvf8m2e8fAn+R5A/onEo5q0f/oCPJp+mc9lvSvUbybuBAgKr6KJ1rJquAHcD9wG+1VkuPfg4lSS2Y66ePJElPgKEgSWoYCpKkhqEgSWoYCpKkhqEgTUGSZUl+kOQZ3eWnd5d7+g60mr8MBWkKqupW4CPA+7ur3g+sr6pbZq8qaf/5OgVpipIcCGwDLqNzp9Jjunf2lHrOnH5FszQTquqRJG8D/hZ4uYGgXubpI2l6nArspnMTPqlnGQrSFCX5t8Bv0LkT6x+09eYn0kwwFKQp6L5L30eA36+qH9K56eBFs1uVtP8MBWlqzqFzt9mvdJf/HHhhkl5+AyPNYz77SJLU8EhBktQwFCRJDUNBktQwFCRJDUNBktQwFCRJDUNBktT4/3MH+lStpVvqAAAAAElFTkSuQmCC\n",
197 |       "text/plain": [
198 |        "<Figure size 432x288 with 1 Axes>"
199 |       ]
200 |      },
201 |      "metadata": {},
202 |      "output_type": "display_data"
203 |     }
204 |    ],
205 |    "source": [
206 |     "plt.scatter(\n",
207 |     "    x=input_data[:,0], \n",
208 |     "    y=input_data[:,1], \n",
209 |     "    color=[('red' if x == 1 else 'blue') for x in predicted_output]\n",
210 |     ")\n",
211 |     "plt.xlabel('X')\n",
212 |     "plt.ylabel('Y')\n",
213 |     "plt.title('Predicted')\n",
214 |     "plt.grid()\n",
215 |     "plt.show()"
216 |    ]
217 |   },
218 |   {
219 |    "cell_type": "code",
220 |    "execution_count": null,
221 |    "metadata": {},
222 |    "outputs": [],
223 |    "source": []
224 |   }
225 |  ],
226 |  "metadata": {
227 |   "kernelspec": {
228 |    "display_name": "Python 3",
229 |    "language": "python",
230 |    "name": "python3"
231 |   },
232 |   "language_info": {
233 |    "codemirror_mode": {
234 |     "name": "ipython",
235 |     "version": 3
236 |    },
237 |    "file_extension": ".py",
238 |    "mimetype": "text/x-python",
239 |    "name": "python",
240 |    "nbconvert_exporter": "python",
241 |    "pygments_lexer": "ipython3",
242 |    "version": "3.6.5"
243 |   }
244 |  },
245 |  "nbformat": 4,
246 |  "nbformat_minor": 2
247 | }
248 | 


--------------------------------------------------------------------------------
/ch.10/R/CustomerSegmentation.R:
--------------------------------------------------------------------------------
  1 | library(dplyr)
  2 | library(readxl)
  3 | library(ggplot2)
  4 | #### 1. Load Data ####
  5 | df <- read_excel(
  6 |   path="~/Documents/data-science-for-marketing/ch.10/data/Online Retail.xlsx", 
  7 |   sheet="Online Retail"
  8 | )
  9 | 
 10 | #### 2. Date Clean-Up ####
 11 | 
 12 | # ignore negative quantity
 13 | dim(df)
 14 | df <- df[which(df$Quantity > 0),]
 15 | dim(df)
 16 | 
 17 | # remove records with NA
 18 | df <- na.omit(df)
 19 | dim(df)
 20 | 
 21 | # excluding incomplete month
 22 | sprintf("Date Range: %s ~ %s", min(df$InvoiceDate), max(df$InvoiceDate))
 23 | dim(df)
 24 | df <- df[which(df$InvoiceDate < '2011-12-01'),]
 25 | dim(df)
 26 | 
 27 | # total sales
 28 | df$Sales <- df$Quantity * df$UnitPrice
 29 | 
 30 | # per customer data
 31 | customerDF <- df %>% 
 32 |   group_by(CustomerID) %>% 
 33 |   summarize(TotalSales=sum(Sales), OrderCount=length(unique(InvoiceDate))) %>%
 34 |   mutate(AvgOrderValue=TotalSales/OrderCount)
 35 | 
 36 | rankDF <- customerDF %>%
 37 |   mutate(TotalSales=rank(TotalSales), OrderCount=rank(OrderCount, ties.method="first"), AvgOrderValue=rank(AvgOrderValue))
 38 | 
 39 | normalizedDF <- rankDF %>%
 40 |   mutate(TotalSales=scale(TotalSales), OrderCount=scale(OrderCount), AvgOrderValue=scale(AvgOrderValue))
 41 | 
 42 | # check for normalization - mean of 0 & std of 1
 43 | summary(normalizedDF)
 44 | sapply(normalizedDF, sd)
 45 | 
 46 | #### 3. Customer Segmentation via K-Means Clustering ####
 47 | 
 48 | cluster <- kmeans(normalizedDF[c("TotalSales", "OrderCount", "AvgOrderValue")], 4)
 49 | 
 50 | # cluster centers
 51 | cluster$centers
 52 | # cluster labels
 53 | normalizedDF$Cluster <- cluster$cluster
 54 | 
 55 | normalizedDF %>% group_by(Cluster) %>% summarise(Count=n())
 56 | 
 57 | ggplot(normalizedDF, aes(x=AvgOrderValue, y=OrderCount, color=Cluster)) +
 58 |   geom_point()
 59 | 
 60 | ggplot(normalizedDF, aes(x=TotalSales, y=OrderCount, color=Cluster)) +
 61 |   geom_point()
 62 | 
 63 | ggplot(normalizedDF, aes(x=TotalSales, y=AvgOrderValue, color=Cluster)) +
 64 |   geom_point()
 65 | 
 66 | 
 67 | # Selecting the best number of cluster
 68 | library(cluster)
 69 | 
 70 | for(n_cluster in 4:8){
 71 |   cluster <- kmeans(normalizedDF[c("TotalSales", "OrderCount", "AvgOrderValue")], n_cluster)
 72 |   
 73 |   silhouetteScore <- mean(
 74 |     silhouette(
 75 |       cluster$cluster, 
 76 |       dist(normalizedDF[c("TotalSales", "OrderCount", "AvgOrderValue")], method = "euclidean")
 77 |     )[,3]
 78 |   )
 79 |   print(sprintf('Silhouette Score for %i Clusters: %0.4f', n_cluster, silhouetteScore))
 80 | }
 81 | 
 82 | # Interpreting customer segments
 83 | cluster <- kmeans(normalizedDF[c("TotalSales", "OrderCount", "AvgOrderValue")], 4)
 84 | normalizedDF$Cluster <- cluster$cluster
 85 | # count per cluster
 86 | normalizedDF %>% group_by(Cluster) %>% summarise(Count=n())
 87 | # cluster centers
 88 | cluster$centers
 89 | 
 90 | # High value cluster summary
 91 | summary(customerDF[which(normalizedDF$Cluster == 4),])
 92 | 
 93 | highValueCustomers <- unlist(
 94 |   customerDF[which(normalizedDF$Cluster == 4),'CustomerID'][,1], use.names = FALSE
 95 | )
 96 | 
 97 | df[which(df$CustomerID %in% highValueCustomers),] %>%
 98 |   group_by(Description) %>%
 99 |   summarise(Count=n()) %>%
100 |   arrange(desc(Count))
101 | 
102 | 
103 | 


--------------------------------------------------------------------------------
/ch.11/R/CustomerRetention.R:
--------------------------------------------------------------------------------
  1 | library(dplyr)
  2 | library(tidyr)
  3 | library(readxl)
  4 | 
  5 | #### 1. Load Data ####
  6 | df <- read_excel(
  7 |   path="~/Documents/data-science-for-marketing/ch.11/data/WA_Fn-UseC_-Telco-Customer-Churn.xlsx"
  8 | )
  9 | 
 10 | #### 2. Date Analysis & Preparation ####
 11 | df <- df %>% drop_na()
 12 | 
 13 | apply(df, 2, function(x) length(unique(x)))
 14 | 
 15 | ggplot(df %>% group_by(gender) %>% summarise(Count=n()), aes(x=gender, y=Count)) +
 16 |   geom_bar(width=0.5, stat="identity") +
 17 |   ggtitle('') +
 18 |   xlab("Gender") +
 19 |   ylab("Count") +
 20 |   theme(plot.title = element_text(hjust = 0.5))
 21 | 
 22 | ggplot(df %>% group_by(InternetService) %>% summarise(Count=n()), aes(x=InternetService, y=Count)) +
 23 |   geom_bar(width=0.5, stat="identity") +
 24 |   ggtitle('') +
 25 |   xlab("Internet Service") +
 26 |   ylab("Count") +
 27 |   theme(plot.title = element_text(hjust = 0.5))
 28 | 
 29 | ggplot(df %>% group_by(PaymentMethod) %>% summarise(Count=n()), aes(x=PaymentMethod, y=Count)) +
 30 |   geom_bar(width=0.5, stat="identity") +
 31 |   ggtitle('') +
 32 |   xlab("Payment Method") +
 33 |   ylab("Count") +
 34 |   theme(plot.title = element_text(hjust = 0.5))
 35 | 
 36 | # Binary & Continuous Vars
 37 | sampleDF <- df %>%
 38 |   select(tenure, MonthlyCharges, TotalCharges, gender, Partner, Dependents, PhoneService, PaperlessBilling, Churn) %>%
 39 |   mutate(
 40 |     # transforming continuous vars
 41 |     tenure=(tenure - mean(tenure))/sd(tenure),
 42 |     MonthlyCharges=(log(MonthlyCharges) - mean(log(MonthlyCharges)))/sd(log(MonthlyCharges)),
 43 |     TotalCharges=(log(TotalCharges) - mean(log(TotalCharges)))/sd(log(TotalCharges)),
 44 |     # encoding binary categorical vars
 45 |     gender=gender %>% as.factor() %>% as.numeric() - 1,
 46 |     Partner=Partner %>% as.factor() %>% as.numeric() - 1,
 47 |     Dependents=Dependents %>% as.factor() %>% as.numeric() - 1,
 48 |     PhoneService=PhoneService %>% as.factor() %>% as.numeric() - 1,
 49 |     PaperlessBilling=PaperlessBilling %>% as.factor() %>% as.numeric() - 1,
 50 |     Churn=Churn %>% as.factor() %>% as.numeric() - 1
 51 |   )
 52 | 
 53 | summary(df[,c("tenure", "MonthlyCharges", "TotalCharges")])
 54 | apply(df[,c("tenure", "MonthlyCharges", "TotalCharges")], 2, sd)
 55 | 
 56 | summary(sampleDF[,c("tenure", "MonthlyCharges", "TotalCharges")])
 57 | apply(sampleDF[,c("tenure", "MonthlyCharges", "TotalCharges")], 2, sd)
 58 | 
 59 | # Dummy vars
 60 | # install.packages('dummies')
 61 | library(dummies)
 62 | 
 63 | sampleDF <- cbind(sampleDF, dummy(df$MultipleLines, sep="."))
 64 | names(sampleDF) = gsub("sampleDF", "MultipleLines", names(sampleDF))
 65 | 
 66 | sampleDF <- cbind(sampleDF, dummy(df$InternetService, sep="."))
 67 | names(sampleDF) = gsub("sampleDF", "InternetService", names(sampleDF))
 68 | 
 69 | sampleDF <- cbind(sampleDF, dummy(df$OnlineSecurity, sep="."))
 70 | names(sampleDF) = gsub("sampleDF", "OnlineSecurity", names(sampleDF))
 71 | 
 72 | sampleDF <- cbind(sampleDF, dummy(df$OnlineBackup, sep="."))
 73 | names(sampleDF) = gsub("sampleDF", "OnlineBackup", names(sampleDF))
 74 | 
 75 | sampleDF <- cbind(sampleDF, dummy(df$DeviceProtection, sep="."))
 76 | names(sampleDF) = gsub("sampleDF", "DeviceProtection", names(sampleDF))
 77 | 
 78 | sampleDF <- cbind(sampleDF, dummy(df$TechSupport, sep="."))
 79 | names(sampleDF) = gsub("sampleDF", "TechSupport", names(sampleDF))
 80 | 
 81 | sampleDF <- cbind(sampleDF, dummy(df$StreamingTV, sep="."))
 82 | names(sampleDF) = gsub("sampleDF", "StreamingTV", names(sampleDF))
 83 | 
 84 | sampleDF <- cbind(sampleDF, dummy(df$StreamingMovies, sep="."))
 85 | names(sampleDF) = gsub("sampleDF", "StreamingMovies", names(sampleDF))
 86 | 
 87 | sampleDF <- cbind(sampleDF, dummy(df$Contract, sep="."))
 88 | names(sampleDF) = gsub("sampleDF", "Contract", names(sampleDF))
 89 | 
 90 | sampleDF <- cbind(sampleDF, dummy(df$PaymentMethod, sep="."))
 91 | names(sampleDF) = gsub("sampleDF", "PaymentMethod", names(sampleDF))
 92 | 
 93 | 
 94 | #### 3. Train & Test Set Split ####
 95 | library(caTools)
 96 | 
 97 | sample <- sample.split(sampleDF$Churn, SplitRatio = .7)
 98 | 
 99 | train <- as.data.frame(subset(sampleDF, sample == TRUE))
100 | test <- as.data.frame(subset(sampleDF, sample == FALSE))
101 | 
102 | trainX <- as.matrix(train[,names(train) != "Churn"])
103 | trainY <- train$Churn
104 | testX <- as.matrix(test[,names(test) != "Churn"])
105 | testY <- test$Churn
106 | 
107 | 
108 | #### 4. Aritificial Neural Network (ANN) with Keras ####
109 | install.packages("devtools")
110 | devtools::install_github("rstudio/tensorflow")
111 | library(tensorflow)
112 | install_tensorflow()
113 | 
114 | devtools::install_github("rstudio/keras")
115 | library(keras)
116 | install_keras()
117 | 
118 | 
119 | model <- keras_model_sequential() 
120 | model %>% 
121 |   layer_dense(units = 16, kernel_initializer = "uniform", activation = 'relu', input_shape=ncol(sampleDF)-1) %>% 
122 |   layer_dense(units = 8, kernel_initializer = "uniform", activation = 'relu') %>%
123 |   layer_dense(units = 1, kernel_initializer = "uniform", activation = 'sigmoid') %>% 
124 |   compile(
125 |     optimizer = 'adam',
126 |     loss      = 'binary_crossentropy',
127 |     metrics   = c('accuracy')
128 |   )
129 | 
130 | history <- model %>% fit(
131 |   trainX, 
132 |   trainY, 
133 |   epochs = 50, batch_size = 100, 
134 |   validation_split = 0.2
135 | )
136 | 
137 | # Evaluating ANN model
138 | inSamplePreds <- as.double(model %>% predict_classes(trainX))
139 | outSamplePreds <- as.double(model %>% predict_classes(testX))
140 | 
141 | # - Accuracy, Precision, and Recall
142 | inSampleAccuracy <- mean(trainY == inSamplePreds)
143 | outSampleAccuracy <- mean(testY == outSamplePreds)
144 | 
145 | inSamplePrecision <- sum(inSamplePreds & trainY) / sum(inSamplePreds)
146 | outSamplePrecision <- sum(outSamplePreds & testY) / sum(outSamplePreds)
147 | 
148 | inSampleRecall <- sum(inSamplePreds & trainY) / sum(trainY)
149 | outSampleRecall <- sum(outSamplePreds & testY) / sum(testY)
150 | 
151 | 
152 | print(sprintf('In-Sample Accuracy: %0.4f', inSampleAccuracy))
153 | print(sprintf('Out-Sample Accuracy: %0.4f', outSampleAccuracy))
154 | print(sprintf('In-Sample Precision: %0.4f', inSamplePrecision))
155 | print(sprintf('Out-Sample Precision: %0.4f', outSamplePrecision))
156 | print(sprintf('In-Sample Recall: %0.4f', inSampleRecall))
157 | print(sprintf('Out-Sample Recall: %0.4f', outSampleRecall))
158 | 
159 | 
160 | # - ROC & AUC
161 | library(ROCR)
162 | 
163 | outSamplePredProbs <- as.double(predict(model, testX))
164 | 
165 | pred <- prediction(outSamplePredProbs, testY)
166 | perf <- performance(pred, measure = "tpr", x.measure = "fpr") 
167 | auc <- performance(pred, measure='auc')@y.values[[1]]
168 | 
169 | plot(
170 |   perf, 
171 |   main=sprintf('Model ROC Curve (AUC: %0.2f)', auc), 
172 |   col='darkorange', 
173 |   lwd=2
174 | ) + grid()
175 | abline(a = 0, b = 1, col='darkgray', lty=3, lwd=2)
176 | 
177 | 
178 | 
179 | 
180 | 


--------------------------------------------------------------------------------
/ch.11/python/CustomerRetention.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "code",
   5 |    "execution_count": 1,
   6 |    "metadata": {},
   7 |    "outputs": [],
   8 |    "source": [
   9 |     "%matplotlib inline"
  10 |    ]
  11 |   },
  12 |   {
  13 |    "cell_type": "code",
  14 |    "execution_count": 2,
  15 |    "metadata": {},
  16 |    "outputs": [],
  17 |    "source": [
  18 |     "import matplotlib.pyplot as plt\n",
  19 |     "import pandas as pd\n",
  20 |     "import numpy as np"
  21 |    ]
  22 |   },
  23 |   {
  24 |    "cell_type": "markdown",
  25 |    "metadata": {},
  26 |    "source": [
  27 |     "# 1. Load Data"
  28 |    ]
  29 |   },
  30 |   {
  31 |    "cell_type": "code",
  32 |    "execution_count": 3,
  33 |    "metadata": {},
  34 |    "outputs": [],
  35 |    "source": [
  36 |     "df = pd.read_excel('../data/WA_Fn-UseC_-Telco-Customer-Churn.xlsx')"
  37 |    ]
  38 |   },
  39 |   {
  40 |    "cell_type": "code",
  41 |    "execution_count": 4,
  42 |    "metadata": {},
  43 |    "outputs": [
  44 |     {
  45 |      "data": {
  46 |       "text/plain": [
  47 |        "(7043, 21)"
  48 |       ]
  49 |      },
  50 |      "execution_count": 4,
  51 |      "metadata": {},
  52 |      "output_type": "execute_result"
  53 |     }
  54 |    ],
  55 |    "source": [
  56 |     "df.shape"
  57 |    ]
  58 |   },
  59 |   {
  60 |    "cell_type": "code",
  61 |    "execution_count": 5,
  62 |    "metadata": {},
  63 |    "outputs": [
  64 |     {
  65 |      "data": {
  66 |       "text/html": [
  67 |        "<div>\n",
  68 |        "<style scoped>\n",
  69 |        "    .dataframe tbody tr th:only-of-type {\n",
  70 |        "        vertical-align: middle;\n",
  71 |        "    }\n",
  72 |        "\n",
  73 |        "    .dataframe tbody tr th {\n",
  74 |        "        vertical-align: top;\n",
  75 |        "    }\n",
  76 |        "\n",
  77 |        "    .dataframe thead th {\n",
  78 |        "        text-align: right;\n",
  79 |        "    }\n",
  80 |        "</style>\n",
  81 |        "<table border=\"1\" class=\"dataframe\">\n",
  82 |        "  <thead>\n",
  83 |        "    <tr style=\"text-align: right;\">\n",
  84 |        "      <th></th>\n",
  85 |        "      <th>customerID</th>\n",
  86 |        "      <th>gender</th>\n",
  87 |        "      <th>SeniorCitizen</th>\n",
  88 |        "      <th>Partner</th>\n",
  89 |        "      <th>Dependents</th>\n",
  90 |        "      <th>tenure</th>\n",
  91 |        "      <th>PhoneService</th>\n",
  92 |        "      <th>MultipleLines</th>\n",
  93 |        "      <th>InternetService</th>\n",
  94 |        "      <th>OnlineSecurity</th>\n",
  95 |        "      <th>...</th>\n",
  96 |        "      <th>DeviceProtection</th>\n",
  97 |        "      <th>TechSupport</th>\n",
  98 |        "      <th>StreamingTV</th>\n",
  99 |        "      <th>StreamingMovies</th>\n",
 100 |        "      <th>Contract</th>\n",
 101 |        "      <th>PaperlessBilling</th>\n",
 102 |        "      <th>PaymentMethod</th>\n",
 103 |        "      <th>MonthlyCharges</th>\n",
 104 |        "      <th>TotalCharges</th>\n",
 105 |        "      <th>Churn</th>\n",
 106 |        "    </tr>\n",
 107 |        "  </thead>\n",
 108 |        "  <tbody>\n",
 109 |        "    <tr>\n",
 110 |        "      <th>0</th>\n",
 111 |        "      <td>7590-VHVEG</td>\n",
 112 |        "      <td>Female</td>\n",
 113 |        "      <td>0</td>\n",
 114 |        "      <td>Yes</td>\n",
 115 |        "      <td>No</td>\n",
 116 |        "      <td>1</td>\n",
 117 |        "      <td>No</td>\n",
 118 |        "      <td>No phone service</td>\n",
 119 |        "      <td>DSL</td>\n",
 120 |        "      <td>No</td>\n",
 121 |        "      <td>...</td>\n",
 122 |        "      <td>No</td>\n",
 123 |        "      <td>No</td>\n",
 124 |        "      <td>No</td>\n",
 125 |        "      <td>No</td>\n",
 126 |        "      <td>Month-to-month</td>\n",
 127 |        "      <td>Yes</td>\n",
 128 |        "      <td>Electronic check</td>\n",
 129 |        "      <td>29.85</td>\n",
 130 |        "      <td>29.85</td>\n",
 131 |        "      <td>No</td>\n",
 132 |        "    </tr>\n",
 133 |        "    <tr>\n",
 134 |        "      <th>1</th>\n",
 135 |        "      <td>5575-GNVDE</td>\n",
 136 |        "      <td>Male</td>\n",
 137 |        "      <td>0</td>\n",
 138 |        "      <td>No</td>\n",
 139 |        "      <td>No</td>\n",
 140 |        "      <td>34</td>\n",
 141 |        "      <td>Yes</td>\n",
 142 |        "      <td>No</td>\n",
 143 |        "      <td>DSL</td>\n",
 144 |        "      <td>Yes</td>\n",
 145 |        "      <td>...</td>\n",
 146 |        "      <td>Yes</td>\n",
 147 |        "      <td>No</td>\n",
 148 |        "      <td>No</td>\n",
 149 |        "      <td>No</td>\n",
 150 |        "      <td>One year</td>\n",
 151 |        "      <td>No</td>\n",
 152 |        "      <td>Mailed check</td>\n",
 153 |        "      <td>56.95</td>\n",
 154 |        "      <td>1889.5</td>\n",
 155 |        "      <td>No</td>\n",
 156 |        "    </tr>\n",
 157 |        "    <tr>\n",
 158 |        "      <th>2</th>\n",
 159 |        "      <td>3668-QPYBK</td>\n",
 160 |        "      <td>Male</td>\n",
 161 |        "      <td>0</td>\n",
 162 |        "      <td>No</td>\n",
 163 |        "      <td>No</td>\n",
 164 |        "      <td>2</td>\n",
 165 |        "      <td>Yes</td>\n",
 166 |        "      <td>No</td>\n",
 167 |        "      <td>DSL</td>\n",
 168 |        "      <td>Yes</td>\n",
 169 |        "      <td>...</td>\n",
 170 |        "      <td>No</td>\n",
 171 |        "      <td>No</td>\n",
 172 |        "      <td>No</td>\n",
 173 |        "      <td>No</td>\n",
 174 |        "      <td>Month-to-month</td>\n",
 175 |        "      <td>Yes</td>\n",
 176 |        "      <td>Mailed check</td>\n",
 177 |        "      <td>53.85</td>\n",
 178 |        "      <td>108.15</td>\n",
 179 |        "      <td>Yes</td>\n",
 180 |        "    </tr>\n",
 181 |        "    <tr>\n",
 182 |        "      <th>3</th>\n",
 183 |        "      <td>7795-CFOCW</td>\n",
 184 |        "      <td>Male</td>\n",
 185 |        "      <td>0</td>\n",
 186 |        "      <td>No</td>\n",
 187 |        "      <td>No</td>\n",
 188 |        "      <td>45</td>\n",
 189 |        "      <td>No</td>\n",
 190 |        "      <td>No phone service</td>\n",
 191 |        "      <td>DSL</td>\n",
 192 |        "      <td>Yes</td>\n",
 193 |        "      <td>...</td>\n",
 194 |        "      <td>Yes</td>\n",
 195 |        "      <td>Yes</td>\n",
 196 |        "      <td>No</td>\n",
 197 |        "      <td>No</td>\n",
 198 |        "      <td>One year</td>\n",
 199 |        "      <td>No</td>\n",
 200 |        "      <td>Bank transfer (automatic)</td>\n",
 201 |        "      <td>42.30</td>\n",
 202 |        "      <td>1840.75</td>\n",
 203 |        "      <td>No</td>\n",
 204 |        "    </tr>\n",
 205 |        "    <tr>\n",
 206 |        "      <th>4</th>\n",
 207 |        "      <td>9237-HQITU</td>\n",
 208 |        "      <td>Female</td>\n",
 209 |        "      <td>0</td>\n",
 210 |        "      <td>No</td>\n",
 211 |        "      <td>No</td>\n",
 212 |        "      <td>2</td>\n",
 213 |        "      <td>Yes</td>\n",
 214 |        "      <td>No</td>\n",
 215 |        "      <td>Fiber optic</td>\n",
 216 |        "      <td>No</td>\n",
 217 |        "      <td>...</td>\n",
 218 |        "      <td>No</td>\n",
 219 |        "      <td>No</td>\n",
 220 |        "      <td>No</td>\n",
 221 |        "      <td>No</td>\n",
 222 |        "      <td>Month-to-month</td>\n",
 223 |        "      <td>Yes</td>\n",
 224 |        "      <td>Electronic check</td>\n",
 225 |        "      <td>70.70</td>\n",
 226 |        "      <td>151.65</td>\n",
 227 |        "      <td>Yes</td>\n",
 228 |        "    </tr>\n",
 229 |        "    <tr>\n",
 230 |        "      <th>5</th>\n",
 231 |        "      <td>9305-CDSKC</td>\n",
 232 |        "      <td>Female</td>\n",
 233 |        "      <td>0</td>\n",
 234 |        "      <td>No</td>\n",
 235 |        "      <td>No</td>\n",
 236 |        "      <td>8</td>\n",
 237 |        "      <td>Yes</td>\n",
 238 |        "      <td>Yes</td>\n",
 239 |        "      <td>Fiber optic</td>\n",
 240 |        "      <td>No</td>\n",
 241 |        "      <td>...</td>\n",
 242 |        "      <td>Yes</td>\n",
 243 |        "      <td>No</td>\n",
 244 |        "      <td>Yes</td>\n",
 245 |        "      <td>Yes</td>\n",
 246 |        "      <td>Month-to-month</td>\n",
 247 |        "      <td>Yes</td>\n",
 248 |        "      <td>Electronic check</td>\n",
 249 |        "      <td>99.65</td>\n",
 250 |        "      <td>820.5</td>\n",
 251 |        "      <td>Yes</td>\n",
 252 |        "    </tr>\n",
 253 |        "    <tr>\n",
 254 |        "      <th>6</th>\n",
 255 |        "      <td>1452-KIOVK</td>\n",
 256 |        "      <td>Male</td>\n",
 257 |        "      <td>0</td>\n",
 258 |        "      <td>No</td>\n",
 259 |        "      <td>Yes</td>\n",
 260 |        "      <td>22</td>\n",
 261 |        "      <td>Yes</td>\n",
 262 |        "      <td>Yes</td>\n",
 263 |        "      <td>Fiber optic</td>\n",
 264 |        "      <td>No</td>\n",
 265 |        "      <td>...</td>\n",
 266 |        "      <td>No</td>\n",
 267 |        "      <td>No</td>\n",
 268 |        "      <td>Yes</td>\n",
 269 |        "      <td>No</td>\n",
 270 |        "      <td>Month-to-month</td>\n",
 271 |        "      <td>Yes</td>\n",
 272 |        "      <td>Credit card (automatic)</td>\n",
 273 |        "      <td>89.10</td>\n",
 274 |        "      <td>1949.4</td>\n",
 275 |        "      <td>No</td>\n",
 276 |        "    </tr>\n",
 277 |        "    <tr>\n",
 278 |        "      <th>7</th>\n",
 279 |        "      <td>6713-OKOMC</td>\n",
 280 |        "      <td>Female</td>\n",
 281 |        "      <td>0</td>\n",
 282 |        "      <td>No</td>\n",
 283 |        "      <td>No</td>\n",
 284 |        "      <td>10</td>\n",
 285 |        "      <td>No</td>\n",
 286 |        "      <td>No phone service</td>\n",
 287 |        "      <td>DSL</td>\n",
 288 |        "      <td>Yes</td>\n",
 289 |        "      <td>...</td>\n",
 290 |        "      <td>No</td>\n",
 291 |        "      <td>No</td>\n",
 292 |        "      <td>No</td>\n",
 293 |        "      <td>No</td>\n",
 294 |        "      <td>Month-to-month</td>\n",
 295 |        "      <td>No</td>\n",
 296 |        "      <td>Mailed check</td>\n",
 297 |        "      <td>29.75</td>\n",
 298 |        "      <td>301.9</td>\n",
 299 |        "      <td>No</td>\n",
 300 |        "    </tr>\n",
 301 |        "    <tr>\n",
 302 |        "      <th>8</th>\n",
 303 |        "      <td>7892-POOKP</td>\n",
 304 |        "      <td>Female</td>\n",
 305 |        "      <td>0</td>\n",
 306 |        "      <td>Yes</td>\n",
 307 |        "      <td>No</td>\n",
 308 |        "      <td>28</td>\n",
 309 |        "      <td>Yes</td>\n",
 310 |        "      <td>Yes</td>\n",
 311 |        "      <td>Fiber optic</td>\n",
 312 |        "      <td>No</td>\n",
 313 |        "      <td>...</td>\n",
 314 |        "      <td>Yes</td>\n",
 315 |        "      <td>Yes</td>\n",
 316 |        "      <td>Yes</td>\n",
 317 |        "      <td>Yes</td>\n",
 318 |        "      <td>Month-to-month</td>\n",
 319 |        "      <td>Yes</td>\n",
 320 |        "      <td>Electronic check</td>\n",
 321 |        "      <td>104.80</td>\n",
 322 |        "      <td>3046.05</td>\n",
 323 |        "      <td>Yes</td>\n",
 324 |        "    </tr>\n",
 325 |        "    <tr>\n",
 326 |        "      <th>9</th>\n",
 327 |        "      <td>6388-TABGU</td>\n",
 328 |        "      <td>Male</td>\n",
 329 |        "      <td>0</td>\n",
 330 |        "      <td>No</td>\n",
 331 |        "      <td>Yes</td>\n",
 332 |        "      <td>62</td>\n",
 333 |        "      <td>Yes</td>\n",
 334 |        "      <td>No</td>\n",
 335 |        "      <td>DSL</td>\n",
 336 |        "      <td>Yes</td>\n",
 337 |        "      <td>...</td>\n",
 338 |        "      <td>No</td>\n",
 339 |        "      <td>No</td>\n",
 340 |        "      <td>No</td>\n",
 341 |        "      <td>No</td>\n",
 342 |        "      <td>One year</td>\n",
 343 |        "      <td>No</td>\n",
 344 |        "      <td>Bank transfer (automatic)</td>\n",
 345 |        "      <td>56.15</td>\n",
 346 |        "      <td>3487.95</td>\n",
 347 |        "      <td>No</td>\n",
 348 |        "    </tr>\n",
 349 |        "  </tbody>\n",
 350 |        "</table>\n",
 351 |        "<p>10 rows × 21 columns</p>\n",
 352 |        "</div>"
 353 |       ],
 354 |       "text/plain": [
 355 |        "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
 356 |        "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
 357 |        "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
 358 |        "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
 359 |        "3  7795-CFOCW    Male              0      No         No      45           No   \n",
 360 |        "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
 361 |        "5  9305-CDSKC  Female              0      No         No       8          Yes   \n",
 362 |        "6  1452-KIOVK    Male              0      No        Yes      22          Yes   \n",
 363 |        "7  6713-OKOMC  Female              0      No         No      10           No   \n",
 364 |        "8  7892-POOKP  Female              0     Yes         No      28          Yes   \n",
 365 |        "9  6388-TABGU    Male              0      No        Yes      62          Yes   \n",
 366 |        "\n",
 367 |        "      MultipleLines InternetService OnlineSecurity  ...  DeviceProtection  \\\n",
 368 |        "0  No phone service             DSL             No  ...                No   \n",
 369 |        "1                No             DSL            Yes  ...               Yes   \n",
 370 |        "2                No             DSL            Yes  ...                No   \n",
 371 |        "3  No phone service             DSL            Yes  ...               Yes   \n",
 372 |        "4                No     Fiber optic             No  ...                No   \n",
 373 |        "5               Yes     Fiber optic             No  ...               Yes   \n",
 374 |        "6               Yes     Fiber optic             No  ...                No   \n",
 375 |        "7  No phone service             DSL            Yes  ...                No   \n",
 376 |        "8               Yes     Fiber optic             No  ...               Yes   \n",
 377 |        "9                No             DSL            Yes  ...                No   \n",
 378 |        "\n",
 379 |        "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
 380 |        "0          No          No              No  Month-to-month              Yes   \n",
 381 |        "1          No          No              No        One year               No   \n",
 382 |        "2          No          No              No  Month-to-month              Yes   \n",
 383 |        "3         Yes          No              No        One year               No   \n",
 384 |        "4          No          No              No  Month-to-month              Yes   \n",
 385 |        "5          No         Yes             Yes  Month-to-month              Yes   \n",
 386 |        "6          No         Yes              No  Month-to-month              Yes   \n",
 387 |        "7          No          No              No  Month-to-month               No   \n",
 388 |        "8         Yes         Yes             Yes  Month-to-month              Yes   \n",
 389 |        "9          No          No              No        One year               No   \n",
 390 |        "\n",
 391 |        "               PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
 392 |        "0           Electronic check          29.85         29.85    No  \n",
 393 |        "1               Mailed check          56.95        1889.5    No  \n",
 394 |        "2               Mailed check          53.85        108.15   Yes  \n",
 395 |        "3  Bank transfer (automatic)          42.30       1840.75    No  \n",
 396 |        "4           Electronic check          70.70        151.65   Yes  \n",
 397 |        "5           Electronic check          99.65         820.5   Yes  \n",
 398 |        "6    Credit card (automatic)          89.10        1949.4    No  \n",
 399 |        "7               Mailed check          29.75         301.9    No  \n",
 400 |        "8           Electronic check         104.80       3046.05   Yes  \n",
 401 |        "9  Bank transfer (automatic)          56.15       3487.95    No  \n",
 402 |        "\n",
 403 |        "[10 rows x 21 columns]"
 404 |       ]
 405 |      },
 406 |      "execution_count": 5,
 407 |      "metadata": {},
 408 |      "output_type": "execute_result"
 409 |     }
 410 |    ],
 411 |    "source": [
 412 |     "df.head(10)"
 413 |    ]
 414 |   },
 415 |   {
 416 |    "cell_type": "markdown",
 417 |    "metadata": {},
 418 |    "source": [
 419 |     "# 2. Data Analysis & Preparation"
 420 |    ]
 421 |   },
 422 |   {
 423 |    "cell_type": "markdown",
 424 |    "metadata": {},
 425 |    "source": [
 426 |     "#### - Encoding target var: Churn"
 427 |    ]
 428 |   },
 429 |   {
 430 |    "cell_type": "code",
 431 |    "execution_count": 6,
 432 |    "metadata": {},
 433 |    "outputs": [],
 434 |    "source": [
 435 |     "df['Churn'] = df['Churn'].apply(lambda x: 1 if x == 'Yes' else 0)"
 436 |    ]
 437 |   },
 438 |   {
 439 |    "cell_type": "code",
 440 |    "execution_count": 7,
 441 |    "metadata": {},
 442 |    "outputs": [
 443 |     {
 444 |      "data": {
 445 |       "text/plain": [
 446 |        "0.2653698707936959"
 447 |       ]
 448 |      },
 449 |      "execution_count": 7,
 450 |      "metadata": {},
 451 |      "output_type": "execute_result"
 452 |     }
 453 |    ],
 454 |    "source": [
 455 |     "df['Churn'].mean()"
 456 |    ]
 457 |   },
 458 |   {
 459 |    "cell_type": "markdown",
 460 |    "metadata": {},
 461 |    "source": [
 462 |     "#### - TotalCharges"
 463 |    ]
 464 |   },
 465 |   {
 466 |    "cell_type": "code",
 467 |    "execution_count": 8,
 468 |    "metadata": {},
 469 |    "outputs": [],
 470 |    "source": [
 471 |     "df['TotalCharges'] = df['TotalCharges'].replace(' ', np.nan).astype(float)"
 472 |    ]
 473 |   },
 474 |   {
 475 |    "cell_type": "code",
 476 |    "execution_count": 9,
 477 |    "metadata": {},
 478 |    "outputs": [
 479 |     {
 480 |      "data": {
 481 |       "text/plain": [
 482 |        "(7043, 21)"
 483 |       ]
 484 |      },
 485 |      "execution_count": 9,
 486 |      "metadata": {},
 487 |      "output_type": "execute_result"
 488 |     }
 489 |    ],
 490 |    "source": [
 491 |     "df.shape"
 492 |    ]
 493 |   },
 494 |   {
 495 |    "cell_type": "code",
 496 |    "execution_count": 10,
 497 |    "metadata": {},
 498 |    "outputs": [
 499 |     {
 500 |      "data": {
 501 |       "text/plain": [
 502 |        "(7032, 21)"
 503 |       ]
 504 |      },
 505 |      "execution_count": 10,
 506 |      "metadata": {},
 507 |      "output_type": "execute_result"
 508 |     }
 509 |    ],
 510 |    "source": [
 511 |     "df.dropna().shape"
 512 |    ]
 513 |   },
 514 |   {
 515 |    "cell_type": "code",
 516 |    "execution_count": 11,
 517 |    "metadata": {},
 518 |    "outputs": [],
 519 |    "source": [
 520 |     "df = df.dropna()"
 521 |    ]
 522 |   },
 523 |   {
 524 |    "cell_type": "markdown",
 525 |    "metadata": {},
 526 |    "source": [
 527 |     "#### - Continuous Vars"
 528 |    ]
 529 |   },
 530 |   {
 531 |    "cell_type": "code",
 532 |    "execution_count": 12,
 533 |    "metadata": {},
 534 |    "outputs": [
 535 |     {
 536 |      "data": {
 537 |       "text/html": [
 538 |        "<div>\n",
 539 |        "<style scoped>\n",
 540 |        "    .dataframe tbody tr th:only-of-type {\n",
 541 |        "        vertical-align: middle;\n",
 542 |        "    }\n",
 543 |        "\n",
 544 |        "    .dataframe tbody tr th {\n",
 545 |        "        vertical-align: top;\n",
 546 |        "    }\n",
 547 |        "\n",
 548 |        "    .dataframe thead th {\n",
 549 |        "        text-align: right;\n",
 550 |        "    }\n",
 551 |        "</style>\n",
 552 |        "<table border=\"1\" class=\"dataframe\">\n",
 553 |        "  <thead>\n",
 554 |        "    <tr style=\"text-align: right;\">\n",
 555 |        "      <th></th>\n",
 556 |        "      <th>tenure</th>\n",
 557 |        "      <th>MonthlyCharges</th>\n",
 558 |        "      <th>TotalCharges</th>\n",
 559 |        "    </tr>\n",
 560 |        "  </thead>\n",
 561 |        "  <tbody>\n",
 562 |        "    <tr>\n",
 563 |        "      <th>count</th>\n",
 564 |        "      <td>7032.000000</td>\n",
 565 |        "      <td>7032.000000</td>\n",
 566 |        "      <td>7032.000000</td>\n",
 567 |        "    </tr>\n",
 568 |        "    <tr>\n",
 569 |        "      <th>mean</th>\n",
 570 |        "      <td>32.421786</td>\n",
 571 |        "      <td>64.798208</td>\n",
 572 |        "      <td>2283.300441</td>\n",
 573 |        "    </tr>\n",
 574 |        "    <tr>\n",
 575 |        "      <th>std</th>\n",
 576 |        "      <td>24.545260</td>\n",
 577 |        "      <td>30.085974</td>\n",
 578 |        "      <td>2266.771362</td>\n",
 579 |        "    </tr>\n",
 580 |        "    <tr>\n",
 581 |        "      <th>min</th>\n",
 582 |        "      <td>1.000000</td>\n",
 583 |        "      <td>18.250000</td>\n",
 584 |        "      <td>18.800000</td>\n",
 585 |        "    </tr>\n",
 586 |        "    <tr>\n",
 587 |        "      <th>25%</th>\n",
 588 |        "      <td>9.000000</td>\n",
 589 |        "      <td>35.587500</td>\n",
 590 |        "      <td>401.450000</td>\n",
 591 |        "    </tr>\n",
 592 |        "    <tr>\n",
 593 |        "      <th>50%</th>\n",
 594 |        "      <td>29.000000</td>\n",
 595 |        "      <td>70.350000</td>\n",
 596 |        "      <td>1397.475000</td>\n",
 597 |        "    </tr>\n",
 598 |        "    <tr>\n",
 599 |        "      <th>75%</th>\n",
 600 |        "      <td>55.000000</td>\n",
 601 |        "      <td>89.862500</td>\n",
 602 |        "      <td>3794.737500</td>\n",
 603 |        "    </tr>\n",
 604 |        "    <tr>\n",
 605 |        "      <th>max</th>\n",
 606 |        "      <td>72.000000</td>\n",
 607 |        "      <td>118.750000</td>\n",
 608 |        "      <td>8684.800000</td>\n",
 609 |        "    </tr>\n",
 610 |        "  </tbody>\n",
 611 |        "</table>\n",
 612 |        "</div>"
 613 |       ],
 614 |       "text/plain": [
 615 |        "            tenure  MonthlyCharges  TotalCharges\n",
 616 |        "count  7032.000000     7032.000000   7032.000000\n",
 617 |        "mean     32.421786       64.798208   2283.300441\n",
 618 |        "std      24.545260       30.085974   2266.771362\n",
 619 |        "min       1.000000       18.250000     18.800000\n",
 620 |        "25%       9.000000       35.587500    401.450000\n",
 621 |        "50%      29.000000       70.350000   1397.475000\n",
 622 |        "75%      55.000000       89.862500   3794.737500\n",
 623 |        "max      72.000000      118.750000   8684.800000"
 624 |       ]
 625 |      },
 626 |      "execution_count": 12,
 627 |      "metadata": {},
 628 |      "output_type": "execute_result"
 629 |     }
 630 |    ],
 631 |    "source": [
 632 |     "df[['tenure', 'MonthlyCharges', 'TotalCharges']].describe()"
 633 |    ]
 634 |   },
 635 |   {
 636 |    "cell_type": "code",
 637 |    "execution_count": 13,
 638 |    "metadata": {},
 639 |    "outputs": [],
 640 |    "source": [
 641 |     "df['MonthlyCharges'] = np.log(df['MonthlyCharges'])\n",
 642 |     "df['MonthlyCharges'] = (df['MonthlyCharges'] - df['MonthlyCharges'].mean())/df['MonthlyCharges'].std()\n",
 643 |     "\n",
 644 |     "df['TotalCharges'] = np.log(df['TotalCharges'])\n",
 645 |     "df['TotalCharges'] = (df['TotalCharges'] - df['TotalCharges'].mean())/df['TotalCharges'].std()\n",
 646 |     "\n",
 647 |     "df['tenure'] = (df['tenure'] - df['tenure'].mean())/df['tenure'].std()"
 648 |    ]
 649 |   },
 650 |   {
 651 |    "cell_type": "code",
 652 |    "execution_count": 14,
 653 |    "metadata": {},
 654 |    "outputs": [
 655 |     {
 656 |      "data": {
 657 |       "text/html": [
 658 |        "<div>\n",
 659 |        "<style scoped>\n",
 660 |        "    .dataframe tbody tr th:only-of-type {\n",
 661 |        "        vertical-align: middle;\n",
 662 |        "    }\n",
 663 |        "\n",
 664 |        "    .dataframe tbody tr th {\n",
 665 |        "        vertical-align: top;\n",
 666 |        "    }\n",
 667 |        "\n",
 668 |        "    .dataframe thead th {\n",
 669 |        "        text-align: right;\n",
 670 |        "    }\n",
 671 |        "</style>\n",
 672 |        "<table border=\"1\" class=\"dataframe\">\n",
 673 |        "  <thead>\n",
 674 |        "    <tr style=\"text-align: right;\">\n",
 675 |        "      <th></th>\n",
 676 |        "      <th>tenure</th>\n",
 677 |        "      <th>MonthlyCharges</th>\n",
 678 |        "      <th>TotalCharges</th>\n",
 679 |        "    </tr>\n",
 680 |        "  </thead>\n",
 681 |        "  <tbody>\n",
 682 |        "    <tr>\n",
 683 |        "      <th>count</th>\n",
 684 |        "      <td>7.032000e+03</td>\n",
 685 |        "      <td>7.032000e+03</td>\n",
 686 |        "      <td>7.032000e+03</td>\n",
 687 |        "    </tr>\n",
 688 |        "    <tr>\n",
 689 |        "      <th>mean</th>\n",
 690 |        "      <td>-1.028756e-16</td>\n",
 691 |        "      <td>4.688495e-14</td>\n",
 692 |        "      <td>7.150708e-15</td>\n",
 693 |        "    </tr>\n",
 694 |        "    <tr>\n",
 695 |        "      <th>std</th>\n",
 696 |        "      <td>1.000000e+00</td>\n",
 697 |        "      <td>1.000000e+00</td>\n",
 698 |        "      <td>1.000000e+00</td>\n",
 699 |        "    </tr>\n",
 700 |        "    <tr>\n",
 701 |        "      <th>min</th>\n",
 702 |        "      <td>-1.280157e+00</td>\n",
 703 |        "      <td>-1.882268e+00</td>\n",
 704 |        "      <td>-2.579056e+00</td>\n",
 705 |        "    </tr>\n",
 706 |        "    <tr>\n",
 707 |        "      <th>25%</th>\n",
 708 |        "      <td>-9.542285e-01</td>\n",
 709 |        "      <td>-7.583727e-01</td>\n",
 710 |        "      <td>-6.080585e-01</td>\n",
 711 |        "    </tr>\n",
 712 |        "    <tr>\n",
 713 |        "      <th>50%</th>\n",
 714 |        "      <td>-1.394072e-01</td>\n",
 715 |        "      <td>3.885103e-01</td>\n",
 716 |        "      <td>1.950521e-01</td>\n",
 717 |        "    </tr>\n",
 718 |        "    <tr>\n",
 719 |        "      <th>75%</th>\n",
 720 |        "      <td>9.198605e-01</td>\n",
 721 |        "      <td>8.004829e-01</td>\n",
 722 |        "      <td>8.382338e-01</td>\n",
 723 |        "    </tr>\n",
 724 |        "    <tr>\n",
 725 |        "      <th>max</th>\n",
 726 |        "      <td>1.612459e+00</td>\n",
 727 |        "      <td>1.269576e+00</td>\n",
 728 |        "      <td>1.371323e+00</td>\n",
 729 |        "    </tr>\n",
 730 |        "  </tbody>\n",
 731 |        "</table>\n",
 732 |        "</div>"
 733 |       ],
 734 |       "text/plain": [
 735 |        "             tenure  MonthlyCharges  TotalCharges\n",
 736 |        "count  7.032000e+03    7.032000e+03  7.032000e+03\n",
 737 |        "mean  -1.028756e-16    4.688495e-14  7.150708e-15\n",
 738 |        "std    1.000000e+00    1.000000e+00  1.000000e+00\n",
 739 |        "min   -1.280157e+00   -1.882268e+00 -2.579056e+00\n",
 740 |        "25%   -9.542285e-01   -7.583727e-01 -6.080585e-01\n",
 741 |        "50%   -1.394072e-01    3.885103e-01  1.950521e-01\n",
 742 |        "75%    9.198605e-01    8.004829e-01  8.382338e-01\n",
 743 |        "max    1.612459e+00    1.269576e+00  1.371323e+00"
 744 |       ]
 745 |      },
 746 |      "execution_count": 14,
 747 |      "metadata": {},
 748 |      "output_type": "execute_result"
 749 |     }
 750 |    ],
 751 |    "source": [
 752 |     "df[['tenure', 'MonthlyCharges', 'TotalCharges']].describe()"
 753 |    ]
 754 |   },
 755 |   {
 756 |    "cell_type": "code",
 757 |    "execution_count": 15,
 758 |    "metadata": {},
 759 |    "outputs": [
 760 |     {
 761 |      "data": {
 762 |       "text/plain": [
 763 |        "['SeniorCitizen', 'tenure', 'MonthlyCharges', 'TotalCharges', 'Churn']"
 764 |       ]
 765 |      },
 766 |      "execution_count": 15,
 767 |      "metadata": {},
 768 |      "output_type": "execute_result"
 769 |     }
 770 |    ],
 771 |    "source": [
 772 |     "continuous_vars = list(df.describe().columns)\n",
 773 |     "continuous_vars"
 774 |    ]
 775 |   },
 776 |   {
 777 |    "cell_type": "markdown",
 778 |    "metadata": {},
 779 |    "source": [
 780 |     "#### - One-Hot Encoding"
 781 |    ]
 782 |   },
 783 |   {
 784 |    "cell_type": "code",
 785 |    "execution_count": 16,
 786 |    "metadata": {},
 787 |    "outputs": [
 788 |     {
 789 |      "name": "stdout",
 790 |      "output_type": "stream",
 791 |      "text": [
 792 |       "customerID 7032\n",
 793 |       "gender 2\n",
 794 |       "SeniorCitizen 2\n",
 795 |       "Partner 2\n",
 796 |       "Dependents 2\n",
 797 |       "tenure 72\n",
 798 |       "PhoneService 2\n",
 799 |       "MultipleLines 3\n",
 800 |       "InternetService 3\n",
 801 |       "OnlineSecurity 3\n",
 802 |       "OnlineBackup 3\n",
 803 |       "DeviceProtection 3\n",
 804 |       "TechSupport 3\n",
 805 |       "StreamingTV 3\n",
 806 |       "StreamingMovies 3\n",
 807 |       "Contract 3\n",
 808 |       "PaperlessBilling 2\n",
 809 |       "PaymentMethod 4\n",
 810 |       "MonthlyCharges 1584\n",
 811 |       "TotalCharges 6530\n",
 812 |       "Churn 2\n"
 813 |      ]
 814 |     }
 815 |    ],
 816 |    "source": [
 817 |     "for col in list(df.columns):\n",
 818 |     "    print(col, df[col].nunique())"
 819 |    ]
 820 |   },
 821 |   {
 822 |    "cell_type": "code",
 823 |    "execution_count": 17,
 824 |    "metadata": {
 825 |     "scrolled": true
 826 |    },
 827 |    "outputs": [
 828 |     {
 829 |      "data": {
 830 |       "image/png": 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\n",
 831 |       "text/plain": [
 832 |        "<Figure size 576x432 with 1 Axes>"
 833 |       ]
 834 |      },
 835 |      "metadata": {},
 836 |      "output_type": "display_data"
 837 |     },
 838 |     {
 839 |      "data": {
 840 |       "image/png": 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\n",
 841 |       "text/plain": [
 842 |        "<Figure size 576x432 with 1 Axes>"
 843 |       ]
 844 |      },
 845 |      "metadata": {},
 846 |      "output_type": "display_data"
 847 |     },
 848 |     {
 849 |      "data": {
 850 |       "image/png": 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\n",
 851 |       "text/plain": [
 852 |        "<Figure size 576x432 with 1 Axes>"
 853 |       ]
 854 |      },
 855 |      "metadata": {},
 856 |      "output_type": "display_data"
 857 |     }
 858 |    ],
 859 |    "source": [
 860 |     "df.groupby('gender').count()['customerID'].plot(\n",
 861 |     "    kind='bar', color='skyblue', grid=True, figsize=(8,6), title='Gender'\n",
 862 |     ")\n",
 863 |     "plt.show()\n",
 864 |     "\n",
 865 |     "df.groupby('InternetService').count()['customerID'].plot(\n",
 866 |     "    kind='bar', color='skyblue', grid=True, figsize=(8,6), title='Internet Service'\n",
 867 |     ")\n",
 868 |     "plt.show()\n",
 869 |     "\n",
 870 |     "df.groupby('PaymentMethod').count()['customerID'].plot(\n",
 871 |     "    kind='bar', color='skyblue', grid=True, figsize=(8,6), title='Payment Method'\n",
 872 |     ")\n",
 873 |     "plt.show()"
 874 |    ]
 875 |   },
 876 |   {
 877 |    "cell_type": "code",
 878 |    "execution_count": 18,
 879 |    "metadata": {},
 880 |    "outputs": [],
 881 |    "source": [
 882 |     "dummy_cols = []\n",
 883 |     "\n",
 884 |     "sample_set = df[['tenure', 'MonthlyCharges', 'TotalCharges', 'Churn']].copy(deep=True)\n",
 885 |     "\n",
 886 |     "for col in list(df.columns):\n",
 887 |     "    if col not in ['tenure', 'MonthlyCharges', 'TotalCharges', 'Churn'] and df[col].nunique() < 5:\n",
 888 |     "        dummy_vars = pd.get_dummies(df[col])\n",
 889 |     "        dummy_vars.columns = [col+str(x) for x in dummy_vars.columns]        \n",
 890 |     "        sample_set = pd.concat([sample_set, dummy_vars], axis=1)"
 891 |    ]
 892 |   },
 893 |   {
 894 |    "cell_type": "code",
 895 |    "execution_count": 19,
 896 |    "metadata": {},
 897 |    "outputs": [
 898 |     {
 899 |      "data": {
 900 |       "text/html": [
 901 |        "<div>\n",
 902 |        "<style scoped>\n",
 903 |        "    .dataframe tbody tr th:only-of-type {\n",
 904 |        "        vertical-align: middle;\n",
 905 |        "    }\n",
 906 |        "\n",
 907 |        "    .dataframe tbody tr th {\n",
 908 |        "        vertical-align: top;\n",
 909 |        "    }\n",
 910 |        "\n",
 911 |        "    .dataframe thead th {\n",
 912 |        "        text-align: right;\n",
 913 |        "    }\n",
 914 |        "</style>\n",
 915 |        "<table border=\"1\" class=\"dataframe\">\n",
 916 |        "  <thead>\n",
 917 |        "    <tr style=\"text-align: right;\">\n",
 918 |        "      <th></th>\n",
 919 |        "      <th>tenure</th>\n",
 920 |        "      <th>MonthlyCharges</th>\n",
 921 |        "      <th>TotalCharges</th>\n",
 922 |        "      <th>Churn</th>\n",
 923 |        "      <th>genderFemale</th>\n",
 924 |        "      <th>genderMale</th>\n",
 925 |        "      <th>SeniorCitizen0</th>\n",
 926 |        "      <th>SeniorCitizen1</th>\n",
 927 |        "      <th>PartnerNo</th>\n",
 928 |        "      <th>PartnerYes</th>\n",
 929 |        "      <th>...</th>\n",
 930 |        "      <th>StreamingMoviesYes</th>\n",
 931 |        "      <th>ContractMonth-to-month</th>\n",
 932 |        "      <th>ContractOne year</th>\n",
 933 |        "      <th>ContractTwo year</th>\n",
 934 |        "      <th>PaperlessBillingNo</th>\n",
 935 |        "      <th>PaperlessBillingYes</th>\n",
 936 |        "      <th>PaymentMethodBank transfer (automatic)</th>\n",
 937 |        "      <th>PaymentMethodCredit card (automatic)</th>\n",
 938 |        "      <th>PaymentMethodElectronic check</th>\n",
 939 |        "      <th>PaymentMethodMailed check</th>\n",
 940 |        "    </tr>\n",
 941 |        "  </thead>\n",
 942 |        "  <tbody>\n",
 943 |        "    <tr>\n",
 944 |        "      <th>0</th>\n",
 945 |        "      <td>-1.280157</td>\n",
 946 |        "      <td>-1.054244</td>\n",
 947 |        "      <td>-2.281382</td>\n",
 948 |        "      <td>0</td>\n",
 949 |        "      <td>1</td>\n",
 950 |        "      <td>0</td>\n",
 951 |        "      <td>1</td>\n",
 952 |        "      <td>0</td>\n",
 953 |        "      <td>0</td>\n",
 954 |        "      <td>1</td>\n",
 955 |        "      <td>...</td>\n",
 956 |        "      <td>0</td>\n",
 957 |        "      <td>1</td>\n",
 958 |        "      <td>0</td>\n",
 959 |        "      <td>0</td>\n",
 960 |        "      <td>0</td>\n",
 961 |        "      <td>1</td>\n",
 962 |        "      <td>0</td>\n",
 963 |        "      <td>0</td>\n",
 964 |        "      <td>1</td>\n",
 965 |        "      <td>0</td>\n",
 966 |        "    </tr>\n",
 967 |        "    <tr>\n",
 968 |        "      <th>1</th>\n",
 969 |        "      <td>0.064298</td>\n",
 970 |        "      <td>0.032896</td>\n",
 971 |        "      <td>0.389269</td>\n",
 972 |        "      <td>0</td>\n",
 973 |        "      <td>0</td>\n",
 974 |        "      <td>1</td>\n",
 975 |        "      <td>1</td>\n",
 976 |        "      <td>0</td>\n",
 977 |        "      <td>1</td>\n",
 978 |        "      <td>0</td>\n",
 979 |        "      <td>...</td>\n",
 980 |        "      <td>0</td>\n",
 981 |        "      <td>0</td>\n",
 982 |        "      <td>1</td>\n",
 983 |        "      <td>0</td>\n",
 984 |        "      <td>1</td>\n",
 985 |        "      <td>0</td>\n",
 986 |        "      <td>0</td>\n",
 987 |        "      <td>0</td>\n",
 988 |        "      <td>0</td>\n",
 989 |        "      <td>1</td>\n",
 990 |        "    </tr>\n",
 991 |        "    <tr>\n",
 992 |        "      <th>2</th>\n",
 993 |        "      <td>-1.239416</td>\n",
 994 |        "      <td>-0.061298</td>\n",
 995 |        "      <td>-1.452520</td>\n",
 996 |        "      <td>1</td>\n",
 997 |        "      <td>0</td>\n",
 998 |        "      <td>1</td>\n",
 999 |        "      <td>1</td>\n",
1000 |        "      <td>0</td>\n",
1001 |        "      <td>1</td>\n",
1002 |        "      <td>0</td>\n",
1003 |        "      <td>...</td>\n",
1004 |        "      <td>0</td>\n",
1005 |        "      <td>1</td>\n",
1006 |        "      <td>0</td>\n",
1007 |        "      <td>0</td>\n",
1008 |        "      <td>0</td>\n",
1009 |        "      <td>1</td>\n",
1010 |        "      <td>0</td>\n",
1011 |        "      <td>0</td>\n",
1012 |        "      <td>0</td>\n",
1013 |        "      <td>1</td>\n",
1014 |        "    </tr>\n",
1015 |        "    <tr>\n",
1016 |        "      <th>3</th>\n",
1017 |        "      <td>0.512450</td>\n",
1018 |        "      <td>-0.467578</td>\n",
1019 |        "      <td>0.372439</td>\n",
1020 |        "      <td>0</td>\n",
1021 |        "      <td>0</td>\n",
1022 |        "      <td>1</td>\n",
1023 |        "      <td>1</td>\n",
1024 |        "      <td>0</td>\n",
1025 |        "      <td>1</td>\n",
1026 |        "      <td>0</td>\n",
1027 |        "      <td>...</td>\n",
1028 |        "      <td>0</td>\n",
1029 |        "      <td>0</td>\n",
1030 |        "      <td>1</td>\n",
1031 |        "      <td>0</td>\n",
1032 |        "      <td>1</td>\n",
1033 |        "      <td>0</td>\n",
1034 |        "      <td>1</td>\n",
1035 |        "      <td>0</td>\n",
1036 |        "      <td>0</td>\n",
1037 |        "      <td>0</td>\n",
1038 |        "    </tr>\n",
1039 |        "    <tr>\n",
1040 |        "      <th>4</th>\n",
1041 |        "      <td>-1.239416</td>\n",
1042 |        "      <td>0.396862</td>\n",
1043 |        "      <td>-1.234860</td>\n",
1044 |        "      <td>1</td>\n",
1045 |        "      <td>1</td>\n",
1046 |        "      <td>0</td>\n",
1047 |        "      <td>1</td>\n",
1048 |        "      <td>0</td>\n",
1049 |        "      <td>1</td>\n",
1050 |        "      <td>0</td>\n",
1051 |        "      <td>...</td>\n",
1052 |        "      <td>0</td>\n",
1053 |        "      <td>1</td>\n",
1054 |        "      <td>0</td>\n",
1055 |        "      <td>0</td>\n",
1056 |        "      <td>0</td>\n",
1057 |        "      <td>1</td>\n",
1058 |        "      <td>0</td>\n",
1059 |        "      <td>0</td>\n",
1060 |        "      <td>1</td>\n",
1061 |        "      <td>0</td>\n",
1062 |        "    </tr>\n",
1063 |        "    <tr>\n",
1064 |        "      <th>5</th>\n",
1065 |        "      <td>-0.994970</td>\n",
1066 |        "      <td>0.974468</td>\n",
1067 |        "      <td>-0.147808</td>\n",
1068 |        "      <td>1</td>\n",
1069 |        "      <td>1</td>\n",
1070 |        "      <td>0</td>\n",
1071 |        "      <td>1</td>\n",
1072 |        "      <td>0</td>\n",
1073 |        "      <td>1</td>\n",
1074 |        "      <td>0</td>\n",
1075 |        "      <td>...</td>\n",
1076 |        "      <td>1</td>\n",
1077 |        "      <td>1</td>\n",
1078 |        "      <td>0</td>\n",
1079 |        "      <td>0</td>\n",
1080 |        "      <td>0</td>\n",
1081 |        "      <td>1</td>\n",
1082 |        "      <td>0</td>\n",
1083 |        "      <td>0</td>\n",
1084 |        "      <td>1</td>\n",
1085 |        "      <td>0</td>\n",
1086 |        "    </tr>\n",
1087 |        "    <tr>\n",
1088 |        "      <th>6</th>\n",
1089 |        "      <td>-0.424595</td>\n",
1090 |        "      <td>0.786142</td>\n",
1091 |        "      <td>0.409363</td>\n",
1092 |        "      <td>0</td>\n",
1093 |        "      <td>0</td>\n",
1094 |        "      <td>1</td>\n",
1095 |        "      <td>1</td>\n",
1096 |        "      <td>0</td>\n",
1097 |        "      <td>1</td>\n",
1098 |        "      <td>0</td>\n",
1099 |        "      <td>...</td>\n",
1100 |        "      <td>0</td>\n",
1101 |        "      <td>1</td>\n",
1102 |        "      <td>0</td>\n",
1103 |        "      <td>0</td>\n",
1104 |        "      <td>0</td>\n",
1105 |        "      <td>1</td>\n",
1106 |        "      <td>0</td>\n",
1107 |        "      <td>1</td>\n",
1108 |        "      <td>0</td>\n",
1109 |        "      <td>0</td>\n",
1110 |        "    </tr>\n",
1111 |        "    <tr>\n",
1112 |        "      <th>7</th>\n",
1113 |        "      <td>-0.913487</td>\n",
1114 |        "      <td>-1.059891</td>\n",
1115 |        "      <td>-0.791550</td>\n",
1116 |        "      <td>0</td>\n",
1117 |        "      <td>1</td>\n",
1118 |        "      <td>0</td>\n",
1119 |        "      <td>1</td>\n",
1120 |        "      <td>0</td>\n",
1121 |        "      <td>1</td>\n",
1122 |        "      <td>0</td>\n",
1123 |        "      <td>...</td>\n",
1124 |        "      <td>0</td>\n",
1125 |        "      <td>1</td>\n",
1126 |        "      <td>0</td>\n",
1127 |        "      <td>0</td>\n",
1128 |        "      <td>1</td>\n",
1129 |        "      <td>0</td>\n",
1130 |        "      <td>0</td>\n",
1131 |        "      <td>0</td>\n",
1132 |        "      <td>0</td>\n",
1133 |        "      <td>1</td>\n",
1134 |        "    </tr>\n",
1135 |        "    <tr>\n",
1136 |        "      <th>8</th>\n",
1137 |        "      <td>-0.180148</td>\n",
1138 |        "      <td>1.059269</td>\n",
1139 |        "      <td>0.696733</td>\n",
1140 |        "      <td>1</td>\n",
1141 |        "      <td>1</td>\n",
1142 |        "      <td>0</td>\n",
1143 |        "      <td>1</td>\n",
1144 |        "      <td>0</td>\n",
1145 |        "      <td>0</td>\n",
1146 |        "      <td>1</td>\n",
1147 |        "      <td>...</td>\n",
1148 |        "      <td>1</td>\n",
1149 |        "      <td>1</td>\n",
1150 |        "      <td>0</td>\n",
1151 |        "      <td>0</td>\n",
1152 |        "      <td>0</td>\n",
1153 |        "      <td>1</td>\n",
1154 |        "      <td>0</td>\n",
1155 |        "      <td>0</td>\n",
1156 |        "      <td>1</td>\n",
1157 |        "      <td>0</td>\n",
1158 |        "    </tr>\n",
1159 |        "    <tr>\n",
1160 |        "      <th>9</th>\n",
1161 |        "      <td>1.205048</td>\n",
1162 |        "      <td>0.009088</td>\n",
1163 |        "      <td>0.783956</td>\n",
1164 |        "      <td>0</td>\n",
1165 |        "      <td>0</td>\n",
1166 |        "      <td>1</td>\n",
1167 |        "      <td>1</td>\n",
1168 |        "      <td>0</td>\n",
1169 |        "      <td>1</td>\n",
1170 |        "      <td>0</td>\n",
1171 |        "      <td>...</td>\n",
1172 |        "      <td>0</td>\n",
1173 |        "      <td>0</td>\n",
1174 |        "      <td>1</td>\n",
1175 |        "      <td>0</td>\n",
1176 |        "      <td>1</td>\n",
1177 |        "      <td>0</td>\n",
1178 |        "      <td>1</td>\n",
1179 |        "      <td>0</td>\n",
1180 |        "      <td>0</td>\n",
1181 |        "      <td>0</td>\n",
1182 |        "    </tr>\n",
1183 |        "  </tbody>\n",
1184 |        "</table>\n",
1185 |        "<p>10 rows × 47 columns</p>\n",
1186 |        "</div>"
1187 |       ],
1188 |       "text/plain": [
1189 |        "     tenure  MonthlyCharges  TotalCharges  Churn  genderFemale  genderMale  \\\n",
1190 |        "0 -1.280157       -1.054244     -2.281382      0             1           0   \n",
1191 |        "1  0.064298        0.032896      0.389269      0             0           1   \n",
1192 |        "2 -1.239416       -0.061298     -1.452520      1             0           1   \n",
1193 |        "3  0.512450       -0.467578      0.372439      0             0           1   \n",
1194 |        "4 -1.239416        0.396862     -1.234860      1             1           0   \n",
1195 |        "5 -0.994970        0.974468     -0.147808      1             1           0   \n",
1196 |        "6 -0.424595        0.786142      0.409363      0             0           1   \n",
1197 |        "7 -0.913487       -1.059891     -0.791550      0             1           0   \n",
1198 |        "8 -0.180148        1.059269      0.696733      1             1           0   \n",
1199 |        "9  1.205048        0.009088      0.783956      0             0           1   \n",
1200 |        "\n",
1201 |        "   SeniorCitizen0  SeniorCitizen1  PartnerNo  PartnerYes  \\\n",
1202 |        "0               1               0          0           1   \n",
1203 |        "1               1               0          1           0   \n",
1204 |        "2               1               0          1           0   \n",
1205 |        "3               1               0          1           0   \n",
1206 |        "4               1               0          1           0   \n",
1207 |        "5               1               0          1           0   \n",
1208 |        "6               1               0          1           0   \n",
1209 |        "7               1               0          1           0   \n",
1210 |        "8               1               0          0           1   \n",
1211 |        "9               1               0          1           0   \n",
1212 |        "\n",
1213 |        "             ...              StreamingMoviesYes  ContractMonth-to-month  \\\n",
1214 |        "0            ...                               0                       1   \n",
1215 |        "1            ...                               0                       0   \n",
1216 |        "2            ...                               0                       1   \n",
1217 |        "3            ...                               0                       0   \n",
1218 |        "4            ...                               0                       1   \n",
1219 |        "5            ...                               1                       1   \n",
1220 |        "6            ...                               0                       1   \n",
1221 |        "7            ...                               0                       1   \n",
1222 |        "8            ...                               1                       1   \n",
1223 |        "9            ...                               0                       0   \n",
1224 |        "\n",
1225 |        "   ContractOne year  ContractTwo year  PaperlessBillingNo  \\\n",
1226 |        "0                 0                 0                   0   \n",
1227 |        "1                 1                 0                   1   \n",
1228 |        "2                 0                 0                   0   \n",
1229 |        "3                 1                 0                   1   \n",
1230 |        "4                 0                 0                   0   \n",
1231 |        "5                 0                 0                   0   \n",
1232 |        "6                 0                 0                   0   \n",
1233 |        "7                 0                 0                   1   \n",
1234 |        "8                 0                 0                   0   \n",
1235 |        "9                 1                 0                   1   \n",
1236 |        "\n",
1237 |        "   PaperlessBillingYes  PaymentMethodBank transfer (automatic)  \\\n",
1238 |        "0                    1                                       0   \n",
1239 |        "1                    0                                       0   \n",
1240 |        "2                    1                                       0   \n",
1241 |        "3                    0                                       1   \n",
1242 |        "4                    1                                       0   \n",
1243 |        "5                    1                                       0   \n",
1244 |        "6                    1                                       0   \n",
1245 |        "7                    0                                       0   \n",
1246 |        "8                    1                                       0   \n",
1247 |        "9                    0                                       1   \n",
1248 |        "\n",
1249 |        "   PaymentMethodCredit card (automatic)  PaymentMethodElectronic check  \\\n",
1250 |        "0                                     0                              1   \n",
1251 |        "1                                     0                              0   \n",
1252 |        "2                                     0                              0   \n",
1253 |        "3                                     0                              0   \n",
1254 |        "4                                     0                              1   \n",
1255 |        "5                                     0                              1   \n",
1256 |        "6                                     1                              0   \n",
1257 |        "7                                     0                              0   \n",
1258 |        "8                                     0                              1   \n",
1259 |        "9                                     0                              0   \n",
1260 |        "\n",
1261 |        "   PaymentMethodMailed check  \n",
1262 |        "0                          0  \n",
1263 |        "1                          1  \n",
1264 |        "2                          1  \n",
1265 |        "3                          0  \n",
1266 |        "4                          0  \n",
1267 |        "5                          0  \n",
1268 |        "6                          0  \n",
1269 |        "7                          1  \n",
1270 |        "8                          0  \n",
1271 |        "9                          0  \n",
1272 |        "\n",
1273 |        "[10 rows x 47 columns]"
1274 |       ]
1275 |      },
1276 |      "execution_count": 19,
1277 |      "metadata": {},
1278 |      "output_type": "execute_result"
1279 |     }
1280 |    ],
1281 |    "source": [
1282 |     "sample_set.head(10)"
1283 |    ]
1284 |   },
1285 |   {
1286 |    "cell_type": "code",
1287 |    "execution_count": 20,
1288 |    "metadata": {},
1289 |    "outputs": [
1290 |     {
1291 |      "data": {
1292 |       "text/plain": [
1293 |        "(7032, 47)"
1294 |       ]
1295 |      },
1296 |      "execution_count": 20,
1297 |      "metadata": {},
1298 |      "output_type": "execute_result"
1299 |     }
1300 |    ],
1301 |    "source": [
1302 |     "sample_set.shape"
1303 |    ]
1304 |   },
1305 |   {
1306 |    "cell_type": "code",
1307 |    "execution_count": 21,
1308 |    "metadata": {
1309 |     "scrolled": true
1310 |    },
1311 |    "outputs": [
1312 |     {
1313 |      "data": {
1314 |       "text/plain": [
1315 |        "['tenure',\n",
1316 |        " 'MonthlyCharges',\n",
1317 |        " 'TotalCharges',\n",
1318 |        " 'Churn',\n",
1319 |        " 'genderFemale',\n",
1320 |        " 'genderMale',\n",
1321 |        " 'SeniorCitizen0',\n",
1322 |        " 'SeniorCitizen1',\n",
1323 |        " 'PartnerNo',\n",
1324 |        " 'PartnerYes',\n",
1325 |        " 'DependentsNo',\n",
1326 |        " 'DependentsYes',\n",
1327 |        " 'PhoneServiceNo',\n",
1328 |        " 'PhoneServiceYes',\n",
1329 |        " 'MultipleLinesNo',\n",
1330 |        " 'MultipleLinesNo phone service',\n",
1331 |        " 'MultipleLinesYes',\n",
1332 |        " 'InternetServiceDSL',\n",
1333 |        " 'InternetServiceFiber optic',\n",
1334 |        " 'InternetServiceNo',\n",
1335 |        " 'OnlineSecurityNo',\n",
1336 |        " 'OnlineSecurityNo internet service',\n",
1337 |        " 'OnlineSecurityYes',\n",
1338 |        " 'OnlineBackupNo',\n",
1339 |        " 'OnlineBackupNo internet service',\n",
1340 |        " 'OnlineBackupYes',\n",
1341 |        " 'DeviceProtectionNo',\n",
1342 |        " 'DeviceProtectionNo internet service',\n",
1343 |        " 'DeviceProtectionYes',\n",
1344 |        " 'TechSupportNo',\n",
1345 |        " 'TechSupportNo internet service',\n",
1346 |        " 'TechSupportYes',\n",
1347 |        " 'StreamingTVNo',\n",
1348 |        " 'StreamingTVNo internet service',\n",
1349 |        " 'StreamingTVYes',\n",
1350 |        " 'StreamingMoviesNo',\n",
1351 |        " 'StreamingMoviesNo internet service',\n",
1352 |        " 'StreamingMoviesYes',\n",
1353 |        " 'ContractMonth-to-month',\n",
1354 |        " 'ContractOne year',\n",
1355 |        " 'ContractTwo year',\n",
1356 |        " 'PaperlessBillingNo',\n",
1357 |        " 'PaperlessBillingYes',\n",
1358 |        " 'PaymentMethodBank transfer (automatic)',\n",
1359 |        " 'PaymentMethodCredit card (automatic)',\n",
1360 |        " 'PaymentMethodElectronic check',\n",
1361 |        " 'PaymentMethodMailed check']"
1362 |       ]
1363 |      },
1364 |      "execution_count": 21,
1365 |      "metadata": {},
1366 |      "output_type": "execute_result"
1367 |     }
1368 |    ],
1369 |    "source": [
1370 |     "list(sample_set.columns)"
1371 |    ]
1372 |   },
1373 |   {
1374 |    "cell_type": "markdown",
1375 |    "metadata": {},
1376 |    "source": [
1377 |     "# 3. Train & Test Sets"
1378 |    ]
1379 |   },
1380 |   {
1381 |    "cell_type": "code",
1382 |    "execution_count": 22,
1383 |    "metadata": {},
1384 |    "outputs": [],
1385 |    "source": [
1386 |     "target_var = 'Churn'\n",
1387 |     "features = [x for x in list(sample_set.columns) if x != target_var]"
1388 |    ]
1389 |   },
1390 |   {
1391 |    "cell_type": "markdown",
1392 |    "metadata": {},
1393 |    "source": [
1394 |     "# 4. Aritificial Neural Network (ANN) with Keras"
1395 |    ]
1396 |   },
1397 |   {
1398 |    "cell_type": "code",
1399 |    "execution_count": 23,
1400 |    "metadata": {},
1401 |    "outputs": [
1402 |     {
1403 |      "name": "stderr",
1404 |      "output_type": "stream",
1405 |      "text": [
1406 |       "/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
1407 |       "  from ._conv import register_converters as _register_converters\n",
1408 |       "Using TensorFlow backend.\n"
1409 |      ]
1410 |     }
1411 |    ],
1412 |    "source": [
1413 |     "from keras.models import Sequential\n",
1414 |     "from keras.layers import Dense"
1415 |    ]
1416 |   },
1417 |   {
1418 |    "cell_type": "markdown",
1419 |    "metadata": {},
1420 |    "source": [
1421 |     "#### - Training a Neural Network Model"
1422 |    ]
1423 |   },
1424 |   {
1425 |    "cell_type": "code",
1426 |    "execution_count": 24,
1427 |    "metadata": {},
1428 |    "outputs": [],
1429 |    "source": [
1430 |     "model = Sequential()\n",
1431 |     "model.add(Dense(16, input_dim=len(features), activation='relu'))\n",
1432 |     "model.add(Dense(8, activation='relu'))\n",
1433 |     "model.add(Dense(1, activation='sigmoid'))"
1434 |    ]
1435 |   },
1436 |   {
1437 |    "cell_type": "code",
1438 |    "execution_count": 25,
1439 |    "metadata": {},
1440 |    "outputs": [],
1441 |    "source": [
1442 |     "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
1443 |    ]
1444 |   },
1445 |   {
1446 |    "cell_type": "code",
1447 |    "execution_count": 26,
1448 |    "metadata": {},
1449 |    "outputs": [],
1450 |    "source": [
1451 |     "from sklearn.model_selection import train_test_split"
1452 |    ]
1453 |   },
1454 |   {
1455 |    "cell_type": "code",
1456 |    "execution_count": 27,
1457 |    "metadata": {},
1458 |    "outputs": [],
1459 |    "source": [
1460 |     "X_train, X_test, y_train, y_test = train_test_split(\n",
1461 |     "    sample_set[features], \n",
1462 |     "    sample_set[target_var], \n",
1463 |     "    test_size=0.3\n",
1464 |     ")"
1465 |    ]
1466 |   },
1467 |   {
1468 |    "cell_type": "code",
1469 |    "execution_count": 28,
1470 |    "metadata": {
1471 |     "scrolled": true
1472 |    },
1473 |    "outputs": [
1474 |     {
1475 |      "name": "stdout",
1476 |      "output_type": "stream",
1477 |      "text": [
1478 |       "Epoch 1/50\n",
1479 |       "4922/4922 [==============================] - 0s 61us/step - loss: 0.5847 - acc: 0.7152\n",
1480 |       "Epoch 2/50\n",
1481 |       "4922/4922 [==============================] - 0s 14us/step - loss: 0.4703 - acc: 0.7678\n",
1482 |       "Epoch 3/50\n",
1483 |       "4922/4922 [==============================] - 0s 13us/step - loss: 0.4310 - acc: 0.7944\n",
1484 |       "Epoch 4/50\n",
1485 |       "4922/4922 [==============================] - 0s 12us/step - loss: 0.4210 - acc: 0.7985\n",
1486 |       "Epoch 5/50\n",
1487 |       "4922/4922 [==============================] - 0s 12us/step - loss: 0.4172 - acc: 0.8023\n",
1488 |       "Epoch 6/50\n",
1489 |       "4922/4922 [==============================] - 0s 13us/step - loss: 0.4146 - acc: 0.8027\n",
1490 |       "Epoch 7/50\n",
1491 |       "4922/4922 [==============================] - 0s 12us/step - loss: 0.4126 - acc: 0.8052\n",
1492 |       "Epoch 8/50\n",
1493 |       "4922/4922 [==============================] - 0s 12us/step - loss: 0.4114 - acc: 0.8054\n",
1494 |       "Epoch 9/50\n",
1495 |       "4922/4922 [==============================] - 0s 16us/step - loss: 0.4104 - acc: 0.8064\n",
1496 |       "Epoch 10/50\n",
1497 |       "4922/4922 [==============================] - 0s 15us/step - loss: 0.4092 - acc: 0.8048\n",
1498 |       "Epoch 11/50\n",
1499 |       "4922/4922 [==============================] - 0s 12us/step - loss: 0.4089 - acc: 0.8070\n",
1500 |       "Epoch 12/50\n",
1501 |       "4922/4922 [==============================] - 0s 15us/step - loss: 0.4093 - acc: 0.8078\n",
1502 |       "Epoch 13/50\n",
1503 |       "4922/4922 [==============================] - 0s 13us/step - loss: 0.4076 - acc: 0.8082\n",
1504 |       "Epoch 14/50\n",
1505 |       "4922/4922 [==============================] - 0s 12us/step - loss: 0.4068 - acc: 0.8090\n",
1506 |       "Epoch 15/50\n",
1507 |       "4922/4922 [==============================] - 0s 13us/step - loss: 0.4074 - acc: 0.8054\n",
1508 |       "Epoch 16/50\n",
1509 |       "4922/4922 [==============================] - 0s 13us/step - loss: 0.4073 - acc: 0.8084\n",
1510 |       "Epoch 17/50\n",
1511 |       "4922/4922 [==============================] - 0s 13us/step - loss: 0.4057 - acc: 0.8082\n",
1512 |       "Epoch 18/50\n",
1513 |       "4922/4922 [==============================] - 0s 13us/step - loss: 0.4058 - acc: 0.8104\n",
1514 |       "Epoch 19/50\n",
1515 |       "4922/4922 [==============================] - 0s 13us/step - loss: 0.4046 - acc: 0.8098\n",
1516 |       "Epoch 20/50\n",
1517 |       "4922/4922 [==============================] - 0s 12us/step - loss: 0.4054 - acc: 0.8102\n",
1518 |       "Epoch 21/50\n",
1519 |       "4922/4922 [==============================] - 0s 12us/step - loss: 0.4040 - acc: 0.8121\n",
1520 |       "Epoch 22/50\n",
1521 |       "4922/4922 [==============================] - 0s 11us/step - loss: 0.4038 - acc: 0.8102\n",
1522 |       "Epoch 23/50\n",
1523 |       "4922/4922 [==============================] - 0s 12us/step - loss: 0.4033 - acc: 0.8094\n",
1524 |       "Epoch 24/50\n",
1525 |       "4922/4922 [==============================] - 0s 11us/step - loss: 0.4031 - acc: 0.8094\n",
1526 |       "Epoch 25/50\n",
1527 |       "4922/4922 [==============================] - 0s 12us/step - loss: 0.4026 - acc: 0.8117\n",
1528 |       "Epoch 26/50\n",
1529 |       "4922/4922 [==============================] - 0s 12us/step - loss: 0.4018 - acc: 0.8125\n",
1530 |       "Epoch 27/50\n",
1531 |       "4922/4922 [==============================] - 0s 14us/step - loss: 0.4030 - acc: 0.8106\n",
1532 |       "Epoch 28/50\n",
1533 |       "4922/4922 [==============================] - 0s 12us/step - loss: 0.4023 - acc: 0.8098\n",
1534 |       "Epoch 29/50\n",
1535 |       "4922/4922 [==============================] - 0s 12us/step - loss: 0.4013 - acc: 0.8135\n",
1536 |       "Epoch 30/50\n",
1537 |       "4922/4922 [==============================] - 0s 12us/step - loss: 0.4004 - acc: 0.8129\n",
1538 |       "Epoch 31/50\n",
1539 |       "4922/4922 [==============================] - 0s 12us/step - loss: 0.4014 - acc: 0.8125\n",
1540 |       "Epoch 32/50\n",
1541 |       "4922/4922 [==============================] - 0s 12us/step - loss: 0.4007 - acc: 0.8145\n",
1542 |       "Epoch 33/50\n",
1543 |       "4922/4922 [==============================] - 0s 10us/step - loss: 0.4000 - acc: 0.8108\n",
1544 |       "Epoch 34/50\n",
1545 |       "4922/4922 [==============================] - 0s 10us/step - loss: 0.4007 - acc: 0.8123\n",
1546 |       "Epoch 35/50\n",
1547 |       "4922/4922 [==============================] - 0s 11us/step - loss: 0.3991 - acc: 0.8121\n",
1548 |       "Epoch 36/50\n",
1549 |       "4922/4922 [==============================] - 0s 12us/step - loss: 0.3988 - acc: 0.8104\n",
1550 |       "Epoch 37/50\n",
1551 |       "4922/4922 [==============================] - 0s 12us/step - loss: 0.3982 - acc: 0.8119\n",
1552 |       "Epoch 38/50\n",
1553 |       "4922/4922 [==============================] - 0s 12us/step - loss: 0.3980 - acc: 0.8121\n",
1554 |       "Epoch 39/50\n",
1555 |       "4922/4922 [==============================] - 0s 12us/step - loss: 0.3976 - acc: 0.8143\n",
1556 |       "Epoch 40/50\n",
1557 |       "4922/4922 [==============================] - 0s 12us/step - loss: 0.3985 - acc: 0.8111\n",
1558 |       "Epoch 41/50\n",
1559 |       "4922/4922 [==============================] - 0s 12us/step - loss: 0.3968 - acc: 0.8125\n",
1560 |       "Epoch 42/50\n",
1561 |       "4922/4922 [==============================] - 0s 13us/step - loss: 0.3972 - acc: 0.8145\n",
1562 |       "Epoch 43/50\n",
1563 |       "4922/4922 [==============================] - 0s 12us/step - loss: 0.3962 - acc: 0.8106\n",
1564 |       "Epoch 44/50\n",
1565 |       "4922/4922 [==============================] - 0s 13us/step - loss: 0.3963 - acc: 0.8119\n",
1566 |       "Epoch 45/50\n",
1567 |       "4922/4922 [==============================] - 0s 13us/step - loss: 0.3959 - acc: 0.8137\n",
1568 |       "Epoch 46/50\n",
1569 |       "4922/4922 [==============================] - 0s 12us/step - loss: 0.3950 - acc: 0.8121\n",
1570 |       "Epoch 47/50\n",
1571 |       "4922/4922 [==============================] - 0s 18us/step - loss: 0.3943 - acc: 0.8133\n",
1572 |       "Epoch 48/50\n",
1573 |       "4922/4922 [==============================] - 0s 12us/step - loss: 0.3944 - acc: 0.8125\n",
1574 |       "Epoch 49/50\n",
1575 |       "4922/4922 [==============================] - 0s 12us/step - loss: 0.3948 - acc: 0.8135\n",
1576 |       "Epoch 50/50\n",
1577 |       "4922/4922 [==============================] - 0s 12us/step - loss: 0.3937 - acc: 0.8139\n"
1578 |      ]
1579 |     },
1580 |     {
1581 |      "data": {
1582 |       "text/plain": [
1583 |        "<keras.callbacks.History at 0x1a2c8c3128>"
1584 |       ]
1585 |      },
1586 |      "execution_count": 28,
1587 |      "metadata": {},
1588 |      "output_type": "execute_result"
1589 |     }
1590 |    ],
1591 |    "source": [
1592 |     "model.fit(X_train, y_train, epochs=50, batch_size=100)"
1593 |    ]
1594 |   },
1595 |   {
1596 |    "cell_type": "markdown",
1597 |    "metadata": {},
1598 |    "source": [
1599 |     "#### - Accuracy, Precision, Recall"
1600 |    ]
1601 |   },
1602 |   {
1603 |    "cell_type": "code",
1604 |    "execution_count": 29,
1605 |    "metadata": {},
1606 |    "outputs": [],
1607 |    "source": [
1608 |     "from sklearn.metrics import accuracy_score, precision_score, recall_score"
1609 |    ]
1610 |   },
1611 |   {
1612 |    "cell_type": "code",
1613 |    "execution_count": 30,
1614 |    "metadata": {},
1615 |    "outputs": [],
1616 |    "source": [
1617 |     "in_sample_preds = [round(x[0]) for x in model.predict(X_train)]\n",
1618 |     "out_sample_preds = [round(x[0]) for x in model.predict(X_test)]"
1619 |    ]
1620 |   },
1621 |   {
1622 |    "cell_type": "code",
1623 |    "execution_count": 31,
1624 |    "metadata": {},
1625 |    "outputs": [
1626 |     {
1627 |      "name": "stdout",
1628 |      "output_type": "stream",
1629 |      "text": [
1630 |       "In-Sample Accuracy: 0.8143\n",
1631 |       "Out-of-Sample Accuracy: 0.8028\n",
1632 |       "\n",
1633 |       "\n",
1634 |       "In-Sample Precision: 0.6837\n",
1635 |       "Out-of-Sample Precision: 0.6604\n",
1636 |       "\n",
1637 |       "\n",
1638 |       "In-Sample Recall: 0.5684\n",
1639 |       "Out-of-Sample Recall: 0.5099\n"
1640 |      ]
1641 |     }
1642 |    ],
1643 |    "source": [
1644 |     "print('In-Sample Accuracy: %0.4f' % accuracy_score(y_train, in_sample_preds))\n",
1645 |     "print('Out-of-Sample Accuracy: %0.4f' % accuracy_score(y_test, out_sample_preds))\n",
1646 |     "\n",
1647 |     "print('\\n')\n",
1648 |     "\n",
1649 |     "print('In-Sample Precision: %0.4f' % precision_score(y_train, in_sample_preds))\n",
1650 |     "print('Out-of-Sample Precision: %0.4f' % precision_score(y_test, out_sample_preds))\n",
1651 |     "\n",
1652 |     "print('\\n')\n",
1653 |     "\n",
1654 |     "print('In-Sample Recall: %0.4f' % recall_score(y_train, in_sample_preds))\n",
1655 |     "print('Out-of-Sample Recall: %0.4f' % recall_score(y_test, out_sample_preds))"
1656 |    ]
1657 |   },
1658 |   {
1659 |    "cell_type": "markdown",
1660 |    "metadata": {},
1661 |    "source": [
1662 |     "#### - ROC & AUC"
1663 |    ]
1664 |   },
1665 |   {
1666 |    "cell_type": "code",
1667 |    "execution_count": 32,
1668 |    "metadata": {},
1669 |    "outputs": [],
1670 |    "source": [
1671 |     "from sklearn.metrics import roc_curve, auc"
1672 |    ]
1673 |   },
1674 |   {
1675 |    "cell_type": "code",
1676 |    "execution_count": 33,
1677 |    "metadata": {},
1678 |    "outputs": [],
1679 |    "source": [
1680 |     "in_sample_preds = [x[0] for x in model.predict(X_train)]\n",
1681 |     "out_sample_preds = [x[0] for x in model.predict(X_test)]"
1682 |    ]
1683 |   },
1684 |   {
1685 |    "cell_type": "code",
1686 |    "execution_count": 34,
1687 |    "metadata": {},
1688 |    "outputs": [],
1689 |    "source": [
1690 |     "in_sample_fpr, in_sample_tpr, in_sample_thresholds = roc_curve(y_train, in_sample_preds)\n",
1691 |     "out_sample_fpr, out_sample_tpr, out_sample_thresholds = roc_curve(y_test, out_sample_preds)"
1692 |    ]
1693 |   },
1694 |   {
1695 |    "cell_type": "code",
1696 |    "execution_count": 35,
1697 |    "metadata": {},
1698 |    "outputs": [
1699 |     {
1700 |      "name": "stdout",
1701 |      "output_type": "stream",
1702 |      "text": [
1703 |       "In-Sample AUC: 0.8659\n",
1704 |       "Out-Sample AUC: 0.8466\n"
1705 |      ]
1706 |     }
1707 |    ],
1708 |    "source": [
1709 |     "in_sample_roc_auc = auc(in_sample_fpr, in_sample_tpr)\n",
1710 |     "out_sample_roc_auc = auc(out_sample_fpr, out_sample_tpr)\n",
1711 |     "\n",
1712 |     "print('In-Sample AUC: %0.4f' % in_sample_roc_auc)\n",
1713 |     "print('Out-Sample AUC: %0.4f' % out_sample_roc_auc)"
1714 |    ]
1715 |   },
1716 |   {
1717 |    "cell_type": "code",
1718 |    "execution_count": 36,
1719 |    "metadata": {},
1720 |    "outputs": [
1721 |     {
1722 |      "data": {
1723 |       "image/png": 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cWW7UHfq8ANH9yn5sEfGItZa0tDTCw8Np2LAht912G2FhRQxZE0AU0kSkQsjOdvHRR1tISXEmEV+79iinTqWxZcsJDh06m6/v0KFt8l5TU7MZNqwtQ4a0oV07L07Yvf0/MHe85/0f3A+1Y7x3fBEp1tmzZ5kzZw7WWsaNG0fv3l74z1k5UkgTkYCSk+Pmt79dyNmzzsj7Bw8ms3z5obz1S3Xt2pisLBd3392F2Ni6TJp0zeUDwBbGWshIKr7fpVKPXgho7cdBg6uK6Gyg3V0KaCLlbPv27cyePZsePXpUuHB2nkKaiPiNtZatW0/yySdbqVYtmP/8ZwtxcUm4XBaARo1qkJPjJiUli0GDYmnWrCbPPXcTNWo4lypq1apGWFgZJjpe9ltY83+l3/7638N1z5V+exHxuqSkJCIjI6lTpw4/+clPaNSokb9LKjWFNBEpV+vWHWX9+qN8+uk2jhw5y549p/O9Hx4eQu/ezfjyyzHUq1fduwfPSHLOgGXmXh49vdOZs/LaZ0u+r2p1oGMJLneKiE+53W5Wr17N0qVLGTNmDFdccYW/SyozhTQR8bnjx1Po3v0tEhLSyMpy5XuvRYtavPDCQO64owPBwUHeecIyOw22vQc56fnbk+Ng/2zn8mR4PYjqDFcMhG4/K/sxRcRvcnJyeO+99wgNDeXBBx+kXr16/i7JKxTSRMSnzpzJoEmTvwFQt244ffpcwZQp3endO5qGDWt470Cpx50zZfMegGOrCu8XFApDPoKoTt47toj4hcvl4uDBg8TExDBw4ECio6P9OiG6tymkiYhXpKRkkZaWzbZtJ5k6dR1BQYbPPtuWr09i4q/L/gM0Ox02vwnZqRfaEjbC7hn5+7W9E/q/DiHh+duDQi9vE5EK58iRI8ycOZP69evTsmXLSnF581IKaSJSaitWHGLGjO18991+tmw5edn7TZvWpEWLWvTvH8OUKT08C2jW7dwrZt2Xv5e4Hb4ZU/i21/0O6neAVrdDqJfvZxORgLF9+3bmzJnD4MGD6dSpU6U6e3YxhTQRKRFrLWPHfsH06RfOkp2/j+yZZ26kceNIOnRoQN++LUt3f9m6l+GHJ4ruE90fbp8BoRddLjVBEFSGJz1FJODFxcURGRlJq1atmDJlCjVqePGWiQCkkCYil8nKcpGens2yZQdJS8sGYNu2BBIStvHqq2tYtuwg4AwWO3Hi1Ywc2cF7B884DSYYbvu04PerR0GLvt47nogEvIyMDBYsWMDevXsZNWpUhZjSyRsU0kQkn4SEVBo2fKmQd7fnLR079ksaN4703oGProLdn8ORpc5ZsbZ3em/fIlJhWWv54IMPaNq0KVOmTCE8vOrcU6qQJlLFud2W99/fyIcfbub77+Pz2rt0aci9915Jv34tiYgIZc2atfTs2QOABg1qlO7JzMxk+Hebgi9Nphx1XkMjoUmv0n0xIlJppKamsm7dOvr06cP48eOpXr3q3WeqkCZSRbndlnXrjtKr17R87X37tqR//xieeaZPvvaEhBp06tSwdAfLOgcr/wjrXnTWqzeAmNsv7xc9ANoX8WCAiFR61lq2bNnC/PnzufLKK3G73VUyoIFCmkiVkZiYRlzcGR57bC6hoUEsXXow3/sHD/6c5s1r+eYpqf/eBod/cJYjm8JDByBIP35E5HJxcXEsX76ccePG0axZM3+X41f6KSlSCbndln37TrN06cG8Jyzvv//rfH1uuKEFjRpFMmZMJ0aP9nBg16xzsOxpyEopWUHnA9rjGRBSrWTbikilZ61l/fr1hIaGcuWVVzJp0iSCg/W0tkKaSCXw44/HOHUqjblz9xAUZHj55YJH3G/atCZvv307N910Rd4k5QVyZcOh7yEnA/Z+BdvepY8JhfXZF/rUbOF5gRENofefFdBE5DKJiYnMmjWLnJwchg0bhjFGAS2XQppIBbZ06QH69HnvsvawsGCys128++5wbrqpJQDBwcbzy5lxc+Hr4fmajjQYQYvolhBWE3o8oVH7RcQrlixZQvv27enZsydBQUH+LiegKKSJVEAul5uoqBc5cyYjr2327LupV6863bo1ISysDP8LPbjoQkAb/pVzxqxGY/at202Lm/qWrXAREeD48eMsWLCAUaNGMXLkyEo7Y0BZKaSJVCA5OW6uuuoNtm9PyGv7/vv7uOmmK7wwJ2YqfD0SDiy40BY7DPL2u7ts+xeRKi8nJ4cffviB9evX079/fyIiIhTQiqCQJhLArLUcOJBMdraLefP28dhjc/PeGzCgFV99Naboe8uKk3UO9s107kFL3u8EtMhm0O8fEHPrRQFNRKRsrLWcO3eO06dPM3nyZGrWrOnvkgKeQppIgDlzJoPFi+MZOfKzAt+vUSOUM2eeIiSkjPduHFkOn/a+vH34f6Fxj7LtW0QkV1ZWFosWLcLlcjF06FDuvFOziXhKIU0kQHzwwSaefPI7jh/PP7zFsGHtuOuujgBcc00TOnTw0px1n9/svEbfDAPfdmYBCKkONRp5Z/8iUuXt37+fWbNmER0dzaBBg/xdToWjkCbiR+vXH+XHH48xadI3+dr/8pf+jBzZnrZt63vvfo3Ms859Z7s+gz1fgrVQOwbuWuid/YuI5MrOziY0NJSkpCSGDBlCmzZt/F1ShaSQJuInL764nF//+rt8bTt3/tQ7wSwtAXZ+CjbHWU85dmFKpvNa9IVuPy/bcURELrFz507mzJnD+PHjueaaa/xdToWmkCZSzjIzc6hf/wVSU52BYcePv5IXXxxIo0aR3jvIpjdgxXOXt3e6D5pcB427QyP98BQR78nIyGDWrFkcP36cO+64gwYNvHRrRhWmkCZSjp59dhHPP780b33fvp/RqlVd7x4kOf5CQJuSAMGhznJQKIRGePdYIlLlWWtJTU2levXqNGvWjBEjRhAaGurvsioFhTSRcvLXvy7LC2gPPdSNqVOHEhxchic00xMh7UT+th0fw+o/OctXDISIqNLvX0SkGMnJyXzzzTeEhIQwZswYrr/+en+XVKkopIn4iLWW99/fxAsvLGfHjlN57VOnDmXy5O5lP8A7bSHjdMHvXfsc3PD7sh9DRKQQW7duZc6cOVx33XUKZz6ikCbiJampWbz33ka+/z6er77aictl870/ceLVTJp0DT17Niv5zq2FI0th9j0QHOYMl5FxGtreCW3vyt+3fieI6lSGr0REpHCJiYnUqlWLqKgo7r//ft175kMKaSJl5HK56d37XVatOpzXVrt2NTp0aECzZjV56aVbiI6uTVBQGZ7Y/PEfsPh/cnceA417QtProPsT0KBLGb8CEZHiuVwuVq5cyYoVKxg7dizR0dH+LqnSU0gTKYPlyw/Su/e7eeu/+91NjBnTmfbtvXgv2IGFFwLa0E+g3WjnTJqISDnJycnhnXfeoXr16kyaNIk6der4u6QqQSFNpIRyctykpWUzfvx/mTlzFwC9ejVj6dL7CQ0N9s5BstNg1R+duTU3vua09X0F2o/1zv5FRDyQk5PDgQMHiI2NZciQITRr1kwTopcjhTQRD1lrufrqN9m0Kf8Tlc8/34+nn+7jvQOln4bt78Oa/4OwmlCtNjTvC9do4FkRKT8HDx5k5syZNG7cmFatWtG8eXN/l1TlKKSJFGPu3D1MnbqOpUsPcuZMBgBPPnkDtWtX4/HHryUiwovjAe2bBV8Nu7B+1yJn4FkRkXK0detW5s2bx6233krHjh39XU6VpZAmUoT9+5MYMuRjAJo0iSQlJYi9ex/jiiu8cD9GTiasfwWW/QaCQiAoDHLSnPea94F+r0KDK8t+HBERD+3du5datWrRpk0bYmNjqV69ur9LqtIU0kSKsGhRHAA//WkP/vWvId7b8ZZ/w8JHwJXlrLe5E2rmXkqIGQLR/bx3LBGRYqSnpzNv3jzi4+MZNWoUDRs29HdJgkKaSIF2706kQ4fXcLudsc5+8YvrSr6TY2vgbDxknoEFD+c2nr/hNncMtTqxMHI21GtX1pJFRErFWsuHH35IixYtmDJlCtWqVfN3SZJLIU0kl7WWJ5/8jq++2smePc5I/tHRtZk0qVvJ59e0bvjsxgtnysAZ26zloAvrbe6Ahld5oXIRkZI7d+4ca9eupV+/ftx3330KZwFIIU0k1z33fMknn2wFoGfPZvTs2ZRXX721+MfNs9Ng79fgviiQWbcT0Lo+Cl2nQHA41Gnlw+pFRDxjrWXTpk0sWLCAbt264Xa7FdAClEKaSK7p07cBcPToL2jSpKbnG+75EuaOL/i9qM5QX09GiUjgiIuLY/Xq1YwfP57GjRv7uxwpgkKaCPCzn83F5bK0bl2vZAEN4ORG53XsMohseqHdBEPNFt4rUkSklNxuN2vXriUsLIyuXbvy0EMPERSkmUsCnUKaVFm7dyfy6quree21tXltb711m2cbZyTBsmcgcRscXuK0RXWBarV8UKmISOklJCQwa9YsjDHcfvvtGGM0a0AFoZAmVdKSJfH07ft+3vq11zbn738fRK9eRYyofXQlHFsNR5bBni8utDe/CWKHKaCJSEBatmwZXbp0oXv37gpnFYxCmlQZcXFJDB78Ec2b18ob/+zRR3vw/PM3U7t2eOEbWjdsfhu+m5y//frfQ6f7oZYuaYpIYDl27Bjz58/nrrvuYuTIkf4uR0pJIU2qhOXLD9K797sAHDt2jhtuaMFtt7Xlqad6F7yBtc6As+kJsPpPkJ3qtA96F1qPgJBw54+ISADJzs5myZIlbNy4kYEDB2rGgApOIU0qJWstf/nLMqZN+5FatarlTYrepEkkR4/+suiN3S44/AMseOhCmwl2Hgxoeq0PqxYRKT1rLampqZw7d47JkycTGRnp75KkjBTSpFJ6+eWVPP30IgBCQoIYMqQN99/flVGjOhS94fb/OJc1z585G/IxtBkFwaFg9CSUiASezMxMFi5ciLWWoUOH6vJmJaKQJpVOTo6bX/1qAQAHDvyc6OjaRW/gdsHmN2HNX+HcQactKARunwEtB0OIBnkUkcC0d+9evvnmG2JiYrjlllv8XY54mUKaVDrNmr0MgDEUH9ByMuHfsZByxFlvMwqu+x006OLbIkVEyiArK4uwsDDOnTvHsGHDaNVKM5pURgppUmns23eaO+/8nJMnUwkJCSI5+an8HbLT4eQGOLgQNv4LgkIvhDOAe9ZC4+7lW7SISAlYa9m+fTvffvst9913H1dffbW/SxIf8mlIM8YMBv4BBAPTrLX/d8n70cD7QJ3cPk9Za+f4siapfLKyXHTr9ibbtiXktU2fficREaHOSsoxWPcSrH85/4bt74bgahAWCTc8r3HORCSgZWRk8PXXX3Pq1ClGjx5NVFSUv0sSH/NZSDPGBAOvAQOBw8BaY8xMa+32i7o9A0y31k41xnQE5gAtfVWTVE49erydF9BefXUwjz3WC5Lj4O0YyE6B9FMXOteJhf5Tnembojr5qWIREc9Zazl79iw1atSgZcuW3HHHHYSE6EJYVeDLT7knsNdaux/AGPMpMBy4OKRZ4Pzpi9rAUR/WI5XQY4/NYfNmZ3iNtLTfUr167tmzHR/D2XhnTLMaTSGyCfR62rlRTUSkgkhKSmLz5s0kJCQwevRoevXq5e+SpBwZa61vdmzMncBga+2DuevjgV7W2kcv6tMEmA/UBWoAA6y16wvY1yRgEkCDBg2umT59uk9qFt9LSUnxytg9brflt7/dyurVpwH43e86ctNNDfLe77u+HwArunxOVpguCXiDtz478Q99fhXPiRMn2Lt3L40aNSI2NlZTOlVQ/fr1W2+tLdUNz748k1bQ36ZLE+E44D1r7d+MMdcBHxpjOltr3fk2svYt4C2Adu3a2b59+/qiXikHixcvpqyfX1JSOm3b/otTp9IAiIt7nJYt6zhvJsfByj84y42u4fpb7izTseQCb3x24j/6/CqOhIQE6tSpw+nTpxkwYABbtmzRZ1dF+TKkHQYuntSwOZdfzpwIDAaw1q40xoQDUcBJH9YlFdDZs5nk5Lh56aUV/OUvy/Lat26d4gS09NOw8BHY9ZnzRkRDZ25NEZEKwuVysWzZMtasWcPYsWNp0ULzAld1vgxpa4E2xpgY4AgwFrj7kj4Hgf7Ae8aYDkB+gJTBAAAgAElEQVQ4kIDIRX71q/n87W8r87U98kh3XnllMGFhwU7D2r9eCGj9X4euU8q5ShGR0svJyWHatGnUqlWLSZMmUbt2MWM8SpXgs5Bmrc0xxjwKzMMZXuMda+02Y8wfgHXW2pnAL4G3jTH/g3MpdIL11U1yUiG5XO68gPbKK4MICjL07x9Dp04N83dc+4IzS8DDxyBC96CJSMWQnZ3NgQMHaN26NcOGDaNJkya690zy+PQZ3twxz+Zc0vbcRcvbgRt8WYNUXElJ6dSr9wIAUVER/PznhUxuvuhxwEDdtgpoIlJhxMfHM2vWLJo3b05sbCxNmzb1d0kSYDTQigSkfftO07r1P/PWN2+eXHDHH56EDa86yzf+tRwqExEpuy1btvDdd98xZMgQ2rVr5+9yJEAppEnAmTDhK95/f1Peutv9XMGn/9NPO5c5Ae7bqsFpRSTg7d69mzp16tC2bVvatGlDeHi4v0uSABbk7wJEznO7LYMG/ScvoL344kCOHPlFwQEt9QS8Xt9Z7vOCApqIBLTU1FS+/PJLvv32W7KysqhWrZoCmhRLZ9IkIFx8/xnAd9+Np3//VgV3Tj8NbzR2liMawpWTyqFCEZHSsdby0UcfccUVVzBlyhRCQ0P9XZJUEApp4nfHjp2jaVNn8vOGDWuwa9ej1KlTxP8wP86dFiWqC/xkk6Z6EpGAdPbsWdasWcPNN9/MhAkTCAsL83dJUsHocqf41ZAhH+UFNIATJ35VdEADOLPXeR3/owKaiAQcay3r16/nzTffJCQkBGutApqUis6kiV8cPnyWFi1eyVt/4YUBPPpoz+I3PLLCeb3yYWdcNBGRABMXF8eGDRu47777aNiwYfEbiBRCv+Wk3KWkZOULaFu3Trl8cNqCWAunNjvLbe7wUXUiIiXndrtZtWoV4eHhXH311TzwwAMEBelilZSN/gZJubHW8sYb+6hZ8y8AtG1bn5ycZz0LaABLn4Lvcqd7qtncR1WKiJTMiRMn+Pe//82ePXto2bIlxhgFNPEKnUkTn0tKSufQobNcddUbeW29e0ezaNFPCA4u5geZtbD6z7Dhn5B2wmkb+Q3Ua+/DikVEPLdy5Uq6detGt27dNKWTeJVCmvjUxo3HufrqN/O1HT78PzRrVsuzHSRsguXPOMudJkDsMGg11LtFioiU0JEjR5g/fz5jxoxhxIgR/i5HKimFNPGp8wHtjjs6MG5cZ+rVO1F8QFv9F+fsWXA1sDlO24CpcFUhU0OJiJST7OxsFi1axJYtWxg8eDDVq1f3d0lSiSmkic/84x+r8pZnzBgNwOLFJ4veaNnTTkADaD8WgkIhNBI6jvdVmSIiHnG73aSmppKRkcEjjzxCRESEv0uSSk4hTXxi1arD/Pzn8wB4883bPN9wU+6l0RGzILYE24mI+EhGRgYLFiwgKCiIoUOHMnz4cH+XJFWEHj8Rr7LW8re/reC66/4NwBtvDGXSpGuK3zD1BMyfBDmpzjRPCmgiEgB2797N1KlTMcYwYMAAf5cjVYzOpIlXpKRk8e23e5k4cSZnz2YC8Mc/9uPhh7sXvWFWCsTNhW+cy6GYYGh2o4+rFREpWmZmJtWqVSMtLY2RI0fSsmVLf5ckVZBCmpSZy+WmV69pbN+ekNd28uSvaNCgRuEbLX8WEnfAni8utDXsBveuBaMTvCLiH9ZatmzZwvz585kwYQJdu3b1d0lShSmkSZlYawkJ+WPe+tatU2jRoja1alUrfKMdH8Oq553l+h0htAYM/gDqtdNcnCLiN+np6fz3v/8lOTmZu+++m6ioKH+XJFWcQpqUypYtJ/jDH35gxozteW3Hjv2Sxo0ji9wuIv0AzJngrNy7Hhp182GVIiLFs9Zy9uxZIiMjadOmDd26dSM4ONjfZYkopEnJffPNbm6//ZO89dtvb8uMGaMJCyv+h1rzkzOchSsnKaCJiN8lJiYya9YsIiMjufPOO+nRo4e/SxLJo5AmHktPz+Y3v1nIP/6xGoAJE7ry4osDiYryYKygpb+B42toemqRs9737z6sVESkeJs2bWLevHn06dOHnj17+rsckcsopEmxNm48zpIl8XnjngF89tmdjB7dybMd5GTCmv+DGk1IrtGZ2j0nQahG6RYR/zhx4gR169alSZMmPPTQQ9StW9ffJYkUSCFNCuV2W9q2/Sf79iXltXXu3JBZs8bRsmUdz3f0w6+d17Z3siFoFH279fVuoSIiHsjJyWHp0qWsW7eOcePG0bx5c3+XJFIkhTQp0P79SXTr9ibJyc6YZzNm3EWvXs1p3tzDidEvlpY7FVTvP8OKdV6sUkTEMzk5Obz99tvUq1ePyZMnU7NmTX+XJFIshTS5zFdf7WTkyM/y1s+efYqaNYsYUqMoJzfCrk+hTmsIK/rJTxERb8vKyiI+Pp62bdsycuRIGjVqhNFQP1JBaNRQyWOt5e231+cFtLFjO3Pu3G9KH9AA5k10XtuN9kKFIiKe279/P1OnTmXHjh1Ya2ncuLECmlQoOpMmACQlpVOv3gt5608/fSPPP39z6XcYNxeW/AqS4yG4GvT+U9mLFBHx0ObNm1m0aBFDhw6lTZs2/i5HpFQU0oTDh8/SsuWFITHWrXuIa65pWvIdpSXAhn+CKwsOL4HE7dB2NLQe4cVqRUQKt3PnTurWrUv79u1p164d1aqV4UqAiJ8ppFVx6enZtGjxSt56YuKvqVevFMNjpCfCO20gM9lZD64GjXvA7Z8VvZ2IiBekpKQwd+5cTpw4wciRIwkLC/N3SSJlppBWxb322loAevVqxqpVD5Z+R18NvxDQfuHSJOkiUm6stXz00UfExsYycuRIQkL0q00qB/1NrsKSkzN44okFAPztb7eUfkfpp+HocmhyLQz/rwKaiJSL5ORkVq9ezYABA3jggQcIDQ31d0kiXqXfplXYmDHOPJqPP96LG26ILv2O4r91XpteDzUae6EyEZHCWWtZs2YNb731FtWrO7dnKKBJZaQzaVXU0aPnmDdvH1DGs2gAJzc4r10mlrEqEZHixcXFsXXrVu6//36ioqL8XY6IzyikVUEpKVk0a/YyAL/5TW+Cg8twQjXrHKx7ybnEWb+jlyoUEcnP7XazYsUKqlevzjXXXENMTIzGPJNKT5c7q5iTJ1OpWfMveetlGgst5Rgs/Y2zXCe2jJWJiBTs+PHjTJs2jfj4eGJjnZ81CmhSFehMWhXTqNFLADRuHMnmzZMJCirhD7rsdPj+Z5B5BnY797RRswXc+6OXKxWRqs5aizGG1atX07NnT6666iqFM6lSFNKqkC+/3JG3fPDgzwkNDS75To4sgy3TILI51OsAUZ3gln9rXk4R8aqDBw8yf/587r77boYPH+7vckT8QiGtCrnjjukArFw5sXQBDWD2GOf1ts+g2fVeqkxExJGVlcXChQvZvn07t956KxEREf4uScRvFNKqiKefXpi3fO21zUu3k0OLISfDWVZAExEvc7lcpKenk5OTwyOPPJI3vIZIVaWQVsklJ2fw+utr+fOflwGwYcPDpduRdcP0fs7y9b/3UnUiIpCens78+fMJCQlh6NCh3H777f4uSSQgKKRVcj/96Rw++mgLAC+8MICuXUs52Ox3jziv3Z+Aa5/1UnUiUtXt3LmT2bNn07FjR/r37+/vckQCikJaJeZ227yAlpDwBFFRpbi3IycTvhkDh5c46z1+BXq6SkTKKCMjg/DwcLKysrjrrruIji7DrCcilZTGSavEpk/fBkCbNvVKF9AAjq+FfV9DZFO4+V8Q0dCLFYpIVWOtZePGjfzzn//k1KlTXHnllQpoIoXQmbRKbMqU2QC8/vrQ0u9kzt3Oa//XoEXfshclIlVWeno6X3zxBampqYwfP15TOokUQyGtkrLWcuaM8yTmgAGtSr+joNxJixXQRKSUrLUkJydTq1YtOnbsSNeuXQkK0oUckeLoX0kl9c47zqTnvXo1K/1ONr8Nyfuh/d1eqkpEqpqEhATeffddFi5cSFBQEN26dVNAE/GQzqRVQnv3nubBB2cBMGPG6NLvaMEk57XThLIXJSJVzsaNG1mwYAF9+/ale/fu/i5HpMJRSKuE2rT5JwA33NCC5s1rlWxjVzZ8fjOcO+ysRw+AlgO9XKGIVGbHjh2jfv36NGvWjEmTJlG7dm1/lyRSISmkVTIvvbQib3nZsgdKtvGuz+HgQmd+zsY9oMVN0PWnXq5QRCqr7OxslixZwsaNGxk7dizNm5dydhMRARTSKpXTp9N54okFAEyd6sETncfWwBe3gAmBoBBIO+G0h9eHG/8K0f18WK2IVCY5OTm89dZbNGrUiMmTJxMZGenvkkQqPI9CmjEmDIi21u71cT1SBlu3ngTgmWduZPLkYu7/WPkHWPG/znL1BtB6hLPcaQI0vdZ3RYpIpZKZmUlcXBzt27fnrrvuomFDjaUo4i3FhjRjzFDgZSAMiDHGdAX+11o70tfFSck8++z3APTpc0XRHa29ENAGvgmdH3DOpImIlMCePXuYPXs2sbGxtGvXTgFNxMs8+c38B6AX8D2AtXajMaa1T6uSEklKSueOO6bzww8HAOjXL6bwzmkJ8N/bnOV2Y+DKSeVQoYhUNps2bWLx4sUMGzaMVq3KMBajiBTKk5CWba09Y/LP12h9VI+UQnT030lJyQJg3rx7CQkpYAyi7FQ4uAi+HgnW5bT1/lM5VikiFZ21lu3bt1O/fn06dOhAhw4dCAsL83dZIpWWJyFthzFmNBBkjIkBHgdW+bYs8dSnn27NC2iZmc8QFhZ8eSdr4Ytb4cjSC23/kwNBBfQVESnAuXPnmDNnDomJiYwcOVLhTKQceBLSHgWeA9zAl8A84De+LEo8s23bScaN+wKAr74aU3BASz0Ob7UAd46zPn4D1G6lgCYiHrPW8vHHH9O2bVvuuOMOQkJ0D6tIefDkX9oga+2TwJPnG4wxo3ACm/jJl1/u4I47pgMQFRXB8OHtL+9kLSx/1glooZEwbjk0uLKcKxWRiiopKYlVq1YxaNAgJk6cqHAmUs48mUDtmQLanvZ2IVIy5wPaxIlXc/ToLwru9Mn1sGWaM0n6o2cU0ETEI263m1WrVvH222/nzRaggCZS/gr9V2eMGQQMBpoZY16+6K1aOJc+xU+GDPkIgAkTujJt2rCCO2Wdg2O5tw7e8a0ub4qIx+Lj49m5cycTJ06kfv36/i5HpMoq6r9GJ4GtQAaw7aL2c8BTvixKCpeRkcPcuc6Yws8916fwjrPvcV5v+htE31wOlYlIReZyuVi2bBk1atSge/fuxMTEcMlT/SJSzgoNadbaDcAGY8xH1tqMcqxJivDww98AzrRPMTF1C+7kyoL9s5zlzveXU2UiUlEdOXKEmTNnUrt2bW67zRlHUQFNxP88ucmgmTHmT0BHIPx8o7W2rc+qksskJqZxzz1fMm/ePqKja/Pgg90K73x8nfPaZhSEFxLkRKTKs9ZijOHHH3+kd+/edO7cWeFMJIB48uDAe8C7gAFuBaYDn/qwJilA797vMm/ePgCefbZPwQPWnrf3K+f1qkfKoTIRqYji4+N5++23SUtL4/bbb6dLly4KaCIBxpMzaRHW2nnGmJestfuAZ4wxS4vdCjDGDAb+AQQD06y1/1dAn9HA73BmMdhkrb3b4+qriKNHz7Fz5ykAcnKeJTi4mGy9+Q3nNbqfjysTkYomIyOD7777jj179jBkyBAiIiL8XZKIFMKTkJZpnP9e7TPGTAaOAMXOomuMCQZeAwYCh4G1xpiZ1trtF/VpgzMw7g3W2iRjjGbnvUhKShYdO77GoUNnAXjmmRuLD2gAEY2gRhMwnpwoFZGqwuVykZmZSVBQEFOmTCE8PLz4jUTEbzz5Lf4/QCTwM+AG4CHgAQ+26wnstdbut9Zm4VwiHX5Jn4eA16y1SQDW2pOeFl4VNG/+cl5Ae/bZPvzxjx48pXl6N5zZCw2LuGdNRKqU1NRUduzYwbfffkvt2rUZMmSIAppIBVDsmTRr7ercxXPAeABjTHMP9t0MOHTR+mGg1yV92ububznOJdHfWWu/vXRHxphJwCSABg0asHjxYg8OX7HFxaWSnJwJwMKFfQgKMh593bGHXqMFsP9cDQ4G4PcpJSWlSnx+lZE+u4opISGBPXv2UK9ePcLDw/UZVkD6t1d1FRnSjDE9cMLWMmvtKWNMJ5zpoW4GigtqBd2Bags4fhugb+7+lhpjOltrz+TbyNq3gLcA2rVrZ/v27VvMoSu+hQsXAfD88/24+eYixkO71KL/QnIdWo2bRisf1VYWixcvpip8fpWRPruKJS0tjYiICLZs2cL111/Pnj179PlVUPq3V3UVernTGPMX4CPgHuBbY8zTwPfAJnLPgBXjMNDiovXmwNEC+nxtrc221sYBu3BCW5V27lwmzz/vPJvxs59devKxGBv/5YyTJiJVkrWW9evX89prr5GYmEiXLl1o1qyZv8sSkVIo6kzacOAqa226MaYeTsC6ylq7y8N9rwXaGGNicB42GAtc+uTmV8A44D1jTBRO+Ntfki+gMurceSoAN9zQgpo1q3m+YeZZsG4Ir+ejykQkkKWlpfH555+TnZ3NfffdpymdRCq4okJahrU2HcBae9oYs7MEAQ1rbY4x5lFgHs79Zu9Ya7cZY/4ArLPWzsx97xZjzHbABTxhrU0s9VdTCaSkZHHwYDIAs2aNK9nGx1Y6rx3v9XJVIhLI3G43Z86coU6dOnTt2pUuXboQFKSnu0UquqJCWitjzJe5ywZoedE61tpRxe3cWjsHmHNJ23MXLVvgF7l/qrzt2xPo1Ol1AP7wh77UrVu9ZDuwubf8xV76EK2IVFYnT57k66+/JioqipEjR3LVVVf5uyQR8ZKiQtodl6z/y5eFVHXZ2a68gNavX0t+/esbSraDlKPw5a3OssZHE6kSfvzxRxYuXEj//v25+uqr/V2OiHhZUROsLyzPQqq6m2/+AICYmDrMnz++6GmfCrLoMee1VktoqB/WIpXZkSNHaNCgAdHR0Tz88MPUqlXL3yWJiA/olEsASE/PZtmygwDs2fNYyQMaQHYaVKsNE/dCcKiXKxSRQJCVlcW8efP45JNPSEhIICoqSgFNpBLzZFoo8bFNm04AMGFCV8+mfSpM3XYQFOylqkQkkOTk5PDWW2/RtGlTHnnkEc25KVIFeBzSjDHVrLWZviymqrruun8DMGZMp9Lv5PhqqNPaSxWJSKDIyMhg//79dOzYkbFjxxIVFeXvkkSknBR72sYY09MYswXYk7t+lTHmnz6vrIp4550NecuDBsWWbidrXoCMJEhL8FJVIhIIdu3axeuvv058fDzWWgU0kSrGkzNprwK34Qw8i7V2kzGmn0+rqkKeffZ7AObPvxdjCppJywO7P3deb/vMS1WJiL9t3LiRpUuXMmrUKFq2bOnvckTEDzwJaUHW2gOXBAiXj+qpUhIT0zh69BxNm9Zk4MBSnkXLyYAT66DlYGjS07sFiki5stayZcsWGjRoQKdOnejUqROhoXoQSKSq8uQu9UPGmJ6ANcYEG2N+Duz2cV1VwtSp6wC49dZS3kuWHA9v5U6PGtHQO0WJiF8kJyfzySefsHz5cowxhIaGKqCJVHGenEmbgnPJMxo4AXyX2yZlMGfOnrxLnS+/PMjzDXMyIOWIs7zqeUg/5SwPeMPLFYpIebHW8tlnn9G+fXvGjBlDcLCe0hYRz0JajrV2rM8rqUJOnUpj6NCPAZg0qRu1ank4iXr8PPhicP62yKbw0AEI0mgqIhVNYmIiq1at4tZbb2XixIkKZyKSjye/2dcaY3YBnwFfWmvP+bimSq9Ro5cAGDy4NW++eXvRna2FpD3gzoJ5E522OrFw3f86y/U6KKCJVDBut5uVK1eyfPly+vTpA6CAJiKXKfa3u7U21hhzPTAW+L0xZiPwqbX2U59XVwm99toa3G5nIvRZs8YVv8EPv4Z1L11Yv2oyDJjqo+pEpDzEx8ezf/9+HnroIerWrevvckQkQHl0CsZauwJYYYz5HfB34CNAIa2E4uKSePTRuQAsWTLBs+mfds9wXge/B6E1oPlNvitQRHwmJyeHH374gZo1a9KjRw9iYmJKP+yOiFQJxYY0Y0wkMBznTFoH4Gvgeh/XVSmdf5pzwoSu9OlzRfEbnNwEZ+OhQVfodJ9vixMRnzl06BAzZ84kKiqKHj16ACigiUixPDmTthWYBbxgrV3q43oqtVdeWQXA668P8WyDuNnOa/df+qgiEfElay3GGDZv3kzfvn3p2LGjwpmIeMyTkNbKWuv2eSVVQE6O822sXt3TsY9yL4e2vdM3BYmIz+zbt48FCxbwk5/8hKFDh/q7HBGpgAoNacaYv1lrfwl8YYyxl75vrR3l08oqmcTENACGDGlTfOeTm+CjHsD5b7v+5y1SUaSnpzN//nzi4uK47bbbiIiI8HdJIlJBFXUm7fxEkP8qj0Iqs0OHkomO/jsAgwd7MP3T6j+BOxua94E2oyDEw3HURMSvcnJyyM7OJjw8nClTplCtmv7tikjpFRrSrLVrchc7WGvzBTVjzKPAQl8WVlksXXqAPn3eA6B163r89KfFzK/pzoHjud/60YtB96+IBLyUlBTmzp1LREQEQ4cOZdCgEswiIiJSCE/m7nyggLaJ3i6kMrLW0r//BwDcc08X9ux5jKCgIkLXzs/gjSZw9gDUaKKAJlIBbN26lTfeeIN69eopnImIVxV1T9oYnGE3YowxX170Vk3gjK8LqwxeeGE52dlumjatyX/+U8gtfKknYO54OLDgQlutljBmcXmUKCKllJqaSo0aNTDGcM8999CkSRN/lyQilUxR96StARKB5sBrF7WfAzb4sqjKYsWKwwBs3PhwwR3OHoK3oy+sX/scxNwKTa8th+pEpDSstaxdu5YlS5bwwAMP0KlTJ3+XJCKVVFH3pMUBccB35VdO5TJz5i6uuaYJDRrUKLjDkdxh5xpdA3d+B+F1yq84ESmxtLQ0PvvsM6y13H///dSvX9/fJYlIJVbU5c4l1tqbjDFJXBgLApzxIKy1tp7Pq6vA1q49AkBYWBGTJu//xnkd8rECmkgAc7lcnDlzhrp169K9e3c6d+6sQWlFxOeKutzZL/c1qjwKqUzOns2kZ89pAAwc2KrwjhlJzmut6ML7iIhfHTt2jJkzZ9KoUSNGjBhBly5d/F2SiFQRRV3uPD/LQAvgqLU2yxjTG7gS+A9wthzqq3CstdSu/X8AjBzZnt//vl/BHRM2Q/y30Kg7hISXY4Ui4ql169bx/fffM3DgQK666ip/lyMiVYwn00J9BfQwxsQCHwCzgY+B23xZWEXkcrkJCflj3vqMGaML7mgtLJjkLLfSt1Ek0Bw6dIiGDRsSExND+/btiYyM9HdJIlIFeTJOmttamw2MAv5urX0MaObbsiqmhx6albd89OgvCh8TbefHcGy1s9zzyXKoTEQ8kZmZyZw5c5g+fTqJiYnUr19fAU1E/MaTkJZjjLkLGA/k3umOpzOEVxmLF8fz7rsbAdiz5zGaNKlZeOekPc7rA7t1qVMkQGRnZ/Pmm2+SnZ3NI488QtOmTf1dkohUcZ5c7nwAeAR4wVq73xgTA3zi27IqlsWL4+nX730APvvsTlq3LubB16zc2/nqejDZuoj4VHp6Ovv27aNz587ce++91KunB9dFJDAUeybNWrsV+BmwzhjTHjhkrf2TzyurQB55ZDYAgwe35s47OxbdefcMWP9KOVQlIsXZvn07r7/+OocPH8Zaq4AmIgGl2DNpxpgbgQ+BIzhjpDU2xoy31i73dXEVxY4dpwCYO/eewjtlpUDSLpj/oLPebmw5VCYihdmwYQMrVqzgrrvuIjpaw+CISODx5HLnK8AQa+12AGNMB5zQ1t2XhVUUM2ZsByA2tm7RHec9ALs/d5Z7/gZu/LOPKxORS1lr2bRpEw0bNqRz58506dKFkBBPfgyKiJQ/T346hZ0PaADW2h3GmDAf1lShvPqq85Tm9Ol3Fd0x8wzUbQc3vQTN+5RDZSJysTNnzjBr1izS0tIYMWIEoaF6/klEApsnIe1HY8ybOGfPAO5BE6wDcPBgMkuXHgSgW7cmxW8QXg9iNS6aSHmz1jJ9+nQ6duzI9ddfT1CQJw+2i4j4lyc/qSYD+4BfA08C+4GHfVlURfH004sAuPHGIu5nyTgD77SFQ9+D5voTKVcJCQnMmjULay0TJ06kd+/eCmgiUmEUeSbNGNMFiAX+a619oXxKqhhcLjf/+c9mwsND+OGH+wvvmHrMGRftilug2+PlV6BIFeZyuVi+fDmrVq2iX79+GGMUzkSkwik0pBljfgtMBH7EmRbqD9bad8qtsgD31786D7dmZOQU3XFf7iwEXR6EVkN8XJWIABw4cIBDhw4xadIk6tSp4+9yRERKpagzafcAV1prU40xDYA5gEJarmnTfgTg1Kkniu6Yley8XjHAxxWJVG3Z2dksWbKEWrVq0bNnT2JiYjC6xUBEKrCiQlqmtTYVwFqbYIzRtYJc6enZxMWdAaB+/YjiNwgKgfBihugQkVI7cOAAM2fOpEmTJlx77bUACmgiUuEVFdJaGWO+zF02QOxF61hrR/m0sgD29tvOWbTnnitmKI2Vf7wwNpqIeJ21FmMM27ZtY+DAgbRv397fJYmIeE1RIe2OS9b/5ctCKpLHH/8WgFGjOhTe6exBWPEcVI/S7AIiPrBnzx4WLFjA/fffz5Ahut9TRCqfQkOatXZheRZSUSQnZ/x/e/cdH1WVPn78c1IgAUILAYGAkBBKEtJIqKIBpFcVAWFFEESDrLu2/aFrYZV1XcvqV0AEBNEVRWVFivQSmgEChBokQIgQQCCUNNLn/P6YMCakQyZT8rxfr7zuzL1n7n1mDpM8nHPuOQD4+DQkMPCe4gtpDQvuNT4OfRk6/62KohPC/mVkZLBu3TrOni5ktlgAACAASURBVD3L0KFDcXV1tXRIQghhFrIeSgXdWgZq/PjAkgtlXjNua9aHsDJuLBBClIvWmtzcXHJzc6lTpw4RERHUqCGLnwgh7JckaRVgMGgmTzZOqTFiRCljX25Nu9HtTZnAVohKkJqays8//0zdunUZNGgQffv2tXRIQghhduVO0pRSNbXWWeYMxtrl5hoA6N69Bf7+jUsuqPOMW5/bh/UJISrq8OHDrF+/ntDQUHr27GnpcIQQosqUOa2GUqqzUuoIcDL/eaBSapbZI7NCeXnGJG3wYB8LRyKE/UtNTUVrjbOzM+PHj6dXr144OUnjvxCi+ijP3GefAEOAqwBa60NAL3MGZa3+97/jANy8mWPhSISwXwaDgaioKObOncu1a9fo0KEDTZo0sXRYQghR5crz31IHrfVvt00MmWemeKza448vB2DChKDSC+ZmVEE0Qtif9PR0vv32W5ycnJg0aRLu7u6WDkkIISymPEnaOaVUZ0ArpRyBPwNx5g3L+ixYsN/0uE2bhqUXPjzPuHVyMWNEQtiPvLw8rl27hru7O926dcPX11dWDBBCVHvl6e6MAF4AWgKXgK75+6qVF1/cAMChQ8+UXTgvB2rUhVoeZo5KCNt3/vx55s+fT1RUFA4ODvj5+UmCJoQQlKMlTWt9GajWU+ZrrUlNzQYgIKCMsTFZyXD9BHiUMo+aEAKA6Ohotm3bRv/+/fH397d0OEIIYVXKTNKUUgsAfft+rfUUs0RkhTZtigfgvvtall4w8zoszZ8iwHu4maMSwnYlJCTQtGlTvL298fX1pXbt2pYOSQghrE55xqRtKvDYBXgIOGeecKzT/PnGBdX/8Y/w0gumnoOrx6DVAAh4yvyBCWFjMjMz2bhxI6dOnWLMmDE0bdrU0iEJIYTVKk9353cFnyul/gtsNFtEVmbhwgOmpaAeeODekgv+thl+HGB8HDAF3DyrIDohbEdOTg7z5s3Dy8uLiIgIXFzkxhohhCjNncwM2RooJVuxLxERPwPw2ms9cXQs5T6LZQ8aty0fhBbh5g9MCBuRnp7OqVOnCAwM5IknnqB+/fqWDkkIIWxCecakXeePMWkOwDVgujmDsib16rnQuXNz3n67d/EFjn8Da8YZH7ceBA+tlvU6hcB4w83Ro0dZv349gYGBaK0lQRNCiAooNUlTxvvgA4Hz+bsMWusiNxHYq8zMXJKSbtKwoWvJhfb+y7gNeBpCX5QETYh8MTEx7Nmzh8cee4zmzZtbOhwhhLA5pSZpWmutlFqute5UVQFZk06d5gNQp47zHztTE+FC1B/P83KgYXvo+1kVRyeE9dFac+DAAe655x4CAgIIDAzE0dHR0mEJIYRNKs+YtL1KqRCt9QGzR2NlHByMrWLvvdf3j52bp8HpFYULth1ZhVEJYZ2uXbvGqlWryMnJYfjw4bIYuhBC3KUSf4sqpZy01rnAfcBTSqnTQDqgMDayhVRRjBZhHE9zmYceao+bW03jzvg1xgStYQcY+sMfhet5WSZIIayE1pply5bRsWNHunTpgoNDeRYzEUIIUZrS/qu7FwgBRlRRLFbj3LlkWrb8GIAzZ278ceDGKeP2vnegkZ8FIhPCuly6dIndu3czZMgQJk+eLMmZEEJUotKSNAWgtT5dRbFYDX//uabHGzc+bnyQcRW2/sX4WKbYENVcbm4uO3bsYN++ffTp0wcHBwdZb1MIISpZaUmah1LqhZIOaq3/U9bJlVIDgP8DHIHPtdbvllBuJPADEKa13lfWec2pe/eFpKRkAaD1m38cODjHuK3bClxkGgFRvZ09e5bff/+dp59+mrp161o6HCGEsEulJWmOQB3yW9QqSinlCMwB+gKJQLRSaqXWOva2cm7Ac8CeO7lOZdqzJ5GoqEQAEhOf/+OAIQ9+yU/YJp20QGRCWF5eXh7r16+nfv36dOnSBS8vGYsphBDmVFqSdlFr/dZdnLszcEprHQ+glFoKDAdibyv3NvAe8NJdXOuuGQyarl0XArBgwVCaNy/QOnDAOD6NWk3AQe5YE9VPfHw8+/bto23btvTs2dPS4QghRLVQ5pi0u9CcwguxJwJdCl1AqWCghdZ6tVKqxCRNKTUFmALg4eFBZGTkXYZW1J49VwGoX9+ZNm1SCl2jzdldeAK72nxKjhmuXZ2kpaWZpf6EeWitUUpx6tQpPD09adCgAXv37rV0WOIOyHfPdkndVV+lJWl97vLcxSV5ptUKlFIOwEfAhLJOpLWeD8wHaNeunQ4PD7/L0IoaOfI9ANaseZxu3VoUPrjlR0htQI++D1f6daubyMhIzFF/ovL9+uuvbNq0iUmTJhEeHi51Z+Ok/myX1F31VWKSprW+dpfnTgQKZjuewIUCz90AfyAy/66we4CVSqlhlrh5wMfHnatXE+na1bOqLy2EVUlPT2ft2rVcvHiRoUOH4upayrJoQgghzMacA6yiAR+lVGuMa3+OAcbeOqi1TgYa3XqulIoEXrLU3Z03bmTSt6+XTCMgqi2tNTk5ORgMBho2bMjw4cNxdnYu+4VCCCHMwmwzT+avVjANWA8cB77XWh9TSr2llBpmruveiW3bEvj11yQyMnKLL5CwzrhGpxB2Kjk5mW+++YZNmzbh5uZG7969JUETQggLM+utilrrNcCa2/a9UULZcHPGUprw8C8BmDYtrPgCqYmQm1GFEQlRdWJiYti0aRNdunShR48elg5HCCFEvmo9n8TOnWfp2fML0/PRo/2LFoqZAzoPgv9chZEJYX4pKSm4ubnh6urKhAkT8PDwsHRIQgghCqjWC+3Nn78fgLCwZkRFTSpawJALW6aBcoTGdr2evKhGDAYDO3fu5LPPPuP69eu0b99eEjQhhLBC1bolbcWKEwDs3ftU8QVOrzJuQ18G/wlVE5QQZpSens6SJUtwdXXlqaeeokGDBpYOSQghRAmqdZJWu7YzGRml3BCQk2bcth9TNQEJYSa5ublcu3YNDw8P7r//ftq1ayd3MgshhJWr1t2dFy+mMWqUX9kFHWuYPxghzOTcuXPMmzePvXv3opSiffv2kqAJIYQNqJYtaTk5edSoMROA335LLr5Q/BrYMNn4WFXrXFbYsD179rBz504GDhxIhw4dLB2OEEKICqiWSVrv3l+ZHv/446iiBfJyYMUIMORAz3ehbquqC06IShAfH0+zZs1o27YtHTt2pFatWpYOSQghRAVVuyaivDwDO3eeBSA393U8PGoXLbRugjFBc64DoS+BdA0JG5GRkcGKFStYuXIlN27coEGDBpKgCSGEjap2SdqkSSsBaN7cDUfHEt7+bxuM22cugINjFUUmxN3Jyclh3rx5ODs7ExERwT333GPpkIQQQtyFatfd+eWXhwCIjX22+AKGPMhIAu/hUMOtCiMT4s6kpaVx8uRJgoODmThxIvXq1bN0SEIIISpBtUvSAPr186Zu3ZrFH9z7L+PWUdYtFNZNa82hQ4fYuHEjISEhaK0lQRNCCDtSrZK0P//ZuIyot3cpE3juet247T2rCiIS4s4dOHCAffv28ac//YmmTZtaOhwhhBCVrNokaQaDZvbsaACee65L6YWbdYfaMp5HWB+tNdHR0TRr1ozAwECCgoJwdJRxk0IIYY+qTZKWlHQTgGHD2tG+faPiC13Ybdx6BFVRVEKUX1JSEitXGm988fLywsmp2nx9hRCiWqo2v+UXLjwAQI8eLUoudPOycdtmWBVEJET5aa356aefCAgIICwsTFYMEEKIaqDaJGmXLqUDMGaMf9mFXT3MHI0Q5XPx4kWioqIYPnw4Tz75JA4O1W7WHCGEqLaqTZJ28ODvALRsWcLdb3vfg5j/q8KIhChZTk4O27ZtIyYmhr59++Lg4CCtZ0IIUc1UmyQtMTGFevVKmHYD4Lf1kJsFAU+Du2/VBSZEMc6dO8e1a9eIiIigTp06lg5HCCGEBVSLJC0zM5fTp6/j4lLC2z30GZzdAs16QN/PqjY4IfJlZWWxefNm3N3d6dKlC15eXpYOSQghhAVViyRt7dqTAAwZ0rbowYQNsCnC+Dj4z1UYlRB/OHXqFKtXr6Z169YEBARYOhwhhBBWoFokabGxVwB44437ix5cMcK4HbQE2o+uwqiEAIPBgIODA/Hx8QwdOhRvb29LhySEEMJKVItbxbZt+w2AFi2KuWmgZj1wcYcOY6s4KlGdaa2JjY1l9uzZZGRk0K9fP0nQhBBCFGL3LWmZmbls3BgPUHS9Tq0h/Xfwn2SByER1lZaWxs8//0xSUhIPPfQQrq6ulg5JCCGEFbL7JG3cuB8B40oDDg63TWFwcI5xm5NWxVGJ6khrTXZ2NgBNmjThkUcekVUDhBBClMju/0Ls2GHs6vz++5GFDxhyIfo94+P7/lnFUYnq5vr166xevZpGjRoxcOBAwsPDLR2SEEIIK2f3SZq7ey2UUtSsedtbTdgAqeeMqwvUl7FAwnz279/P5s2b6d69O927d7d0OEIIIWyE3SdpKSlZ3H//vUUPrJ9o3A7/qWoDEtXGjRs3qFevHm5ubkyaNAl3d3dLhySEEMKG2PXdnVev3uTChVRu3MgsfCA364/F1Jt1rfrAhF3Ly8tj+/btLFiwgOvXr9O2bVtJ0IQQQlSYXbek/fnPawHo2rV54QMJ64zbrm+Asus8VVSxtLQ0vv76a9zc3JgyZQr16pWwVqwQQghRBrtO0r799igAb7zxwB87DbmwOn/SWp+HLRCVsEc5OTlcvXqVJk2a0Lt3b3x8fGRBdCGEEHfFbpuRTp++BoCHRy2cnR2NO9MuwN53IS8LHGtAIz8LRijsRUJCAp999hkHDhxAKUXbtm0lQRNCCHHX7LYlbffuRADeeafPHzsXtftjTrThK8DBbt++qCK7d+/ml19+YdCgQbRv397S4QghhLAjdpulZGfnARAS0tS44/KhPxK0aclQs66FIhP24NSpU3h6etK+fXuCgoJwcXGxdEhCCCHsjN12d8bE/A6Au3v+kjsZxkXWGfK9JGjijt28eZMff/yRn3/+meTkZOrXry8JmhBCCLOw2yTt7NlkoJhF1Ws3tUA0wh7k5OQwb948ateuTUREBE2aNLF0SEIIIeyY3Xd3FlmvU4gKSk1NJS4ujk6dOjF58mTc3NwsHZIQQohqwG5b0tauPUXHjo0tHYawYVprDhw4wGeffUZqaipaa0nQhBBCVBm7bUkDOHPmhqVDEDZs//79xMTEMH78eOnaFEIIUeXsOkl74QVZ8klUjMFgYM+ePXh6ehIUFERISAgODnbb4CyEEMKK2WWSdvSocV3Oa9cy/tiZnWahaIStuHz5MitXrsTJyYl27drh5GSXXw8hhBA2wi7/Cr3yymYAeva894+dq0YatzVkTJEoSmvNqlWrCA4OJiQkRFYMEEIIYXF214+jtWb16jgAHn64Q4EDxrs98QiwQFTCWp0/f55ly5ZhMBh48skn6dSpkyRoQgghrILdtaRdvWrs4uzZsyVOTvk5aPol47bLqyB/gAXGOc+2bt3K4cOH6d+/Pw4ODpKcCSGEsCp2l6RprQEYPTp/8fTcTJjvaXxcu5mFohLWRGtNYmIiqampREREULt2bUuHJIQQQhRhd0laEb9+C4Zc4+OgCMvGIiwqMzOTjRs34uHhQdeuXWndurWlQxJCCCFKZHdj0nJyDIV3JB0xbiedAmV3b1eU04kTJ5g7dy5KKYKCgiwdjhBCCFEmu2tJmzNnL1AgWfttk3FbSyYjrY7y8vJwdHTk3LlzjBgxQlrPhBBC2Ay7a1rKyjLexfnUUyHGHc61oUknqFHHglGJqqa15siRI8yePZuMjAwefPBBSdCEEELYFLtrSQOoXduZ2rVr5D9T4NLQovGIqpWamsqqVatITk5m5MiRuLq6WjokIYQQosLsLkk7fz6V7Ow8S4chLEBrTVZWFkopWrRowejRo3F0dLR0WEIIIcQdsaskTWvN0qVHLR2GsICrV6+yatUq7rnnHgYMGEDPnj0tHZIQQghxV+xqTNrZs8kANG+ev/ST1nAxyoIRiaoQHR3NwoULadeuHf369bN0OEIIIUSlsKuWtO3bfwPgnXf6GHeknjNus5ItFJEwp2vXrtGgQQPq16/PU089RYMGDSwdkhBCCFFp7KolbfnyXwHw9fUw7khYZ9wGTbVQRMIccnNz2bp1KwsXLuT69ev4+PhIgiaEEMLu2FVL2q5d56hRw5HQ0PzlnyJfNG7reVkuKFGp0tLS+Oqrr2jYsCFPP/00devWtXRIQgghhFnYTZKWk5PH5cvp1KmTP/XG9VOQkwatB4GnDCK3ddnZ2SQlJdG0aVP69++Pl5eXLIguhBDCrtlNknby5DUAevfOn7A067px23akhSISlSU+Pp5Vq1bRrl07mjVrhre3t6VDEkIIIczObpK0AwcuAjB0aNvCB2o1tkA0orL88ssv7NmzhyFDhuDj42PpcIQQQogqYzdJ2o4dxjs7TePRDn6af0S6xGzRiRMnaNmyJX5+fnTq1ImaNWtaOiQhhBCiStnN3Z21ajkDEBCQv5D6scXGbfMelglI3JG0tDR++OEHNmzYQGpqKvXq1ZMETQghRLVkNy1pAHXr1sTBIb/lrGY9aN7TuBU2IScnhwULFuDv78+IESNwdna2dEhCCCGExdhVklaIcoK691o6ClEOycnJxMXFERYWxpQpU6hdu7alQxJCCCEszm66O03SLsCHDpB5FZT9vT17orUmOjqa+fPnk5mZidZaEjQhhBAin1mzGKXUAKXUCaXUKaXU9GKOv6CUilVKHVZKbVZK3VHTV05OHh9/vIfs7FyY1xzQ4OIOgRF3/R6E+ezfv5/Dhw8zYcIEevbsKfOeCSGEEAWYrbtTKeUIzAH6AolAtFJqpdY6tkCxGCBUa31TKRUBvAeMrui1tmw5A0CjegbjjtaDYMRKcHC8q/cgKp/Wmp07d9KyZUuCg4MJCQnBwUFaPIUQQojbmfOvY2fglNY6XmudDSwFhhcsoLXeqrW+mf90N+B5JxdKSckCYNEbtYw7Bn0tCZoV+v333zlw4ABnzpyhbt26ODo6SoImhBBClMCcNw40B84VeJ4IdCml/CRgbXEHlFJTgCkAHh4eREZGFjq+enUCADk3z4GCnTt3kuvkdqdxCzPQWnPw4EHc3d3x9PTk4MGDlg5JVFBaWlqR756wHVJ/tkvqrvoyZ5JW3AAjXWxBpf4EhAIPFHdcaz0fmA/Qrl07HR4eXuh4r17/MG67t4YouO+++8ClwR0HLirPuXPniIqK4pFHHiE8PJxt27Zxe/0J2xAZGSl1Z8Ok/myX1F31Zc4kLRFoUeC5J3Dh9kJKqQeBvwMPaK2z7uRCNWo4kp2dh6uLDDy3FtnZ2WzevJnY2FgGDhyIo6N0PwshhBAVYc4kLRrwUUq1Bs4DY4CxBQsopYKBecAArfXlO71QjRqOTJsWBvx6F+GKyqK15vz582RlZTF16lRcXV0tHZIQQghhc8yWpGmtc5VS04D1gCOwSGt9TCn1FrBPa70SeB+oA/yQP/3CWa31sIpcx2DQpKVlV3L04k5kZGSwYcMGGjduTLdu3WjdurWlQxJCCCFslllXHNBarwHW3LbvjQKPH7zba0ybZjy9i4tTgUXVRVU7fvw4a9asoUOHDoSEhFg6HCGEEMLm2fyyUAcOXATghWd84Ps4486a9S0YUfWSl5eHo6MjFy9e5NFHH6Vly5aWDkkIIYSwCzY/SdXx40n4+nrgXit/urX7/gkyc73Z3ZpSY9asWWRmZtK7d29J0IQQQohKZNMtaQaDJiUli8zMXPh1iXFnww6WDaoaSElJYeXKlaSnpzN69GhcXFwsHZIQQghhd2w6SduzJxGAkJCmcPgd406vwRaMyL5prcnMzMTR0REvLy+6dOkiU2sIIYQQZmLT3Z0JCTcAeGiYN9y8BI41wbGGhaOyT1euXOGLL75gx44d1K5dm+7du0uCJoQQQpiRTbek5U/bQYj6n3FH+8csGI392rNnj2mlgLCwMEuHI4QQQlQLNp2kmWSnGre9/s+ycdiZpKQk3N3dadSoEVOmTKF+fblrVgghhKgqNt3dmZqav4rUiW+hhhvUrGvZgOxETk4OmzZt4osvvuDGjRt4e3tLgiaEEEJUMZtuSTtx4ioA9WoDtZpYNhg7kZqaypdffkmTJk2IiIigTp06lg5JCCGEqJZsOkn74YdYAO6plQStxlg4GtuWlZVFUlISzZo1Y/DgwbKkkxBCCGFhNpuk5eUZOHs2mZo1lHHu2mbdLB2SzTp58iQ///wzvr6+NG/eXBI0IYQQwgrYbJK2ZcsZADp7njXucPe1YDS2a9euXezbt4+hQ4fi7e1t6XCEEEIIkc9mk7Rb3hm40figUUfLBmJDtNYcP36c1q1b07FjR8LCwqhRQ+aXE0IIIayJzSdpCg1PHAEHmVi1PFJTU1mzZg1JSUl4eHjg4eFh6ZCEEEIIUQybTdK++eYoAI4OGpzlDsTyyM7O5vPPPycwMJBHHnkEJyebrX4hhBDC7tnsX+m9e88D0MnzAjg4Wzga63b9+nXi4uLo0qULTz/9NLVq1bJ0SEIIIYQog81OZnv5cjot3DNxdjSAW3NLh2OVDAYDu3fvZsGCBeTl5aG1lgRNCCGEsBE225IG4KgzoFl3S4dhtfbv38+vv/7KpEmTcHd3t3Q4QgghhKgAm0zSDAZNUtJNhnU+AxlXLR2OVcnLy2Pnzp20atWKkJAQQkNDTQvRCyGEEMJ22GSStm1bAgA1HPOg0/OWDcaKXLhwgZUrV+Lm5kZQUBCOjnLHqxBCCGGrbDJJu3AhFYCxwUegzUILR2MdtNasX7+e7t2707FjR2k9E0IIIWycbd44kHEdgHvc0qBWYwsHY1kJCQksXboUg8HAhAkTCAgIkARNCCGEsAM22ZJ28Xz+OLSuf4dqmpBkZWWxceNG4uLiGDx4sHRtCiGEEHbGJpO0TdsuA+DetJmFI7EMrTUXL17EYDAwdepUXFxcLB2SEEIIISqZTSZpdWobw25Yv3q1Ht28eZN169bRtGlTunXrRqtWrSwdkhBCCCHMxCbHpDnkptKh8RVLh1Gljh49yqeffkrt2rXp1KmTpcMRQgghhJnZZEvavkM3qAnQsJ2lQzG73NxcnJycSEpKYsyYMXh6elo6JCGEEEJUAZtrSVMYqGVI4mKKG3gEWDocs9Fas3//fmbNmkVmZibh4eGSoAkhhBDViM21pDkYsjh2qTFDOv5m6VDMJjk5mZ9++ons7GzGjh0rNwYIIYQQ1ZDNJWmOeRkAqCbBFo6k8hkMBjIzM3FycqJdu3Z07twZBweba+wUQphJTk4OiYmJZGZmVvi19erV4/jx42aISpib1J1tcHFxwdPTE2dn50o7p80laQ46hxqOufgFtLB0KJXq8uXLrFixglatWtG3b1+6du1q6ZCEEFYmMTERNzc3WrVqVeFJq1NTU3FzczNTZMKcpO6sn9aaq1evkpiYSOvWrSvtvDaXpAHGCWxr1LF0FJUmKiqKnTt30rt3b0JCQiwdjhDCSmVmZt5RgiaEMC+lFO7u7ly5UrkzT9hmkmYnLl++jIeHB02aNOHpp5+mbt26lg5JCGHlJEETwjqZ47spA54sICcnh/Xr1/PVV19x48YNvLy8JEETQgghRCGSpFWx1NRU5s6dS3p6OhERETRo0MDSIQkhRLklJiYyfPhwfHx88Pb25i9/+QvZ2dllvu6dd94p8dilS5cYMmQIgYGB+Pr6MmjQoMoMuYiEhAT8/f0r9BpHR0eCgoLw9/dn6NCh3Lhxw3Ts2LFj9O7dm7Zt2+Lj48Pbb7+N1tp0fO3atYSGhtKhQwfat2/PSy+9VGnvpbLExMQwefJkS4dRqn/961+0adOGdu3asX79+mLLbN68mZCQEIKCgrjvvvs4deqU6dj333+Pr68vfn5+jB071rT/Vt0GBQUxbNgw0/4tW7YQEhKCv78/TzzxBLm5uQCsXr2aN99800zv8jZaa5v6CWhVT9dwel1Pn75R25KMjAx99uxZbTAY9G+//WbpcCxm69atlg5B3CGpO8uLjY2949empKTc9fUNBoMOCwvTixYt0lprnZubq5988kn90ksvlfna2rVrl3hsypQp+uOPPzY9P3To0F3HWpozZ85oPz+/Cr2mYPzjx4/XM2fO1FprffPmTe3l5aXXr1+vtdY6PT1dDxgwQM+ePVtrrfWRI0e0l5eXPn78uNZa65ycHD1nzpwKXbususvJyanQ+YozcuRIffDgwXKXr4xrVsSxY8d0QECAzszM1PHx8drLy0vn5uYWKefj42P6nsyZM0c/8cQTWmut4+LidFBQkL527ZrWWutLly6ZXlPcv828vDzt6empT5w4obXW+vXXX9eff/651tr4PQgKCtLp6elFXlfcdxTYp+8w55GWtCpw4sQJPv30U06cOIFSipYtW1o6JCGErdv6V/guvNw/rqsGlV1u619LveSWLVtwcXFh4sSJgLEF4qOPPmLRokXcvHmTxYsXM23aNFP5IUOGEBkZyfTp08nIyCAoKIhx48YVOe/FixcLTdYdEGCcqDwtLY0+ffoQEhJCx44dWbFiBWBsCWvfvj2TJ0/G39+fcePGsWnTJnr06IGPjw979+4FYMaMGTz++OP07t0bHx8fFixYUOTaeXl5vPzyy4SFhREQEMC8efNK/QwAunXrxvnz5wH45ptv6NGjB/369QOgVq1azJ49m3fffReA9957j7///e+0b98eACcnJ6ZOnVrknGlpaUycOJGOHTsSEBDA//73PwDq1PnjJrlly5YxYcIEACZMmMALL7xAr169ePnll2nVqlWh1r02bdpw6dIlrly5wiOPPEJYWBhhYWHs2rWryLVTU1M5fPgwgYGBAOzdu5fu3bsTHBxM9+7dOXHiBACLFy/m0UcfZejQoab3pfxavgAAIABJREFU+/7775s+u4KtSyNGjKBTp074+fkxf/78Mj/TsqxYsYIxY8ZQs2ZNWrduTZs2bUz1XJBSipSUFMA452izZs0AWLBgAc8++6yp96px48alXu/q1avUrFmTtm3bAtC3b19TnSilCA8PZ/Xq1Xf9vsoiNw6Y2Y4dO4iJieHhhx+WBdGFEDbt2LFjRdYOrlu3Li1btizUrXS7d999l9mzZ3Pw4MFijz/77LOMHj2a2bNn8+CDDzJx4kSaNWuGi4sLy5cvp27duiQlJdG1a1dTd9SpU6f44YcfmD9/PmFhYXzzzTfs3LmTlStX8s477/DTTz8BcPjwYXbv3k16ejrBwcEMHjy40LUXLlxIvXr1iI6OJisry5RwlTSNQl5eHps3b2bSpEklfibe3t6kpaWRkpLC0aNHefHFF0v5VI3efvtt6tWrx5EjRwC4fv16ma+Ji4tj06ZNODo6YjAYWL58ORMnTmTPnj20atWKJk2aMHbsWJ5//nnuu+8+zp49S//+/YvMubZv375C3b/t27dn+/btODk5sWnTJl599VVTghIVFcXhw4dp2LAhGzZs4OTJk+zduxetNcOGDWP79u3cf//9LFq0iIYNG5KRkUFYWBiPPPII7u7uha77/PPPs3Xr1iLva8yYMUyfPr3QvvPnzxeamsrT09OUKBf0+eefM2jQIFxdXalbty67d+82fVYAPXr0IC8vjxkzZjBgwADAeNd0aGgoTk5OTJ8+nREjRtCoUSNycnLYt28foaGhLFu2jHPnzpmuExoayo4dOxg1alSZ9XQ3bC5Jc8y7aekQyqS15ujRo3h7exMUFETXrl0rdXI7IYSg18cVKp5RCXNtaa2LvYOtpP3l1b9/f+Lj41m3bh1r164lODiYo0ePUr9+fV599VW2b9+Og4MD58+f59KlSwC0bt2ajh07AuDn50efPn1QStGxY0cSEhJM5x4+fDiurq64urrSq1cv9u7dS1BQkOn4hg0bOHz4MMuWLQOMrS8nT54skqTdaglMSEigU6dO9O3bt8z3XpHPZNOmTSxdutT0vDzjlR999FEcHR0BGD16NG+99RYTJ05k6dKljB492nTe2NhY02tSUlKKzLt28eJFPDw8TM+Tk5N54oknOHnyJEopcnJyTMf69u1Lw4YNAeNnt2HDBoKDjZPLp6WlcfLkSe6//34++eQTli9fDsC5c+c4efJkkSTto48+Kt+HA4XG+N1S3Of70UcfsWbNGrp06cL777/PCy+8wOeff05ubi4nT54kMjKSxMREevbsafo3dvbsWZo1a0Z8fDy9e/emY8eOeHt7s3TpUp5//nmysrLo168fTk5/pEyNGzfmwoUL5Y7/Ttlckuagc6CYyrIWycnJ/PzzzyQnJ9O0aVMaNWpk6ZCEEKJS+Pn5mVpUbklJSeHcuXN4e3tz6NAhDAaD6VhJKyPMmTPH1PW4Zs0amjVrRsOGDRk7dixjx45lyJAhbN++ndTUVK5cucL+/ftxdnamVatWpnPWrFnTdD4HBwfTcwcHB9MAbyj6h/z251prZs2aRf/+/Ut9766urhw8eJDk5GSGDBnCnDlzeO655/Dz82P79u2FysbHx1OnTh3c3Nzw8/Nj//79pq7EkpSU7BXcd/vnWbt2bdPjbt26cerUKa5cucJPP/3Ea6+9BhhXsomKisLV1bXU91bw3K+//jq9evVi+fLlJCQkEB4eXuw1tda88sorPP3004XOFxkZyaZNm4iKiqJWrVqEh4cX+2+hIi1pnp6ehVqyEhMTTV2Zt1y5coVDhw7RpUsXwJi43mot8/T0NDWYtG7dmnbt2nHy5EnCwsJM5/Hy8iI8PJyYmBi8vb3p1q0bO3bsAIwJ6a3WODDWRWmfaWWxwTFpiuw868wts7OzWbhwIc2bN2fKlCmSoAkh7EqfPn24efMmX331FWDs+nvxxReZMGECtWrVolWrVhw8eBCDwcC5c+cKjRlydnY2tcg8++yzHDx4kIMHD9KsWTO2bNnCzZvGXpLU1FROnz5Ny5YtSU5OpnHjxjg7O7N161Z++63iazavWLGCzMxMrl69SmRkJGFhYYWO9+/fn7lz55pii4uLIz09vcTz1atXj08++YQPPviAnJwcxo0bx86dO9m0aRNgbHF77rnn+Nvf/gbAyy+/zDvvvGP6A28wGPjPf/5T5Lz9+vVj9uzZpue3ujubNGnCiRMnTN2ZJVFK8dBDD/HCCy/QoUMHU6vV7ectrsu5Q4cOhbqrk5OTad68OWAch1aS/v37s2jRItLS0gBjl+Tly5dJTk6mQYMG1KpVi19//dXU5Xi7jz76yPTvoODP7QkawLBhw1i6dClZWVmcOXOGkydP0rlz50JlGjRoQHJysumz3rhxIx06dACMY+RuJYRJSUnExcXh5eXF9evXycrKMu3ftWsXvr6+gHEuU4CsrCz+/e9/88wzz5iuFRcXV+E7hO+EzSVpOQZjyFevZlg4kj9cvXqVqKgoatSowdSpU3nggQdMTdBCCGEvlFIsX76cH374AR8fH9q2bYuLi4tpeo0ePXqYuiFfeumlQiuoTJkyhYCAgGJvHNi/fz+hoaEEBATQrVs3Jk+eTFhYGOPGjTONCVqyZIlp8H1FdO7cmcGDB9O1a1def/31Iq0vkydPxtfX1zTVwtNPP12oJa44wcHBBAYGsnTpUlxdXVmxYgUzZ86kXbt2dOzYkbCwMNMNFAEBAXz88cc89thjdOjQAX9/fy5evFjknK+99hrXr1/H39+fwMBAU0Lx7rvv8uijj9K7d2+aNm1aalyjR4/m66+/NnV1AnzyySfs27ePgIAAfH19+eyzz4q8rn379iQnJ5OamgrA3/72N1555RXT+K2S9OvXj7Fjx9KtWzc6duzIyJEjSU1NZcCAAeTm5hIQEMDrr79eKcsc+vn5MWrUKHx9fRkwYABz5swx/Z0dNGgQFy5cwMnJiQULFvDII48QGBjIf//7X95//33AmFC6u7vj6+tLr169eP/993F3d+f48eOEhoYSGBhIr169mD59uilJe//99+nQoQMBAQEMHTqU3r17m+LZunVrkfGN5qCK6+e1Zh2bOuujv7/GggVDmTzZskso3WpG3rVrF/fffz9dunSR2cDLEBkZWajpXNgOqTvLO378uKlloKKq4/qPM2bMoE6dOlY5L1lFVEXdffTRR7i5uVn9XGnW4NKlS4wdO5bNmzcXOVbcd1QptV9rHXon17K5lrSsPGPmbDBYPrncv38/p0+f5qmnnqJr166SoAkhhLBJERERhcb5iZKdPXuWDz/8sEquZZ2Du0qhtTER8vKyzEz9ubm57Nixg9atW9OpUydCQ0MlORNCCCs0Y8YMS4dgM1xcXHj88cctHYZNuH1coznZXEuaIT9Jq1OnRpVf+9y5c8ybN49Lly7h7u6Og4ODJGhCCCGEMAuba0nLzDGG7OpataFrrdmyZQvh4eH4+vpKciaEEEIIs7K5ljQHZRyLdu+99avkevHx8SxZsgSDwcD48ePx8/OTBE0IIYQQZmdzLWl5hrLLVIbMzEzWr1/PmTNnGDx4sEypIYQQQogqZXMtaZm5xuWVatY0X9Kkteb333/HycmJiIgIfHx8zHYtIYSwJQUX/C4Pg8HAc889h7+/v2kOsTNnzpgpOqNWrVqRlJRU7vLh4eG0a9eOwMBAwsLCCk34mpyczPjx4/H29sbb25vx48eTnJxsOh4XF8egQYNo06YNHTp0YNSoUaalq6xFRkYGDzzwQKlznlnaunXraNeuHW3atDEtTn+7s2fP0qtXL4KDgwkICGDNmjVFjtepU4cPPvjAtO/GjRuMHDmS9u3b06FDB6KiokzHZs2aRbt27fDz8zNNPnzkyBHTIvbWwOaSNEcHAzVrOuDqWvlrYaalpfHDDz+we/duWrVqxeDBg+WWZCGEuAvfffcdFy5c4PDhwxw5coTly5dTv37VDFepiCVLlnDo0CGmTp3Kyy+/bNo/adIkvLy8OH36NKdPn6Z169amucQyMzMZPHgwERERnDp1iuPHjxMREcGVK1cqLa6yJtYtj0WLFvHwww+Xu0dIa11oeS9zy8vL49lnn2Xt2rXExsby7bffFlpv9JaZM2cyatQoYmJiWLp0KVOnTi10/Pnnn2fgwIGF9v3lL39hwIAB/Prrrxw6dMg0h9nWrVtZsWIFhw8f5tixY6a59Dp27EhiYiJnz54107utGJvr7gSoX7fyE7RDhw6xceNGgoKCCA29oznnhBCiyvz1r+s4ePD3cpfPy8sr8490UNA9fPzxgHKdLzIykhkzZtCoUSOOHj1Kp06d+Prrr4uM2b148SJNmzbFwcHYJuDp6Wk6FhERQXR0NBkZGYwcOZJ//OMfgLElbOzYsWzdupWcnBzmz5/PK6+8wqlTp3j55Zd55plniIyM5I033sDd3Z0TJ05w//338+mnn5quc8vXX3/NJ598QnZ2Nl26dOHTTz8t9XPo1q2baZb6U6dOsX//fr777jvT8TfeeIM2bdpw+vRptm3bRrdu3Rg6dKjpeK9evYo973vvvcd///tfHBwcGDhwIO+++y7h4eF88MEHhIaGkpSURGhoKAkJCSxevJiff/6ZzMxM0tPTadCgAZMmTWLQoEEATJgwgaFDhzJixAimT59OZGQkWVlZPPvss0XW0QRjAvrNN98AxsaI4cOHc/36dXJycpg5cybDhw8nISGBgQMH0qtXL6Kiovjpp584ceIEb775JllZWXh7e/PFF19Qp04d3nrrLVatWkVGRgbdu3dn3rx5dzVWe+/evbRp0wYvLy/AuHbnihUrTDP/36KUIiUlBTC2cBZcPeKnn37Cy8ur0NqiKSkpbN++3bS0VY0aNahRwzgzxNy5c5k+fbqpIaZx48am1w0dOpSlS5eaWtcsyeZa0irbrfXaUlJSGDt2LA8++CDOzpWfBAohhL2JiYnh448/JjY2lvj4eHbt2lWkzKhRo1i1ahVBQUG8+OKLxMTEmI7985//ZN++fRw+fJht27Zx+PBh07EWLVoQFRVFz549mTBhAsuWLWP37t288cYbpjJ79+7lww8/5MiRI5w+fZoff/yx0LWPHz/Od999x65duzh48CCOjo4sWbKk1Pe0bt06RowYAUBsbCxBQUGFkjpHR0eCgoI4duyYKTkty9q1a/npp5/Ys2cPhw4dKtcf/6ioKL788ku2bNnCI488YkoUs7Oz2bx5M4MGDWLhwoXUq1eP6OhooqOjWbBgQZGu5OzsbOLj42nVqhVgnA9t+fLlHDhwgK1bt/Liiy9ya+WhEydOMH78eGJiYqhduzYzZ85k06ZNHDhwgNDQUNOao9OmTSM6OpqjR4+SkZHB6tWri8S/ZMkSgoKCivyMHDmySNnz58/TokUL03NPT0/Onz9fpNyMGTP4+uuv8fT0ZNCgQcyaNQuA9PR0/v3vf/Pmm28WKh8fH4+HhwcTJ04kODiYyZMnm9ZljYuLY8eOHXTp0oUHHniA6Oho0+tCQ0NNC6tbmk22pOVVwmoDWmuio6PZuXMnU6dOpWfPnpUQmRBCVI3ytnjdYo6lhTp37mxqGQsKCiIhIYH77ruvUBlPT09OnDjBli1b2LJlC3369OGHH36gT58+fP/998yfP5/c3FwuXrxIbGwsAQEBgHFBbTB2P6WlpeHm5oabmxsuLi7cuHHDdP1brS+PPfYYO3fuLJQEbN68mf3795smH83IyCjUYlLQuHHjSE9PJy8vjwMHDgDGvxPFtRCVtL8kmzZtYuLEidSqVQuAhg0blvmavn37msr17duX//f//h9ZWVmsW7eO+++/H1dXVzZs2MDhw4dZtmwZYGxdOnnyJK1btzadJykpqVD3staaV199le3bt+Pg4MD58+dNY+juvfde0zqbu3fvJjY2lh49egDGZK9bt26Asavwvffe4+bNm1y7dg0/P79CrYlg/DyLW6e1OMUtT1nc5/vtt98yYcIEXnzxRaKionj88cc5evQob775Js8//3yR8ZK5ubkcOHCAWbNm0aVLF/7yl7/w7rvv8vbbb5Obm8v169fZvXs30dHRjBo1ivj4eJRSNG7cmAsXLpQrdnOzuSQtKb0WdR3urq/8xo0bLF++HIPBwOOPP46Li0slRSeEENVHwTG7jo6O5ObmsmfPHlOX21tvvcWwYcOoWbMmAwcOZODAgTRp0sTUNfXBBx8QHR1NgwYNmDBhApmZmUXO7eDgUOg6Dg4OpnFat/8hv/251ponnniCf/3rX2W+lyVLlhAYGMj06dN59tln+fHHH/Hz8yMmJgaDwWDqRjUYDKaxTZcvX2bbtm1lnrukpM7Jyck09qvgewcKddu5uLgQHh7O+vXr+e6773jsscdM5501axb9+/cv8dqurq6Fzr1kyRKuXLnC/v37cXZ2plWrVqbjBa+ptaZv3758++23hc6XmZnJ1KlT2bdvHy1atGDGjBlFYr91nVvdxgW1adPGlFTe4unpyblz50zPExMTC3Vl3rJw4ULWrVsHGLulMzMzSUpKYs+ePSxbtoy//e1v3LhxAwcHB1xcXBg5ciSenp506dIFgJEjR5puSvD09OThhx9GKUXnzp1xcHAgKSkJDw8PMjMzcXV1LfEzrUo22d2ZknpnAykNBgPp6enUqFEDf39/Jk6ciIeHRyVHJ4QQ1VeXLl04ePAgBw8eZNiwYRw4cMDUKmEwGDh8+DD33nsvKSkp1K5dm3r16nHp0iXWrl1b4Wvt3buXM2fOYDAY+O6774q04vXp04dly5Zx+fJlAK5du8Zvv/1W4vmcnZ2ZOXMmu3fv5vjx47Rp04bg4GBmzpxpKjNz5kxCQkJo06YNY8eO5ZdffuHnn382HV+3bh1HjhwpdN5+/fqxaNEibt68aYoDjGPv9u/fD1AkcbndmDFj+OKLL9ixY4cpKevfvz9z5841DduJi4szdefd0qBBA/Ly8kyJVHJyMo0bN8bZ2ZmtW7eW+Hl07dqVXbt2cerUKQBu3rxJXFyc6TyNGjUiLS2txLjHjRtn+ndQ8Ke48mFhYZw8eZIzZ86QnZ3N0qVLTS2pBbVs2dK0qPnx48fJzMzEw8ODHTt2kJCQQEJCAn/961959dVXmTZtGvfccw8tWrTgxIkTgLFl9dY4txEjRrBlyxbT55adnU2jRo1Mz/39/Yt9X1XNJpO0D/9R8Q/v999/5/PPPycqKopatWoRFhZWZICpEEKIynX58mWGDh2Kv78/AQEBODk5MW3aNAIDAwkODsbPz48nn3zS1K1WEd26dWP69On4+/vTunVrHnrooULHfX19mTlzJv369SMgIIC+ffty8eLFUs/p6urKiy++aJrGYeHChcTFxdGmTRu8vb2Ji4tj4cKFprKrV69m1qxZ+Pj44Ovry+LFi4t0qQ4YMIBhw4YRGhpKUFCQ6dwvvfQSc+fOpXv37mVOGdKvXz+2b9/Ogw8+aBr8PnnyZHx9fQkJCcHf35+nn3662LtB+/Xrx86dOwFj8rRv3z5CQ0NZsmQJ7du3L/Z6Hh4eLF68mMcee4yAgAC6du3Kr7/+Sv369Xnqqafo2LEjI0aMqJR1LJ2cnJg9ezb9+/c3TWPi5+cHGG/UWLlyJQAffvghCxYsIDAwkMcee4zFixeX2e08a9Ysxo0bR0BAAAcPHuTVV18F4MknnyQ+Ph5/f3/GjBnDl19+aTrX1q1bGTx48F2/r8qgiusLtmZKNdP/fv0T/vZW0cGHJdm1axe//PILffv2JTAwUFYMsKDIyEjCw8MtHYa4A1J3lnf8+HHTFAIVZY4xaZYUGRnJBx98UOygdXtzt3UXExPDf/7zH/773/9WYlT2KSsriwceeICdO3fi5FTxEWHFfUeVUvu11nc0bYTNjUkDGDmkaF91cS5dukTjxo1p3rw5zzzzjF39ghJCCCHKIzg4mF69epVrGpbq7uzZs7z77rt3lKCZg3VEUclu3aIcGxvLpEmTTLceCyGEsA/h4eHSslsBTz75pKVDsAk+Pj5WtcqQTQ7KanxvyxKPpaSk8Omnn5KVlcXUqVOtcmZrIYS4U7Y2REWI6sIc302bbEmr08SzyL6MjAyuXLlCixYtTLfdCiGEPXFxceHq1au4u7vL2FohrIjWmqtXr1b6lF42maTdLjY2lrVr1xIcHEzLli0lQRNC2CVPT08SExPvaG3IzMxMmRPSRknd2QYXF5dKzz9sPknbtm0bR44c4dFHH6Vly5K7QYUQwtY5OzsXmk2+IiIjIwkODq7kiERVkLqrvsw6Jk0pNUApdUIpdUopNb2Y4zWVUt/lH9+jlGpVnvNqrTl06BA3b96kU6dOPPPMM5KgCSGEEMKumC1JU0o5AnOAgYAv8JhSyve2YpOA61rrNsBHwL/LOq+jo3G5id27d5ORkUGdOnWs5lZZIYQQQojKYs7spjNwSmsdD6CUWgoMB2ILlBkOzMh/vAyYrZRSupRbJNzdjUtpdOvWTeZ7EUIIIYTdMmeS1hw4V+B5ItClpDJa61ylVDLgDhRaH0MpNQWYkv80q2fPnkfNErGoCo24rX6FzZC6s21Sf7ZL6s62tbvTF5ozSSvu/vDbW8jKUwat9XxgPoBSat+dLq8gLE/qz3ZJ3dk2qT/bJXVn25RS++70tea8cSARaFHguSdwoaQySiknoB5wzYwxCSGEEELYBHMmadGAj1KqtVKqBjAGWHlbmZXAE/mPRwJbShuPJoQQQghRXZituzN/jNk0YD3gCCzSWh9TSr0F7NNarwQWAv9VSp3C2II2phynnm+umEWVkPqzXVJ3tk3qz3ZJ3dm2O64/JQ1XQgghhBDWxyYXWBdCCCGEsHeSpAkhhBBCWCGrTdLMtaSUML9y1N0LSqlYpdRhpdRmpdS9lohTFK+s+itQbqRSSiulZGoAK1Ke+lNKjcr/Dh5TSn1T1TGK4pXjd2dLpdRWpVRM/u/PQZaIUxSllFqklLqslCp2Hldl9El+3R5WSoWU57xWmaSZa0kpYX7lrLsYIFRrHYBxpYn3qjZKUZJy1h9KKTfgOWBP1UYoSlOe+lNK+QCvAD201n7AX6s8UFFEOb97rwHfa62DMd5o92nVRilKsRgYUMrxgYBP/s8UYG55TmqVSRoFlpTSWmcDt5aUKmg48GX+42VAH6VUcZPjiqpVZt1prbdqrW/mP92NcQ49YR3K890DeBtjcp1ZlcGJMpWn/p4C5mitrwNorS9XcYyieOWpOw3UzX9cj6JzjwoL0Vpvp/R5XocDX2mj3UB9pVTTss5rrUlacUtKNS+pjNY6F7i1pJSwrPLUXUGTgLVmjUhURJn1p5QKBlporVdXZWCiXMrz/WsLtFVK7VJK7VZKlfa/f1F1ylN3M4A/KaUSgTXAn6smNFEJKvq3ETDvslB3o9KWlBJVrtz1opT6ExAKPGDWiERFlFp/SikHjMMLJlRVQKJCyvP9c8LY5RKOsRV7h1LKX2t9w8yxidKVp+4eAxZrrT9USnXDOM+ov9baYP7wxF26o5zFWlvSZEkp21WeukMp9SDwd2CY1jqrimITZSur/twAfyBSKZUAdAVWys0DVqO8vztXaK1ztNZngBMYkzZhWeWpu0nA9wBa6yjABePi68L6letv4+2sNUmTJaVsV5l1l99dNg9jgibjYaxLqfWntU7WWjfSWrfSWrfCOKZwmNb6jhcQFpWqPL87fwJ6ASilGmHs/oyv0ihFccpTd2eBPgBKqQ4Yk7QrVRqluFMrgfH5d3l2BZK11hfLepFVdneacUkpYWblrLv3gTrAD/n3epzVWg+zWNDCpJz1J6xUOetvPdBPKRUL5AEva62vWi5qAeWuuxeBBUqp5zF2lU2QxgnroJT6FuMQgkb5YwbfBJwBtNafYRxDOAg4BdwEJpbrvFK/QgghhBDWx1q7O4UQQgghqjVJ0oQQQgghrJAkaUIIIYQQVkiSNCGEEEIIKyRJmhBCCCGEFZIkTQhRqZRSeUqpgwV+WpVStpVS6mglXDNSKXVCKXUof7mjdndwjmeUUuPzH09QSjUrcOzz4haav8s4o5VSQeV4zV+VUrXu9tpCCNsjSZoQorJlaK2DCvwkVNF1x2mtA4EvMc7FVyFa68+01l/lP50ANCtwbLLWOrZSovwjzk8pX5x/BSRJE6IakiRNCGF2+S1mO5RSB/J/uhdTxk8ptTe/9e2wUsonf/+fCuyfp5RyLONy24E2+a/to5SKUUodUUotUkrVzN//rlIqNv86H+Tvm6GUekkpNRLjmrJL8q/pmt8CFqqUilBKvVcg5glKqVl3GGcUBRZYVkrNVUrtU0odU0r9I3/fcxiTxa1Kqa35+/oppaLyP8cflFJ1yriOEMJGSZImhKhsrgW6Opfn77sM9NVahwCjgU+Ked0zwP9prYMwJkmJ+UvfjAZ65O/PA8aVcf2hwBGllAuwGBitte6IcYWVCKVUQ+AhwE9rHQDMLPhirfUyYB/GFq8grXVGgcPLgIcLPB8NfHeHcQ7AuETTLX/XWocCAcADSqkArfUnGNf366W17pW/jNNrwIP5n+U+4IUyriOEsFFWuSyUEMKmZeQnKgU5A7Pzx2DlYVwv8nZRwN+VUp7Aj1rrk0qpPkAnIDp/CTFXjAlfcZYopTKABODPQDvgjNY6Lv/4l8CzwGwgE/hcKfUzsLq8b0xrfUUpFZ+/9t7J/Gvsyj9vReKsjXHpn5AC+0cppaZg/L3cFPAFDt/22q75+3flX6cGxs9NCGGHJEkTQlSF54FLQCDGFvzM2wtorb9RSu0BBgPrlVKTAQV8qbV+pRzXGFdwoXellHtxhfLXSOyMcaHqMcA0oHcF3st3wCjgV2C51lorY8ZU7jiBQ8C7wBzgYaVUa+AlIExrfV0ptRjj4tm3U8BGrfVjFYhXCGGjpLtTCFEV6gEXtdYG4HGMrUiFKKW8gPj8Lr6VGLvLB4hDAAABOElEQVT9NgMjlVKN88s0VErdW85r/gq0Ukq1yX/+OLAtfwxXPa31GoyD8ou7wzIVcCvhvD8CI4DHMCZsVDROrXUOxm7LrvldpXWBdCBZKdUEGFhCLLuBHrfek1KqllKquFZJIYQdkCRNCFEVPgWeUErtxtjVmV5MmdHAUaXUQaA98FX+HZWvARuUUoeBjRi7Asuktc4EJgI/KKWOAAbgM4wJz+r8823D2Mp3u8XAZ7duHLjtvNeBWOBerfXe/H0VjjN/rNuHwEta60NADHAMWISxC/WW+cBapdRWrfUVjHeefpt/nd0YPyshhB1SWmtLxyCEEEIIIW4jLWlCCCGEEFZIkjQhhBBCCCskSZoQQgghhBWSJE0IIYQQwgpJkiaEEEIIYYUkSRNCCCGEsEKSpAkhhBBCWKH/DwdWx4eKzgx5AAAAAElFTkSuQmCC\n",
1724 |       "text/plain": [
1725 |        "<Figure size 720x504 with 1 Axes>"
1726 |       ]
1727 |      },
1728 |      "metadata": {},
1729 |      "output_type": "display_data"
1730 |     }
1731 |    ],
1732 |    "source": [
1733 |     "plt.figure(figsize=(10,7))\n",
1734 |     "\n",
1735 |     "plt.plot(\n",
1736 |     "    out_sample_fpr, out_sample_tpr, color='darkorange', label='Out-Sample ROC curve (area = %0.4f)' % in_sample_roc_auc\n",
1737 |     ")\n",
1738 |     "plt.plot(\n",
1739 |     "    in_sample_fpr, in_sample_tpr, color='navy', label='In-Sample ROC curve (area = %0.4f)' % out_sample_roc_auc\n",
1740 |     ")\n",
1741 |     "plt.plot([0, 1], [0, 1], color='gray', lw=1, linestyle='--')\n",
1742 |     "plt.grid()\n",
1743 |     "plt.xlim([0.0, 1.0])\n",
1744 |     "plt.ylim([0.0, 1.05])\n",
1745 |     "plt.xlabel('False Positive Rate')\n",
1746 |     "plt.ylabel('True Positive Rate')\n",
1747 |     "plt.title('ROC Curve')\n",
1748 |     "plt.legend(loc=\"lower right\")\n",
1749 |     "\n",
1750 |     "plt.show()"
1751 |    ]
1752 |   },
1753 |   {
1754 |    "cell_type": "code",
1755 |    "execution_count": null,
1756 |    "metadata": {},
1757 |    "outputs": [],
1758 |    "source": []
1759 |   }
1760 |  ],
1761 |  "metadata": {
1762 |   "kernelspec": {
1763 |    "display_name": "Python 3",
1764 |    "language": "python",
1765 |    "name": "python3"
1766 |   },
1767 |   "language_info": {
1768 |    "codemirror_mode": {
1769 |     "name": "ipython",
1770 |     "version": 3
1771 |    },
1772 |    "file_extension": ".py",
1773 |    "mimetype": "text/x-python",
1774 |    "name": "python",
1775 |    "nbconvert_exporter": "python",
1776 |    "pygments_lexer": "ipython3",
1777 |    "version": "3.6.5"
1778 |   }
1779 |  },
1780 |  "nbformat": 4,
1781 |  "nbformat_minor": 2
1782 | }
1783 | 


--------------------------------------------------------------------------------
/ch.12/R/ABTesting.R:
--------------------------------------------------------------------------------
  1 | library(dplyr)
  2 | library(readxl)
  3 | library(ggplot2)
  4 | 
  5 | #### 1. Load Data ####
  6 | df <- read_excel(
  7 |   path="~/Documents/data-science-for-marketing/ch.12/data/WA_Fn-UseC_-Marketing-Campaign-Eff-UseC_-FastF.xlsx"
  8 | )
  9 | 
 10 | #### 2. Date Analysis ####
 11 | 
 12 | # - total sales
 13 | summary(df$SalesInThousands)
 14 | 
 15 | salesPerPromo <- df %>% 
 16 |   group_by(Promotion) %>%
 17 |   summarise(Sales=sum(SalesInThousands))
 18 | 
 19 | salesPerPromo
 20 | 
 21 | ggplot(salesPerPromo, aes(x="", y=Sales, fill=Promotion)) + 
 22 |   geom_bar(width=1, stat = "identity", position=position_fill()) +
 23 |   geom_text(aes(x=1.25, label=Sales), position=position_fill(vjust = 0.5), color='white') +
 24 |   coord_polar("y") +
 25 |   ggtitle('sales distribution across different promotions')
 26 | 
 27 | # - market size
 28 | df %>% 
 29 |   group_by(MarketSize) %>%
 30 |   summarise(Count=n())
 31 | 
 32 | marketSizePerPromo <- df %>% 
 33 |   group_by(Promotion, MarketSize) %>%
 34 |   summarise(Count=n())
 35 | 
 36 | marketSizePerPromo
 37 | 
 38 | ggplot(marketSizePerPromo, aes(x=Promotion, y=Count, fill=MarketSize)) + 
 39 |   geom_bar(width=0.5, stat="identity", position="dodge") +
 40 |   ylab("Count") +
 41 |   xlab("Promotion") +
 42 |   ggtitle("breakdowns of market sizes across different promotions") +
 43 |   theme(plot.title=element_text(hjust=0.5))
 44 | 
 45 | ggplot(marketSizePerPromo, aes(x=Promotion, y=Count, fill=MarketSize)) + 
 46 |   geom_bar(width=0.5, stat="identity", position="stack") +
 47 |   ylab("Count") +
 48 |   xlab("Promotion") +
 49 |   ggtitle("breakdowns of market sizes across different promotions") +
 50 |   theme(plot.title=element_text(hjust=0.5))
 51 | 
 52 | 
 53 | # - store age
 54 | summary(df$AgeOfStore)
 55 | 
 56 | overallAge <- df %>%
 57 |   group_by(AgeOfStore) %>%
 58 |   summarise(Count=n())
 59 | 
 60 | overallAge
 61 | 
 62 | ggplot(overallAge, aes(x=AgeOfStore, y=Count)) + 
 63 |   geom_bar(width=0.5, stat="identity") +
 64 |   ylab("Count") +
 65 |   xlab("Store Age") +
 66 |   ggtitle("overall distributions of age of store") +
 67 |   theme(plot.title=element_text(hjust=0.5))
 68 | 
 69 | AgePerPromo <- df %>% 
 70 |   group_by(Promotion, AgeOfStore) %>%
 71 |   summarise(Count=n())
 72 | 
 73 | AgePerPromo
 74 | 
 75 | ggplot(AgePerPromo, aes(x=AgeOfStore, y=Count, fill=Promotion)) + 
 76 |   geom_bar(width=0.5, stat="identity", position="dodge2") +
 77 |   ylab("Count") +
 78 |   xlab("Store Age") +
 79 |   ggtitle("distributions of age of store") +
 80 |   theme(plot.title=element_text(hjust=0.5))
 81 | 
 82 | tapply(df$AgeOfStore, df$Promotion, summary)
 83 | 
 84 | 
 85 | # - week number
 86 | df %>% 
 87 |   group_by(Week) %>%
 88 |   summarise(Count=n())
 89 | 
 90 | weekPerPromo <- df %>% 
 91 |   group_by(Week, Promotion) %>%
 92 |   summarise(Count=n())
 93 | 
 94 | weekPerPromo
 95 | 
 96 | ggplot(weekPerPromo, aes(x="", y=Count, fill=Promotion)) + 
 97 |   geom_bar(width=1, stat = "identity", position=position_fill()) +
 98 |   geom_text(aes(x=1.25, label=Count), position=position_fill(vjust = 0.5), color='white') +
 99 |   coord_polar("y") +
100 |   facet_wrap(~Week) +
101 |   ggtitle('distribution across different weeks')
102 | 
103 | 
104 | #### 3. Statistical Significance ####
105 | 
106 | # Promotion 1 vs. 2
107 | promo_1 <- df[which(df$Promotion == 1),]$SalesInThousands
108 | promo_2 <- df[which(df$Promotion == 2),]$SalesInThousands
109 | 
110 | mean_1 <- mean(promo_1)
111 | mean_2 <- mean(promo_2)
112 | std_1 <- sd(promo_1)
113 | std_2 <- sd(promo_2)
114 | n_1 <- length(promo_1)
115 | n_2 <- length(promo_2)
116 | 
117 | df_1_2 <- n_1 + n_2 - 2
118 | 
119 | t_val <- (
120 |   mean_1 - mean_2
121 | ) / sqrt(
122 |   (std_1**2/n_1 + std_2**2/n_2)
123 | )
124 | 
125 | p_val <- 2 * pt(t_val, df_1_2, lower=FALSE)
126 | 
127 | # - using t.test
128 | t.test(
129 |   promo_1, 
130 |   promo_2
131 | )
132 | 
133 | # Promotion 1 vs. 3
134 | promo_1 <- df[which(df$Promotion == 1),]$SalesInThousands
135 | promo_3 <- df[which(df$Promotion == 3),]$SalesInThousands
136 | 
137 | mean_1 <- mean(promo_1)
138 | mean_3 <- mean(promo_3)
139 | std_1 <- sd(promo_1)
140 | std_3 <- sd(promo_3)
141 | n_1 <- length(promo_1)
142 | n_3 <- length(promo_3)
143 | df_1_3 <- n_1 + n_3 - 2
144 | 
145 | t_val <- (
146 |   mean_1 - mean_3
147 | ) / sqrt(
148 |   (std_1**2/n_1 + std_3**2/n_3)
149 | )
150 | 
151 | p_val <- 2 * pt(t_val, df_1_3, lower=FALSE)
152 | 
153 | # - using t.test
154 | t.test(
155 |   promo_1, 
156 |   promo_3
157 | )
158 | 
159 | 
160 | 
161 | 
162 | 


--------------------------------------------------------------------------------
/ch.2/R/ConversionRate.R:
--------------------------------------------------------------------------------
  1 | library(dplyr)  # Data Wrangling and Manipulation
  2 | library(ggplot2)
  3 | 
  4 | conversionsDF <- read.csv(
  5 |   file="~/Documents/data-science-for-marketing/ch.2/data/bank-additional-full.csv", 
  6 |   header=TRUE, 
  7 |   sep=";"
  8 | )
  9 | 
 10 | # Shape of conversionsDF
 11 | dim(conversionsDF)
 12 | # Quick look at conversionsDF
 13 | head(conversionsDF)
 14 | 
 15 | # Encode conversions as 0s and 1s
 16 | conversionsDF$conversion <- as.integer(conversionsDF$y) - 1
 17 | tail(conversionsDF)
 18 | 
 19 | #### 1. Aggregate Conversion Rate ####
 20 | sprintf("total conversions: %i out of %i", sum(conversionsDF$conversion), nrow(conversionsDF))
 21 | sprintf("conversion rate: %0.2f%%", sum(conversionsDF$conversion)/nrow(conversionsDF)*100.0)
 22 | 
 23 | #### 2. Conversion Rates by Number of Contacts ####
 24 | conversionsByNumContact <- conversionsDF %>% 
 25 |   group_by(NumContact=campaign) %>% 
 26 |   summarise(TotalCount=n(), NumConversions=sum(conversion)) %>%
 27 |   mutate(ConversionRate=NumConversions/TotalCount*100.0)
 28 | 
 29 | head(conversionsByNumContact, 10)
 30 | 
 31 | # line chart
 32 | ggplot(data=head(conversionsByNumContact, 10), aes(x=NumContact, y=ConversionRate)) +
 33 |   geom_line() +
 34 |   ggtitle('Conversion Rates by Number of Contacts') +
 35 |   xlab("Number of Contacts") +
 36 |   ylab("Conversion Rate (%)") +
 37 |   ylim(c(0, 15)) +
 38 |   theme(plot.title = element_text(hjust = 0.5))
 39 | 
 40 | 
 41 | #### 3. Conversion Rates by Age ####
 42 | 
 43 | # a. by age
 44 | conversionsByAge <- conversionsDF %>% 
 45 |   group_by(Age=age) %>% 
 46 |   summarise(TotalCount=n(), NumConversions=sum(conversion)) %>%
 47 |   mutate(ConversionRate=NumConversions/TotalCount*100.0)
 48 | 
 49 | head(conversionsByAge)
 50 | 
 51 | # line chart
 52 | ggplot(data=conversionsByAge, aes(x=Age, y=ConversionRate)) +
 53 |   geom_line() +
 54 |   ggtitle('Conversion Rates by Age') +
 55 |   xlab("Age") +
 56 |   ylab("Conversion Rate (%)") +
 57 |   theme(plot.title = element_text(hjust = 0.5))
 58 | 
 59 | # b. by age groups
 60 | conversionsByAgeGroup <- conversionsDF %>% 
 61 |   group_by(AgeGroup=cut(age, breaks= seq(20, 70, by = 10)) ) %>% 
 62 |   summarise(TotalCount=n(), NumConversions=sum(conversion)) %>%
 63 |   mutate(ConversionRate=NumConversions/TotalCount*100.0)
 64 | 
 65 | conversionsByAgeGroup$AgeGroup <- as.character(conversionsByAgeGroup$AgeGroup)
 66 | conversionsByAgeGroup$AgeGroup[6] <- "70+"
 67 | 
 68 | # bar chart
 69 | ggplot(conversionsByAgeGroup, aes(x=AgeGroup, y=ConversionRate)) +
 70 |   geom_bar(width=0.5, stat="identity") +
 71 |   ggtitle('Conversion Rates by Age Groups') +
 72 |   xlab("Age") +
 73 |   ylab("Conversion Rate (%)") +
 74 |   theme(plot.title = element_text(hjust = 0.5)) 
 75 | 
 76 | #### 4. Conversions vs. Non-Conversions ####
 77 | 
 78 | # 4.1. Marital Status
 79 | conversionsByMaritalStatus <- conversionsDF %>% 
 80 |   group_by(Marital=marital, Conversion=conversion) %>% 
 81 |   summarise(Count=n())
 82 | 
 83 | conversionsByMaritalStatus
 84 | 
 85 | # pie chart
 86 | ggplot(conversionsByMaritalStatus, aes(x="", y=Count, fill=Marital)) + 
 87 |   geom_bar(width=1, stat = "identity", position=position_fill()) +
 88 |   geom_text(aes(x=1.25, label=Count), position=position_fill(vjust = 0.5)) +
 89 |   coord_polar("y") +
 90 |   facet_wrap(~Conversion) +
 91 |   ggtitle('Marital Status (0: Non Conversions, 1: Conversions)') +
 92 |   theme(
 93 |     axis.title.x=element_blank(),
 94 |     axis.title.y=element_blank(),
 95 |     plot.title=element_text(hjust=0.5),
 96 |     legend.position='bottom'
 97 |   )
 98 | 
 99 | # 4.2. Education
100 | conversionsByEducation <- conversionsDF %>% 
101 |   group_by(Education=education, Conversion=conversion) %>% 
102 |   summarise(Count=n())
103 | 
104 | conversionsByEducation
105 | 
106 | # pie chart
107 | ggplot(conversionsByEducation, aes(x="", y=Count, fill=Education)) + 
108 |   geom_bar(width=1, stat = "identity", position=position_fill()) +
109 |   geom_text(aes(x=1.25, label=Count), position=position_fill(vjust = 0.5)) +
110 |   coord_polar("y") +
111 |   facet_wrap(~Conversion) +
112 |   ggtitle('Education (0: Non Conversions, 1: Conversions)') +
113 |   theme(
114 |     axis.title.x=element_blank(),
115 |     axis.title.y=element_blank(),
116 |     plot.title=element_text(hjust=0.5),
117 |     legend.position='bottom'
118 |   )
119 | 
120 | # 4.3. Last Contact Duration
121 | conversionsDF$duration <- conversionsDF$duration / (60*60)
122 | 
123 | ggplot(conversionsDF, aes(x="", y=duration)) + 
124 |   geom_boxplot() +
125 |   facet_wrap(~conversion) +
126 |   ylab("duration (in hours)") +
127 |   xlab("0: Non-Conversion, 1: Conversion") +
128 |   ggtitle("Conversion vs. Non-Conversions: Last Contact Duration") +
129 |   theme(plot.title=element_text(hjust=0.5))
130 | 
131 | #### 5. Conversions by Age Groups & Marital Status ####
132 | conversionsByAgeMarital <- conversionsDF %>% 
133 |   group_by(AgeGroup=cut(age, breaks= seq(20, 70, by = 10)), Marital=marital) %>% 
134 |   summarise(Count=n(), NumConversions=sum(conversion)) %>%
135 |   mutate(TotalCount=sum(Count)) %>%
136 |   mutate(ConversionRate=NumConversions/TotalCount)
137 | 
138 | conversionsByAgeMarital$AgeGroup <- as.character(conversionsByAgeMarital$AgeGroup)
139 | conversionsByAgeMarital$AgeGroup[is.na(conversionsByAgeMarital$AgeGroup)] <- "70+"
140 | 
141 | # bar chart
142 | ggplot(conversionsByAgeMarital, aes(x=AgeGroup, y=ConversionRate, fill=Marital)) + 
143 |   geom_bar(width=0.5, stat="identity", position="dodge") +
144 |   ylab("Conversion Rate (%)") +
145 |   xlab("Age") +
146 |   ggtitle("Conversion Rates by Age and Marital Status") +
147 |   theme(plot.title=element_text(hjust=0.5))
148 | 
149 | # stacked bar chart
150 | ggplot(conversionsByAgeMarital, aes(x=AgeGroup, y=ConversionRate, fill=Marital)) + 
151 |   geom_bar(width=0.5, stat="identity", position="stack") +
152 |   ylab("Conversion Rate (%)") +
153 |   xlab("Age") +
154 |   ggtitle("Conversion Rates by Age and Marital Status") +
155 |   theme(plot.title=element_text(hjust=0.5))
156 |   
157 | 


--------------------------------------------------------------------------------
/ch.3/R/RegressionAnalysis.R:
--------------------------------------------------------------------------------
  1 | library(dplyr)
  2 | library(ggplot2)
  3 | 
  4 | # Load data
  5 | df <- read.csv(
  6 |   file="~/Documents/data-science-for-marketing/ch.3/data/WA_Fn-UseC_-Marketing-Customer-Value-Analysis.csv", 
  7 |   header=TRUE, 
  8 |   sep=","
  9 | )
 10 | 
 11 | #### 1. Engagement Rate ####
 12 | 
 13 | # Encode Response as 0s and 1s
 14 | df$Engaged <- as.integer(df$Response) - 1
 15 | 
 16 | engagementRate <- df %>% 
 17 |   group_by(Engaged) %>% 
 18 |   summarise(Count=n()) %>%
 19 |   mutate(Percentage=Count/nrow(df)*100.0)
 20 | 
 21 | engagementRate
 22 | 
 23 | # Transpose
 24 | transposed <- t(engagementRate)
 25 | 
 26 | colnames(transposed) <- engagementRate$Engaged
 27 | transposed <- transposed[-1,]
 28 | transposed
 29 | 
 30 | #### 2. Renewal Offer Type ####
 31 | renewalOfferType <- df %>% 
 32 |   group_by(Engaged, Type=Renew.Offer.Type) %>% 
 33 |   summarise(Count=n())
 34 | 
 35 | renewalOfferType
 36 | 
 37 | # pie chart
 38 | ggplot(renewalOfferType, aes(x="", y=Count, fill=Type)) + 
 39 |   geom_bar(width=1, stat = "identity", position=position_fill()) +
 40 |   geom_text(aes(x=1.25, label=Count), position=position_fill(vjust = 0.5)) +
 41 |   coord_polar("y") +
 42 |   facet_wrap(~Engaged) +
 43 |   ggtitle('Renwal Offer Type (0: Not Engaged, 1: Engaged)') +
 44 |   theme(
 45 |     axis.title.x=element_blank(),
 46 |     axis.title.y=element_blank(),
 47 |     plot.title=element_text(hjust=0.5),
 48 |     legend.position='bottom'
 49 |   )
 50 | 
 51 | #### 3. Sales Channel ####
 52 | salesChannel <- df %>% 
 53 |   group_by(Engaged, Channel=Sales.Channel) %>% 
 54 |   summarise(Count=n())
 55 | 
 56 | salesChannel
 57 | 
 58 | # pie chart
 59 | ggplot(salesChannel, aes(x="", y=Count, fill=Channel)) + 
 60 |   geom_bar(width=1, stat = "identity", position=position_fill()) +
 61 |   geom_text(aes(x=1.25, label=Count), position=position_fill(vjust = 0.5)) +
 62 |   coord_polar("y") +
 63 |   facet_wrap(~Engaged) +
 64 |   ggtitle('Sales Channel (0: Not Engaged, 1: Engaged)') +
 65 |   theme(
 66 |     axis.title.x=element_blank(),
 67 |     axis.title.y=element_blank(),
 68 |     plot.title=element_text(hjust=0.5),
 69 |     legend.position='bottom'
 70 |   )
 71 | 
 72 | #### 4. Total Claim Amount ####
 73 | ggplot(df, aes(x="", y=Total.Claim.Amount)) + 
 74 |   geom_boxplot() +
 75 |   facet_wrap(~Engaged) +
 76 |   ylab("Total Claim Amount") +
 77 |   xlab("0: Not Engaged, 1: Engaged") +
 78 |   ggtitle("Engaed vs. Not Engaged: Total Claim Amount") +
 79 |   theme(plot.title=element_text(hjust=0.5))
 80 | 
 81 | # without outliers
 82 | ggplot(df, aes(x="", y=Total.Claim.Amount)) + 
 83 |   geom_boxplot(outlier.shape = NA) +
 84 |   scale_y_continuous(limits = quantile(df$Total.Claim.Amount, c(0.1, 0.9))) +
 85 |   facet_wrap(~Engaged) +
 86 |   ylab("Total Claim Amount") +
 87 |   xlab("0: Not Engaged, 1: Engaged") +
 88 |   ggtitle("Engaed vs. Not Engaged: Total Claim Amount") +
 89 |   theme(plot.title=element_text(hjust=0.5))
 90 | 
 91 | #### 5. Income ####
 92 | 
 93 | # boxplot
 94 | ggplot(df, aes(x="", y=Income)) + 
 95 |   geom_boxplot() +
 96 |   facet_wrap(~Engaged) +
 97 |   ylab("Income") +
 98 |   xlab("0: Not Engaged, 1: Engaged") +
 99 |   ggtitle("Engaed vs. Not Engaged: Income") +
100 |   theme(plot.title=element_text(hjust=0.5))
101 | 
102 | # summary statistics
103 | incomeDescription <- df %>% 
104 |   group_by(Engaged) %>% 
105 |   summarise(
106 |     Min=min(Income), Q1=quantile(Income, 0.25), 
107 |     Median=median(Income), Q3=quantile(Income, 0.75),
108 |     Max=max(Income)
109 |   )
110 | 
111 | incomeDescription
112 | 
113 | 
114 | #### 6. Regression Analysis ####
115 | # summary statistics per column
116 | summary(df)
117 | # get data types of each column
118 | sapply(df, class)
119 | 
120 | ## 6.1. Continuous Variables ##
121 | 
122 | # get numeric columns
123 | continuousDF <- select_if(df, is.numeric)
124 | colnames(continuousDF)
125 | 
126 | # Fit regression model with continuous variables
127 | logit.fit <- glm(Engaged ~ ., data = continuousDF, family = binomial)
128 | summary(logit.fit)
129 | 
130 | 
131 | ## 6.2. Categorical Variables ##
132 | 
133 | # a. Education
134 | # Fit regression model with Education factor variables
135 | logit.fit <- glm(Engaged ~ factor(Education), data = df, family = binomial)
136 | summary(logit.fit)
137 | 
138 | # b. Education + Gender
139 | # Fit regression model with Education & Gender variables
140 | logit.fit <- glm(Engaged ~ factor(Education) + factor(Gender), data = df, family = binomial)
141 | summary(logit.fit)
142 | 
143 | 
144 | ## 6.3. Continuous & Categorical Variables ##
145 | 
146 | continuousDF$Gender <- factor(df$Gender)
147 | continuousDF$Education <- factor(df$Education)
148 | 
149 | # Fit regression model with Education & Gender variables
150 | logit.fit <- glm(Engaged ~ ., data = continuousDF, family = binomial)
151 | summary(logit.fit)
152 | 
153 | 


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/ch.4/R/FromEngagementToConversions.R:
--------------------------------------------------------------------------------
  1 | library(dplyr)
  2 | library(ggplot2)
  3 | 
  4 | # install.packages('rattle')
  5 | library(rattle)	
  6 | 
  7 | # install.packages('rpart')
  8 | library(rpart) # used to build decision tree
  9 | 
 10 | # install.packages('rpart.plot')
 11 | library(rpart.plot)
 12 | 
 13 | #### 1. Load Data ####
 14 | df <- read.csv(
 15 |   file="~/Documents/data-science-for-marketing/ch.4/data/bank-full.csv", 
 16 |   header=TRUE, 
 17 |   sep=";"
 18 | )
 19 | 
 20 | # Encode conversions as 0s and 1s
 21 | df$conversion <- as.integer(df$y) - 1
 22 | 
 23 | head(df)
 24 | 
 25 | #### 2. Data Analysis ####
 26 | 
 27 | # column names
 28 | colnames(df)
 29 | 
 30 | #### 2.1. Conversion Rate ####
 31 | sprintf("total conversions: %i out of %i", sum(df$conversion), nrow(df))
 32 | sprintf("conversion rate: %0.2f%%", sum(df$conversion)/nrow(df)*100.0)
 33 | 
 34 | #### 2.2. Conversion Rates by Marital Status ####
 35 | conversionsByMarital <- df %>% 
 36 |   group_by(Marital=marital) %>% 
 37 |   summarise(Count=n(), NumConversions=sum(conversion)) %>%
 38 |   mutate(ConversionRate=NumConversions/Count*100.0)
 39 | 
 40 | conversionsByMarital
 41 | 
 42 | ggplot(conversionsByMarital, aes(x=Marital, y=ConversionRate)) +
 43 |   geom_bar(width=0.5, stat="identity") +
 44 |   ggtitle('Conversion Rates by Marital Status') +
 45 |   xlab("Marital Status") +
 46 |   ylab("Conversion Rate (%)") +
 47 |   theme(plot.title = element_text(hjust = 0.5)) 
 48 | 
 49 | #### 2.2. Conversion Rates by Job ####
 50 | conversionsByJob <- df %>% 
 51 |   group_by(Job=job) %>% 
 52 |   summarise(Count=n(), NumConversions=sum(conversion)) %>%
 53 |   mutate(ConversionRate=NumConversions/Count*100.0)
 54 | 
 55 | conversionsByJob
 56 | 
 57 | ggplot(conversionsByJob, aes(x=Job, y=ConversionRate)) +
 58 |   geom_bar(width=0.5, stat="identity") +
 59 |   coord_flip() +
 60 |   ggtitle('Conversion Rates by Job') +
 61 |   xlab("Job") +
 62 |   ylab("Conversion Rate (%)") +
 63 |   theme(plot.title = element_text(hjust = 0.5)) 
 64 | 
 65 | #### 2.3. Default Rates by Conversions ####
 66 | defaultByConversion <- df %>% 
 67 |   group_by(Default=default, Conversion=conversion) %>% 
 68 |   summarise(Count=n())
 69 | 
 70 | defaultByConversion
 71 | 
 72 | ggplot(defaultByConversion, aes(x="", y=Count, fill=Default)) + 
 73 |   geom_bar(width=1, stat = "identity", position=position_fill()) +
 74 |   geom_text(aes(x=1.25, label=Count), position=position_fill(vjust = 0.5)) +
 75 |   coord_polar("y") +
 76 |   facet_wrap(~Conversion) +
 77 |   ggtitle('Default (0: Non Conversions, 1: Conversions)') +
 78 |   theme(
 79 |     axis.title.x=element_blank(),
 80 |     axis.title.y=element_blank(),
 81 |     plot.title=element_text(hjust=0.5),
 82 |     legend.position='bottom'
 83 |   )
 84 | 
 85 | 
 86 | #### 2.4. Bank Balance by Conversions ####
 87 | ggplot(df, aes(x="", y=balance)) + 
 88 |   geom_boxplot() +
 89 |   facet_wrap(~conversion) +
 90 |   ylab("balance") +
 91 |   xlab("0: Non-Conversion, 1: Conversion") +
 92 |   ggtitle("Conversion vs. Non-Conversions: Balance") +
 93 |   theme(plot.title=element_text(hjust=0.5))
 94 | 
 95 | ggplot(df, aes(x="", y=balance)) + 
 96 |   geom_boxplot(outlier.shape = NA) +
 97 |   scale_y_continuous(limits = c(-2000, 5000)) +
 98 |   facet_wrap(~conversion) +
 99 |   ylab("balance") +
100 |   xlab("0: Non-Conversion, 1: Conversion") +
101 |   ggtitle("Conversion vs. Non-Conversions: Balance") +
102 |   theme(plot.title=element_text(hjust=0.5))
103 | 
104 | #### 2.5. Conversions by Number of Contacts ####
105 | conversionsByNumContacts <- df %>% 
106 |   group_by(Campaign=campaign) %>% 
107 |   summarise(Count=n(), NumConversions=sum(conversion)) %>%
108 |   mutate(ConversionRate=NumConversions/Count*100.0)
109 | 
110 | conversionsByNumContacts
111 | 
112 | ggplot(conversionsByNumContacts, aes(x=Campaign, y=ConversionRate)) +
113 |   geom_bar(width=0.5, stat="identity") +
114 |   ggtitle('Conversion Rates by Number of Contacts') +
115 |   xlab("Number of Contacts") +
116 |   ylab("Conversion Rate (%)") +
117 |   theme(plot.title = element_text(hjust = 0.5)) 
118 | 
119 | #### 3. Encoding Categorical Variables ####
120 | rapply(df, function(x) length(unique(x)))
121 | 
122 | #### 3.1. encoding 'month' ####
123 | # unique values
124 | unique(df$month)
125 | # convert to numbers
126 | months = lapply(month.abb, function(x) tolower(x))
127 | months
128 | # test
129 | match(unique(df$month), months)
130 | 
131 | # encode
132 | df$month <- match(df$month, months)
133 | # check
134 | df %>% 
135 |   group_by(month) %>% 
136 |   summarise(Count=n())
137 | 
138 | #### 3.2. encoding job, housing, marital ####
139 | df$job <- factor(df$job)
140 | df$housing <- factor(df$housing)
141 | df$marital <- factor(df$marital)
142 | 
143 | 
144 | #### 4. Fitting Decision Trees ####
145 | 
146 | # grow tree 
147 | fit <- rpart(
148 |   conversion ~ age + balance + campaign + previous + housing + job + marital,
149 |   method="class", 
150 |   data=df,
151 |   control=rpart.control(maxdepth=4, cp=0.0001)
152 | )
153 | 
154 | # plot tree 
155 | fancyRpartPlot(fit)
156 | 


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/ch.5/R/ProductAnalytics.R:
--------------------------------------------------------------------------------
  1 | library(dplyr)
  2 | library(ggplot2)
  3 | 
  4 | # install.packages("readxl")
  5 | library(readxl)
  6 | # install.packages("lubridate")
  7 | library(lubridate)
  8 | 
  9 | 
 10 | #### 1. Load Data ####
 11 | df <- read_excel(
 12 |   path="~/Documents/research/data-science-marketing/ch.5/data/Online Retail.xlsx", 
 13 |   sheet="Online Retail"
 14 | )
 15 | 
 16 | #### 2. Product Analytics ####
 17 | 
 18 | #### - Quantity Distribution ####
 19 | summary(df$Quantity)
 20 | 
 21 | ggplot(df, aes(x="", y=Quantity)) + 
 22 |   geom_boxplot(outlier.shape = NA) +
 23 |   ylim(c(-15, 25))+
 24 |   ylab("order quantity") +
 25 |   xlab("") +
 26 |   ggtitle("Quantity Distribution") +
 27 |   theme(plot.title=element_text(hjust=0.5))
 28 | 
 29 | # filter out orders with negative quantity (cancel orders)
 30 | dim(df[which(df$Quantity > 0),])
 31 | dim(df)
 32 | 
 33 | df <- df[which(df$Quantity > 0),]
 34 | dim(df)
 35 | 
 36 | #### 2.1. Time-series Number of Orders ####
 37 | timeSeriesNumInvoices <- df %>%
 38 |   group_by(InvoiceDate=floor_date(InvoiceDate, "month")) %>%
 39 |   summarise(NumOrders=n_distinct(InvoiceNo))
 40 | 
 41 | ggplot(timeSeriesNumInvoices, aes(x=InvoiceDate, y=NumOrders)) +
 42 |   geom_line() +
 43 |   ylim(c(0, max(timeSeriesNumInvoices$NumOrders) + 1000)) +
 44 |   ylab("number of orders") +
 45 |   xlab("date") +
 46 |   ggtitle("Number of Orders over Time") +
 47 |   theme(plot.title=element_text(hjust=0.5))
 48 | 
 49 | summary(df[which(df$InvoiceDate >= as.Date("2011-12-01")),"InvoiceDate"])
 50 | 
 51 | dim(df[which(df$InvoiceDate < as.Date("2011-12-01")),])
 52 | dim(df)
 53 | 
 54 | df <- df[which(df$InvoiceDate < as.Date("2011-12-01")),]
 55 | dim(df)
 56 | 
 57 | timeSeriesNumInvoices <- df %>%
 58 |   group_by(InvoiceDate=floor_date(InvoiceDate, "month")) %>%
 59 |   summarise(NumOrders=n_distinct(InvoiceNo))
 60 | 
 61 | ggplot(timeSeriesNumInvoices, aes(x=InvoiceDate, y=NumOrders)) +
 62 |   geom_line()+
 63 |   ylim(c(0, max(timeSeriesNumInvoices$NumOrders) + 100)) +
 64 |   ylab("number of orders") +
 65 |   xlab("date") +
 66 |   ggtitle("Number of Orders over Time") +
 67 |   theme(plot.title=element_text(hjust=0.5))
 68 | 
 69 | 
 70 | #### 2.2. Time-series Revenue ####
 71 | df$Sales <- df$Quantity * df$UnitPrice
 72 | 
 73 | timeSeriesRevenue <- df %>%
 74 |   group_by(InvoiceDate=floor_date(InvoiceDate, "month")) %>%
 75 |   summarise(Sales=sum(Sales))
 76 | 
 77 | ggplot(timeSeriesRevenue, aes(x=InvoiceDate, y=Sales)) +
 78 |   geom_line() +
 79 |   ylim(c(0, max(timeSeriesRevenue$Sales) + 10000)) +
 80 |   ylab("sales") +
 81 |   xlab("date") +
 82 |   ggtitle("Revenue over Time") +
 83 |   theme(plot.title=element_text(hjust=0.5))
 84 | 
 85 | #### 2.3. Revenue from Repeat Customers ####
 86 | 
 87 | # Repeat Customers
 88 | invoiceCustomerDF <- df %>%
 89 |   group_by(InvoiceNo, InvoiceDate) %>%
 90 |   summarise(CustomerID=max(CustomerID), Sales=sum(Sales))
 91 | 
 92 | timeSeriesCustomerDF <- invoiceCustomerDF %>%
 93 |   group_by(InvoiceDate=floor_date(InvoiceDate, "month"), CustomerID) %>%
 94 |   summarise(Count=n_distinct(InvoiceNo), Sales=sum(Sales))
 95 | 
 96 | repeatCustomers <- na.omit(timeSeriesCustomerDF[which(timeSeriesCustomerDF$Count > 1),])
 97 | 
 98 | timeSeriesRepeatCustomers <- repeatCustomers %>%
 99 |   group_by(InvoiceDate) %>%
100 |   summarise(Count=n_distinct(CustomerID), Sales=sum(Sales))
101 | 
102 | # Unique Customers
103 | timeSeriesUniqCustomers <- df %>%
104 |   group_by(InvoiceDate=floor_date(InvoiceDate, "month")) %>%
105 |   summarise(Count=n_distinct(CustomerID))
106 | 
107 | timeSeriesRepeatCustomers$Perc <- timeSeriesRepeatCustomers$Sales / timeSeriesRevenue$Sales*100.0
108 | timeSeriesRepeatCustomers$Total <- timeSeriesUniqCustomers$Count
109 | 
110 | ggplot(timeSeriesRepeatCustomers)  + 
111 |   geom_line(aes(x=InvoiceDate, y=Total), stat="identity", color="navy") +
112 |   geom_line(aes(x=InvoiceDate, y=Count), stat="identity", color="orange") +
113 |   geom_bar(aes(x=InvoiceDate, y=Perc*20), stat="identity", fill='gray', alpha=0.5) +
114 |   scale_y_continuous(sec.axis = sec_axis(~./20, name="Percentage (%)")) +
115 |   ggtitle("Number of Unique vs. Repeat & Revenue from Repeat Customers") +
116 |   theme(plot.title=element_text(hjust=0.5))
117 | 
118 | #### 2.4. Popular Items Over Time ####
119 | popularItems <- df %>%
120 |   group_by(InvoiceDate=floor_date(InvoiceDate, "month"), StockCode) %>%
121 |   summarise(Quantity=sum(Quantity))
122 | 
123 | top5Items <- popularItems[
124 |     which(popularItems$InvoiceDate == as.Date("2011-11-01")),
125 |   ] %>%
126 |   arrange(desc(Quantity)) %>%
127 |   head(5)
128 | 
129 | timeSeriesTop5 <- popularItems[
130 |   which(popularItems$StockCode %in% top5Items$StockCode),
131 | ]
132 | 
133 | ggplot(timeSeriesTop5, aes(x=InvoiceDate, y=Quantity, color=StockCode)) +
134 |   geom_line() +
135 |   ylab("number of purchases") +
136 |   xlab("date") +
137 |   ggtitle("Top 5 Popular Items over Time") +
138 |   theme(plot.title=element_text(hjust=0.5))
139 | 
140 | 
141 | 
142 | 
143 | 


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/ch.6/R/ProductRecommendation.R:
--------------------------------------------------------------------------------
  1 | library(dplyr)
  2 | library(readxl)
  3 | 
  4 | #### 1. Load Data ####
  5 | df <- read_excel(
  6 |   path="~/Documents/research/data-science-marketing/ch.6/data/Online Retail.xlsx", 
  7 |   sheet="Online Retail"
  8 | )
  9 | 
 10 | # ignore cancel orders
 11 | df <- df[which(df$Quantity > 0),]
 12 | 
 13 | #### 2. Data Preparation ####
 14 | 
 15 | ## 2.1. Handle NaNs in CustomerID field
 16 | 
 17 | # there are 133,361 records with no CustomerID
 18 | sum(is.na(df$CustomerID))
 19 | # sneak peek at records with no CustomerID
 20 | head(df[which(is.na(df$CustomerID)),])
 21 | 
 22 | # current DataFrame shape
 23 | dim(df)
 24 | 
 25 | # remove records with NA
 26 | df <- na.omit(df)
 27 | dim(df)
 28 | 
 29 | ## 2.2. Customer-Item Matrix
 30 | # install.packages("reshape2")
 31 | library(reshape2)
 32 | 
 33 | customerItemMatrix <- dcast(
 34 |   df, CustomerID ~ StockCode, value.var="Quantity"
 35 | )
 36 | # 0-1 encode 
 37 | encode_fn <- function(x) {as.integer(x > 0)}
 38 | customerItemMatrix <- customerItemMatrix %>% 
 39 |   mutate_at(vars(-CustomerID), funs(encode_fn))
 40 | 
 41 | #### 3. Collaborative Filtering ###
 42 | # install.packages("coop")
 43 | library(coop)
 44 | 
 45 | ## 3.1. User-based Collaborative Filtering
 46 | 
 47 | # User-to-User Similarity Matrix
 48 | userToUserSimMatrix <- cosine(
 49 |   as.matrix(
 50 |     # excluding CustomerID column
 51 |     t(customerItemMatrix[, 2:dim(customerItemMatrix)[2]])
 52 |   )
 53 | )
 54 | colnames(userToUserSimMatrix) <- customerItemMatrix$CustomerID
 55 | 
 56 | # Making Recommendations
 57 | top10SimilarCustomersTo12350 <- customerItemMatrix$CustomerID[
 58 |   order(userToUserSimMatrix[,"12350"], decreasing = TRUE)[1:11]
 59 | ]
 60 | 
 61 | itemsBoughtByA <- customerItemMatrix[
 62 |   which(customerItemMatrix$CustomerID == "12350"),
 63 | ]
 64 | itemsBoughtByA <- colnames(customerItemMatrix)[which(itemsBoughtByA != 0)]
 65 | 
 66 | itemsBoughtByB <- customerItemMatrix[
 67 |   which(customerItemMatrix$CustomerID == "17935"),
 68 | ]
 69 | itemsBoughtByB <- colnames(customerItemMatrix)[which(itemsBoughtByB != 0)]
 70 | 
 71 | itemsToRecommendToB <- setdiff(itemsBoughtByA, itemsBoughtByB)
 72 | itemsToRecommendToB
 73 | 
 74 | itemsToRecommendToBDescriptions <- unique(
 75 |   df[
 76 |     which(df$StockCode %in% itemsToRecommendToB), 
 77 |     c("StockCode", "Description")
 78 |     ]
 79 | )
 80 | itemsToRecommendToBDescriptions <- itemsToRecommendToBDescriptions[
 81 |   match(itemsToRecommendToB, itemsToRecommendToBDescriptions$StockCode),
 82 | ]
 83 | 
 84 | 
 85 | ## 3.2. Item-based Collaborative Filtering
 86 | 
 87 | # Item-to-Item Similarity Matrix
 88 | itemToItemSimMatrix <- cosine(
 89 |   as.matrix(
 90 |     # excluding CustomerID column
 91 |     customerItemMatrix[, 2:dim(customerItemMatrix)[2]]
 92 |   )
 93 | )
 94 | 
 95 | # Making Recommendations
 96 | top10SimilarItemsTo23166 <- colnames(itemToItemSimMatrix)[
 97 |   order(itemToItemSimMatrix[,"23166"], decreasing = TRUE)[1:11]
 98 | ]
 99 | top10SimilarItemsTo23166
100 | 
101 | top10SimilarItemDescriptions <- unique(
102 |   df[
103 |     which(df$StockCode %in% top10SimilarItemsTo23166), 
104 |     c("StockCode", "Description")
105 |   ]
106 | )
107 | top10SimilarItemDescriptions <- top10SimilarItemDescriptions[
108 |   match(top10SimilarItemsTo23166, top10SimilarItemDescriptions$StockCode),
109 | ]
110 | 
111 | 
112 | 
113 | 


--------------------------------------------------------------------------------
/ch.7/R/CustomerBehaviors.R:
--------------------------------------------------------------------------------
  1 | library(dplyr)
  2 | library(ggplot2)
  3 | 
  4 | #### 1. Load Data ####
  5 | df <- read.csv(
  6 |   file="~/Documents/data-science-for-marketing/ch.7/data/WA_Fn-UseC_-Marketing-Customer-Value-Analysis.csv", 
  7 |   header=TRUE
  8 | )
  9 | 
 10 | # Encode engaged customers as 0s and 1s
 11 | df$Engaged <- as.integer(df$Response) - 1
 12 | 
 13 | #### 2. Analytics on Engaged Customers ####
 14 | 
 15 | ## - Overall Engagement Rates ##
 16 | engagementRate <- df %>% group_by(Response) %>%
 17 |   summarise(Count=n())  %>%
 18 |   mutate(EngagementRate=Count/nrow(df)*100.0)
 19 | 
 20 | ggplot(engagementRate, aes(x=Response, y=EngagementRate)) +
 21 |   geom_bar(width=0.5, stat="identity") +
 22 |   ggtitle('Engagement Rate') +
 23 |   xlab("Engaged") +
 24 |   ylab("Percentage (%)") +
 25 |   theme(plot.title = element_text(hjust = 0.5)) 
 26 | 
 27 | 
 28 | ## - Engagement Rates by Offer Type ##
 29 | engagementRateByOfferType <- df %>% 
 30 |   group_by(Renew.Offer.Type) %>%
 31 |   summarise(Count=n(), NumEngaged=sum(Engaged))  %>%
 32 |   mutate(EngagementRate=NumEngaged/Count*100.0)
 33 | 
 34 | ggplot(engagementRateByOfferType, aes(x=Renew.Offer.Type, y=EngagementRate)) +
 35 |   geom_bar(width=0.5, stat="identity") +
 36 |   ggtitle('Engagement Rates by Offer Type') +
 37 |   xlab("Offer Type") +
 38 |   ylab("Engagement Rate (%)") +
 39 |   theme(plot.title = element_text(hjust = 0.5)) 
 40 | 
 41 | 
 42 | ## - Offer Type & Vehicle Class ##
 43 | engagementRateByOfferTypeVehicleClass <- df %>% 
 44 |   group_by(Renew.Offer.Type, Vehicle.Class) %>%
 45 |   summarise(NumEngaged=sum(Engaged))  %>%
 46 |   left_join(engagementRateByOfferType[,c("Renew.Offer.Type", "Count")], by="Renew.Offer.Type") %>%
 47 |   mutate(EngagementRate=NumEngaged/Count*100.0)
 48 | 
 49 | ggplot(engagementRateByOfferTypeVehicleClass, aes(x=Renew.Offer.Type, y=EngagementRate, fill=Vehicle.Class)) +
 50 |   geom_bar(width=0.5, stat="identity", position = "dodge") +
 51 |   ggtitle('Engagement Rates by Offer Type & Vehicle Class') +
 52 |   xlab("Offer Type") +
 53 |   ylab("Engagement Rate (%)") +
 54 |   theme(plot.title = element_text(hjust = 0.5)) 
 55 | 
 56 | 
 57 | ## - Engagement Rates by Sales Channel ##
 58 | engagementRateBySalesChannel <- df %>% 
 59 |   group_by(Sales.Channel) %>%
 60 |   summarise(Count=n(), NumEngaged=sum(Engaged))  %>%
 61 |   mutate(EngagementRate=NumEngaged/Count*100.0)
 62 | 
 63 | ggplot(engagementRateBySalesChannel, aes(x=Sales.Channel, y=EngagementRate)) +
 64 |   geom_bar(width=0.5, stat="identity") +
 65 |   ggtitle('Engagement Rates by Sales Channel') +
 66 |   xlab("Sales Channel") +
 67 |   ylab("Engagement Rate (%)") +
 68 |   theme(plot.title = element_text(hjust = 0.5)) 
 69 | 
 70 | 
 71 | ## - Sales Channel & Vehicle Size ##
 72 | engagementRateBySalesChannelVehicleSize <- df %>% 
 73 |   group_by(Sales.Channel, Vehicle.Size) %>%
 74 |   summarise(NumEngaged=sum(Engaged))  %>%
 75 |   left_join(engagementRateBySalesChannel[,c("Sales.Channel", "Count")], by="Sales.Channel") %>%
 76 |   mutate(EngagementRate=NumEngaged/Count*100.0)
 77 | 
 78 | ggplot(engagementRateBySalesChannelVehicleSize, aes(x=Sales.Channel, y=EngagementRate, fill=Vehicle.Size)) +
 79 |   geom_bar(width=0.5, stat="identity", position = "dodge") +
 80 |   ggtitle('Engagement Rates by Sales Channel & Vehicle Size') +
 81 |   xlab("Sales Channel") +
 82 |   ylab("Engagement Rate (%)") +
 83 |   theme(plot.title = element_text(hjust = 0.5)) 
 84 | 
 85 | 
 86 | ## - Engagement Rates by Months Since Policy Inception ##
 87 | engagementRateByPolicyAge <- df %>% 
 88 |   group_by(Months.Since.Policy.Inception) %>%
 89 |   summarise(Count=n(), NumEngaged=sum(Engaged))  %>%
 90 |   mutate(EngagementRate=NumEngaged/Count*100.0)
 91 | 
 92 | ggplot(engagementRateByPolicyAge, aes(x=Months.Since.Policy.Inception, y=EngagementRate)) +
 93 |   geom_line() +
 94 |   ylab("Engagement Rate (%)") +
 95 |   xlab("Months Since Policy Inception") +
 96 |   ggtitle("Engagement Rates by Months Since Policy Inception") +
 97 |   theme(plot.title=element_text(hjust=0.5))
 98 | 
 99 | 
100 | #### 3. Customer Segmentation by CLV & Months Since Inception ####
101 | summary(df$Customer.Lifetime.Value)
102 | summary(df$Months.Since.Policy.Inception)
103 | 
104 | clv_encode_fn <- function(x) {if(x > median(df$Customer.Lifetime.Value)) "High" else "Low"}
105 | df$CLV.Segment <- sapply(df$Customer.Lifetime.Value, clv_encode_fn)
106 | 
107 | policy_age_encode_fn <- function(x) {if(x > median(df$Months.Since.Policy.Inception)) "High" else "Low"}
108 | df$Policy.Age.Segment <- sapply(df$Months.Since.Policy.Inception, policy_age_encode_fn)
109 | 
110 | ggplot(
111 |   df[which(df$CLV.Segment=="High" & df$Policy.Age.Segment=="High"),], 
112 |   aes(x=Months.Since.Policy.Inception, y=log(Customer.Lifetime.Value))
113 | ) +
114 |   geom_point(color='red') +
115 |   geom_point(
116 |     data=df[which(df$CLV.Segment=="High" & df$Policy.Age.Segment=="Low"),], 
117 |     color='orange'
118 |   ) +
119 |   geom_point(
120 |     data=df[which(df$CLV.Segment=="Low" & df$Policy.Age.Segment=="Low"),], 
121 |     color='green'
122 |   ) +
123 |   geom_point(
124 |     data=df[which(df$CLV.Segment=="Low" & df$Policy.Age.Segment=="High"),], 
125 |     color='blue'
126 |   ) +
127 |   ggtitle('Segments by CLV and Policy Age') +
128 |   xlab("Months Since Policy Inception") +
129 |   ylab("CLV (in log scale)") +
130 |   theme(plot.title = element_text(hjust = 0.5)) 
131 | 
132 | engagementRateBySegment <- df %>% 
133 |   group_by(CLV.Segment, Policy.Age.Segment) %>%
134 |   summarise(Count=n(), NumEngaged=sum(Engaged))  %>%
135 |   mutate(EngagementRate=NumEngaged/Count*100.0)
136 | 
137 | ggplot(engagementRateBySegment, aes(x=CLV.Segment, y=EngagementRate, fill=Policy.Age.Segment)) +
138 |   geom_bar(width=0.5, stat="identity", position = "dodge") +
139 |   ggtitle('Engagement Rates by Customer Segments') +
140 |   ylab("Engagement Rate (%)") +
141 |   theme(plot.title = element_text(hjust = 0.5)) 
142 | 
143 | 


--------------------------------------------------------------------------------
/ch.8/R/PredictingEngagement.R:
--------------------------------------------------------------------------------
  1 | library(dplyr)
  2 | library(ggplot2)
  3 | 
  4 | #### 1. Load Data ####
  5 | df <- read.csv(
  6 |   file="~/Documents/data-science-for-marketing/ch.8/data/WA_Fn-UseC_-Marketing-Customer-Value-Analysis.csv", 
  7 |   header=TRUE
  8 | )
  9 | 
 10 | #### 2. Variable Encoding ####
 11 | 
 12 | ## 2.1. Response Variable: Response
 13 | df$Engaged <- as.integer(df$Response) - 1
 14 | mean(df$Engaged)
 15 | 
 16 | ## 2.2. Categorical Features
 17 | categoricalVars = c(
 18 |   'Sales.Channel', 'Vehicle.Size', 'Vehicle.Class', 'Policy', 'Policy.Type',
 19 |   'EmploymentStatus', 'Marital.Status', 'Education', 'Coverage', 'Gender'
 20 | )
 21 | 
 22 | encodedDF <- model.matrix(~.-1, df[categoricalVars])
 23 | 
 24 | ## 2.3. Continuous Features
 25 | continuousFeatures <- c(
 26 |   'Customer.Lifetime.Value', 'Income', 'Monthly.Premium.Auto',
 27 |   'Months.Since.Last.Claim', 'Months.Since.Policy.Inception',
 28 |   'Number.of.Open.Complaints', 'Number.of.Policies', 'Total.Claim.Amount'
 29 | )
 30 | 
 31 | encodedDF <- cbind(encodedDF, df[continuousFeatures])
 32 | 
 33 | 
 34 | #### 3. Training & Testing ####
 35 | 
 36 | # install.packages('caTools')
 37 | library(caTools)
 38 | 
 39 | sample <- sample.split(df$Customer, SplitRatio = .7)
 40 | 
 41 | trainX <- as.matrix(subset(encodedDF, sample == TRUE))
 42 | trainY <- as.double(as.matrix(subset(df$Engaged, sample == TRUE)))
 43 | 
 44 | testX <- as.matrix(subset(encodedDF, sample == FALSE))
 45 | testY <- as.double(as.matrix(subset(df$Engaged, sample == FALSE)))
 46 | 
 47 | ## 3.1. Building Random Forest Model
 48 | 
 49 | # - Training
 50 | # install.packages('randomForest')
 51 | library(randomForest)
 52 | 
 53 | rfModel <- randomForest(x=trainX, y=factor(trainY), ntree=200, maxnodes=24)
 54 | 
 55 | # - Individual Tree Predictions
 56 | getTree(rfModel, 1)
 57 | predict(rfModel, trainX, predict.all=TRUE)$individual
 58 | 
 59 | # - Feature Importances
 60 | importance(rfModel)
 61 | 
 62 | ## 3.2. Evaluating Models
 63 | 
 64 | inSamplePreds <- as.double(predict(rfModel, trainX)) - 1
 65 | outSamplePreds <- as.double(predict(rfModel, testX)) - 1
 66 | 
 67 | # - Accuracy, Precision, and Recall
 68 | inSampleAccuracy <- mean(trainY == inSamplePreds)
 69 | outSampleAccuracy <- mean(testY == outSamplePreds)
 70 | print(sprintf('In-Sample Accuracy: %0.4f', inSampleAccuracy))
 71 | print(sprintf('Out-Sample Accuracy: %0.4f', outSampleAccuracy))
 72 | 
 73 | inSamplePrecision <- sum(inSamplePreds & trainY) / sum(inSamplePreds)
 74 | outSamplePrecision <- sum(outSamplePreds & testY) / sum(outSamplePreds)
 75 | print(sprintf('In-Sample Precision: %0.4f', inSamplePrecision))
 76 | print(sprintf('Out-Sample Precision: %0.4f', outSamplePrecision))
 77 | 
 78 | inSampleRecall <- sum(inSamplePreds & trainY) / sum(trainY)
 79 | outSampleRecall <- sum(outSamplePreds & testY) / sum(testY)
 80 | print(sprintf('In-Sample Recall: %0.4f', inSampleRecall))
 81 | print(sprintf('Out-Sample Recall: %0.4f', outSampleRecall))
 82 | 
 83 | # - ROC & AUC
 84 | # install.packages('ROCR')
 85 | library(ROCR)
 86 | 
 87 | inSamplePredProbs <- as.double(predict(rfModel, trainX, type='prob')[,2])
 88 | outSamplePredProbs <- as.double(predict(rfModel, testX, type='prob')[,2])
 89 | 
 90 | pred <- prediction(outSamplePredProbs, testY)
 91 | perf <- performance(pred, measure = "tpr", x.measure = "fpr") 
 92 | auc <- performance(pred, measure='auc')@y.values[[1]]
 93 | 
 94 | plot(
 95 |   perf, 
 96 |   main=sprintf('Random Forest Model ROC Curve (AUC: %0.2f)', auc), 
 97 |   col='darkorange', 
 98 |   lwd=2
 99 | ) + grid()
100 | abline(a = 0, b = 1, col='darkgray', lty=3, lwd=2)
101 | 
102 | 
103 | 
104 | 


--------------------------------------------------------------------------------
/ch.9/R/CustomerLifetimeValue.R:
--------------------------------------------------------------------------------
  1 | library(dplyr)
  2 | library(readxl)
  3 | library(ggplot2)
  4 | 
  5 | #### 1. Load Data ####
  6 | df <- read_excel(
  7 |   path="~/Documents/data-science-for-marketing/ch.9/data/Online Retail.xlsx", 
  8 |   sheet="Online Retail"
  9 | )
 10 | 
 11 | #### 2. Date Clean-Up ####
 12 | 
 13 | # ignore negative quantity
 14 | dim(df)
 15 | df <- df[which(df$Quantity > 0),]
 16 | dim(df)
 17 | 
 18 | # remove records with NA
 19 | df <- na.omit(df)
 20 | dim(df)
 21 | 
 22 | # excluding incomplete month
 23 | sprintf("Date Range: %s ~ %s", min(df$InvoiceDate), max(df$InvoiceDate))
 24 | dim(df)
 25 | df <- df[which(df$InvoiceDate < '2011-12-01'),]
 26 | dim(df)
 27 | 
 28 | # total sales
 29 | df$Sales <- df$Quantity * df$UnitPrice
 30 | 
 31 | # per order data
 32 | ordersDF <- df %>% 
 33 |   group_by(CustomerID, InvoiceNo) %>% 
 34 |   summarize(Sales=sum(Sales), InvoiceDate=max(InvoiceDate))
 35 | 
 36 | #### 3. Date Analysis ####
 37 | 
 38 | # order amount & frequency summary
 39 | summaryDF <- ordersDF %>%
 40 |   group_by(CustomerID) %>%
 41 |   summarize(
 42 |     SalesMin=min(Sales), SalesMax=max(Sales), SalesSum=sum(Sales), SalesAvg=mean(Sales), SalesCount=n(),
 43 |     InvoiceDateMin=min(InvoiceDate), InvoiceDateMax=max(InvoiceDate), 
 44 |     PurchaseDuration=as.double(floor(max(InvoiceDate)-min(InvoiceDate))),
 45 |     PurchaseFrequency=as.double(floor(max(InvoiceDate)-min(InvoiceDate)))/n()
 46 |   )
 47 | 
 48 | # customers with repeat purchases
 49 | dim(summaryDF)
 50 | summaryDF <- summaryDF[which(summaryDF$PurchaseDuration > 0),]
 51 | dim(summaryDF)
 52 | 
 53 | salesCount <- summaryDF %>% 
 54 |   group_by(SalesCount) %>% 
 55 |   summarize(Count=n())
 56 | 
 57 | ggplot(salesCount[1:19,], aes(x=SalesCount, y=Count)) +
 58 |   geom_bar(width=0.5, stat="identity") +
 59 |   ggtitle('') +
 60 |   xlab("Sales Count") +
 61 |   ylab("Count") +
 62 |   theme(plot.title = element_text(hjust = 0.5))
 63 | 
 64 | summary(summaryDF$SalesCount)
 65 | summary(summaryDF$SalesAvg)
 66 | 
 67 | hist(
 68 |   summaryDF$PurchaseFrequency, 
 69 |   breaks=20,
 70 |   xlab='avg. number of days between purchases',
 71 |   ylab='count',
 72 |   main=''
 73 | )
 74 | 
 75 | summary(summaryDF$PurchaseDuration)
 76 | summary(summaryDF$PurchaseFrequency)
 77 | 
 78 | 
 79 | #### 4. Predicting 3-Month CLV ####
 80 | 
 81 | ## 4.1. Data Prep ##
 82 | 
 83 | # group data into every 3 months
 84 | library(lubridate)
 85 | 
 86 | ordersDF$Quarter = as.character(round_date(ordersDF$InvoiceDate, '3 months'))
 87 | 
 88 | dataDF <- ordersDF %>%
 89 |   group_by(CustomerID, Quarter) %>%
 90 |   summarize(SalesSum=sum(Sales), SalesAvg=mean(Sales), SalesCount=n())
 91 | 
 92 | dataDF$Quarter[dataDF$Quarter == "2012-01-01"] <- "Q1"
 93 | dataDF$Quarter[dataDF$Quarter == "2011-10-01"] <- "Q2"
 94 | dataDF$Quarter[dataDF$Quarter == "2011-07-01"] <- "Q3"
 95 | dataDF$Quarter[dataDF$Quarter == "2011-04-01"] <- "Q4"
 96 | dataDF$Quarter[dataDF$Quarter == "2011-01-01"] <- "Q5"
 97 | 
 98 | # building sample set
 99 | # install.packages('reshape2')
100 | library(reshape2)
101 | 
102 | salesSumFeaturesDF <- dcast(
103 |   dataDF[which(dataDF$Quarter != "Q1"),], 
104 |   CustomerID ~ Quarter, 
105 |   value.var="SalesSum"
106 | )
107 | colnames(salesSumFeaturesDF) <- c("CustomerID", "SalesSum.Q2", "SalesSum.Q3", "SalesSum.Q4", "SalesSum.Q5")
108 | 
109 | salesAvgFeaturesDF <- dcast(
110 |   dataDF[which(dataDF$Quarter != "Q1"),], 
111 |   CustomerID ~ Quarter, 
112 |   value.var="SalesAvg"
113 | )
114 | colnames(salesAvgFeaturesDF) <- c("CustomerID", "SalesAvg.Q2", "SalesAvg.Q3", "SalesAvg.Q4", "SalesAvg.Q5")
115 | 
116 | salesCountFeaturesDF <- dcast(
117 |   dataDF[which(dataDF$Quarter != "Q1"),], 
118 |   CustomerID ~ Quarter, 
119 |   value.var="SalesCount"
120 | )
121 | colnames(salesCountFeaturesDF) <- c("CustomerID", "SalesCount.Q2", "SalesCount.Q3", "SalesCount.Q4", "SalesCount.Q5")
122 | 
123 | featuresDF <- merge(
124 |   merge(salesSumFeaturesDF, salesAvgFeaturesDF, by="CustomerID"),
125 |   salesCountFeaturesDF, by="CustomerID"
126 | )
127 | featuresDF[is.na(featuresDF)] <- 0
128 | 
129 | responseDF <- dataDF[which(dataDF$Quarter == "Q1"),] %>% 
130 |   select(CustomerID, SalesSum)
131 | colnames(responseDF) <- c("CustomerID", "CLV_3_Month")
132 | 
133 | sampleDF <- merge(featuresDF, responseDF, by="CustomerID", all.x=TRUE)
134 | sampleDF[is.na(sampleDF)] <- 0
135 | 
136 | summary(sampleDF$CLV_3_Month)
137 | 
138 | ## 4.2. Regression Models ##
139 | 
140 | # train/test set split
141 | library(caTools)
142 | 
143 | sample <- sample.split(sampleDF$CustomerID, SplitRatio = .8)
144 | 
145 | train <- as.data.frame(subset(sampleDF, sample == TRUE))[,-1]
146 | test <- as.data.frame(subset(sampleDF, sample == FALSE))[,-1]
147 | 
148 | # Linear Regression model
149 | regFit <- lm(CLV_3_Month ~ ., data=train)
150 | 
151 | summary(regFit)
152 | 
153 | ## 4.3. Evaluation ##
154 | train_preds <- predict(regFit, train)
155 | test_preds <- predict(regFit, test)
156 | 
157 | # R-squared
158 | # install.packages('miscTools')
159 | library(miscTools)
160 | 
161 | inSampleR2 <- rSquared(train$CLV_3_Month, resid=train$CLV_3_Month - train_preds)
162 | outOfSampleR2 <- rSquared(test$CLV_3_Month, resid=test$CLV_3_Month - test_preds)
163 | 
164 | sprintf('In-Sample R-Squared: %0.4f', inSampleR2)
165 | sprintf('Out-of-Sample R-Squared: %0.4f', outOfSampleR2)
166 | 
167 | # Median Absolute Error
168 | inSampleMAE <- median(abs(train$CLV_3_Month - train_preds))
169 | outOfSampleMAE <- median(abs(test$CLV_3_Month - test_preds))
170 | 
171 | sprintf('In-Sample MAE: %0.4f', inSampleMAE)
172 | sprintf('Out-of-Sample MAE: %0.4f', outOfSampleMAE)
173 | 
174 | # Actual vs. Predicted Scatter Plot
175 | plot(
176 |   train$CLV_3_Month, 
177 |   train_preds, 
178 |   xlab='actual', 
179 |   ylab='predicted', 
180 |   main='In-Sample Actual vs. Predicted'
181 | )
182 | abline(a=0, b=1)
183 | 
184 | plot(
185 |   test$CLV_3_Month, 
186 |   test_preds, 
187 |   xlab='actual', 
188 |   ylab='predicted', 
189 |   main='Out-of-Sample Actual vs. Predicted'
190 | )
191 | abline(a=0, b=1)
192 | 
193 | 
194 | 
195 | 
196 | 


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