Foundations_of_Predictive_Analytics_in_Python .ipynb

{
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Foundations of Predictive Analytics in Python \n",
    "\n",
    "## Introduction and base table structure\n",
    "\n",
    "### Structure of the base table\n",
    "Consider the predictive modeling problem where you want to predict whether a candidate donor will make a donation in the next year. To build the model, you use historical data and calculate the target in 2017. The target is 1 if a donation is made in 2017 and 0 otherwise. \n",
    "\n",
    "![Screen%20Shot%202019-01-22%20at%209.32.18%20AM.png](attachment:Screen%20Shot%202019-01-22%20at%209.32.18%20AM.png)\n",
    "\n",
    "### Exploring the base table\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   target  gender_F  income_high  income_low  country_USA  country_India  \\\n",
      "0       0         1            0           1            0              1   \n",
      "1       0         1            0           0            0              1   \n",
      "2       0         1            0           0            0              1   \n",
      "3       0         1            0           1            1              0   \n",
      "4       0         1            1           0            1              0   \n",
      "\n",
      "   country_UK  age  time_since_last_gift  time_since_first_gift  max_gift  \\\n",
      "0           0   65                   530                   2265     166.0   \n",
      "1           0   71                   715                    715      90.0   \n",
      "2           0   28                   150                   1806     125.0   \n",
      "3           0   52                   725                   2274     117.0   \n",
      "4           0   82                   805                    805      80.0   \n",
      "\n",
      "   min_gift  mean_gift  number_gift  \n",
      "0      87.0     116.00            7  \n",
      "1      90.0      90.00            1  \n",
      "2      74.0      96.00            9  \n",
      "3      97.0     104.25            4  \n",
      "4      80.0      80.00            1  \n"
     ]
    }
   ],
   "source": [
    "basetable = pd.read_csv('data/basetable_ex2_4.csv')\n",
    "print(basetable.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25000\n"
     ]
    }
   ],
   "source": [
    "# Assign the number of rows in the basetable \n",
    "basetable_size = len(basetable)\n",
    "print(basetable_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1187\n"
     ]
    }
   ],
   "source": [
    "# Assign the number of targets to the variable 'targets_count'\n",
    "target_count =sum(basetable['target'])\n",
    "print(target_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.04748\n"
     ]
    }
   ],
   "source": [
    "# Print the target incidence.\n",
    "print(target_count / basetable_size )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Female:12579 , Male:12421\n"
     ]
    }
   ],
   "source": [
    "#Let's find out whether how many  males and females in the population\n",
    "female = sum(basetable['gender_F'] == 1)\n",
    "male = sum(basetable['gender_F'] == 0)\n",
    "print('Female:{} , Male:{}'.format(female, male))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Female: 50.316 %, Male: 49.684 %\n"
     ]
    }
   ],
   "source": [
    "per_femele = (female/basetable_size)*100\n",
    "per_male = (male/basetable_size)*100\n",
    "print('Female: {} %, Male: {} %'.format(per_femele, per_male)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {
    "Screen%20Shot%202019-01-22%20at%2011.58.08%20AM.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic regression\n",
    "![Screen%20Shot%202019-01-22%20at%2011.58.08%20AM.png](attachment:Screen%20Shot%202019-01-22%20at%2011.58.08%20AM.png)\n",
    "\n",
    "If we plot the target in function of age for all donors in the we that a 1 accurs more to the right, where the older donors are. If we fit a regression line though these points, it is of the form a*x+b, with a positive number. A is called the coefficient of age, and b is called the intercept. If we plot the target as a function of the time since the last donation for each donor, it can be seen that who recently donated, are more likely to danate. in this case coefficient  of recency is negative. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_copy = basetable.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "      <th>gender_F</th>\n",
       "      <th>income_high</th>\n",
       "      <th>income_low</th>\n",
       "      <th>country_USA</th>\n",
       "      <th>country_India</th>\n",
       "      <th>country_UK</th>\n",
       "      <th>age</th>\n",
       "      <th>time_since_last_gift</th>\n",
       "      <th>time_since_first_gift</th>\n",
       "      <th>max_gift</th>\n",
       "      <th>min_gift</th>\n",
       "      <th>mean_gift</th>\n",
       "      <th>number_gift</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "      <td>530</td>\n",
       "      <td>2265</td>\n",
       "      <td>166.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>116.00</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>715</td>\n",
       "      <td>715</td>\n",
       "      <td>90.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>90.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>150</td>\n",
       "      <td>1806</td>\n",
       "      <td>125.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>96.00</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>725</td>\n",
       "      <td>2274</td>\n",
       "      <td>117.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>104.25</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>82</td>\n",
       "      <td>805</td>\n",
       "      <td>805</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   target  gender_F  income_high  income_low  country_USA  country_India  \\\n",
       "0       0         1            0           1            0              1   \n",
       "1       0         1            0           0            0              1   \n",
       "2       0         1            0           0            0              1   \n",
       "3       0         1            0           1            1              0   \n",
       "4       0         1            1           0            1              0   \n",
       "\n",
       "   country_UK  age  time_since_last_gift  time_since_first_gift  max_gift  \\\n",
       "0           0   65                   530                   2265     166.0   \n",
       "1           0   71                   715                    715      90.0   \n",
       "2           0   28                   150                   1806     125.0   \n",
       "3           0   52                   725                   2274     117.0   \n",
       "4           0   82                   805                    805      80.0   \n",
       "\n",
       "   min_gift  mean_gift  number_gift  \n",
       "0      87.0     116.00            7  \n",
       "1      90.0      90.00            1  \n",
       "2      74.0      96.00            9  \n",
       "3      97.0     104.25            4  \n",
       "4      80.0      80.00            1  "
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_copy.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpretation of coefficients\n",
    "Assume you built a logistic regression model to predict which donors are most likely to donate for a project, using age and time_since_last_gift (number of months since the last gift) as predictors. The output of the logistic regression model is as follows:\n",
    "```python\n",
    "y = 0.3 + 4.5*age - 2.3*time_since_last_gift\n",
    "```\n",
    "### Building a logistic regression model\n",
    "You can build a logistic regression model using the module linear_model from sklearn. First, you create a logistic regression model using the LogisticRegression() method:\n",
    "```python\n",
    "logreg = linear_model.LogisticRegression()\n",
    "```\n",
    "Next, you need to feed data to the logistic regression model, so that it can be fit. X contains the predictive variables, whereas y has the target.\n",
    "```python\n",
    "X = basetable[[\"predictor_1\",\"predictor_2\",\"predictor_3\"]]`\n",
    "y = basetable[[\"target\"]]\n",
    "logreg.fit(X,y)\n",
    "```\n",
    "### Showing the coefficients and intercept\n",
    "Once the logistic regression model is ready, it can be interesting to have a look at the coefficients to check whether the model makes sense.\n",
    "\n",
    "Given a fitted logistic regression model logreg, you can retrieve the coefficients using the attribute `coef_`. The order in which the coefficients appear, is the same as the order in which the variables were fed to the model. The intercept can be retrieved using the attribute `intercept_`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import linear_model from sklearn.\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dataframe X that only contains the candidate predictors age, gender_F and time_since_last_gift.\n",
    "X = basetable[['age', 'gender_F', 'time_since_last_gift']]\n",
    "# Create a dataframe y that contains the target\n",
    "y =basetable['target']\n",
    "# Create a logistic regression model logreg and fit it to the data.\n",
    "logreg = linear_model.LogisticRegression()\n",
    "logreg.fit(X,y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.007178355658921441 age\n",
      "0.11430414536794431 gender_F\n",
      "-0.0013087501133203447 time_since_last_gift\n",
      "[-2.54149728]\n"
     ]
    }
   ],
   "source": [
    "predictors = [\"age\",\"gender_F\",\"time_since_last_gift\"]\n",
    "\n",
    "# Assign the coefficients to a list coef\n",
    "coef = logreg.coef_\n",
    "\n",
    "for c,p in zip(predictors,list(coef[0])):\n",
    "    print(p , c)\n",
    "print(logreg.intercept_)\n",
    "\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2.0137"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-2.5 + 0.0072* 70 + 0.1143 *1 - 0.0011* 120\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.93427169 0.06572831]\n",
      " [0.9454883  0.0545117 ]\n",
      " [0.9185279  0.0814721 ]\n",
      " [0.95269877 0.04730123]\n",
      " [0.94745512 0.05254488]]\n"
     ]
    }
   ],
   "source": [
    "basetable_predict = basetable[predictors]\n",
    "prediction= logreg.predict_proba(basetable_predict)\n",
    "print(prediction[0:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating AUC\n",
    "\n",
    "The AUC value assesses how well a model can order observations from low probability to be target to high probability to be target. In Python, the roc_auc_score function can be used to calculate the AUC of the model. It takes the true values of the target and the predictions as arguments.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.63\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "# Make predictions\n",
    "prediction_X = logreg.predict_proba(X)\n",
    "prediction_X_target = prediction_X[:,1]\n",
    "\n",
    "# Calculate the AUC value\n",
    "auc = roc_auc_score(y, prediction_X_target)\n",
    "print(round(auc,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Trying differetn model \n",
    "variable_1 = ['mean_gift', 'income_low']\n",
    "variable_2 = ['mean_gift', 'income_low', 'gender_F', 'country_India', 'age']\n",
    "\n",
    "X_1 = basetable[variable_1]\n",
    "X_2 = basetable[variable_2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "logreg.fit(X_1, y)\n",
    "prediction_1 = logreg.predict_proba(X_1)[:,1]\n",
    "auc_1 = roc_auc_score(y, prediction_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "logreg.fit(X_2,y)\n",
    "prediction_2 = logreg.predict_proba(X_2)[:,1]\n",
    "auc_2 = roc_auc_score(y, prediction_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.68\n",
      "0.69\n"
     ]
    }
   ],
   "source": [
    "print(round(auc_1,2))\n",
    "print(round(auc_2,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Forward stepwise variable selection\n",
    "#### Selecting the next best variable\n",
    "The forward stepwise variable selection method starts with an empty variable set and proceeds in steps, where in each step the next best variable is added. To implement this procedure, two handy functions have been implemented for you.\n",
    "\n",
    "The `auc_score` function calculates for a given variable set variables the AUC of the model that uses this variable set as predictors. The `next_best_variable` function calculates which variable should be added in the next step to the variable list.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def auc_score(variable, target, df):\n",
    "    X = df[variable]\n",
    "    y = df[target]\n",
    "    \n",
    "    logreg = linear_model.LogisticRegression()\n",
    "    logreg.fit(X,y)\n",
    "    predict = logreg.predict_proba(X)[:,1]\n",
    "    auc = roc_auc_score(y, predict)\n",
    "    return (auc)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#defining a functiong that loop the candidate variable to find out next variable combination\n",
    "def next_best_variable(current_varibales, candidate_variables, target, df):\n",
    "    best_auc = -1\n",
    "    best_variable = None\n",
    "    \n",
    "    for v in candidate_variables:\n",
    "        auc_v = auc_score(current_varibales + [v], target, df)\n",
    "        \n",
    "        if auc_v >= best_auc:\n",
    "            best_auc = auc_v\n",
    "            best_variable =v \n",
    "    return best_variable    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7125\n"
     ]
    }
   ],
   "source": [
    "# Calculate the AUC of a model that uses \"max_gift\", \"mean_gift\" and \"min_gift\" as predictors\n",
    "current_auc = auc_score([\"max_gift\", \"mean_gift\",\"min_gift\"],'target', basetable)\n",
    "print(round(current_auc, 4))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age\n"
     ]
    }
   ],
   "source": [
    "# Calculate which variable among \"age\" and \"gender_F\" should be added to the variables \n",
    "#\"max_gift\", \"mean_gift\" and \"min_gift\"\n",
    "\n",
    "next_variable = next_best_variable([\"max_gift\", \"mean_gift\",\"min_gift\"],[\"age\",\"gender_F\"], 'target', basetable)\n",
    "print(next_variable)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7148\n"
     ]
    }
   ],
   "source": [
    "# Calculate the AUC of a model that uses \"max_gift\", \"mean_gift\", \"min_gift\" and \"age\" as predictors\n",
    "current_auc_age = auc_score([\"max_gift\", \"mean_gift\",\"min_gift\",\"age\"],'target', basetable)\n",
    "print(round(current_auc_age,4))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.713\n"
     ]
    }
   ],
   "source": [
    "current_auc_gender_F = auc_score([\"max_gift\", \"mean_gift\",\"min_gift\",\"gender_F\"],'target', basetable)\n",
    "print(round(current_auc_gender_F,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Nice! The model that has age as next variable has a better AUC than the model that has gender_F as next variable. Therefore, age is selected as the next best variable.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Finding the order of variables\n",
    "The forward stepwise variable selection procedure starts with an empty set of variables, and adds predictors one by one. In each step, the predictor that has the highest AUC in combination with the current variables is selected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['target', 'gender_F', 'income_high', 'income_low', 'country_USA', 'country_India', 'country_UK', 'age', 'time_since_last_gift', 'time_since_first_gift', 'max_gift', 'min_gift', 'mean_gift', 'number_gift']\n"
     ]
    }
   ],
   "source": [
    "# Find the candidate variables\n",
    "#Create the candidates variable \n",
    "candidate_variable = list(basetable.columns.values)\n",
    "print(candidate_variable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "candidate_variable.remove('target')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the current variables\n",
    "current_variable =[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable added in step 1 is max_gift\n",
      "Variable added in step 2 is number_gift\n",
      "Variable added in step 3 is time_since_last_gift\n",
      "Variable added in step 4 is mean_gift\n",
      "Variable added in step 5 is age\n",
      "['max_gift', 'number_gift', 'time_since_last_gift', 'mean_gift', 'age']\n"
     ]
    }
   ],
   "source": [
    "# The forward stepwise variable selection procedure\n",
    "number_of_iteration =5\n",
    "\n",
    "for i in range (0, number_of_iteration):\n",
    "    \n",
    "    next_variable = next_best_variable(current_variable, candidate_variable, 'target', basetable)\n",
    "    current_variable = current_variable + [next_variable]\n",
    "    candidate_variable.remove(next_variable)\n",
    "    \n",
    "    print('Variable added in step '+str(i+1)+ ' is '+next_variable)\n",
    "print(current_variable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.768756710130262\n"
     ]
    }
   ],
   "source": [
    "current_list_auc = auc_score(current_variable, 'target', basetable)\n",
    "print(current_list_auc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlated variables\n",
    "you can test this calculating the correlation between these variables:\n",
    "```python\n",
    "import numpy\n",
    "numpy.corrcoef(basetable[\"variable_1\"],basetable[\"variable_2\"])[0,1]\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "#if we look at first 10 variable\n",
    "candidate_variable_10 = list(basetable.columns.values)\n",
    "current_variable_10 = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable added in step 1 is target\n",
      "Variable added in step 2 is number_gift\n",
      "Variable added in step 3 is mean_gift\n",
      "Variable added in step 4 is min_gift\n",
      "Variable added in step 5 is max_gift\n",
      "Variable added in step 6 is time_since_last_gift\n",
      "Variable added in step 7 is time_since_first_gift\n",
      "Variable added in step 8 is age\n",
      "Variable added in step 9 is country_UK\n",
      "Variable added in step 10 is country_India\n",
      "['target', 'number_gift', 'mean_gift', 'min_gift', 'max_gift', 'time_since_last_gift', 'time_since_first_gift', 'age', 'country_UK', 'country_India']\n"
     ]
    }
   ],
   "source": [
    "for i in range (0, 10):\n",
    "    \n",
    "    next_variable_10 = next_best_variable(current_variable_10, candidate_variable_10, 'target', basetable)\n",
    "    current_variable_10 = current_variable_10 + [next_variable_10]\n",
    "    candidate_variable_10.remove(next_variable_10)\n",
    "    \n",
    "    print('Variable added in step '+str(i+1)+ ' is '+next_variable_10)\n",
    "print(current_variable_10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Partitioning\n",
    "In order to properly evaluate a model, one can partition the data in a train and test set. The train set contains the data the model is built on, and the test data is used to evaluate the model. This division is done randomly, but when the target incidence is low, it could be necessary to stratify, that is, to make sure that the train and test data contain an equal percentage of targets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_basetable = basetable.drop('target', 1)\n",
    "y_basetable = basetable['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Carry out 50-50 partititioning with stratification\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_basetable, y_basetable, test_size=0.3, stratify = y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the final train and test basetables\n",
    "train = pd.concat([X_train, y_train], 1)\n",
    "test  = pd.concat([X_test, y_test], 1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.05\n",
      "0.05\n"
     ]
    }
   ],
   "source": [
    "# Check whether train and test have same percentage targets\n",
    "print(round(sum(train['target'])/len(train),2))\n",
    "print(round(sum(test['target'])/len(test),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "def auc_train_test(var, target, df_train, df_test):\n",
    "    logreg =linear_model.LogisticRegression()\n",
    "    train_X = df_train[var]\n",
    "    train_y = df_train[target]\n",
    "    \n",
    "    test_X  = df_test[var]\n",
    "    test_y  = df_test[target]\n",
    "    \n",
    "    logreg.fit(train_X, train_y)\n",
    "    predict_train = logreg.predict_proba(train_X)[:,1]\n",
    "    auc_train = roc_auc_score(train_y, predict_train)\n",
    "      \n",
    "    logreg.fit(test_X, test_y)\n",
    "    predict_test = logreg.predict_proba(test_X)[:,1]\n",
    "    auc_test = roc_auc_score(test_y, predict_test)\n",
    "    return (auc_train, auc_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.54\n",
      "0.56\n"
     ]
    }
   ],
   "source": [
    "# Apply the auc_train_test function\n",
    "auc_train, auc_test = auc_train_test(['age', 'gender_F'], 'target', train, test)\n",
    "print(round(auc_train,2))\n",
    "print(round(auc_test, 2))\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Building the AUC curves\n",
    "The forward stepwise variable selection procedure provides an order in which variables are optimally added to the predictor set. In order to decide where to cut off the variables, you can make the train and test AUC curves. These curves plot the train and test AUC using the first, first two, first three, ... variables in the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['gender_F', 'income_high', 'income_low', 'country_USA', 'country_India', 'country_UK', 'age', 'time_since_last_gift', 'time_since_first_gift', 'max_gift', 'min_gift', 'mean_gift', 'number_gift']\n"
     ]
    }
   ],
   "source": [
    "variables = list(train.columns.values)\n",
    "variables.remove('target')\n",
    "print(variables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "auc_values_train = []\n",
    "auc_values_test   = []\n",
    "variables_evaluate = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "for v in variables:   \n",
    "    variables_evaluate.append(v)\n",
    "    auc_train, auc_test = auc_train_test(variables_evaluate, 'target', train, test)\n",
    "    auc_values_train.append(auc_train)\n",
    "    auc_values_test.append(auc_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_range = np.array(range(0,len(auc_values_train)))\n",
    "y_plt_train = np.array(auc_values_train)\n",
    "y_plt_test = np.array(auc_values_test)\n",
    "plt.xticks(x_range, variables, rotation=90)\n",
    "plt.plot(x_range, y_plt_train)\n",
    "plt.plot(x_range, y_plt_test)\n",
    "plt.show()\n",
    "             "
   ]
  },
  {
   "attachments": {
    "Screen%20Shot%202019-01-24%20at%2010.13.10%20AM.png": {
     "image/png": 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"
    },
    "Screen%20Shot%202019-01-24%20at%2010.24.51%20AM.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The cumulative gains curve\n",
    "![Screen%20Shot%202019-01-24%20at%2010.13.10%20AM.png](attachment:Screen%20Shot%202019-01-24%20at%2010.13.10%20AM.png)\n",
    "\n",
    "First you order all the observation according to the output of the model.On the left hand side are observations with highest probability to be target according to the model, on the rigt hand side are observations with lowest probability to be target. On the horizantal axis of the cumulative gains curve is indicated which percentage of the observations considered. For instance, the 30% observations with highest probability to be target is considered. On the vertical axis, the curves indicade which percentage of all target is included in this group. \n",
    "\n",
    "The cumulative gains curves is great tool to compare to the models. The more the line is situated to upper corner , the better the model.\n",
    "![Screen%20Shot%202019-01-24%20at%2010.24.51%20AM.png](attachment:Screen%20Shot%202019-01-24%20at%2010.24.51%20AM.png)\n",
    "When the two models curves cross each other, it is not straightforword to decide which model is better. In this case, we could say that model2 is better to distinguish the top 10% observations from the rest while, model 1 is better to distinguish the top 70% observations from the rest. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Constructing the cumulative gains curve\n",
    "The cumulative gains curve is an evaluation curve that assesses the performance of your model. It shows the percentage of targets reached when considering a certain percentage of your population with the highest probability to be target according to your model.\n",
    "\n",
    "To construct this curve, you can use the .plot_cumulative_gain() method in the scikitplot module and the matplotlib.pyplot module. As for each model evaluation metric or curve, you need the true target values on the one hand and the predictions on the other hand to construct the cumulative gains curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scikitplot as skplt\n",
    "model_1_variable = ['time_since_last_gift', 'max_gift', 'min_gift','mean_gift', 'number_gift']\n",
    "\n",
    "model_1_feture_train = train[model_1_variable]\n",
    "model_1_target_train = train['target']\n",
    "\n",
    "logreg.fit(model_1_feture_train, model_1_target_train)\n",
    "model_1_train = logreg.predict_proba(model_1_feture_train)\n",
    "#prediction_model_1_train = logreg.predict_proba(model_1_feture_train)[:1]\n",
    "#auc_score_model_1 = roc_auc_score(model_1_target_train, prediction_model_1_train)\n",
    "#print(auc_score_model_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/matplotlib/cbook/deprecation.py:107: MatplotlibDeprecationWarning: Passing one of 'on', 'true', 'off', 'false' as a boolean is deprecated; use an actual boolean (True/False) instead.\n",
      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a16085898>"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "skplt.metrics.plot_cumulative_gain(model_1_target_train,model_1_train)"
   ]
  },
  {
   "attachments": {
    "lift_curve.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Constructing the lift curve\n",
    "![lift_curve.png](attachment:lift_curve.png)\n",
    "\n",
    "\n",
    "The lift curve is an evaluation curve that assesses the performance of your model. It shows how many times more than average the model reaches targets.\n",
    "\n",
    "To construct this curve, you can use the plot_lift_curve method in the scikitplot module and the matplotlib.pyplot module. As for each model evaluation metric or curve, you need the true target values on the one hand and the predictions on the other hand to construct the cumulative gains curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/matplotlib/cbook/deprecation.py:107: MatplotlibDeprecationWarning: Passing one of 'on', 'true', 'off', 'false' as a boolean is deprecated; use an actual boolean (True/False) instead.\n",
      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "skplt.metrics.plot_lift_curve(model_1_target_train,model_1_train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Business case using lift curve\n",
    "```python\n",
    "profit = profit(perc_targets, perc_selected, population_size, campaign_cost, campaign_reward)\n",
    "```\n",
    "In this method, perc_targets is the percentage of targets in the group that you select for your campaign, perc_selected the percentage of people that is selected for the campaign, population_size the total population size, campaign_cost the cost of addressing a single person for the campaign, and campaign_reward the reward of addressing a target."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "def profit(perc_targets, perc_selected, population_size, campaign_cost, campaign_reward):\n",
    "    cost = campaign_cost * perc_selected * population_size\n",
    "    reward = campaign_reward * perc_targets * perc_selected * population_size\n",
    "    return reward -cost   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "perc_selected = 0.4\n",
    "lift = 2 #(from lift curve at 40% selected)\n",
    "\n",
    "# Information about the campaign\n",
    "population_size, target_incidence, campaign_cost, campaign_reward = 100000, 0.01, 1, 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    }
   ],
   "source": [
    "# perc_targets =target_incidence *lift (lift here 1 because at 100%)\n",
    "#perc_slected =1 (100%)\n",
    "profit_all = profit(target_incidence,1, population_size, campaign_cost, campaign_reward)\n",
    "print(profit_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40000.0\n"
     ]
    }
   ],
   "source": [
    "profit_40 = profit(target_incidence*lift,0.4, population_size, campaign_cost, campaign_reward)\n",
    "print(profit_40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**When addressing the entire donor base, you do not make any profit at all. When using the predictive model, you can make 40,000 Euro profit!**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Business case using cumulative gains curve\n",
    "The cumulative gains graph can be used to estimate how many donors one should address to make a certain profit. Indeed, the cumulative gains graph shows which percentage of all targets is reached when addressing a certain percentage of the population. If one knows the reward of a campaign, it follows easily how many donors should be targeted to reach a certain profit.\n",
    "\n",
    "In this exercise, you will calculate how many donors you should address to obtain a 30 000 Euro profit.\n",
    "1-Assume the cost of a campaign is 0 Euro and the reward of addressing a target is 50 Euro. Fill out how many targets should be reached to make 30 000 Euro profit.\n",
    "2-There are 1000 targets in total. Fill out which percentage of the targets should be addressed.\n",
    "3-Use the cumulative gains curve to know which percentage of the population should be addressed. Round to the tenth.\n",
    "4-Given that the population consists of 10 000 donors, fill out how many donors should be addressed.\n",
    "```python\n",
    "# Number of targets you want to reach\n",
    "number_targets_toreach = 30000 / 50\n",
    "perc_targets_toreach = number_targets_toreach / 1000\n",
    "cumulative_gains = 0.4\n",
    "number_donors_toreach = cumulative_gains * 10000\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discretization of a certain variable\n",
    "In order to make predictor insight graphs for continuous variables, you first need to discretize them. In Python, you can discretize pandas columns using the `qcut` method. To check whether the variable was nicely discretized, you can verify that the bins have equal size using the groupby method:\n",
    "```python\n",
    "print(basetable.groupby(\"discretized_variable\").size()\n",
    "      \n",
    "# Discretize the variable time_since_last_donation in 10 bins\n",
    "discretize_df['bins_recency'] = pd.qcut(discretize_df['time_since_last_gift'], 10)\n",
    "print(discretize_df.groupby('bins_recency').size())\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "      <th>gender_F</th>\n",
       "      <th>income_high</th>\n",
       "      <th>income_low</th>\n",
       "      <th>country_USA</th>\n",
       "      <th>country_India</th>\n",
       "      <th>country_UK</th>\n",
       "      <th>age</th>\n",
       "      <th>time_since_last_gift</th>\n",
       "      <th>time_since_first_gift</th>\n",
       "      <th>max_gift</th>\n",
       "      <th>min_gift</th>\n",
       "      <th>mean_gift</th>\n",
       "      <th>number_gift</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "      <td>530</td>\n",
       "      <td>2265</td>\n",
       "      <td>166.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>116.00</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>715</td>\n",
       "      <td>715</td>\n",
       "      <td>90.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>90.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>150</td>\n",
       "      <td>1806</td>\n",
       "      <td>125.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>96.00</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>725</td>\n",
       "      <td>2274</td>\n",
       "      <td>117.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>104.25</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>82</td>\n",
       "      <td>805</td>\n",
       "      <td>805</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   target  gender_F  income_high  income_low  country_USA  country_India  \\\n",
       "0       0         1            0           1            0              1   \n",
       "1       0         1            0           0            0              1   \n",
       "2       0         1            0           0            0              1   \n",
       "3       0         1            0           1            1              0   \n",
       "4       0         1            1           0            1              0   \n",
       "\n",
       "   country_UK  age  time_since_last_gift  time_since_first_gift  max_gift  \\\n",
       "0           0   65                   530                   2265     166.0   \n",
       "1           0   71                   715                    715      90.0   \n",
       "2           0   28                   150                   1806     125.0   \n",
       "3           0   52                   725                   2274     117.0   \n",
       "4           0   82                   805                    805      80.0   \n",
       "\n",
       "   min_gift  mean_gift  number_gift  \n",
       "0      87.0     116.00            7  \n",
       "1      90.0      90.00            1  \n",
       "2      74.0      96.00            9  \n",
       "3      97.0     104.25            4  \n",
       "4      80.0      80.00            1  "
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#to work on Discretize stuty first copy the basetable data frame\n",
    "discretize_df =basetable.copy()\n",
    "discretize_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['target', 'gender_F', 'income_high', 'income_low', 'country_USA',\n",
      "       'country_India', 'country_UK', 'age', 'time_since_last_gift',\n",
      "       'time_since_first_gift', 'max_gift', 'min_gift', 'mean_gift',\n",
      "       'number_gift'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "      <th>gender_F</th>\n",
       "      <th>income_high</th>\n",
       "      <th>income_low</th>\n",
       "      <th>country_USA</th>\n",
       "      <th>country_India</th>\n",
       "      <th>country_UK</th>\n",
       "      <th>age</th>\n",
       "      <th>time_since_last_gift</th>\n",
       "      <th>time_since_first_gift</th>\n",
       "      <th>max_gift</th>\n",
       "      <th>min_gift</th>\n",
       "      <th>mean_gift</th>\n",
       "      <th>number_gift</th>\n",
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       "      <td>715</td>\n",
       "      <td>90.0</td>\n",
       "      <td>90.0</td>\n",
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       "      <td>28</td>\n",
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       "      <td>1806</td>\n",
       "      <td>125.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>96.00</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>82</td>\n",
       "      <td>805</td>\n",
       "      <td>805</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   target  gender_F  income_high  income_low  country_USA  country_India  \\\n",
       "0       0         1            0           1            0              1   \n",
       "1       0         1            0           0            0              1   \n",
       "2       0         1            0           0            0              1   \n",
       "3       0         1            0           1            1              0   \n",
       "4       0         1            1           0            1              0   \n",
       "\n",
       "   country_UK  age  time_since_last_gift  time_since_first_gift  max_gift  \\\n",
       "0           0   65                   530                   2265     166.0   \n",
       "1           0   71                   715                    715      90.0   \n",
       "2           0   28                   150                   1806     125.0   \n",
       "3           0   52                   725                   2274     117.0   \n",
       "4           0   82                   805                    805      80.0   \n",
       "\n",
       "   min_gift  mean_gift  number_gift  \n",
       "0      87.0     116.00            7  \n",
       "1      90.0      90.00            1  \n",
       "2      74.0      96.00            9  \n",
       "3      97.0     104.25            4  \n",
       "4      80.0      80.00            1  "
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(discretize_df.columns)\n",
    "discretize_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discretizing all variables\n",
    "Instead of discretizing the continuous variables one by one, it is easier to discretize them automatically. To get a list of all the columns in Python, you can use\n",
    "\n",
    "variables  = basetable.columns\n",
    "Only variables that are continuous should be discretized. You can verify whether variables should be discretized by checking whether they have more than a predefined number of different values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['gender_F', 'income_high', 'income_low', 'country_USA', 'country_India', 'country_UK', 'age', 'time_since_last_gift', 'time_since_first_gift', 'max_gift', 'min_gift', 'mean_gift', 'number_gift']\n"
     ]
    }
   ],
   "source": [
    "# Get all the variable names except \"target\"\n",
    "varibale_disc = list(discretize_df.columns.values)\n",
    "varibale_disc.remove('target')\n",
    "print(varibale_disc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['target', 'gender_F', 'income_high', 'income_low', 'country_USA',\n",
      "       'country_India', 'country_UK', 'age', 'time_since_last_gift',\n",
      "       'time_since_first_gift', 'max_gift', 'min_gift', 'mean_gift',\n",
      "       'number_gift', 'disc_age', 'disc_time_since_last_gift',\n",
      "       'disc_time_since_first_gift', 'disc_max_gift', 'disc_min_gift',\n",
      "       'disc_mean_gift', 'disc_number_gift'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Loop through all the variables and discretize in 10 bins if there are more than 5 different values\n",
    "for var in varibale_disc:\n",
    "    if len(discretize_df.groupby(var)) > 5:\n",
    "        new_variable_disc = 'disc_' + var\n",
    "        discretize_df[new_variable_disc] = pd.qcut(discretize_df[var], 5, duplicates='drop')\n",
    "print(discretize_df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "disc_age\n",
      "(18.999, 38.0]    5298\n",
      "(38.0, 49.0]      5110\n",
      "(49.0, 59.0]      4885\n",
      "(59.0, 68.0]      4907\n",
      "(68.0, 109.0]     4800\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(discretize_df.groupby('disc_age').size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age_DISC\n",
      "(18, 30]     2516\n",
      "(30, 40]     3632\n",
      "(40, 50]     4766\n",
      "(50, 60]     6166\n",
      "(60, 110]    7920\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "discretize_df['Age_DISC'] = pd.cut(discretize_df['age'],[18,30,40,50,60,110])\n",
    "print(discretize_df.groupby('Age_DISC').size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "disc_number_gift\n",
      "(0.999, 3.0]    7506\n",
      "(3.0, 4.0]      2999\n",
      "(4.0, 6.0]      6448\n",
      "(6.0, 8.0]      4667\n",
      "(8.0, 18.0]     3380\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(discretize_df.groupby('disc_number_gift').size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "disc_time_since_last_gift\n",
      "(31.999, 459.0]     5001\n",
      "(459.0, 656.0]      5044\n",
      "(656.0, 832.0]      4962\n",
      "(832.0, 1047.0]     5006\n",
      "(1047.0, 2305.0]    4987\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(discretize_df.groupby('disc_time_since_last_gift').size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function that creates predictor insight graph table\n",
    "def create_pig_table(df, target, variable):\n",
    "    # Create groups for each variable\n",
    "    groups = df[[target, variable]].groupby(variable)\n",
    "    # Calculate size and target incidence for each group\n",
    "    pig_table = groups[target].agg({'Incidence':np.mean, 'Size':np.size}).reset_index()\n",
    "    return pig_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   gender_F  Incidence   Size\n",
      "0         0   0.044924  12421\n",
      "1         1   0.050004  12579\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:6: FutureWarning: using a dict on a Series for aggregation\n",
      "is deprecated and will be removed in a future version\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "print(create_pig_table(discretize_df, 'target', 'gender_F'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['target', 'gender_F', 'income_high', 'income_low', 'country_USA',\n",
      "       'country_India', 'country_UK', 'age', 'time_since_last_gift',\n",
      "       'time_since_first_gift', 'max_gift', 'min_gift', 'mean_gift',\n",
      "       'number_gift', 'disc_age', 'disc_time_since_last_gift',\n",
      "       'disc_time_since_first_gift', 'disc_max_gift', 'disc_min_gift',\n",
      "       'disc_mean_gift', 'disc_number_gift', 'Age_DISC'],\n",
      "      dtype='object')\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>target</th>\n",
       "      <th>gender_F</th>\n",
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       "      <th>income_low</th>\n",
       "      <th>country_USA</th>\n",
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       "      <td>7</td>\n",
       "      <td>(59.0, 68.0]</td>\n",
       "      <td>(459.0, 656.0]</td>\n",
       "      <td>(1705.0, 2279.0]</td>\n",
       "      <td>(128.0, 194.0]</td>\n",
       "      <td>(86.0, 194.0]</td>\n",
       "      <td>(103.0, 194.0]</td>\n",
       "      <td>(6.0, 8.0]</td>\n",
       "      <td>(60, 110]</td>\n",
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       "      <td>715</td>\n",
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       "      <td>90.00</td>\n",
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       "      <td>(68.0, 109.0]</td>\n",
       "      <td>(656.0, 832.0]</td>\n",
       "      <td>(31.999, 1705.0]</td>\n",
       "      <td>(17.999, 95.0]</td>\n",
       "      <td>(86.0, 194.0]</td>\n",
       "      <td>(86.857, 94.2]</td>\n",
       "      <td>(0.999, 3.0]</td>\n",
       "      <td>(60, 110]</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>150</td>\n",
       "      <td>1806</td>\n",
       "      <td>...</td>\n",
       "      <td>96.00</td>\n",
       "      <td>9</td>\n",
       "      <td>(18.999, 38.0]</td>\n",
       "      <td>(31.999, 459.0]</td>\n",
       "      <td>(1705.0, 2279.0]</td>\n",
       "      <td>(117.0, 128.0]</td>\n",
       "      <td>(64.0, 74.0]</td>\n",
       "      <td>(94.2, 103.0]</td>\n",
       "      <td>(8.0, 18.0]</td>\n",
       "      <td>(18, 30]</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>725</td>\n",
       "      <td>2274</td>\n",
       "      <td>...</td>\n",
       "      <td>104.25</td>\n",
       "      <td>4</td>\n",
       "      <td>(49.0, 59.0]</td>\n",
       "      <td>(656.0, 832.0]</td>\n",
       "      <td>(1705.0, 2279.0]</td>\n",
       "      <td>(107.0, 117.0]</td>\n",
       "      <td>(86.0, 194.0]</td>\n",
       "      <td>(103.0, 194.0]</td>\n",
       "      <td>(3.0, 4.0]</td>\n",
       "      <td>(50, 60]</td>\n",
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       "      <td>805</td>\n",
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       "      <td>80.00</td>\n",
       "      <td>1</td>\n",
       "      <td>(68.0, 109.0]</td>\n",
       "      <td>(656.0, 832.0]</td>\n",
       "      <td>(31.999, 1705.0]</td>\n",
       "      <td>(17.999, 95.0]</td>\n",
       "      <td>(74.0, 86.0]</td>\n",
       "      <td>(78.0, 86.857]</td>\n",
       "      <td>(0.999, 3.0]</td>\n",
       "      <td>(60, 110]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   target  gender_F  income_high  income_low  country_USA  country_India  \\\n",
       "0       0         1            0           1            0              1   \n",
       "1       0         1            0           0            0              1   \n",
       "2       0         1            0           0            0              1   \n",
       "3       0         1            0           1            1              0   \n",
       "4       0         1            1           0            1              0   \n",
       "\n",
       "   country_UK  age  time_since_last_gift  time_since_first_gift    ...      \\\n",
       "0           0   65                   530                   2265    ...       \n",
       "1           0   71                   715                    715    ...       \n",
       "2           0   28                   150                   1806    ...       \n",
       "3           0   52                   725                   2274    ...       \n",
       "4           0   82                   805                    805    ...       \n",
       "\n",
       "   mean_gift  number_gift        disc_age  disc_time_since_last_gift  \\\n",
       "0     116.00            7    (59.0, 68.0]             (459.0, 656.0]   \n",
       "1      90.00            1   (68.0, 109.0]             (656.0, 832.0]   \n",
       "2      96.00            9  (18.999, 38.0]            (31.999, 459.0]   \n",
       "3     104.25            4    (49.0, 59.0]             (656.0, 832.0]   \n",
       "4      80.00            1   (68.0, 109.0]             (656.0, 832.0]   \n",
       "\n",
       "  disc_time_since_first_gift   disc_max_gift  disc_min_gift  disc_mean_gift  \\\n",
       "0           (1705.0, 2279.0]  (128.0, 194.0]  (86.0, 194.0]  (103.0, 194.0]   \n",
       "1           (31.999, 1705.0]  (17.999, 95.0]  (86.0, 194.0]  (86.857, 94.2]   \n",
       "2           (1705.0, 2279.0]  (117.0, 128.0]   (64.0, 74.0]   (94.2, 103.0]   \n",
       "3           (1705.0, 2279.0]  (107.0, 117.0]  (86.0, 194.0]  (103.0, 194.0]   \n",
       "4           (31.999, 1705.0]  (17.999, 95.0]   (74.0, 86.0]  (78.0, 86.857]   \n",
       "\n",
       "  disc_number_gift   Age_DISC  \n",
       "0       (6.0, 8.0]  (60, 110]  \n",
       "1     (0.999, 3.0]  (60, 110]  \n",
       "2      (8.0, 18.0]   (18, 30]  \n",
       "3       (3.0, 4.0]   (50, 60]  \n",
       "4     (0.999, 3.0]  (60, 110]  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(discretize_df.columns)\n",
    "discretize_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "disc_age\n",
      "(18.999, 38.0]    5298\n",
      "(38.0, 49.0]      5110\n",
      "(49.0, 59.0]      4885\n",
      "(59.0, 68.0]      4907\n",
      "(68.0, 109.0]     4800\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(discretize_df.groupby(\"disc_age\").size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:6: FutureWarning: using a dict on a Series for aggregation\n",
      "is deprecated and will be removed in a future version\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# Create the list of variables for our predictor insight graph tables\n",
    "pig_table_variable = [\"income_high\",\"gender_F\",\"disc_mean_gift\",\"disc_time_since_last_gift\",\"disc_number_gift\",\"Age_DISC\"]\n",
    "\n",
    "# Create an empty dictionary\n",
    "pig_tables = {}\n",
    "# Loop through the variables\n",
    "for variable in pig_table_variable:\n",
    "    # Create a predictor insight graph table\n",
    "    pig_table = create_pig_table(discretize_df, 'target', variable)\n",
    "    \n",
    "    # Add the table to the dictionary\n",
    "    pig_tables[variable] = pig_table  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   income_high  Incidence   Size\n",
      "0            0   0.044993  20892\n",
      "1            1   0.060127   4108\n"
     ]
    }
   ],
   "source": [
    "print(pig_tables['income_high'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_pig(pig_table, variable):\n",
    "    \n",
    "    # Plot formatting\n",
    "    plt.ylabel(\"Size\", rotation=0, rotation_mode=\"anchor\", ha=\"right\")\n",
    "    plt.xlabel(variable)\n",
    "    \n",
    "    # Plot the bars with sizes \n",
    "    pig_table[\"Size\"].plot(kind=\"bar\", width=0.5, color=\"lightgray\", edgecolor=\"none\") \n",
    "    \n",
    "    # Plot the incidence line on secondary axis\n",
    "    pig_table[\"Incidence\"].plot(secondary_y=True)\n",
    "    \n",
    "    # Plot formatting\n",
    "    plt.xticks(np.arange(len(pig_table)), pig_table[variable])\n",
    "    plt.xlim([-0.5, len(pig_table) - 0.5])\n",
    "    plt.ylabel(\"Incidence\", rotation=0, rotation_mode=\"anchor\", ha=\"left\")\n",
    "    \n",
    "    # Show the graph\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:6: FutureWarning: using a dict on a Series for aggregation\n",
      "is deprecated and will be removed in a future version\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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l4Uh5YjazZmb2rpktMLN8M/tZKO9lZu+Y2Uoze9LMskN50/B8VZjeM2ZZt4fy5WY2JjVrJCJH2paiCu6dsZ0+bbP5yol5qQ5HpFalPDEDpcBZ7n4CMAQ438xGAncD97p7X6AQuDbUvxYodPc+wL2hHmY2ABgPDATOB/5oZhl1uiYicsRVVDl3v7mVKnduHd2erAxdV5aGJeWJ2SNF4WlW+HPgLOCZUP4wcHF4PDY8J0w/28wslE9091J3fw9YBYyog1UQkTr06PwdLN9Wxo0j29GlZVaqwxGpdSlPzABmlmFm84EtwFRgNbDD3StClQ1A1/C4K7AeIEzfCbSLLU8wT/zrXWdms81sdkVFRaIqIpKGZn1Qwj+X7OKCvi0YfXRuqsMROSLSIjG7e6W7DwG6EZ3l9k9ULfxP1G7l+ylP9HoT3H24uw/PzFRPTpH6YNueCu59exs922Tx1WG6rpzuzOz80N9nlZndlmD6/voLDTazGaHf0SIza1aXsadaWiTmau6+A5gOjATamFl11uwGbAyPNwDdAcL01kBBbHmCeUSkHquscn7z1jbKKp3bRnegaWZafXVJnNC/5wHgAmAAcHnoBxQrWX+hTOAfwDfcfSBwBlBeR6GnhZTv3WbWwczahMc5wDnAUuBVYFyodg3wXHg8KTwnTH/F3T2Ujw9HYb2AvsC7dbMWInIkPb5wJ/lbSrl+RFu6tdZ15XpgBLDK3de4exkwkagfUKxk/YXOAxa6+wIAd9/u7pV1FHdaqFFiNrMfhSaFhWY238xONrO/JjgCOhRdgFfNbCEwC5jq7s8DtwK3mNkqomvID4b6DwLtQvktwG0A7p4PPAUsAV4Erm9sb6ZIQzRnYwlPLd7JOcfkcmbvFqkOR2qmJn1+kvUXOhZwM5tiZnPN7Ad1EG9aOeAFVjMbBVwEnOjupWbWHsh296/WRgDuvhAYmqB8DQl6Vbv7XuDSJMv6JfDL2ohLRFLL3Xl+xW4enFNI99ZZfOOktqkOST4u08xmxzyf4O4TwuOa9PlJVicTOA04CSgGppnZHHefdrgB1xc16fnUBdjm7qUA7r4NwMymA98DjgLuDHVziJJ2LzMbBtwDtAC2AV9y9021G76INETFZVX84Z3tvPF+MSd1zeGWU9rRTNeV002Fuw9PMq0mfX6q62xI0F/otZhcMxk4EWg0ibkme/pLQHczW2FmfzSzT8VOdPdJ7j4k9KpeAPzWzLKAPwDj3H0Y8BA6kxWRGnivsIyb/7uJt9YVc82QNvzPGR1o2VRjBdUzs4C+YQTHbKLBnybF1UnWX2gKMNjMmoeE/SmiS5SNxgHPmN29KJz9jgbOBJ5M0vX9B0CJuz9gZoOAQcDU6Fo+GYDOlkVkv6auLuJP7xbQIrsJvzynE8d3alS/kmkw3L3CzG4gSrIZwEPunm9mdwKz3X0SUX+hR0N/oQKi5I27F5rZPUTJ3YHJ7v5CSlYkRWr0I97QiWo6MN3MFrHvKAcAMzub6Lrv6dVFQL67j6q9UEWkodpbUcWfZxXw8uo9DO7cjO+f2p68HJ0l12fuPhmYHFd2R8zj/fUX+gfRT6YapZp0/uoHVLn7ylA0BHif6IwYMzsa+CNwvruXhDrLgQ5mNsrdZ4Sm7WNDz2kRkY98sKucX72+lbU7yrlsUGuuGNyaDN1XWRqxmpwxtwD+EH5rXEE0BvV17BvH+ktEXdyfDc3WG939QjMbB9xnZq3D6/wOUGIWkY+88f4e7puxnawM42dndWTYUTmpDkkk5WpyjXkOcEqCSWeE/7OBnyWYbz77mrZFRD5SXuk8NLeQ/yzfTb/22dw2ugMdcjU8rgjU8BqziEht2VJUwV1vbGXF9jLGHteSLw3N060bRWLoh4EiUmdmbSjmxsmb2LCrnNtPb8/XhrdVUpbDYmZFB66VcL7hZnZfkmlrw2BaKaEzZhE54iqrnEcX7OCZ/F30zsvittM7cJTupSwp5O6ziS7Fph2dMYvIEVVQXMGPXt7MM/m7GNOnBb8Z01lJWWqdmZ1hZtPN7BkzW2Zmj4WbYmBmJ5nZ22a2wMzeNbOWof7zYXo7M3vJzOaZ2V+IGS7UzL4Y5plvZn8Jd87CzIrM7JdhmTPNrFMo72Rmz4byBWZ2yv6Wk4gSs4gcMW+v3saNkzexcnsZt5zSjm+PbKdbNsqRNBS4mehWk72BU8PIY08CN7n7CUR3MCyJm+8nwJvuPpRoRLIeAGbWH7gMODWMblkJXBnmyQVmhmW+DnwtlN9HNKToCURDieYfYDmfoKZsEal1VVXOA6+u4t6XV3BUy0x+eU4Hjm6TneqwpOF71903AJjZfKAn0V2rNrn7LAB33xWmx853OnBJmP6CmRWG8rOBYcCsUD8H2BKmlQHPh8dzgHPD47OAq8OyKoGdZnbVfpbzCUrMIlKrCvaU8Z0n5/Paiq189oSjuOq4DHKydJYsdaI05nElUY4zPnlnq0QS1THgYXe/PcG08jC2d+xrJbO/5XyCPi0iUmvmrivkovveYMbq7fzi4kH8fvwQJWVJtWXAUWZ2EkC4vhyfRF8nNC2b2QVAXiifBowzs45hWtsw2uX+TAO+GepnmFmrg12OPjEictjcnQfffI8v/HkGGRnGP795Cl8ceXR8c6FInXP3MqLru38wswXAVCD+7ig/A043s7nAecC6MO8S4MfAS2a2MMzb5QAveRNwZrivxBxg4MEuR03ZInJYdu0t59ZnFvLfxR9yTv9O/N+lJ9C6uXpdS91w9xbh/3Simy1Vl98Q83gWMDJu1o/qu/t2ooRc7Tsx8z5J1Hks4euGx88Qhql2983A2AT1Ey4nESVmETlk+Rt3cv1jc1lfWMIPLzyOr43urbNkkcOkxCwiB83deXLWeu6YlE9e8ywmXjeSk3q2TXVYIg2CErOIHJTisgp+/O/F/GvuB5zWpz2/Gz+E9i2apjoskQZDiVlEamzVliK+9dgcVm4p4uZz+vLts/rq3skitUyJWURq5Ln5H3D7vxbRLCuDR74ygtF9O6Q6JJEGSYlZRPartKKSnz+/hH/MXMfwo/O4/4oT6dw6/tcmIlJblJhFJKn1BcV867G5LPpgJ9ed3pvvj+lHVoaGPxA5kpSYRSShqUs2892n5uPAhKuGcd7AzqkOSaRRUGIWkY8pr6zit1OW85fX1zCoayv+eMUwerRrnuqwRBoNJWYR+ciHO/fy7SfmMmttIVee3IP/uWgAzbKS3jZWRI4AJWYRAeDNldu4aeI8Ssor+f34IYwd0jXVIYk0SkrMIo1cZZXzh1dW8vtpK+nToQV/+uKJ9OnYMtVhiTRaSswijdj2olJufnI+b6zcxiVDu/KLzw2ieba+FkRSSZ9AkUZq9toCbnh8HgXFZfzqkuMZf1J33YBCJA0oMYs0Mu7OX994j7teXEa3vBz+9c1TGNS1darDEpEg5SMFmFl3M3vVzJaaWb6Z3RTK25rZVDNbGf7nhXIzs/vMbJWZLTSzE2OWdU2ov9LMrknVOomkq50l5Xz90Tn8cvJSzunfkf98+zQlZZE0kw5nzBXAd919rpm1BOaY2VTgS8A0d7/LzG4DbgNuBS4A+oa/k4E/ASebWVvgJ8BwwMNyJrl7YZ2vkUgaWvzBTr712Fw27ijhx5/uz7Wn9VLTtUgaSvkZs7tvcve54fFuYCnQFRgLPByqPQxcHB6PBR7xyEygjZl1AcYAU929ICTjqcD5dbgqImnJ3Xnsnfe55E9vU15ZxZNfH8lXR/dWUhZJU+lwxvwRM+sJDAXeATq5+yaIkreZdQzVugLrY2bbEMqSlSd6neuA6wCys7NrbwVE0sye0gp++Owinpu/kdOP7cDvLhtC21zt8yLpLG0Ss5m1AP4J3Ozuu/ZzNJ9ogu+n/JOF7hOACQC5ubkJ64jUd+t2lHHzA2+xZmsR3z33WK4/sw9NdO9kkbSXFonZzLKIkvJj7v6vULzZzLqEs+UuwJZQvgHoHjN7N2BjKD8jrnz6kYxbJF29uqaI+98poGVOFv+49mRO6dM+1SGJSA2l/BqzRafGDwJL3f2emEmTgOqe1dcAz8WUXx16Z48EdoYm7ynAeWaWF3pwnxfKRBqNskrn/pnb+b+3t9OnXTYv3DhaSVmknkmHM+ZTgauARWY2P5T9ELgLeMrMrgXWAZeGaZOBC4FVQDHwZQB3LzCznwOzQr073b2gblZBJPU27S7nrte3sbqwjHEDW3HVCW3o1KpZqsMSkYOU8sTs7m+S+PowwNkJ6jtwfZJlPQQ8VHvRidQPb68r5vcztmFm3HFGB0Z0020aReqrlCdmETl0FVXO3+cV8u+lu+nbNpvbTu9Apxb6WIvUZym/xiwih2brngpue2kz/166m4v6teTXYzorKUvaMLPzzWx5GKXxtgTTm5rZk2H6O+HnsrHTe5hZkZl9r65iThf6FIvUQ3M2lvB/b22jvNL5wWntOb1nbqpDEvmImWUADwDnEv1iZlYYiXFJTLVrgUJ372Nm44G7gctipt8L/LeuYk4nSswi9UhllfPEop08uWgnPdpkcfvoDnRrnZXqsETijQBWufsaADObSDRqY2xiHgv8NDx+BrjfzMzd3cwuBtYAe+ou5PShxCxSTxSWVPLbt7ax4MO9nNM7l2+MaEuzTF2NkpTJNLPZMc8nhMGbIPFIjCfHzf9RHXevMLOdQDszKyG6L8K5QKNrxgYlZpF6YfGWvfz6jW0UlVVx48h2nNenRapDEqlw9+FJptVkJMZkdX4G3OvuRY11PHclZpE0VuXOv5bs4pH5O+jcIpOfntWZ3nka61rSXrIRGhPV2WBmmUBroIDozHqcmf0aaANUmdled7//yIedHpSYRdJUUWkl97y9nXc/KOHUHs25aWQ7mmer6VrqhVlAXzPrBXwAjAeuiKtTPbrjDGAc8EoYp2J0dQUz+ylQ1JiSMigxi6Sd8kpn7qYSJswqYHtJJdcNz+Mz/VrqNo1Sb4RrxjcQDYucATzk7vlmdicw290nEQ3F/KiZrSI6Ux6fuojTixKzSBooLqti9sYSZqwvZvbGEkrKnQ65Gdx9Xmf6tW+a6vBEDpq7TyYaQjm27I6Yx3vZN9RysmX89IgEl+aUmEVSZMfeSmauL2bm+hLmf1hCRRW0adaE04/OZVT35pzQuRlZGTpLFmlslJhF6tCHReXMWBedGS/dWooDnVpkclG/lozq3pzj2jclQ/dMFmnUlJhFjiB3Z+2OcmasL2bG+mLeKywHoGebLMYf35pRPZrTq02Wrh+LyEeUmEVqWWWVs2TLXmasj86MPyyqwID+HZpy7Yl5jOqeQ+eWGq1LRBJTYhapBaUVlby9ejsv5X/I1CVb2FZUSmYTOKFzM8YNbMXJ3ZqTl5OR6jBFpB5QYhY5REWlFUxfvoUp+ZuZvmwLu0sryM3O4MzjOtK/VTknHZWj3x2LyEFTYhY5CNuKSpm2dDNT8jfz5qptlFVU0S43m08P7sKYgZ05pU87mmZmsHjx4lSHKiL1lBKzyAGsLyhmSv6HvJS/mdnvF1Dl0C0vh6tGHs2YgZ0ZdnSeelKLSK1RYhaJ4+4s37ybKYs3MyX/Q5Zs2gXAcZ1bcsNZfRkzsBMDurRST2oROSKUmEWAqipn3vpCpuRHyfj97cWYwbAeefzowv6cN7ATR7fLTXWYItIIKDFLo1VWUcXbq7cxJX8zU5dsZltRKVkZxinHtOfrpx/DOQM60rFls1SHKSKNjBKzNCp7SiuYvnwrU/I/5NWYntRnHNeRMQM7c0a/DrRqpt8Yi0jqKDFLg7e9qJRpS7cwJf9D3ojpSX3h8V0YM6gTpxzTnmZZ+o2xiKQHJWZpkDYUFjMlfzMv5X/IrLVRT+qubXL44slHM2ZgJ4b3bKue1CKSlpSYpUFwd97fWc6MdcXMf+UN8jfG9KQ+sw/nDezMwKPUk1pE0p8Ss9RbVe4s31b20Q0iNu2OxqQ+8eg8fnjhcZw3oDM926sntYjUL0rMUq+UVzqLNu9lxvpi3tlQQkFJJZlNYHCnZlzSvxUjuzdn9EknpDpMEZFDpsQsaa+kvIo5G6M7Nc3+oIQ95U6zTGP4UTmM6t6c4V1zyNWY1CLSQCgxS1raubeSdz8oYca6YuZtKqGXQts6AAAYYElEQVS8Clo1bcIpPZozqntzhnTJITtD14tFpOFRYpa0sWNvJa+v3cOM9cXkbymlyqFDbgYXHNuSUd2bM6BDU/WkFpEGT4lZUm5PWRX/WrKL55btYm+Fc3SbLL4wqDWjujend16WelKLSKOSFonZzB4CLgK2uPugUNYWeBLoCawFvuDuhRZ9S/8euBAoBr7k7nPDPNcAPw6L/YW7P1yX6yEHp7SiihdWFPH04p3sLqti9NHNufz41vRok53q0EREUiYtEjPwd+B+4JGYstuAae5+l5ndFp7fClwA9A1/JwN/Ak4OifwnwHDAgTlmNsndC+tsLaRGKqucaWuKeHzhTrYVVzLsqGZcPSSPY9oqIYuIpEVidvfXzaxnXPFY4Izw+GFgOlFiHgs84u4OzDSzNmbWJdSd6u4FAGY2FTgfeOIIhy815O68vb6YR+fvYMOuCvq1z+aWU9ozuLNuFCEiUi0tEnMSndx9E4C7bzKzjqG8K7A+pt6GUJasXNLA/E0lPDxvBysLyujROosff6oDJ3fL0fVjEZE46ZyYk0n0Te77Kf/kAsyuA64DyM5W8+mRtGJbKQ/P38GCD/fSITeD74xqxxm9ctW7WkQkiXROzJvNrEs4W+4CbAnlG4DuMfW6ARtD+Rlx5dMTLdjdJwATAHJzcxMmbzk863eW8+iCHby9rpjWTZvwteF5XNi3JVn67bGIyH6lc2KeBFwD3BX+PxdTfoOZTSTq/LUzJO8pwP+aWV6odx5wex3H3Oht3VPBEwt38vKaIppmGlcObs3Y/q1onqWRuUREaiItErOZPUF0ttvezDYQ9a6+C3jKzK4F1gGXhuqTiX4qtYro51JfBnD3AjP7OTAr1LuzuiOYHHkFe8r465wCXli+Gwc+068lXxjUmtbNdJ9jEZGDkRaJ2d0vTzLp7AR1Hbg+yXIeAh6qxdDkAPaUVvDgm+8x4fU1FJdVcFbvXK4Y3IaOuWmxa4mI1Dv69pRDUlpRyRPvrOP+V1exraiMMQM7MbZXEw0OIiJymJSY5aBUVjnPzf+Ae6auYENhCaN6t+P/Xd2PoT3yWLx4carDExGp95SYpUbcnZeXbuG3U5azfPNuBnVtxa8uOZ7T+rTXb5FFRGqRErMc0DtrtnP3i8uYu24Hvdvn8sAVJ3LBoM400W+RRURqnRKzJJW/cSe/mbKc6cu30rlVM+665HjGDetGZoZ++iQicqQoMcsnrN22h3umrmDSgo20zsnihxcex9WjetIsSz99EhE50pSY5SObd+3lvmkreXLWerIymnDDmX342um9aZ2TlerQREQaDSVmYWdxOX9+fTV/e+s9KiqdK07uwQ1n9aFjS931SUQOjZmdD/weyAD+6u53xU1vSnSr32HAduAyd19rZucSDTCVDZQB33f3V+o0+BRTYm7ESsoq+fvba/nT9FXsLq1g7AlHccu5/ejRrnmqQxOReszMMoAHgHOJ7mMwy8wmufuSmGrXAoXu3sfMxgN3A5cB24DPuPtGMxsETKGR3SlQibkRKq+s4slZ67lv2kq27C7l7OM68r0x/ejfpVWqQxORhmEEsMrd1wCEexuMBWIT81jgp+HxM8D9ZmbuPi+mTj7QzMyaunvpkQ87PSgxNyJVVc7zizZxz0vLWbu9mOFH5/HAlSdyUs+2qQ5NRBqWrsD6mOcbiG46lLCOu1eY2U6gHdEZc7XPA/MaU1IGJeZGwd15bcVWfv3icpZs2sVxnVvy0JeGc2a/jhocREQOVaaZzY55PiHcUhcg0RdL/C1291vHzAYSNW+fd1hR1kNKzA3cnPcL+fWLy3jnvQK6t83hd5cN4bMnHKXBQUTkcFW4+/Ak0zYA3WOedwM2JqmzwcwygdZAAYCZdQOeBa5299W1GnU9oMTcQK3YvJvfTFnO1CWbad+iKT8fO5DLTupBdqYGBxGRI24W0NfMegEfAOOBK+LqTAKuAWYA44BX3N3NrA3wAnC7u79VhzGnDSXmBmZ9QTH3vryCZ+d9QIvsTL4/ph9fPrUnzbP1VotI3QjXjG8g6lGdATzk7vlmdicw290nAQ8Cj5rZKqIz5fFh9huAPsD/mNn/hLLz3H1L3a5F6ujbuoHYVlTK/a+s4rF33qeJGdeN7s03PnUMebm6DaOI1D13nwxMjiu7I+bxXuDSBPP9AvjFEQ8wjSkx13PFZVU8u3QXzz31KqUVVXxheDduPLsvXVrnpDo0ERE5BErM9VRZpfPC8t08nb+TXaVVfHpwF7577rH07tAi1aGJiMhhUGKuZyqrnGlr9vDEwh1sLa5kaJdmXDOkDRd/6sRUhyYiIrVAibmecHdmrC/hkfmFbNhVwbHtsrn5lHac0FlN1iIiDYkScz2w4MMSHp63gxXby+jeOosfnt6BUd1zNDiIiEgDpMScxlZuL+WR+TuYt2kvHZpncNOodpzVK5cMDQ4iItJgKTGnoQ07y3l0wQ7eWldMq6ZN+NqwPC44tiXZGUrIIiINnRJzGtm2p4LHF+3k5dVFNM0wLj++NZ/r34rm2RqtS0SksVBiTgO7Sit5ZvEu/rN8Fw5c1K8lXxjUmjbNMlIdmoiI1DEl5hR7YfluHp5fyN4K56xeuVwxuA0dW+htERFprJQB0sDgzs246oQ2HN1Gw2eKiDR2SswpduGxLfh0v5apDkNERNKEehWlmH6LLCIisZSYRURE0ogSs4iISBpRYhYREUkjDS4xm9n5ZrbczFaZ2W2pjkdERORgNKjEbGYZwAPABcAA4HIzG5DaqERERGquQSVmYASwyt3XuHsZMBEYm+KYREREaqyhJeauwPqY5xtCmYiISL3Q0AYYSfSjYP9EJbPrgOuqp5tZyRGN6sAygYoUx5AutC320bbYR9tin3TZFjmpDqChamiJeQPQPeZ5N2BjfCV3nwBMqKugDsTMZrv78FTHkQ60LfbRtthH22IfbYuGr6E1Zc8C+ppZLzPLBsYDk1Ick4iISI01qDNmd68wsxuAKUAG8JC756c4LBERkRprUIkZwN0nA5NTHcdBSptm9TSgbbGPtsU+2hb7aFs0cOb+ib5RIiIikiIN7RqziIhIvabELCIikkYadWI2sxwzey0M5YmZvWhmO8zs+bh6b5jZ/PC30cz+nWR5d5vZ4vB3WUz5WWY2N5Q/bGaZoTzPzJ41s4Vm9q6ZDapBzAljMbPWZvYfM1tgZvlm9uUk8w8zs0VhLPH7LNwQ2szuNbMyM/u+mZ0Z8xrzzWyvmV0c6p0d1mW+mb1pZn2SvM7t4TWWm9mYGqxX0uWa2RfMbElYr8eTzH95WK+F4X1sH8p/amYfxKzLhaH8yrh1rDKzIWHaa2ZWaWYjwvNfh9deGrfNss1sgpmtMLNlZvb5BHFlhfd8UZj/9phpa0P5fDObHVP+ZExca81sfg22301h/8o3s5sTTP+emXn1dombNsTMZoR5F8btu4+ZWYGZjbNPfl4S7u8x8/7BzIqSxHuumc0J6z/HzM6qwTpeGmKsMrPhcdM+sb+ZWTOLPlfVn4mfJVlu07DNV5nZO2bWM5SPDvvd4vC8pt8XvcJyVoblZofyb8S8329akuGC7SDH+zez08Nnp8LMxsVNO+T3KExPtF1zwjqUJdqfpBa4e6P9A64Hbop5fjbwGeD5/czzT+DqBOWfBqYSdajLBWYDrYgOftYDx4Z6dwLXhse/AX4SHh8HTDvI+D+KBfghcHd43AEoALITzPMuMIpoMJb/AhfEbIv/At+Lq982LKt5eL4C6B8efwv4e4LXGAAsAJoCvYDVQMYB1iXhcoG+wDwgLzzvmGDeTGAL0D48/zXw0/D4p/HrlGD+44E1cfvFSmA4cArwFlEv/wxgBnBGqPcz4BfhcZPq149b9hXAxPC4ObAW6Bmer000T9z8/wfccYA6g4DFYfmZwMtA35jp3Yl+qfB+khiPra4PHAVsAtrETP87MI6YzwtJ9veYeYYDjwJFSWIeChwVE/8HNdjf+wP9gOnA8APtb0T7eItQJwt4BxiZYLnfAv4cHo8HnoyZ1hNYfDDfF8BTwPjw+M/AN8Pj2O3zWeDFBLFkhPh7A9lhvQYcYLv0BAYDjwDjYsoP9z3a7+e4Jvuv/g7tr1GfMQNXAs9VP3H3acDuZJXNrCVwFpDojHkA8Jq7V7j7HqId+nygHVDq7itCvanA52PmmRZeexnQ08w61STwBLE40DKczbUgSqYVcfN0IfpgzvDok/UIcHGYfCWwPMFLjQP+6+7FMa/TKjxuTYIBXIjGJ5/o7qXu/h6wimgc8/1JttyvAQ+4eyGAu29JMK+Fv9yw/q2SxJXM5cATMc+vBLbHxNWM6EuyKdEX/OYw7SvAr0JcVe6+Lcl65VrUSpIDlAG7ahJUWJcvxMWWSH9gprsXu3sF8BrwuZjp9wI/IMEoeCH2Fe6+MjzeSHSQ0yFB1djPS7L9vfpmMr8Jr5mQu88LrwWQDzQzs6b7W0l3X+ruifbRhPubR6rPBrPCX6JtMBZ4ODx+Bji7ulUkzgG/L8J8Z4XlEJZ7cagf+77nJonloMf7d/e17r4QqIqbdFjvEYf2OZZa0GgTc2he6u3uaw9its8RndUm+mJdAFxgZs1D886ZRGcq24CsmKa3cewbnWwBcEmIZwRwNNFoZYcSy/1EX9AbgUVER/bxH9SuRKOjVdsAdK3eFsCOBK8zno8nhq8Ck81sA3AVcFeCeQ5lzPJkyz0WONbM3jKzmWZ2fvyM7l4OfJNovTcSfSE9GFPlhtBE+5CZ5SV47cuq1zFmW+wNy54BvEp0FrkJmOLuS82sTZj356EZ8ekkB1XPAHvCvOuA37p7QXXowEuhKfe6BPOOBjZXJ839WAycbmbtzKw5cCFhHzOzzxKdjS44wDII9UcQHYSsjpuUycc/L8n2d4AbgEnuvqkmr0l0oDrP3UtrWD9e0v3NzDIsuhSwBZjq7u/sb/5wYLOT6ID6IwfxfdEO2BGW87FYwnKuN7PVRK06Nx7MuhyCw32PdO+BFGm0iRloT+JEtD/xZ1YfcfeXiH4//XaoMwOoCGem44F7zexdoiPs6g/tXUBe+OL4NlGTbU3HwI2PZQwwn6gpcghwv5m1ipsn2VjiCbdFOMM+nqgZtNp3gAvdvRvwN+CeBMus0ZjlcZItN5OoOfsMonX+a0xSrI4ziygxDyVa/4VA9bXcPwHHEG2TTURNw7HzngwUu/viUPSxbWHRte7+RAdMXYGzzOz0EFc34C13P5Ho/f5tgvUaAVSGuHoB3zWz3mHaqWHeC4Drw3JjJd3fYrn7UuBuotaYF4m+kCtCkv4RcMeBlhHWtQtR0+aXExzUtSRmuyTb383sKOBS4A81fM2BIfav16R+ssUkKPMQZ6W7DyF6r0ZY4n4cNdlfa/p9sd9lufsD7n4McCvw40OMpUZq4T2qtVjk4DTmxFxC1ERZI2bWjuhL9oVkddz9l+4+xN3PJdqpq5sHZ7j7aHcfAbweU77L3b8cvjiuJmo+fO8QY/ky8K/QfLcqLOe4uFk38PEz8uqxxJNtiy8Az4YzUsysA3BCzFnHk0TXYOPVaMzymPXZ33I3AM+5e3loTltOlKhjDQFw99XhQOip6vndfXP4cq4C/h+fbIqLbxGI3xafI2omLgrNov8FRhI1dRcDz4Z6TwMnJli9K4iuJZaHZvi3iK7tVTcbVzfPPxsbW2j6viRsiwNy9wfd/UR3P53oMsZKogOSXsACM1tL9D7MNbPO8fOHg7gXgB+7+8wEL1FG3D6SZH8fCvQBVoXXbG5mqxLFbGbdwnpf7e7xZ+gH44D7m7vvILo2/YkWl9j5w3ZvTbQNY9X0+2Ib0CYsJ2EswUT2XUZKGMsB5q+Rw3yPajUWqblGm5jDNcsMM6tpcr6UqJPH3kQTQ5NZu/B4MFFnjJfC847hf1OiI+U/h+dtqntsEjXlvl7dNG1m08wsWbNRoljWEXVGITSp9gPWxK3zJmC3mY0M18KuJkp6hUSdTuJHgos/YysEWpvZseH5ucDSBPFNAsZb1Nu1F1EifXc/67W/5f6bqAmO0Bx3bPx6AR8AA0KC/9j84Syw2ueImn0J05oQbcuJ1WUx26L6s7EO+JSZZYYz808BS8MBwH+IzuQh2vZLEmyLdURn2WZmuURJfZmZ5YZ+AoTy82JjA84Blrn7R5cezKyrmU1L8Bqx+1gPooT+hLsvcveO7t7T3XsSfdGe6O4fxs2bTZQgH3H3pxMtn6g5/qPPS7L93d1fcPfOMa9Z7O6f6LkfWj1eAG5397fipj0SmtRrKuH+ZmYdqltXzCyHsE2TzH9NeDwOeCW8vx+p6fdFmO/VsBzCcp8LMcQeUH6acIAeJ+l4/2b2KzP7XIJ5Ejrc94j9fI7lCPM06IGWqj+i65DnxDx/A9hKdHS8ARgTM206cH7c/MOBv4bHzYi+mJcAM4EhMfV+Q5QolgM3x5SPIvpwLgP+xb6ex02IetDmJIk7USxHER0ILCL6gv9izLT5cTEvJrqGeD/7Rn97kKgz2PfC855ECa9J3Ot8LrzGghBH71D+WeDOmHo/Cq+xnH09v5Ou136Wa0TN2kvC9PFJ1usbYRsvJEqY7UL5o2G+hURfNF1i5jmD6Gw40X6xIGyrDOAvYdlLgHti6h1N1AKykKgTX4/4bUHUEe9pog5OS4Dvh/Le4TUWhGk/iovh78A3EuxvU5LsE2+E5S8Azk5SZy37eq7H7rtfBMqJLoVU/w2Ji2UcMZ8X9rO/x71mUczj2O3yY6JkH/uaHavfV6B7kn1kA1BK1AFvSsy0RPvbYKLLQwuJ9vk7YurfCXw2Zl2eJurc9C5h34v5HFT3yq7R90V4b98Ny3saaBrKfx/e6/lEyXtgzGd3csxyLyT6lcLq2P0CeB4YlWC7nBRefw9RS05+bbxHybZrov1Jf7X716iH5DSzocAt7n5VqmOJFa6DfcXdb6nD1xxK9AX8qLsnulZaG69R5+t1KMK2+C9wkbvPPlD9umLRDVrWuXud3jHNzP5OlBRWc4Q/L6FJ/UF3v/RIvcbBsOg3zc+7+6BUf1+Y2RR3P+CYAHUlNIMP98S/RpDD0GibsiH6yQbwqoUBA9KFuy9OQfK6AuhMdN30iEjReh2Ke4h+WlOZ6kBiufv9KUjKjxE13++ti8+LR/0u0iUpjyZqfdkGqf++SJekbGGAEaLPSHwnQakFjfqMWUREJN006jNmERGRdKPELCIikkaUmEVERNJI/O9WRYTorlRAEdG426+7+8upjejICkN3DnD3u8LvwZ8nGprzRmC0u/9vSgMUaUTU+UskgerEfKR+OpbOzGw80W9WrwnPi9y9RYrDEmk01JQtEpjZjyy67+zLRCOnYWZ/t3CPWzO7y6L78y40s9+Gsk4W3VN7QfhLNEQpZtbTons2/9Wie+M+ZmbnWHRzjpW2797PuRbdbGOWmc0zs7Ex879h0Q0z5la/jpmdYWbTzeyZsPzHwqhuydbxwlDvTYvuLf18KP+Smd1v0T2pfw1caNE9d+8Gqu+/+1htbWsRSU5N2SKAmQ0jGv5wKNHnYi4wJ2Z6W6KRp45zd7d9N9K4j+jWep8Lv2/d35llH6IhQK8jGnrxCuA0otGWfkg0dvKPiIaE/Ep4jXfDgcIW4Fx33xuGdnyCMOZ2iHkg0TjGbwGnAm8mWMdmRKOYne7u75nZJ26Q4e7zzewOooEjbgjzXe/ReO4iUgd0xiwSGU10w45ij8Yrjx/IYxfRrSD/amaXsG8glrOI7mCFRzfL2Lmf13jPo/Grq4iGZpzm0bWkRURDP0I0ZvZtYQCH6UTDKvYgGszh/5nZIqJhHgfELPddd98Qljs/ZlnxjgPWeHQzEKjBnatEpO7pjFlkn6QdLty9IjQ3n010Zn0DUVI+GLH3G66KeV7Fvs+iAZ939+WxM4Zr3puBE4gOqGNvYBK73EqSf66TNnGLSPrQGbNI5HXgc2G4wZbAZ2InmlkLoLW7TwZuJtxqkujmFd8MdTLsk/fAPlhTgG9XXycO4zNDdCvCTeGs+Cqim2scrGVA7zD+M8BlNZyv3KI7a4lIHVBiFgHcfS7RvY/nA/8kunNQrJbA82a2EHgN+E4ovwk4MzQxzyG61ns4fk7UbL3QzBaH5wB/BK4xs5lEt77cc7ALdvcS4FvAi2b2JtEZ+P6a3qtNCPGo85dIHdDPpUQaETNr4e5F4Yz8AWClu9+b6rhEZB+dMYs0Ll8LHcvyiZrH/5LieEQkjs6YRWqRmbUjuu4c72x3316HcTwL9IorvtXdp9RVDCJyaJSYRURE0oiaskVERNKIErOIiEgaUWIWERFJI0rMIiIiaUSJWUREJI38f+mhecGXltQwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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TRtOpkxt8NbOZwAhJwyWVABNJbkPmyr1deRHwRHri9RpwRnq7shQ4GdhVg+B2x4nZzDqcJWs2cdffl3PJ2KEcM7Qs63DanfSe8dUk7XwWAvdFxHxJN0i6IK32M6CfpHLgS+zsROpWoAdJW6OZwF0RMbdVdyBjmV/KNjNrTRHBdQ/Np7RLMV8994isw2m3ImIqSZug3LJrcqa3sbPtUG6dzfnKOxKfMZtZh/LIvDf4e/lbfPnsw+hb6n4MrPD4jNnMWtS8efOyDgGA0aNHU1Vdw7ceXsDIQb247KSDsg7JLC+fMZtZh3Hrk+Ws2rCNGyaMosgNvqxAOTGbWYewbN0WfvqXZXzk+CGMHdY363DMGuXEbGbtXkRw/UPzKSnuxOTz3ODLCpsTs5m1e8+t3MqMxW/yhTNHMKBn16zDMdslJ2Yza9e219Rx++z1jBjQg0mnDMs6HLPdcqtsM2vX/rBgI2s213DzpaPoXORzESt8/pSaWbu1ZnMNv5u/kdMO6s4ph/TPOhyzJnFiNrN2647ZFQi48vg+WYdi1mROzGbWLs1etZWnV2xl4lG96V/qu3bWdjQpMUv6D0nzJc2VNEfSSZLukDSypQM0M9tTO2qDn8ysYHDPYi48slfW4Zjtkd1+jZT0HuB84PiI2C6pP1ASEZ9u8ejMzPbCg4s2smpTDdefMYDORe7hy9qWppwxDwLWRcR2gIhYFxGrJM2QNFbSBelZ9BxJiyUtA5A0RtJTkmZLekzSoJbcETMzgHVbarj3pQ2cPLQbYwZ3yzocsz3WlMT8ODBU0suSfiTp9NyZETElIo6NiGOBF4HvSuoM/AC4KCLGAHcC327u4M3MGrrz+fXUBXx6jBt8Wdu020vZEbFZ0hjgNOD9wG8lTW5YT9JXga0Rcauk0cBoYJokgCJgdbNGbmbWwNw3tvGXV6u47Oje7N+jc9bhmO2VJjVVjIhaYAYwQ9JLwKTc+ZLGkwxs/b76ImB+RLynqYFIKgJmAa9HxPmShgP3An2B54FPRES1pC7APcAY4C3gkohYnq7j68CVQC3wbxHxWFO3b2ZtW01dcNvMCgb2KOajI93gy9qu3V7KlnS4pBE5RccCr+bMPwj4EfBPEbE1LV4M7Jc2HENSZ0mjdrOpzwMLc97fBNwcESOA9SQJl/R1fUQcCtyc1iNtIT4RGAWcC/woTfZm1gH8afEmXtuwg6vG9KFLsZ8EtbarKZ/eHsDdkhZImguMBK7LmX8F0A94IG0ANjUiqoGLgJskvQjMAU5pbAOSDgA+CNyRvhdwBnB/WuVu4MJ0ekL6nnT++LT+BODeiNgeEcuAcuDEJuyfmbVx67fW8qu5lYwZ3JUTD3CDL2vbmnKPeTb5k+q49HUWcH2e5eaw89L27nwf+CrQM33fD6iMiJr0/UpgSDo9BFiRbqNG0oa0/hDgmZx15i5jZu3Yz19YT3VtcNXYvqTtWszarMyv90g6H1ibfgF4uzhP1djNvF0t03CbV0maJWlWTU1Nvipm1kYsfHMb05du4cNH9mJILzf4srYv88QMnApcIGk5SWOvM0jOoMsk1Z/RHwCsSqdXAkMB0vm9gYrc8jzLvENE3B4RYyNibHGxu+oza6tq64IfP1dB/+5FXHJU76zDsQxI2ryXy42VdEsj85annWllIvPEHBFfj4gDImIYSeOtJyLiY8CTJPepIWkF/mA6PYWdrcIvSutHWj5RUpe0RfcI4LlW2g0zy8Bj5ZtZun4Hnx7Th65u8GV7ICJmRcS/ZR1HPoX8Sf4a8CVJ5ST3kH+Wlv8M6JeWfwmYDBAR84H7gAXAo8Dn0se8zKwd2rCtlnvmVHL0/l059cDuWYdjGZM0Lu2R8n5JiyT9Km0YjKQTJP1D0ouSnpPUM63/cDq/n6THJb0g6Sfk3BqV9PF0mTmSflL/tI+kzZK+na7zGUkD0/KBkh5Iy1+UdMqu1pNPQSXmiJgREeen00sj4sSIODQiLs7pEnRb+v7QdP7SnOW/HRGHRMThEfFIVvthZi3vnjmVbN1Rx2fG9nGDL6t3HPAFkqeHDgZOlVQC/Bb4fEQcA5wJbG2w3LXA3yLiOJKrrwcCSDoSuAQ4Ne3dshb4WLpMKfBMus6/AP+clt8CPJWWHw/M38163sU3WM2szXl53XYeL9/MhCN7cmBZSdbhWOF4LiJWAkiaAwwDNgCrI2ImQERsTOfnLvc+4CPp/D9JWp+WjyfpzGpmWr8bsDadVw08nE7PBs5Kp88ALk/XVQtskPSJXaznXZyYzaxNqYukh6+yrkVcdlRZ1uFYYdmeM11LkuNEI0/oNJCvjoC7I+LreebtSNs35W6rMbtaz7sU1KVsM7Pd+fMrW3j5rWo+dXwZ3Uv8L8x2axEwWNIJAOn95YZJ9C+kl5YlnQfUj4AyHbhI0oB0Xt+0t8tdmQ58Nq1fJKnXnq7Hn2ozazM2b6/l7hfWM3K/LowbXpp1ONYGpD1RXgL8IO2JchrQtUG164H3SXoeOBt4LV12AfAN4PG058tpJEMh78rngfen40rMBkbt6Xq080y8YyotLY0tW7bs9fLz5s1rxmj23ujRo7MOwXL4c7FTcx6L22ZWMPXlTXz/vEEc3HfP7i0XwrFoTyRVRYS/HbUAnzGbWZuwdH01U1/exHkjeu5xUjZrS5yYzazgRQS3PVdBj5JOfOIY9/Bl7ZsTs5kVvBnLt7Dgze1ccVwZPbp4NFdr35yYzaygVVXXcefsSkb0K+HMQ3pkHY41kaRzJS2WVC5pcp75XST9Np3/rKRhOfOOlvS0pPmSXpLUsLFWu+bEbGYFq6Kqhu8//RaV22r57Al96eQevtqEtLvJW4HzSHrhulTSyAbVrgTWR8ShwM3ATemyxcAvgc9ExCiSIYZ3tFLoBcEdjJhZwamqruP3Czbwx4WbqI3gE8eWcVj/LlmHZU13IlBe32WypHuBCSRjGdSbAFyXTt8P/DDt2/psYG5EvAgQEW+1VtCFwonZzArGjtrgkSWbuPelDWzcXsf7DurOJ44tY1BPj7PcxgwBVuS8Xwmc1FidiKiRtIFkwKLDgJD0GLAfcG9EfKflQy4cTsxmlrm6CP76ahW/mFPJG5trOHr/rnzyuDJG9GtfZ8nt7Pn2Ykmzct7fHhG3p9P57jk07DSjsTrFwHuBE4AqYLqk2RExfV8DbiucmM0sUy++sZW7nq+kvKKaYWWduf6MARw/qKtHjCp8NRExtpF5K4GhOe8PAFY1Umdlel+5N1CRlj8VEesAJE0lGaXJidnMrCUtW1/Nz19Yz+xV29ivtIgvntKPccNKKerkhNwOzARGSBoOvA5MBC5rUGcKMAl4GrgIeCIi6i9hf1VSd5IRnE4naRzWYTgxm1mrWru5hl/OreTJpVsoLenEp47vw/mH96SkyAm5vUjvGV8NPAYUAXdGxHxJNwCzImIK8DPgF5LKSc6UJ6bLrpf0PZLkHsDUiPhTJjuSESdmM2sVm7bXct+8jTy8eCMAHxnZi4tH9XKHIe1UREwFpjYouyZnehtwcSPL/pLkkakOyYnZzFrU9po6Hlq8id/N30hVdR3jDy7lY8eUsV+p//2Y5ZP5X0bao8tfgC4k8dwfEdem9ybuBfoCzwOfiIhqSV2Ae4AxwFvAJRGxPF3X10keWq8F/i0iHmvt/TGzRG1d8IfnV/Kdqat4s6qWsYO7ccVxZQzr4wEozHYl88QMbAfOiIjNkjoDf5P0CPAl4OaIuFfSbSQJ98fk9BYjaSJJbzGXpL3KTARGAYOBP0s6LCJqs9gps44qIpix+E1uenQRi97YxIh+JXzxlP4cvX+H6lXRbK9lnpgjGRB6c/q2c/oTwBnsbMV3N0kPMT+m8d5iJpA8iL4dWJY2KDiRpMWfmbWCF1dU8t+PLOSZpRUc1K87P7zsOA7SW370yWwPZJ6Y4e1+VWcDh5L0r/oKUBkRNWmVlSS9xEDjvcUMAZ7JWW3uMmbWgpav28L/PL6YP81dTb/SEq6/YBSXnnggJcWdmDevIuvwzNqUgkjM6eXmYyWVAQ8AR+arlr421ltMU3qaSVYgXQVcBVBS4vtdzaWd9WpkTbBu83Z+MH0Jv3r2NToXdeLfzjiUf37fwfTs6i40zfZWQSTmehFRKWkGcDJQJqk4PWvO7TVmV73F7K6nmfrt3A7cDlBaWpo3eZtZ46qqa7jjr8v4yVOvsK2mjoknDOXzZ45gQE/fRzbbV5knZkn7ATvSpNwNOJOkQdeTJL3B3EvSO8yD6SKN9RYzBfh1+mD6YGAE8Fyr7oxZO7ejto77Zq3g+39ewpubtnPuqP35yrmHc8h+HifZrLlknpiBQcDd6X3mTsB9EfGwpAXAvZK+BbxA0ksMNN5bzHxJ95EMK1YDfM4tss2aR0Tw2Pw3+M6ji1m6bgsnDOvDbR8fw5iD+mQdmlm7k3lijoi5wHF5ypeStKpuWL6r3mK+DXy7uWM068hmLq/gv6cu5PnXKjl0QA9+evlYzjxygFtam7WQzBOzmRWm8rWbuPGRxfx54RoG9urCjR85iovGHEBxUaesQzNr15yYzewd1mzcxs3TXua+WSsoLSnmK+cczqdOHU63EvdpbdYanJjNDICN23bwk6de4Wd/W0ZtXXDFKcO5+oxD6VvqRwrNWpMTs1kHt72mll8+8xo/fGIJ66t2MOHYwfz7WYdzYL/uWYdm1iE5MZt1UHV1wUNzV/HdxxezomIr7z20P5PPO4LRQ3pnHZpZh+bEbNYB/W3JOm58dCHzXt/IyEG9uOdTR/G+w/bLOiwzw4nZrEOZv2oDNz6yiL8uWceQsm7cfMkxTDhmCJ06+dEns0LhxGzWAayoqOJ7017mj3Nep3e3znzjg0fyifccRJdit7Q2KzROzGbt2Pot1dz6ZDn3PP0qEvyf9x3CZ8cdQu9uHmTCrFA5MZu1Q9tr6nho8SZ+f/+TbNlew0VjDuCLZx3GoN7dsg7NzHbDidmsHamtC55YtoVfvVjJuqpaxh8xgK+eewSH798z69DMrImcmM3agYhg5utb+fkLlby2YQeH9Svhy6f2Z+L4sVmHZmZ7yInZrI1bvG47dz2/nnlrtzO4ZzGTT+vPqQd29yATZm2UE7NZG/X6xh3cM6eSv79WRVnXTnz2hL6cM6IHxX70yaxNc2I2a2PWb63l3pcqeXTJZjoXicuO7s2FR/aie2eP+mTWHjgxm7URW3fU8cDCjTywYCPba4PzRvRg4lFl9OnmZ5HN2hMnZrMCV1MXPF6+mV/PraRyWx2nHtidy48tY0gvP4ts1h45MZsVqIjgHyuquOeFSl7fVMPoAV34z3F9OLx/l6xDM7MW5MRsVoDmrd3GXc+vZ/G6ag7s3Zlrxu3HCUO6uaW1WQeQeWsRSUMlPSlpoaT5kj6flveVNE3SkvS1T1ouSbdIKpc0V9LxOeualNZfImlSVvtktreWV1Zz/ZNrmfz4GtZV1fJvJ/fjBx8cxIkH+PEns46iEM6Ya4B/j4jnJfUEZkuaBlwBTI+IGyVNBiYDXwPOA0akPycBPwZOktQXuBYYC0S6nikRsb7V98hsD725pYZfza3kiaVb6FYsrjiujA8d3pMuxZl/dzazVpZ5Yo6I1cDqdHqTpIXAEGACMC6tdjcwgyQxTwDuiYgAnpFUJmlQWndaRFQApMn9XOA3rbYzZnto8/Zafjd/Iw8t3kRdBBOO6MU/je5Fzy5uaW3WUWWemHNJGgYcBzwLDEyTNhGxWtKAtNoQYEXOYivTssbKzQpOdW3w0KKN3Dd/I1XVdbz/4FI+fnQZA3oU1J+kmWWgYP4LSOoB/B74QkRs3MX9tHwzYhfl+bZ1FXAVQElJyZ4Ha7aXauuCGcu28MsXK3mzqpYxg7tyxXF9GN7Hn0NrXySdC/wvUATcERE3NpjfBbgHGAO8BVwSEctz5h8ILACui4jvtlbchaAgErOkziRJ+VcR8Ye0eI2kQenZ8iBgbVq+Ehias/gBwKq0fFyD8hn5thcRtwO3A5SWluZN3mbNKSKY9fpWfj6nklcrdzCiXwlfPKU/R+/fNevQzJqdpCLgVuAskv/NM9M2Pwtyql0JrI+IQyVNBG4CLsmZfzPwSGvFXEgyb1mi5NT4Z8DCiPhezqwpQH153O6KAAAWz0lEQVTL6knAgznll6ets08GNqSXvB8DzpbUJ23BfXZaZpapxeu28/Vpa7h+xptU1waTT+vP987d30nZ2rMTgfKIWBoR1cC9JO2Dck0gaT8EcD8wPs0HSLoQWArMb6V4C0ohnDGfCnwCeEnSnLTs/wI3AvdJuhJ4Dbg4nTcV+ABQDlQBnwSIiApJ3wRmpvVuqG8IZpYFDzJhHVi+Nj8nNVYnImokbQD6SdpK0tD3LODLrRBrwck8MUfE38h/fxhgfJ76AXyukXXdCdzZfNGZ7bm1m7Zx67Nv8Vj5ZkrSQSY+fGQvunmQCWtfiiXNynl/e3qbEJrW5qexOtcDN0fE5o767H7midmsvdi8vYbbn3qFn/51GdU1tZw3oicTj+rtQSasvaqJiLGNzGusLVC+OislFQO9gQqSM+uLJH0HKAPqJG2LiB82a/QFzInZbB9V19Tx62df5QdPlPPWlmo+ePQgJgwXg3t6kAnrsGYCIyQNB14HJgKXNahT347oaeAi4In0iuhp9RUkXQds7khJGZyYzfZaXV3w8Eur+e5ji3mtoor3HNyPyecdwTFDy5g3b17W4ZllJr1nfDVJA9wi4M6ImC/pBmBWREwhafT7C0nlJGfKE7OLuLA4MZvthb+Xr+PGRxbx0usbOGL/nvz8kydw+mH7uT9rs1RETCVprJtbdk3O9DZ2NuptbB3XtUhwBc6J2WwPzF+1gRsfWcRfl6xjSFk3vvdPx3DhsUPo5JbWZtZMnJjNmmBFRRX/7/HF/HHOKsq6d+YbHzySj598EF07u2GXmTUvJ2azXajYUs0Pnyjnl8+8igSfHXcInzn9EHp3c8MuM2sZTsxmeWytruXOvy/jthmvsKW6hovHDOWLZx3G/r3dW5eZtSwnZrMcNbV1/G72Sm6e9jJrN23nzCMH8rVzD2fEwJ5Zh2ZmHYQTsxnJIBOPL1jDdx5dxCtvbmHMQX249WPHc8KwvlmHZmYdjBOzdXgzl1dw4yOLmP3qeg7Zr5SffGIMZ48c6EefzCwTTszWYS1Zs4mbHl3MnxeuYUDPLvz3R47i4jEHUFzkPq3NLDtOzNbhrN6wle9PW8LvZq+gtKSYr5xzOJ86dTjdSvzok5llz4nZOowNW3dw21OvcOffllEXwRWnDOfqMw6lb2lJ1qGZmb3NidnavW07avnlM6/ywyfLqazawYXHDubfzz6coX27Zx2amdm7ODFbu1VbF/zxhdf53rSXeb1yK+87bD++es7hjB7SO+vQzMwa5cRs7U5EMOPlN7npkUUsemMTo4f04jsXHc2ph/bPOjQzs91yYrZ25eV12/n2T5/l6aVvcWDf7txy6XGcf9QgDzJhZm2GE7O1C6s27eAXcyr566tV9C0t4boPjeSykw6ipNiPPplZ21IQ/7Uk3SlpraR5OWV9JU2TtCR97ZOWS9ItksolzZV0fM4yk9L6SyRNymJfrHWt31rLj597i89OWcVzK7cy8ajePPWVcVxx6nAnZTNrkwrljPnnwA+Be3LKJgPTI+JGSZPT918DzgNGpD8nAT8GTpLUF7gWGAsEMFvSlIhY32p7Ya2makcdDyzYyAMLN1JdG5xzaA8uO7qMPt2K6NnVIz+ZWdtVEIk5Iv4iaViD4gnAuHT6bmAGSWKeANwTEQE8I6lM0qC07rSIqACQNA04F/hNC4dvraimLnh0yWbufamSym11nHpgdy4/towhvZyMzax9KIjE3IiBEbEaICJWSxqQlg8BVuTUW5mWNVb+LpKuAq4CKClx5xJtQUTwt9equGdOJas31TB6QBf+c1wfDu/fJevQzMyaVSEn5sbka14buyh/d2HE7cDtAKWlpXnrWOGY+8Y27np+PUsqqjmorDPXjtuPsUO6eZAJM2uXCjkxr5E0KD1bHgSsTctXAkNz6h0ArErLxzUon9EKcVoLWba+mp+/sJ7Zq7bRv3sRX3hPP94/vJQiP/pkZu1YISfmKcAk4Mb09cGc8qsl3UvS+GtDmrwfA/6rvvU2cDbw9VaO2ZrB2s01/OLFSmYs20L3kk588rgyzj+8J13cytrMOoCCSMySfkNytttf0kqS1tU3AvdJuhJ4Dbg4rT4V+ABQDlQBnwSIiApJ3wRmpvVuqG8IZoWvLoLVm2p4ZMkmHl68CQEfGdmLi0f1okcXj/pkZh1HQSTmiLi0kVnj89QN4HONrOdO4M5mDM1awLaaOl6t3MGy9dUsXV/N0oodLK+sZltNIGD8IaV87Ogy9istiI+nmVmr8n8+a1Hrt9aydH11koQrqllWuYPXN+6gLm1y172zOLhPCWcd0oPhfUoYNaCLH30ysw7NidmaRW1dsGLDjncm4fU7WL+t9u06A0qLGN6nhPce2J3hfUo4uG9nBpYWu3W1mVkOJ2bbY1u217DojU0sWL2Rhas3smDVRha9sZFtO+oAKO4EQ3t35vjBXTm4T0mShPt09r1iM7MmcGK2RkUEazdtZ8GqjSxYnfwsXLWRZW9tIdJL0b27debIQT257MSD6F23keF9SxjaqzOdi3wWbGa2N5yYDYCa2jqWrtvCglXpWXB6JvzWluq36wzt242Rg3ox4dghjBzci5GDezG4d9e3L0XPmzevsdWbmVkTOTF3QJu27UguReck4UVvbKK6JrkUXVLUicP278H4IwcwclAvRg7uzRGDetLLg0OYmbU4J+Z2LCJYvWHbO8+CV2/k1beq3q7Tp3tnRg7uxaT3HJScBQ/qzcH7ldK5yJ15mJllwYm5ndhRW0f52s1vN8aqT8KVVTverjOsX3dGDe7FxWMOYOTgXhw5qBf79+rqVtFmZgXEibkN2lxdx7L6x5LWV7OsYgcrfrOC6trkUnSX4k4csX9Pzhu9PyMHJQn4iEG96NHFv24zs0Ln/9QFLCJYu6V2Zw9Z66tZVlHNmi07nw3u3aUTB/ct4ZNHD0vuBw/qxfD+pRT7UrSZWZvkxFwgdtTu7KBjaU4vWVuqk7NgAUN6FXNY/y6cM6KEg9Nng/t0K0ISo0cfme0OmJnlkHQu8L9AEXBHRNzYYH4X4B5gDPAWcElELJd0FslYCSVANfCViHiiVYPPmBNzxh5atJHHyjezYsMOatNng7sUiWF9OnPaQd3TBFzCsD6d6erRlcysDZBUBNwKnEUyJO9MSVMiYkFOtSuB9RFxqKSJwE3AJcA64EMRsUrSaOAxYEjr7kG2nJgzFkC/7kWMHdLt7SQ8qGexxxw2s7bsRKA8IpYCpMP0TgByE/ME4Lp0+n7gh5IUES/k1JkPdJXUJSK2t3zYhcGJOWMXHNGLC47olXUYZmbNaQiwIuf9SuCkxupERI2kDUA/kjPmeh8FXuhISRmcmM3MbO8US5qV8/72iLg9nc53yS8avN9lHUmjSC5vn71PUbZBTsxmZrY3aiJibCPzVgJDc94fAKxqpM5KScVAb6ACQNIBwAPA5RHxSrNG3Qa4NZGZmTW3mcAIScMllQATgSkN6kwBJqXTFwFPRERIKgP+BHw9Iv7eahEXECdmMzNrVhFRA1xN0qJ6IXBfRMyXdIOkC9JqPwP6SSoHvgRMTsuvBg4F/lPSnPRnQCvvQqba3aXs3T07Z2ZmLS8ipgJTG5RdkzO9Dbg4z3LfAr7V4gEWsHZ1xpzz7Nx5wEjgUkkjs43KzMys6dpVYibn2bmIqAbqn50zMzNrE9pbYs737FyH6jHGzMzatvZ2j7kpz84h6Srgqvr5kra2aFS7VwzUZBxDofCx2MnHYicfi50K5Vh0yzqA9qq9JeamPDtH+hD87Q3LsyJp1i6eB+xQfCx28rHYycdiJx+L9q+9XcpuyrNzZmZmBatdnTGn/a3WPztXBNwZEfMzDsvMzKzJ2lVihvzPzrUBBXNZvQD4WOzkY7GTj8VOPhbtnCLe1TbKzMzMMtLe7jGbmZm1aU7MZmZmBcSJOQ9J3SQ9lXbxiaRJkpakP5MaWeYYSU9LeknSQ5J6peUlku5Ky1+UNC5nmUskzZU0X9J3mhDXiTmdur8o6cON1Bsu6dk03t+mLdSR9EVJr0n64V4clobbePsYSTpI0uw0rvmSPtPIMn0lTUvjmiapTxO31UvS643F3dh60+NbLunhvd/TJsX3js/LvsS8m+0cKOlxSQslLZA0LE+dLunvvDz9DAxLy09Ll5m3t/vZFHn+dvY65t1s54vpZ22epN9I6trU9bbksciz/99J41wo6RZJ7+prYS8/C49Kqmz42ZY0XtLz6d/i3yQd2sjyX0+Py2JJ5+TEPkdStaT+e3cErFlEhH8a/ACfAz6fTvcFlqavfdLpPnmWmQmcnk5/CvhmzrruSqcHALNJvhD1A14D9kvn3Q2M301c3YHidHoQsLb+fYN69wET0+nbgM/mzLsC+GEzH6MSoEs63QNYDgzOs8x3gMnp9GTgpiZu63+BXzcW967WC4wDHm6tz0tzxLyL7cwAzso5zt3z1PkX4LZ0eiLw25x5w4B5rXks9jXmRrYxBFgGdEvf3wdcUQjHosHfxSnA30meECkCngbGNdNnYTzwoYafbeBl4Mic/f95nmVHAi8CXYDhwCtAUc785UD/lvyc+GfXPz5jzu9jwIPp9DnAtIioiIj1wDTg3DzLHA78JZ2eBnw0nR4JTAeIiLVAJTAWOBh4OSLeTOv9OWeZvCKiKpLh1AC6kr9XMwFnAPenRXcDF+5qvXvp7WMUEdURsT0t70LjV2ImpPE0OS5JY4CBwOO7qLbH621muZ+XFolZyWAsxRExDSAiNkdE1W7Wez8wPt9ZWgt6+1i0cMzFQDdJxSRfWN/VkdBerndf5X4WguTvtITk76IzsGY3cTbp8xsR04FN+WYBvdLp3jR+XO6NiO0RsQwoJxlnwAqEE3MD6WXfgyNieVrU1P635wH144xezM4eyF4EJkgqljQcGJPOKweOkDQs/edyIe/stayx+E6SNB94CfhMTqKu1w+ozClv9v7C8xwjJA2VNJfkWN0UEfn+IQyMiNUA6esux1iV1An4f8BXdhPSHq23OTU8Fi0Y82FApaQ/SHpB0v/kXjrP8fbnNf0MbCD5TLS4PJ+LFok5Il4HvktyxWk1sCEi8n0JatVj0XD/I+Jp4Mk0xtXAYxGxMM+izfn5/TQwVdJK4BNAvmFvPaZAgXNifrf+JGe19ZrU/zbJ5evPSZoN9ASq0/I7ST74s4DvA/8AatKz788CvwX+SnL5aLf930bEsxExCjgB+Hqee2tNjXdfNDxGRMSKiDiaZIDzSZIGNsN2/gWYGhErdlszOw2PRUvFXAycBnyZ5Hd/MMltiYZa4/ffmIbHokViTu/BTiC5DDsYKJX08X1dbzN4x/6n93ePJOkaeAhwhqT3teD2Ab4IfCAiDgDuAr6Xp06WnxFrAifmd9tKcvmpXlP7314UEWdHxBjgNyT3bYiImoj4YkQcGxETgDJgSTrvoYg4KSLeAyyuL2+K9Jv3FmB0g1nrgLL0LLzRePdRw2OUG9cqYD7JP+SG1kgaBJC+rt3Ndt4DXC1pOckZ0uWS8p0B7Ol6m1PDY9FSMa8EXohkSNMa4I/A8Y3UG5qut5jkcmZF03dnn+T722mJmM8ElkXEmxGxA/gDyf3cfV3vvmq4/x8Gnkkv4W8GHgFOzrNcs3x+Je0HHBMRz6ZFv2U3xyXVEv8jbB84MTeQnskW5ZyJPgacLalP+k397LTsHSQNSF87Ad8gaXSFpO6SStPps0jOlhc0WKYPyZnWHen7D0v67zzbGF6fcCUdRHJfe3mD+IPk8tlFadEkcu5/NoeGx0jSAZK65ezLqSRfNBqaksbzjriUtDa/J892PhYRB0bEMJKzrnsiYnJT19saGh6LfY25sWNB0riwT/rPF5J2BAt2s96LgCfSz0SLy/O3s08xSxoiaXqe+q8BJ6d/WyJpCJXvEnGrHos8+/8acHp6G6szcHoT4mzKZ6Ex64Hekg5L35+1i+1NVNJqfTgwAnhuD7ZjLcyJOb/HgfcCREQF8E2SfzIzgRvSMiTdIal+lJdLJb0MLCL59nlXWj4AeF7SQuBrJPd96v2vpAUkLTdvjIiX0/JDgI154nov8KKkOcADwL9ExLo0lqmSBqf1vgZ8SVI5yT21n+39oWjU28eI5HLds5JeBJ4CvhsRL6Vx5R6jG4GzJC0h+adRfyZ5IMnZRpM1cb2tJfdYNGpfjkVE1JIk+umSXiK5HPnTdL03SKpv3/AzoF/6u/8SSSvf1pT7t7OvMQ8iz+2d9IzwfuB5krYWnUi7qSyAY5H7Wbif5MrZSyRtTV6MiIfSOPfp70LSX4HfkTRoWynpnPSqxD8Dv0//Fj9B2tZB0gWSbgCIZPyA+0i+JD0KfC79XVmhaOlm323xBzgO+EWG2/8l6WNULbDuK2iex6Wa7RgB/wMc3UL7O46Wf1yqrRyLYbT841LNeSyuBi5oS8eirXwWdrPd5fhxqUx/2t0gFs0hIl6Q9KSkosjgm2RE5GvIss8kfRH4DPD7fV1Xcx6jiNhdC+a9IukS4FqSZ8dbTBs5FqcBPyJpg9BimvlY7HNHOPm05LFoC5+FxqS3o54meayrrjW3be/kQSzMzMwKiO8xm5mZFRAnZjMzswLixGxmZlZAnJitw5N0naQvp4/anJl1PLsiaUbOYzatsb07lPR5jaSLlYyS9KSkYyV9oLXiMOtI3CrbLBUR12QdQ0uSVBzv7lt9lyLi0zlvryR5dv5JSVeQDMYytRlDNDN8xmwdlKT/UDIW7Z9JelBD0s8lXZRO36hkzN65kr6blg2U9ICSsbBflJSvu0OUDEyyUNJPlYzF+3hOz2hvn/FK6q+k604kXSHpj0rG8l4m6WpJX1Iy+MMzkvrmbOLjkv6hZCziE9PlSyXdKWlmusyEnPX+TtJDNDLalaROkn6Uxvpw2llN/XGYIWmspGtIOs64TdLNwA3AJUrG771kX34XZvZOPmO2DkfJsIwTSTqDKCbpQWp2zvy+JP0cHxERIaksnXUL8FREfFjJKEk9drGZEcClEfHPku4jGdLzl7sJbXQaU1eS0ce+FhHHpYnwcpJBUABKI+IUJQMi3Jku9x8kXU5+Ko33ufRLByT9dx8daY91eXyEpMONo0h6qluYrvdtEXGDpDOAL0fErLRnqbERcfVu9snM9pDPmK0jOg14IJLxrTeS9B2cayOwDbhD0keA+jGEzwB+DEl3kxGxYRfbWBYRc9Lp2SSJb3eejIhNkYzRvQF4KC1/qcHyv0lj+AvQK03EZwOTlXTXOoMkuR+Y1p+2i6QMyZnw7yKiLiLeIOlr3cwy4sRsHVWjPeuk92FPJOkh7UKS/oT31Pac6Vp2Xp2qYeffXcMRunKXqct5X8c7r241jD1I+qH+aCSjmB0byUAa9QMYbNlNrPmGATSzjDgxW0f0F+DDkrpJ6gl8KHempB5A74iYCnwBODadNZ1kDG0kFUnqtRfbXg6MSacv2kW9XbkkjeG9wIb0zP0x4F8lKZ133B6s72/AR9N7zQNJ+hffnU0k446bWTNzYrYOJyKeJxmrdg7JWfFfG1TpCTwsaS7JaFlfTMs/D7xfyUhJs4FRe7H57wKflfQPoP9eLA+wPl3+NpKW0pCMgNYZmCtpXvq+qX5PMkbvPOAnwLMkl9J35UlgpBt/mTU/95VtZkjqERGbJfUjGZv31PR+s5m1MrfKNjNIrhCUASXAN52UzbLjM2azvZSeXU7PM2t8RLzV2vHsjqSjgF80KN4eESdlEY+Z5efEbGZmVkDc+MvMzKyAODGbmZkVECdmMzOzAuLEbGZmVkCcmM3MzArI/weCof8mHf13eQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for variable in pig_table_variable: \n",
    "    \n",
    "    # Create the predictor insight graph table\n",
    "    pig_table = create_pig_table(discretize_df, \"target\", variable)\n",
    "    \n",
    "    # Plot the predictor insight graph\n",
    "    plot_pig(pig_table, variable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}