{
"cells": [
{
"cell_type": "markdown",
"id": "dc95c62b-6fcc-41e0-942c-e4cf1d416b59",
"metadata": {},
"source": [
"# Purchase Predictive Analytics"
]
},
{
"cell_type": "markdown",
"id": "77b00848-c963-4a75-9c85-dabaeff62824",
"metadata": {},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8940f82c-404d-44d6-ab35-3dfeba4d00f9",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import scipy as sp\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt \n",
"import sklearn\n",
"import numpy as np\n",
"import pickle\n",
"import joblib\n",
"import itertools\n",
"from sklearn.linear_model import LogisticRegression"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c8f20c03-2f0a-4c88-ba0f-1d5e7e40b904",
"metadata": {},
"outputs": [],
"source": [
"sns.set()"
]
},
{
"cell_type": "markdown",
"id": "46e96698-8fc2-4bab-9c6f-9be7ee2032dd",
"metadata": {},
"source": [
"## Read and Prepare Dataset"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "81c0f212-6857-4c1d-8024-c536c998d361",
"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>ID</th>\n",
" <th>Day</th>\n",
" <th>Incidence</th>\n",
" <th>Brand</th>\n",
" <th>Quantity</th>\n",
" <th>Last_Inc_Brand</th>\n",
" <th>Last_Inc_Quantity</th>\n",
" <th>Price_1</th>\n",
" <th>Price_2</th>\n",
" <th>Price_3</th>\n",
" <th>...</th>\n",
" <th>Promotion_3</th>\n",
" <th>Promotion_4</th>\n",
" <th>Promotion_5</th>\n",
" <th>Sex</th>\n",
" <th>Marital status</th>\n",
" <th>Age</th>\n",
" <th>Education</th>\n",
" <th>Income</th>\n",
" <th>Occupation</th>\n",
" <th>Settlement size</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>200000001</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.59</td>\n",
" <td>1.87</td>\n",
" <td>2.01</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>47</td>\n",
" <td>1</td>\n",
" <td>110866</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>200000001</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.51</td>\n",
" <td>1.89</td>\n",
" <td>1.99</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>47</td>\n",
" <td>1</td>\n",
" <td>110866</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>200000001</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.51</td>\n",
" <td>1.89</td>\n",
" <td>1.99</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>47</td>\n",
" <td>1</td>\n",
" <td>110866</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>200000001</td>\n",
" <td>16</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.52</td>\n",
" <td>1.89</td>\n",
" <td>1.98</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>47</td>\n",
" <td>1</td>\n",
" <td>110866</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>200000001</td>\n",
" <td>18</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.52</td>\n",
" <td>1.89</td>\n",
" <td>1.99</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>47</td>\n",
" <td>1</td>\n",
" <td>110866</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 24 columns</p>\n",
"</div>"
],
"text/plain": [
" ID Day Incidence Brand Quantity Last_Inc_Brand \\\n",
"0 200000001 1 0 0 0 0 \n",
"1 200000001 11 0 0 0 0 \n",
"2 200000001 12 0 0 0 0 \n",
"3 200000001 16 0 0 0 0 \n",
"4 200000001 18 0 0 0 0 \n",
"\n",
" Last_Inc_Quantity Price_1 Price_2 Price_3 ... Promotion_3 \\\n",
"0 0 1.59 1.87 2.01 ... 0 \n",
"1 0 1.51 1.89 1.99 ... 0 \n",
"2 0 1.51 1.89 1.99 ... 0 \n",
"3 0 1.52 1.89 1.98 ... 0 \n",
"4 0 1.52 1.89 1.99 ... 0 \n",
"\n",
" Promotion_4 Promotion_5 Sex Marital status Age Education Income \\\n",
"0 0 0 0 0 47 1 110866 \n",
"1 0 0 0 0 47 1 110866 \n",
"2 0 0 0 0 47 1 110866 \n",
"3 0 0 0 0 47 1 110866 \n",
"4 0 0 0 0 47 1 110866 \n",
"\n",
" Occupation Settlement size \n",
"0 1 0 \n",
"1 1 0 \n",
"2 1 0 \n",
"3 1 0 \n",
"4 1 0 \n",
"\n",
"[5 rows x 24 columns]"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv(\"purchase data.csv\"); df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a6a5cf05-2d61-4edd-b4e4-fe5b751c494b",
"metadata": {},
"outputs": [],
"source": [
"def do_clustering(df, pipeline, drop_cols=None, sel_cols=None, do_fit=False):\n",
" y = None\n",
" df_new = df.copy()\n",
" if drop_cols: df_new = df_new.drop(columns=drop_cols, axis=1)\n",
" df_filter = df_new.copy() \n",
" if sel_cols: df_filter = df_new[sel_cols]\n",
" if do_fit:y = pipeline.fit_predict(df_filter)\n",
" else: y = pipeline.predict(df_filter)\n",
" if 'pca' in pipeline.named_steps:\n",
" m = pipeline.named_steps['pca']\n",
" comp_names = [f\"PCA{i+1}\" for i in range(m.n_components)]\n",
" transform_df = df_filter.copy()\n",
" for step in pipeline.named_steps:\n",
" transform_df = pipeline.named_steps[step].transform(transform_df)\n",
" if step == \"pca\": break\n",
" pca_df = pd.DataFrame(transform_df, \n",
" columns=comp_names, \n",
" index=df_filter.index)\n",
" df_new = pd.concat([df_new, pca_df], axis=1)\n",
" df_new['y'] = y+1\n",
" return df_new, pipeline"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3a2f361c-f8b7-4ddf-8f75-0db9fb286809",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
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" 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>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>ID</th>\n",
" <td>200000001</td>\n",
" <td>200000001</td>\n",
" <td>200000001</td>\n",
" <td>200000001</td>\n",
" <td>200000001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Day</th>\n",
" <td>1</td>\n",
" <td>11</td>\n",
" <td>12</td>\n",
" <td>16</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Incidence</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Brand</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Quantity</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Last_Inc_Brand</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Last_Inc_Quantity</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Price_1</th>\n",
" <td>1.59</td>\n",
" <td>1.51</td>\n",
" <td>1.51</td>\n",
" <td>1.52</td>\n",
" <td>1.52</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Price_2</th>\n",
" <td>1.87</td>\n",
" <td>1.89</td>\n",
" <td>1.89</td>\n",
" <td>1.89</td>\n",
" <td>1.89</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Price_3</th>\n",
" <td>2.01</td>\n",
" <td>1.99</td>\n",
" <td>1.99</td>\n",
" <td>1.98</td>\n",
" <td>1.99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Price_4</th>\n",
" <td>2.09</td>\n",
" <td>2.09</td>\n",
" <td>2.09</td>\n",
" <td>2.09</td>\n",
" <td>2.09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Price_5</th>\n",
" <td>2.66</td>\n",
" <td>2.66</td>\n",
" <td>2.66</td>\n",
" <td>2.66</td>\n",
" <td>2.66</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Promotion_1</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Promotion_2</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Promotion_3</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Promotion_4</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Promotion_5</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sex</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Marital status</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Age</th>\n",
" <td>47</td>\n",
" <td>47</td>\n",
" <td>47</td>\n",
" <td>47</td>\n",
" <td>47</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Education</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Income</th>\n",
" <td>110866</td>\n",
" <td>110866</td>\n",
" <td>110866</td>\n",
" <td>110866</td>\n",
" <td>110866</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Occupation</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Settlement size</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PCA1</th>\n",
" <td>0.362152</td>\n",
" <td>0.362152</td>\n",
" <td>0.362152</td>\n",
" <td>0.362152</td>\n",
" <td>0.362152</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PCA2</th>\n",
" <td>-0.639557</td>\n",
" <td>-0.639557</td>\n",
" <td>-0.639557</td>\n",
" <td>-0.639557</td>\n",
" <td>-0.639557</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PCA3</th>\n",
" <td>1.462706</td>\n",
" <td>1.462706</td>\n",
" <td>1.462706</td>\n",
" <td>1.462706</td>\n",
" <td>1.462706</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PCA4</th>\n",
" <td>-0.593242</td>\n",
" <td>-0.593242</td>\n",
" <td>-0.593242</td>\n",
" <td>-0.593242</td>\n",
" <td>-0.593242</td>\n",
" </tr>\n",
" <tr>\n",
" <th>y</th>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>labels</th>\n",
" <td>Fewer-Opportunities</td>\n",
" <td>Fewer-Opportunities</td>\n",
" <td>Fewer-Opportunities</td>\n",
" <td>Fewer-Opportunities</td>\n",
" <td>Fewer-Opportunities</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 \\\n",
"ID 200000001 200000001 \n",
"Day 1 11 \n",
"Incidence 0 0 \n",
"Brand 0 0 \n",
"Quantity 0 0 \n",
"Last_Inc_Brand 0 0 \n",
"Last_Inc_Quantity 0 0 \n",
"Price_1 1.59 1.51 \n",
"Price_2 1.87 1.89 \n",
"Price_3 2.01 1.99 \n",
"Price_4 2.09 2.09 \n",
"Price_5 2.66 2.66 \n",
"Promotion_1 0 0 \n",
"Promotion_2 1 0 \n",
"Promotion_3 0 0 \n",
"Promotion_4 0 0 \n",
"Promotion_5 0 0 \n",
"Sex 0 0 \n",
"Marital status 0 0 \n",
"Age 47 47 \n",
"Education 1 1 \n",
"Income 110866 110866 \n",
"Occupation 1 1 \n",
"Settlement size 0 0 \n",
"PCA1 0.362152 0.362152 \n",
"PCA2 -0.639557 -0.639557 \n",
"PCA3 1.462706 1.462706 \n",
"PCA4 -0.593242 -0.593242 \n",
"y 3 3 \n",
"labels Fewer-Opportunities Fewer-Opportunities \n",
"\n",
" 2 3 \\\n",
"ID 200000001 200000001 \n",
"Day 12 16 \n",
"Incidence 0 0 \n",
"Brand 0 0 \n",
"Quantity 0 0 \n",
"Last_Inc_Brand 0 0 \n",
"Last_Inc_Quantity 0 0 \n",
"Price_1 1.51 1.52 \n",
"Price_2 1.89 1.89 \n",
"Price_3 1.99 1.98 \n",
"Price_4 2.09 2.09 \n",
"Price_5 2.66 2.66 \n",
"Promotion_1 0 0 \n",
"Promotion_2 0 0 \n",
"Promotion_3 0 0 \n",
"Promotion_4 0 0 \n",
"Promotion_5 0 0 \n",
"Sex 0 0 \n",
"Marital status 0 0 \n",
"Age 47 47 \n",
"Education 1 1 \n",
"Income 110866 110866 \n",
"Occupation 1 1 \n",
"Settlement size 0 0 \n",
"PCA1 0.362152 0.362152 \n",
"PCA2 -0.639557 -0.639557 \n",
"PCA3 1.462706 1.462706 \n",
"PCA4 -0.593242 -0.593242 \n",
"y 3 3 \n",
"labels Fewer-Opportunities Fewer-Opportunities \n",
"\n",
" 4 \n",
"ID 200000001 \n",
"Day 18 \n",
"Incidence 0 \n",
"Brand 0 \n",
"Quantity 0 \n",
"Last_Inc_Brand 0 \n",
"Last_Inc_Quantity 0 \n",
"Price_1 1.52 \n",
"Price_2 1.89 \n",
"Price_3 1.99 \n",
"Price_4 2.09 \n",
"Price_5 2.66 \n",
"Promotion_1 0 \n",
"Promotion_2 0 \n",
"Promotion_3 0 \n",
"Promotion_4 0 \n",
"Promotion_5 0 \n",
"Sex 0 \n",
"Marital status 0 \n",
"Age 47 \n",
"Education 1 \n",
"Income 110866 \n",
"Occupation 1 \n",
"Settlement size 0 \n",
"PCA1 0.362152 \n",
"PCA2 -0.639557 \n",
"PCA3 1.462706 \n",
"PCA4 -0.593242 \n",
"y 3 \n",
"labels Fewer-Opportunities "
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pipeline = joblib.load(\"cluster_pipeline.pkl\"); pipeline\n",
"sel_cols = ['Sex','Marital status','Age','Education','Income','Occupation','Settlement size']\n",
"df_segments, _ = do_clustering(df, pipeline, sel_cols=sel_cols)\n",
"names = {1:\"Standard\",\n",
" 2:\"Career-Focussed\",\n",
" 3:\"Fewer-Opportunities\",\n",
" 4:\"Well-off\"}\n",
"\n",
"df_segments['labels'] = df_segments['y'].map(names)\n",
"df_segments.head().T\n",
"## May be we might include pca names later"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a029e780-a01c-46ef-8212-6dfc5bb8c19c",
"metadata": {},
"outputs": [
{
"data": {
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" Career-Focussed Fewer-Opportunities Standard Well-off\n",
"0 0 1 0 0\n",
"1 0 1 0 0\n",
"2 0 1 0 0\n",
"3 0 1 0 0\n",
"4 0 1 0 0"
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"execution_count": null,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"df_segments_dummies = pd.get_dummies(df_segments['labels']); df_segments_dummies.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3f8353bd-a20a-40ff-b80d-0e99028c75f0",
"metadata": {},
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{
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" <th>Brand</th>\n",
" <th>Quantity</th>\n",
" <th>Last_Inc_Brand</th>\n",
" <th>Last_Inc_Quantity</th>\n",
" <th>Price_1</th>\n",
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" <th>...</th>\n",
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" <th>PCA4</th>\n",
" <th>y</th>\n",
" <th>labels</th>\n",
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" <th>Fewer-Opportunities</th>\n",
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" <td>3</td>\n",
" <td>Fewer-Opportunities</td>\n",
" <td>0</td>\n",
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" <td>1.89</td>\n",
" <td>1.99</td>\n",
" <td>...</td>\n",
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" <td>-0.639557</td>\n",
" <td>1.462706</td>\n",
" <td>-0.593242</td>\n",
" <td>3</td>\n",
" <td>Fewer-Opportunities</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
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" <td>200000001</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.51</td>\n",
" <td>1.89</td>\n",
" <td>1.99</td>\n",
" <td>...</td>\n",
" <td>0.362152</td>\n",
" <td>-0.639557</td>\n",
" <td>1.462706</td>\n",
" <td>-0.593242</td>\n",
" <td>3</td>\n",
" <td>Fewer-Opportunities</td>\n",
" <td>0</td>\n",
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" <td>200000001</td>\n",
" <td>16</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.52</td>\n",
" <td>1.89</td>\n",
" <td>1.98</td>\n",
" <td>...</td>\n",
" <td>0.362152</td>\n",
" <td>-0.639557</td>\n",
" <td>1.462706</td>\n",
" <td>-0.593242</td>\n",
" <td>3</td>\n",
" <td>Fewer-Opportunities</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
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" <td>200000001</td>\n",
" <td>18</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
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"text/plain": [
" ID Day Incidence Brand Quantity Last_Inc_Brand \\\n",
"0 200000001 1 0 0 0 0 \n",
"1 200000001 11 0 0 0 0 \n",
"2 200000001 12 0 0 0 0 \n",
"3 200000001 16 0 0 0 0 \n",
"4 200000001 18 0 0 0 0 \n",
"\n",
" Last_Inc_Quantity Price_1 Price_2 Price_3 ... PCA1 PCA2 \\\n",
"0 0 1.59 1.87 2.01 ... 0.362152 -0.639557 \n",
"1 0 1.51 1.89 1.99 ... 0.362152 -0.639557 \n",
"2 0 1.51 1.89 1.99 ... 0.362152 -0.639557 \n",
"3 0 1.52 1.89 1.98 ... 0.362152 -0.639557 \n",
"4 0 1.52 1.89 1.99 ... 0.362152 -0.639557 \n",
"\n",
" PCA3 PCA4 y labels Career-Focussed \\\n",
"0 1.462706 -0.593242 3 Fewer-Opportunities 0 \n",
"1 1.462706 -0.593242 3 Fewer-Opportunities 0 \n",
"2 1.462706 -0.593242 3 Fewer-Opportunities 0 \n",
"3 1.462706 -0.593242 3 Fewer-Opportunities 0 \n",
"4 1.462706 -0.593242 3 Fewer-Opportunities 0 \n",
"\n",
" Fewer-Opportunities Standard Well-off \n",
"0 1 0 0 \n",
"1 1 0 0 \n",
"2 1 0 0 \n",
"3 1 0 0 \n",
"4 1 0 0 \n",
"\n",
"[5 rows x 34 columns]"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_segments = pd.concat([df_segments, df_segments_dummies], axis=1); df_segments.head()"
]
},
{
"cell_type": "markdown",
"id": "c3df92f3-6e82-4b0f-9076-f8ae69163f86",
"metadata": {},
"source": [
"## Purchase Probability"
]
},
{
"cell_type": "markdown",
"id": "5853fbca-a60a-4735-8546-8f9e5fd0b7b8",
"metadata": {},
"source": [
"```{note}\n",
"We can estimate purchase probability by a simple binomial/ logistics regression model with average price as X. Since we are only interested in probabilities we might not even need to estimate train-test split ? I think this intuition might be weird. My thinking is we should do a test-train split\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "baf09e41-3517-4a75-8d8f-ca2afa00542f",
"metadata": {},
"outputs": [],
"source": [
"df_segments['Avg_Price'] = df_segments.filter(regex='Price*').mean(axis=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "577140c1-4f38-4853-add7-6b10ffaae983",
"metadata": {},
"outputs": [
{
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" <th>Price_2</th>\n",
" <th>Price_3</th>\n",
" <th>...</th>\n",
" <th>PCA2</th>\n",
" <th>PCA3</th>\n",
" <th>PCA4</th>\n",
" <th>y</th>\n",
" <th>labels</th>\n",
" <th>Career-Focussed</th>\n",
" <th>Fewer-Opportunities</th>\n",
" <th>Standard</th>\n",
" <th>Well-off</th>\n",
" <th>Avg_Price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>200000001</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2.044</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>200000001</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.51</td>\n",
" <td>1.89</td>\n",
" <td>1.99</td>\n",
" <td>...</td>\n",
" <td>-0.639557</td>\n",
" <td>1.462706</td>\n",
" <td>-0.593242</td>\n",
" <td>3</td>\n",
" <td>Fewer-Opportunities</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2.028</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>200000001</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>1.462706</td>\n",
" <td>-0.593242</td>\n",
" <td>3</td>\n",
" <td>Fewer-Opportunities</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2.028</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>200000001</td>\n",
" <td>16</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.52</td>\n",
" <td>1.89</td>\n",
" <td>1.98</td>\n",
" <td>...</td>\n",
" <td>-0.639557</td>\n",
" <td>1.462706</td>\n",
" <td>-0.593242</td>\n",
" <td>3</td>\n",
" <td>Fewer-Opportunities</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2.028</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>200000001</td>\n",
" <td>18</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.52</td>\n",
" <td>1.89</td>\n",
" <td>1.99</td>\n",
" <td>...</td>\n",
" <td>-0.639557</td>\n",
" <td>1.462706</td>\n",
" <td>-0.593242</td>\n",
" <td>3</td>\n",
" <td>Fewer-Opportunities</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
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" <td>0</td>\n",
" <td>2.030</td>\n",
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"<p>5 rows × 35 columns</p>\n",
"</div>"
],
"text/plain": [
" ID Day Incidence Brand Quantity Last_Inc_Brand \\\n",
"0 200000001 1 0 0 0 0 \n",
"1 200000001 11 0 0 0 0 \n",
"2 200000001 12 0 0 0 0 \n",
"3 200000001 16 0 0 0 0 \n",
"4 200000001 18 0 0 0 0 \n",
"\n",
" Last_Inc_Quantity Price_1 Price_2 Price_3 ... PCA2 PCA3 \\\n",
"0 0 1.59 1.87 2.01 ... -0.639557 1.462706 \n",
"1 0 1.51 1.89 1.99 ... -0.639557 1.462706 \n",
"2 0 1.51 1.89 1.99 ... -0.639557 1.462706 \n",
"3 0 1.52 1.89 1.98 ... -0.639557 1.462706 \n",
"4 0 1.52 1.89 1.99 ... -0.639557 1.462706 \n",
"\n",
" PCA4 y labels Career-Focussed Fewer-Opportunities \\\n",
"0 -0.593242 3 Fewer-Opportunities 0 1 \n",
"1 -0.593242 3 Fewer-Opportunities 0 1 \n",
"2 -0.593242 3 Fewer-Opportunities 0 1 \n",
"3 -0.593242 3 Fewer-Opportunities 0 1 \n",
"4 -0.593242 3 Fewer-Opportunities 0 1 \n",
"\n",
" Standard Well-off Avg_Price \n",
"0 0 0 2.044 \n",
"1 0 0 2.028 \n",
"2 0 0 2.028 \n",
"3 0 0 2.028 \n",
"4 0 0 2.030 \n",
"\n",
"[5 rows x 35 columns]"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_segments.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d6ef8405-5102-434e-b5d7-b8dc1eb47f66",
"metadata": {},
"outputs": [],
"source": [
"model_purchase = LogisticRegression()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d166dc2a-3a2f-496e-b459-bc6b0a0a2bb0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([2.044, 2.028, 2.028, ..., 2.086, 2.092, 2.092]),\n",
" array([0, 0, 0, ..., 0, 1, 0]))"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_segments['Avg_Price'].values, df_segments['Incidence'].values"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c7f4a5ba-343d-4dfc-9bca-bbfaa100dfab",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LogisticRegression()"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model_purchase.fit(df_segments.Avg_Price.values[:, np.newaxis],df_segments.Incidence.values )"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "01cebff9-a936-4e33-8aa7-b4bcbedfcebe",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"-2.3480548048384446"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model_purchase.coef_[0][0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "23f8c793-dc62-4f80-9d8d-90f9f7c362db",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "36076e81-0499-41de-b023-d6b6caa42435",
"metadata": {},
"source": [
"## Price Elasticity"
]
},
{
"cell_type": "markdown",
"id": "df5ddc82-e539-4207-a6fd-cb5038d53513",
"metadata": {},
"source": [
"### Determining Price Ranges"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "77532dc3-f4e8-4d8d-b480-4d67b840f754",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1.1, 2.8)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_segments.filter(regex='Price*').min().min(),df_segments.filter(regex='Price*').max().max()"
]
},
{
"cell_type": "markdown",
"id": "fcf88827-c731-4578-bfb4-7e16d8ffad1a",
"metadata": {},
"source": [
"Based on this we can choose the price to be between (0.5, 3.5). Will give us some space if we want to increase of decrease price"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c8ee6b06-679e-4dd3-b8ac-018bb5982486",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((300,),\n",
" array([0.5 , 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6 ,\n",
" 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7 , 0.71,\n",
" 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8 , 0.81, 0.82,\n",
" 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9 , 0.91, 0.92, 0.93,\n",
" 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1. , 1.01, 1.02, 1.03, 1.04,\n",
" 1.05, 1.06, 1.07, 1.08, 1.09, 1.1 , 1.11, 1.12, 1.13, 1.14, 1.15,\n",
" 1.16, 1.17, 1.18, 1.19, 1.2 , 1.21, 1.22, 1.23, 1.24, 1.25, 1.26,\n",
" 1.27, 1.28, 1.29, 1.3 , 1.31, 1.32, 1.33, 1.34, 1.35, 1.36, 1.37,\n",
" 1.38, 1.39, 1.4 , 1.41, 1.42, 1.43, 1.44, 1.45, 1.46, 1.47, 1.48,\n",
" 1.49, 1.5 , 1.51, 1.52, 1.53, 1.54, 1.55, 1.56, 1.57, 1.58, 1.59,\n",
" 1.6 , 1.61, 1.62, 1.63, 1.64, 1.65, 1.66, 1.67, 1.68, 1.69, 1.7 ,\n",
" 1.71, 1.72, 1.73, 1.74, 1.75, 1.76, 1.77, 1.78, 1.79, 1.8 , 1.81,\n",
" 1.82, 1.83, 1.84, 1.85, 1.86, 1.87, 1.88, 1.89, 1.9 , 1.91, 1.92,\n",
" 1.93, 1.94, 1.95, 1.96, 1.97, 1.98, 1.99, 2. , 2.01, 2.02, 2.03,\n",
" 2.04, 2.05, 2.06, 2.07, 2.08, 2.09, 2.1 , 2.11, 2.12, 2.13, 2.14,\n",
" 2.15, 2.16, 2.17, 2.18, 2.19, 2.2 , 2.21, 2.22, 2.23, 2.24, 2.25,\n",
" 2.26, 2.27, 2.28, 2.29, 2.3 , 2.31, 2.32, 2.33, 2.34, 2.35, 2.36,\n",
" 2.37, 2.38, 2.39, 2.4 , 2.41, 2.42, 2.43, 2.44, 2.45, 2.46, 2.47,\n",
" 2.48, 2.49, 2.5 , 2.51, 2.52, 2.53, 2.54, 2.55, 2.56, 2.57, 2.58,\n",
" 2.59, 2.6 , 2.61, 2.62, 2.63, 2.64, 2.65, 2.66, 2.67, 2.68, 2.69,\n",
" 2.7 , 2.71, 2.72, 2.73, 2.74, 2.75, 2.76, 2.77, 2.78, 2.79, 2.8 ,\n",
" 2.81, 2.82, 2.83, 2.84, 2.85, 2.86, 2.87, 2.88, 2.89, 2.9 , 2.91,\n",
" 2.92, 2.93, 2.94, 2.95, 2.96, 2.97, 2.98, 2.99, 3. , 3.01, 3.02,\n",
" 3.03, 3.04, 3.05, 3.06, 3.07, 3.08, 3.09, 3.1 , 3.11, 3.12, 3.13,\n",
" 3.14, 3.15, 3.16, 3.17, 3.18, 3.19, 3.2 , 3.21, 3.22, 3.23, 3.24,\n",
" 3.25, 3.26, 3.27, 3.28, 3.29, 3.3 , 3.31, 3.32, 3.33, 3.34, 3.35,\n",
" 3.36, 3.37, 3.38, 3.39, 3.4 , 3.41, 3.42, 3.43, 3.44, 3.45, 3.46,\n",
" 3.47, 3.48, 3.49]))"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prices = np.arange(0.5, 3.5,0.01); prices.shape, prices"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "102b2723-0961-40df-ab93-7fabb209974b",
"metadata": {},
"outputs": [
{
"data": {
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" 0.90859965, 0.90663088, 0.90462415, 0.90257893, 0.90049467,\n",
" 0.89837085, 0.89620692, 0.89400234, 0.8917566 , 0.88946917,\n",
" 0.88713951, 0.88476711, 0.88235145, 0.87989203, 0.87738834,\n",
" 0.87483988, 0.87224617, 0.86960672, 0.86692105, 0.86418871,\n",
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" 0.70844058, 0.70356699, 0.69864658, 0.69368006, 0.68866816,\n",
" 0.68361166, 0.67851138, 0.67336816, 0.66818289, 0.66295652,\n",
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" 0.60305518, 0.59742104, 0.59176106, 0.58607665, 0.58036921,\n",
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" 0.54572339, 0.53989635, 0.53405839, 0.52821108, 0.52235602,\n",
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" 0.48715294, 0.48128872, 0.47542965, 0.46957733, 0.46373337,\n",
" 0.45789935, 0.45207685, 0.44626745, 0.44047268, 0.43469409,\n",
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" 0.40044584, 0.39482183, 0.38922552, 0.38365825, 0.37812131,\n",
" 0.37261597, 0.36714347, 0.361705 , 0.35630173, 0.35093481,\n",
" 0.34560531, 0.34031433, 0.33506286, 0.32985192, 0.32468244,\n",
" 0.31955535, 0.31447152, 0.30943179, 0.30443696, 0.29948778,\n",
" 0.29458499, 0.28972927, 0.28492126, 0.28016156, 0.27545074,\n",
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" 0.22698282, 0.22288931, 0.21884873, 0.21486115, 0.2109266 ,\n",
" 0.2070451 , 0.20321664, 0.19944116, 0.19571859, 0.19204884,\n",
" 0.18843178, 0.18486725, 0.18135509, 0.17789508, 0.17448702,\n",
" 0.17113066, 0.16782574, 0.16457196, 0.16136904, 0.15821665,\n",
" 0.15511445, 0.15206209, 0.1490592 , 0.14610539, 0.14320027,\n",
" 0.14034341, 0.1375344 , 0.1347728 , 0.13205816, 0.12939002,\n",
" 0.12676792, 0.12419137, 0.12165989, 0.119173 , 0.11673018,\n",
" 0.11433094, 0.11197476, 0.10966112, 0.10738951, 0.1051594 ,\n",
" 0.10297025, 0.10082155, 0.09871274, 0.09664331, 0.0946127 ,\n",
" 0.09262039, 0.09066583, 0.08874848, 0.0868678 , 0.08502326,\n",
" 0.08321431, 0.08144043, 0.07970108, 0.07799571, 0.07632382,\n",
" 0.07468485, 0.0730783 , 0.07150363, 0.06996034, 0.0684479 ,\n",
" 0.0669658 , 0.06551354, 0.0640906 , 0.0626965 , 0.06133074,\n",
" 0.05999282, 0.05868227, 0.0573986 , 0.05614133, 0.05491 ,\n",
" 0.05370413, 0.05252328, 0.05136698, 0.05023479, 0.04912626,\n",
" 0.04804096, 0.04697845, 0.0459383 , 0.0449201 , 0.04392342,\n",
" 0.04294787, 0.04199303, 0.04105851, 0.04014392, 0.03924886,\n",
" 0.03837296, 0.03751585, 0.03667715, 0.0358565 , 0.03505355,\n",
" 0.03426794, 0.03349932, 0.03274736, 0.03201172, 0.03129207,\n",
" 0.03058809, 0.02989945, 0.02922585, 0.02856698, 0.02792254,\n",
" 0.02729223, 0.02667575, 0.02607283, 0.02548317, 0.02490651,\n",
" 0.02434258, 0.0237911 , 0.02325182, 0.02272447, 0.02220882,\n",
" 0.0217046 , 0.02121159, 0.02072954, 0.02025821, 0.01979739,\n",
" 0.01934684, 0.01890634, 0.01847569, 0.01805466, 0.01764306,\n",
" 0.01724068, 0.01684731, 0.01646277, 0.01608687, 0.01571941,\n",
" 0.01536021, 0.0150091 , 0.01466589, 0.01433041, 0.0140025 ,\n",
" 0.01368199, 0.01336872, 0.01306252, 0.01276325, 0.01247075,\n",
" 0.01218487, 0.01190546, 0.01163238, 0.0113655 , 0.01110467,\n",
" 0.01084976, 0.01060063, 0.01035717, 0.01011925, 0.00988673])"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model_purchase.predict_proba(prices[:, np.newaxis])[:,1]"
]
},
{
"cell_type": "markdown",
"id": "aa95016c-e9f1-49ef-8674-1d362190efbc",
"metadata": {},
"source": [
"$$Elasiticity = E = \\beta*price*(1-Pr)$$"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "931276eb-b755-4b2b-aa28-33e26ce019fa",
"metadata": {},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
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" <th>Probabilities</th>\n",
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"text/plain": [
" Price Probabilities E Is_Elastic\n",
"0 0.50 0.917893 -0.096396 False\n",
"1 0.51 0.916106 -0.100464 False\n",
"2 0.52 0.914284 -0.104659 False\n",
"3 0.53 0.912425 -0.108984 False\n",
"4 0.54 0.910531 -0.113442 False\n",
".. ... ... ... ...\n",
"295 3.45 0.010850 -8.012897 True\n",
"296 3.46 0.010601 -8.038147 True\n",
"297 3.47 0.010357 -8.063363 True\n",
"298 3.48 0.010119 -8.088544 True\n",
"299 3.49 0.009887 -8.113692 True\n",
"\n",
"[300 rows x 4 columns]"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_price_elasticity = pd.DataFrame()\n",
"df_price_elasticity[\"Price\"]= prices\n",
"df_price_elasticity[\"Probabilities\"]= model_purchase.predict_proba(prices[:, np.newaxis])[:,1]\n",
"df_price_elasticity[\"E\"] = model_purchase.coef_[0][0]*df_price_elasticity[\"Price\"]*(1-df_price_elasticity[\"Probabilities\"])\n",
"df_price_elasticity[\"Is_Elastic\"] = df_price_elasticity[\"E\"].abs() >1 \n",
"df_price_elasticity"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "734b8945-a08a-48aa-a32a-6918cb7c0114",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='Price', ylabel='Probabilities'>"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = sns.lineplot(data=df_price_elasticity, x='Price', y='E', hue='Is_Elastic')\n",
"ax2 = ax.twinx()\n",
"sns.lineplot(data=df_price_elasticity, x='Price', y='Probabilities', hue='Is_Elastic', ax=ax2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dfa42629-5e12-4514-bab0-b794c7140926",
"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>Price</th>\n",
" <th>Probabilities</th>\n",
" <th>Is_Elastic</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>75</th>\n",
" <td>1.25</td>\n",
" <td>0.65769</td>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Price Probabilities Is_Elastic\n",
"75 1.25 0.65769 True"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_price_elasticity[['Price','Probabilities', 'Is_Elastic']][df_price_elasticity['Is_Elastic']][:1] #Price Point where demand becomes elastic -> We can increase the price below this to increase revenue"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ae25c588-baa2-4676-b617-81b55e7b55e6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"-3.2199999999999998"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"-2.3*2*(1-0.3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "552045a2-c1b4-4609-a3ce-106e41af9553",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.953125"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"0.02/2.56*100 * 1.22"
]
},
{
"cell_type": "markdown",
"id": "3e9b267d-14dc-44a7-aafa-f042316995b2",
"metadata": {},
"source": [
"## Price Elasticity By Segments"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "143b15e7-70af-49ff-8f8f-4e4c870fa21e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[None, 'Fewer-Opportunities', 'Well-off', 'Career-Focussed', 'Standard']"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"segments = df_segments['labels'].unique().tolist(); segments\n",
"segments.insert(0, None)\n",
"segments"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ffd05fb9-8100-4f6f-9ea9-7c1ab2c05204",
"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>Price</th>\n",
" <th>Probabilities</th>\n",
" <th>E</th>\n",
" <th>Is_Elastic</th>\n",
" <th>label</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.50</td>\n",
" <td>0.917893</td>\n",
" <td>-0.096396</td>\n",
" <td>False</td>\n",
" <td>Aggregate</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.51</td>\n",
" <td>0.916106</td>\n",
" <td>-0.100464</td>\n",
" <td>False</td>\n",
" <td>Aggregate</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.52</td>\n",
" <td>0.914284</td>\n",
" <td>-0.104659</td>\n",
" <td>False</td>\n",
" <td>Aggregate</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.53</td>\n",
" <td>0.912425</td>\n",
" <td>-0.108984</td>\n",
" <td>False</td>\n",
" <td>Aggregate</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.54</td>\n",
" <td>0.910531</td>\n",
" <td>-0.113442</td>\n",
" <td>False</td>\n",
" <td>Aggregate</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Price Probabilities E Is_Elastic label\n",
"0 0.50 0.917893 -0.096396 False Aggregate\n",
"1 0.51 0.916106 -0.100464 False Aggregate\n",
"2 0.52 0.914284 -0.104659 False Aggregate\n",
"3 0.53 0.912425 -0.108984 False Aggregate\n",
"4 0.54 0.910531 -0.113442 False Aggregate"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def get_elasticity_df(df_segments, segment=None):\n",
" label = \"Aggregate\"\n",
" df = df_segments.copy()\n",
" if segment: \n",
" label = segment\n",
" df = df_segments[df_segments['labels'] == segment].copy()\n",
" df['Avg_Price'] = df.filter(regex='Price*').mean(axis=1)\n",
" model_purchase = LogisticRegression()\n",
" model_purchase.fit(df.Avg_Price.values[:, np.newaxis],df.Incidence.values)\n",
" df_price_elasticity = pd.DataFrame()\n",
" df_price_elasticity[\"Price\"]= np.arange(0.5, 3.5,0.01)\n",
" df_price_elasticity[\"Probabilities\"]= model_purchase.predict_proba(df_price_elasticity[\"Price\"].values[:, np.newaxis])[:,1]\n",
" df_price_elasticity[\"E\"] = model_purchase.coef_[0][0]*df_price_elasticity[\"Price\"]*(1-df_price_elasticity[\"Probabilities\"])\n",
" df_price_elasticity[\"Is_Elastic\"] = df_price_elasticity[\"E\"].abs() >1 \n",
" df_price_elasticity['label'] = label\n",
" return df_price_elasticity\n",
" \n",
"get_elasticity_df(df_segments,segment=segments[0]).head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ecdc14e9-cf64-4122-aebe-6c73e6506eff",
"metadata": {},
"outputs": [],
"source": [
"df_price_elasticity_all = pd.concat([get_elasticity_df(df_segments,segment=s) for s in segments]).reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d7b35abf-5738-461c-bd97-400a667be21c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='Price', ylabel='E'>"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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pvoA6JgZD1jAMWcMISE09YvU7eM2u0lrtNZGGfHIbCihr8s7wkpBIMMSSGpziNZTgFMIDQo8p43j4Y720F6HFPxHG0U6EcfivFtnehG35f3AVbEWVNpKAs/6EpNLidHlYu7ucn9YWUFlvJTHKwIyxKQztE0l0lJGqqkZsViclBfUU5dVRlFeLuTkLcFhkIElpoSSlhROTYESpPHG3krO2lqZtWzBv3YrlwD5wu1GFhxM0fASGYSOPayKHsDgt5JmKyGso8P6YClqmAwdrjGSEpJIWkkJGcCpxhhiRPqUb0dO1CONAGAf4txZZlnFs/wnHhq9RRCShO/cuFIZwANweD+v3VPDjmgIqai3ERwRy9bS+9Dmqe0qWZeqqLRTm1lKYW0tZUQMej4xaoyQhJZSktEOtkRN3KbmbmjBv24p580aadu/ymkhEBEHDRhA0fATalBObCIBH9lBqLie3IZ+chnyy6/Oot3t3MdGpAkgNTiYjOJX0kFSSjYmoFSq/rpe2IrT4J8I42okwju6hxVW4Deuv7yCpNOjOvQtldEbLMY9HZsO+CuavKaC0uomYMD0zxqUwKjP6uOMbDruLkoL6FiM5sjXiNZGTtUZaTGTTBpr27D7SREaMRJuccsquKFmWqbXVk9OQR3Z9Hjn1eZRbKgFQKVQkByUwMK4PcZp40oKT0al0Jy3P3+kO91hr6elaurVxPPHEE6xduxaNRoNer+ehhx5i4MCBbS5HGEf30eKuK8H686vI5loCJt2EutfYI457ZJnsMjMfL9xLcZWZ+MhA/jAhjSG9Ik74RX54a6Qgp5by4qNaI4fGRoKO3xrxmsgWGjduxLLXayLqiEgMw0cQNHwk2uTkVo1nAJgdTeQ05JNTn0d2Qx7FjSW4ZQ8SEnGGGDJCUkkPTiE9JJUQbXDbPjwf013usdbQ07V0a+NYtmwZ48ePR61Ws2zZMp555hmWLFnS5nKEcXQvLbLNjHXJm7hL96IZcSmaIRce8cUcGRlERaWJTfsqmbcqj4paC2lxRi6ZmEZmStgpyz9RayQ8MpDkjHCSM8KIij3+TC232Yx521YaN23AsneP10SiowkaORrjqDFoYmLapDUoVMOmnD1kN3hbJHkNBTg83mzBEQFhpIekkt48ThKlj2y1QfmC7nSPnYqerqVbG8fh1NXVMWHCBHbs2IFC0bY59MI4up8W2e3CtuI/uLLXoe43Be3Ya5Ga6/1wLW6PhzU7y/l+dR61JjuZyaH8YVIa6XGt+2v9UGukILeWwpwayooakGXv5ldJ6WGkZISTkBKKNuDYfc/cZjPmrZsxrV+Hdf8+kGW0ySkYR40maOQoVCGhp7z+0fXi9rgpNpe2dG3lNORjdjYBEKQ2kBGSSkZoGr1C0ogNjParRI7d7R47GT1dyxljHG+88Qb79u3jjTfeaPN7hXF0Ty2y7MGx4Wsc239ClTKUgCm3Iak0x9XidLlZvq2U+WvyabQ4GZIRwR8mppEQ1bZ9z+02F0V5tRRk11CQU4vd5kKhkIhNDCY53dsaCQnTH/M+V30djRs2YNqwDnt+HkgSuj59MY4ajWHYcJT6468+b80GWxWWKnLq8zhYn0d2fS519noAAtV6MoJ/N5J4Q6xPjaQ73mMnoqdr8WvjuPjiiyltzi90NGvWrEGp9OYMWrBgAa+99hqffPIJERERXRmiwA9o2PgTNb/MQZvQh9gr/g9FwIlTgFjtLn5YlcO8ZdlY7C6mjkjimml9CQ9u+8CzxyNTXFDHwT0VHNxTSWW59+ELjwykV79oeveLIjE17JgBdmtJKVUrV1G1chW20jIklYrQYUOJnDSR0OFDUWrbn1hRlmWqmmrYU3WQPZUH2VN1gMqmGgD0ah19I9LpF9WLfpG9SQ1N7Lb7lAj8F58bR2tYvHgxL7zwAnPnziUhIaFdZYgWR/fX4szdgO3Xd1FEJJF47WPUNp38fLPVyYK1+fy6uRiFQuK8EUlMG5WETntsl1NrMdXbKMypIT+7hpLCejxuGY1WSVJaGMnp4SSmhaHTq1vOl2UZe0E+pvXraNywHndDPYqAAAxDhxE0agz6vplExYScdr3U2eo5WJ9Ldn0uB+tzqbRUA6BVakgLTqFXSBq9QtNICkpApWi//lPR3e+xw+npWvy6xXEqli1bxlNPPcWHH35IcnJyu8sRxnFmaHEVbsO6+A3UYXFozrsHhT7klO+prLfy7YocNuytxBio4aIJqUwYFIuyjeNkR+N0uCnOryM/u4bCnFosTQ5vqvh4Y3OXVjhhEfqWwWzZ48G6fx+m9Wsxb96Ex2pFGRxM9FkTUQ8ZgTbx+FvttocGu6nZRPI4WJ9LefMKd7VCTVpwMr1C0sgISSPFmIhaqT5Faa3nTLjHDtHTtXRr4xg9ejRqtZqwsN9nysydO5fQ0FMPOh6OMI4zR4urZA+2X14DfQj6C+9rWSh4KnJKG/hyaTYHixuIiwjk8snpDEwL75BZSrIsU1Vubh4XqaGq3AxAkFHbPEsrnLikEFQqr1l5nA6aduygcd1amnZuR3a50MQnYBw7DuOo0a0aVG8LjQ4z2c0mkl2fS6m5HBkZlUJFqjGJjBDvGElqcBIapabd1zlT7jEQWrq1cXQUwjjOLC1B9hJKP30aSatHP/0BFMaoVr1PlmW2HKjmq+XZVNZZyUwO5fLJGSTHBHVofOZGO4U53gH24vw6XC4PKrWCxJRQr5Gkh6M3eL+gQ7SQv+hXTGvXYMvNAUlC368/xjFjMWQNQ3Ea4yEnoslpaR5s9xpJUWMpMjJKSUmyMaHFSNKCU9qUvPFMusd6uhZhHAjjgDNPS/nenVgX/BNUavQzHmy1eQC43B6Wby3hh9X5NFmdjB8Uyx8mpRMc2P6/tk94LaebksJ6CrJrKcipaVkzEhUbRHJGOFkjElGoJSRJwlFejmndakxr1+CqqUHSBhA0bBjGMePQ9enbMh25o7G6rOTU57e0Sgobi/HIHhSSgsSgeO8YSUga6SEpJ13dfqbdYz1ZizAOhHHAmanFXVOEZf7zSOoAr3kEtW3GncXm5Mc1+SzZVIxGrWDG2FSmDk9AdZKkiKeDLMvUVDZRkF1Dfk4NlaXe+ggM0pKcEUZKejjxySEolRLW7IOY1qzGvHkjHqsVVWgYQaPHYBwzFm1cfKfEdwiby05eQwHZ9bkcqM+lwFSEW3YjIZEYFNfSIskISUWv/n1q8pl4j50JCONoJ8I4zlwt7up8LPNfRNIGes3DcOqV40dTVtPEF0uz2ZFTQ3SojivP7sXgjM6f9m1pclBXaWHn1hKK8mpxOT2oVAriU0JJyQgnOT0MnVaiadtWTGtXexMvejzeRYZjxnkXGRqNnR6nw+0gr6GwpWsrz1SIy+NqSZNyqEUyKmMQdtOZ8ZVypj4vrUUYB8I44MzW4q7MxbLgJSS9Ef302SgC2ze4vCOnhs9/PUh5rYWBaeFceXYGseGdt20sHNZ6cnmau7RqKMiuobG5SysyxkByRjgpGeGEBLgxb1yPae0a7IUFoFQS2H8AxrHjCBw8BIW647vajofT7STfVNTSIslrKMDZnCYlLjDG2yIJ9bZIjJqOHT/qKs7k56U1CONAGAec+VrcFdlYfvonisBQdNNno9C3L0Ggy+1h6eZivl+dj8Pp5uxhCcwcl4I+oOOmrR7O8bTIskxttaXFRMpLTAAEGjQkpYeTkhFGpMaCZeM6TOvW4K6vR6HXEzRiJMYx4whIz+jSnFYuj4sCUzGlzmK2l+wjpyEfR/O+JNH6KHqFpHq7tkLTuk3ixjP9eTkVwjgQxgE9Q4urbD/Whf9CERSFfuaDSNr2txZMTQ7mrcpl5bZSAnVq/jApjYmD4k66RW17aE29WC0OCppnaRXl1eF0uFGqFCQkh5CcHkakpxr3tnWYt2xGdjhQR0VjHDMW45ixqCMiOzTek9HSevK4KWws4WB9Dgfrc8mtz8fmbp4UoItoaZH0CkkjNCCky+JrCz3heTkZwjgQxgE9R4urZA/Whf9GEZmC/oL7kNSnN521oLyRz5Yc4EBxA0lRBq4+pze9E0NOq8zDaWu9uN0eyooayD/oXcHe2GADICLaQFKKkUh7Gaqda7Ht3wuArncfjGPHYRg2AqWuc/f7OJGWQ4kbD42RZNfnY3VZAQgPCGtpjfQKSWv1lrudTU95Xk6EMA6EcUDP0uLM24RtyZsoEwagO/dvSMrTS7MhyzIb91Xy5bJsak12xvSP5rLJGYQYTn+NxenUiyzL1NV4u7Tys2uoKDEhy6AP1JCQoCfSWoJu92/IFaVIajWGrGEYx45D369/p0ztba0Wj+yhxFzuXd1el0N2fR5NLgsAodqQltZIRkgakbqOWaTZVnrS83I8hHEgjAN6nhbHvhXYV36IKn00AVNuQeqArLF2h5sF6/JZtL4QlVLBrPGpnD3s9KbvdmS9WC1OinJryc+uoSivFofdjVIpEROpIdJWimH/WrSNVSiDQzCOHoNx7Di08e3L/3Y82qvFI3soa6rwtkjqvPm2DqWSD9YYmwfavWYS3UV7kvS05+VohHEgjAN6phb7tgU4NnyFuv/Z3v08OugLp6LOwmdLDrIjp4a4iECumdqrVRtIHY/OqpdDXVoFOTUUZNfSUOftGgo1SETYywgu2ILRUkFAUjLGMWMJGjXmtKf2dpQWbyr5Sg7U/Z640eTwlhukMbRM/81o3pOkM4ykJz4vhyOMA2Ec0DO1yLKMff0XOHcsQjPsIrTDLuqwGGRZZnt2DZ8uOUB1g40RfaO4YkoGYcaANpXTFfUiyzL1tdaWLq3yYu9mVVqVTKSjnNCKvYTZygjun4lx7HgCBw9u19TeztIiyzKV1uqW1sjB+lzq7Q0AGNSB3s2tms0kzhDTIXuS9MTn5XCEcSCMA3quFlmWsa2Yg+vAKrRjr0UzYGqHxuJwulm0oZAFawuQJJgxNoVzRyShVrXuy8sX9WKzOlv2Xy/MqcVhd6FAJtRRSXhDHlGeKqKGDsA4djwBaemt/ou+q7TIskyNrfaIFkmtrQ4AvUpHekgqvZsH3BMMce0ykp76vBxCGAfCOKBna5E9bmyL38BVsI2AqbejThvR4TFV11v5fGk2Ww5UER2q4+pzejMw7dSZe31dL263h4oSE/nZNeQfrGnp0gp01BNhLiBG20TiiH6EjD311F5faqmx1rWYyMH6XKqt3s2tdKoA0oNTWqYAJxriW7W5la/rpSMRxtFOhHEILbLLgWXBi3iq89Ff+ADKmF6dEtuu3Bo+WXyAijorWb0iuPLsXkSGdJ/EgPW1zbO0DlRRVtyIDKjdNiKaiogN9pA+qg+ho0Yed2qvP2lp3eZW6SQHJRzXSPxJy+kijKOdCOMQWgA8tkYs3z8Ntib0sx5GERLTCdGB0+Vh8aYiflydj0eWuWB0MuePSkKj7l5fUIf2X8/dXUpRXh0OtwJJdhNqqyQ+XCJjVB+iRwxqmdrrz1qO2NyqLodySyUAGoWatMNaJMnGRNQKlV9raSvCONqJMA6h5RAeUyWW754CdQD6ix5Boeu8JIG1JhtfLstmw95KIoIDuGpqL4ZkRBwxZtBd6sXjkSkvbiBncw4FObU0uryD54EuE/ERCjJG9Wbo1Cxqasw+jrR1NDrMv7dI6nIpbSoHQK1QkWJMYnB8JvGaeFKMyWg6cJdEXyCMo50I4xBaDsddmYvlx+dRhMWjnz77tFeXn4q9BXV8svgApdVNDEwL5+qpvYgO86Yj7671UlfVSPZvO8nPrqXGFYgsKVDLTuLCFaQPTyelXzzagM7b37yjMTubWja3OliXS4m5zLtLoqQk2ZhERkgq6SGppAUno1O1beacrxHG0U6EcQgtR+PK34p18WsoEwejO/dOpFYMmJ7W9ZqTJ373Wx4ut4fzRiYxfUwKCfEh3b5eLFV1HFy2mcKcWio8wTiVAUjIRIcqSR2cRGqfKIJDOzfdSUejC1awIWcXB5unABebS/HIHiQkEgyxpDcbSXfIACyMo50I4xBajodj96/YV3+Eut8UtOOu65IVyQ1mO18uy2Ht7nJCg7TccvFAescG+UV+ptMlMjKIwi17yF++iYKDVVQqo2jSelPcGw0qkvtGk5weRmzi7/uv+ytH32M2l408UyE59Xlk1+eRbyrE6XEB3sSNLUYSnEqELsyv6lMYRzsRxiG0nAj7+i9xbP8JzcjL0A65sEPKbA0Hiur5ZPEBiirNZCaHcvXUXsRHHv9B7S4cXi+yx4Nl7x7KVm2gILeWGm0sdfpYPJISlUoiPjmUpPRwktLCMIb4X9fPqe4xl8dFYWMJOfV55DTkkVOfj6U5cWOwJqjFSNKDU4nvoEWJ7UUYRzsRxiG0nAhZ9mBb+i6unPUETLkNdcboDim3Nbg9HjYfrOGjhXux2t1MGRrPrAmpBHbS3h+dzYnqxWOz0rhpE3Vr1lJabKJGH09tSBoWvIYRGq4nKS2MpPQwYhOCUfpBa6St95hH9lDeVEl2s5Fk1+e1rG7XqQJIDU4mI9hrJodmbnUVwjjaiTAOoeVkyG4n1gUv4a7KRXfh/ahiendY2aciMjKI3IIa5q3KY8XWEgJ1ai49K53xA2M7fO+PzqY19eKsrsK0bi0Na1djqrVSY0ymPqov1W4DHg+o1AoSUkK9RpIWRlCwb1ojp3uPybJMra2u2UjyyanPa5kCrFKoSA5K7LIBd2Ec7UQYh9ByKmSbmabvn0K2mQm86BEUwZ2zxuNoDtdSUN7Ip0sOcLC4geSYIK6Z2puMhO6xYx60IxVMbg6mNatp3Lgep9VBQ0QGDYmDqXQbMTd5xw9CI/Qkp3tNJCYhGOVpZCJuC51xjzU6zOQ25HvNpD6fInPJEQPuac2D7enBqQRrO27Avccax/r167nxxht56KGHuPbaa9v8fmEcQktr8DRUeBcIavToL3oYRUDnz5Y5Wossy6zfW8FXy3Koa7Qzpn8Ml56VTmhQ504Z7gjavTDT6aRpxzZMa1bTtGsnstuNKykTU+pQKgmjvKwJj0dGrVF6WyPNRmLoxM+kK54Xm8tOvqmw2UjyyDMVtuzdHqkLbxlsTw9JPa19SXqkcZjNZv74xz8SFhbGhAkThHG0E6GldbgrsrHMfx5FRAr6C+9HUrU9S2xbOJEWm8PFgrUF/LyhEKVSwcyxKUwdntjq5Im+oEOmSZtMNG5Yh2ndWuz5eSBJqHtl0tR7JNWqKIoKTZhN3m1owyMDW0wkOt7Yoa0RXzwvh7bcPTRGkluf37LBVZDGQHpwCunBKaSFpLQ65xb0UON4+OGHGT9+PMuXL2fAgAHCONqJ0NJ6nLkbsS15E1XaSALOvq1DNoE6EafSUlln4fNfs9mWXU10qI6rpvZiUHpEp8VzOnR0vTgqymncsB7TurU4K8pBqUQ/YBDygFFUaaIpKmigvNiExyOj0XpbI4lpYSSlhmJoY3r7o/GH5+XQgLt31lYBuQ351NhqAVAr1KQYE5vHSFJIC05Cpzr+WpmONg6/X9a5YsUKGhsbmTZtGsuXL/d1OIIegjptBPKoy71TdYMi0I663GexRIXquevSQezMreGzJQd55asdDEoP56qpvYgO1fssrq5AEx1D+IxZhE2fib2ggMb1azFtXI97+1aCtAGMHDqUgHNGURsQQ1F+PYW5teTu9yYzDA3Xk5jqNZK4xGBUx8kT5u8oJAVxhhjiDDFMiB8DQL29gdyGAnLq88htyOeXgmUt4yRxhhhviyQ4hfSQFMICQjslLp+3OC6++GJKS0uPe2zRokXccMMNfPjhh4SHhzN79ux2tzgEgrYiyzLVi96jccsvRJx/K8ah5/o6JJwuD/N/y+WzX/bjdHm4aFI6l0/tjU7r938Ddhiy203D7j1Ur/yN6jVrcTc1oQ42Ej5uLBETxmMLjSf3QDU5+6ooyK3B7fKgVClITgsjvU8k6X2jiIw2+NUCvdPB5rRxsDaffVXZ7K/O5UBNLjaXtysvxhDJ41PuIUwX0qHX9LlxnIxNmzZx5513omtO31xXV4dGo+G6667jjjvuaFNZoqtKaGkPsseN9edXcRfvQjftblSJgzr8Gu3R0mC28/WKHFbvLCfEoOGyszIY1T8ahY+/DLv6HvM4nVh27cS0fh1N27ciO52oIiIwjhxN0KgxKKNiKC1qoCi3lqK8OupqvOMFgUEaElPDSEwNJSEllADdsetmuuvz4va4KW0qJ6chnxprLReknkNSbGTPG+M4xOm0OIRxCC3tRXZYsfz4HB5TJfqZ/4cyPKlDyz8dLTmlDXy6+AB5ZY2kxhq58uwMeiWEdGh8bcGX95jbaqVp6xZM69di2bsHPB40CYkYR40maOQo1OERmE02CvPqKMqtpTi/HofdhSRBZGwQianesZGoOCMKhdTjnxdhHAjjAKHldPA01WH57kkA9LMeQWEI67CyT1eLR5ZZu6ucb1bkUG92MKJvFJeelX7SzaM6C3+5x1wNDTRu3kjj+nXYcrIBCEjPIGjESIKGj0AVEorHI1NZZqIwt46ivFqqyhqRZdBoVSSkhNBvUByhkbrTHmT3B3q0cZwOwjiEltPFXVOE5YdnUBgj0c/4PyRNx3wxd5QWu8PNwvUFLFpfiEeGc0YkMH1MSpeOf/jjPeaoqqRxw3oaN27AUVwEkoSuV2+CRozEMHQ4qmDvAkub1Ulxfh1FeV4jaWp0AGfGILswjnYijENo6QhcRTuxLnoZZUJ/dOfd3SGp2DtaS12jnW9W5LBmVzlGvZqLJqYxcVBcl6Qv8fd7zFFWSuOmjTRuXI+jtNRrIn36EjRiFEFDh6EM8i74lGUZPBI7NhdRmFtHWVE9breMUikRmxjSMjYSHhXYLQbZhXG0E2EcQktH4di7HPuquagzz0I7/obT/uLoLC15ZSY+//UgB4sbSIgM5Iopveif2nFdbMejO91j9pJiGjd6WyLOigpQKNBn9iNoxCgMWUOJSYlp0eJ0uik7ziC7Tq8mISW05cdg9M/V/cI42okwDqGlI7Fv+ArHtgVoR12OZvAFp1VWZ2qRZZnN+6v4clk21Q02BqWHc8WUDGLDAzvler6ul/YgyzL2okIaN6zHvGkjzuoqUCoJHToE7aBhBA7JQqk7slvSbLJTnF/X8mO1eNOEhITrSUwJJSElhLikEDR+Mk1aGEc7EcYhtHQksuzB9us7uHI3EDD1dtRpI9tdVldocbo8LNlcxPw1+dgdHiZnxTNjfApGfcemU/F1vZwusixjz8+jceMGmrZswlFdjaRSoR84yDsmMjgLhVZ7zHtqKptaTKSsqAGXy4NCIREVF+Q1ktRQomKNPst2LIyjnQjjEFo6Gtnl8KZir85Df+EDKGN6taucrtRianLw/W95LN9Wglat5PzRyZw7PBGtpmMGfP2hXjqKiPBAitZvo3HjBho3bcTdUI+k0RA4cBCGYcMxDBqMIuDYCRIul4fy4oZmI6mnqtz7eWi0SuKSQlqMJDhU12XjI8I42okwDqGlM/DYGrF89zTYm9Bf9AiK4Og2l+ELLWU1TXy9PIetB6sJNmi4aHwq4wfFolScXk4uf6mXjuDo3Qyt2Qdp3Lge85bNuBsavC2RAQMJGjacwMFDUOqP3/1nszopKainKM/bImlssAFgMGoPGx8JQdfBrb8TaWktwjgQxgFCS2fhaSj3mkdAIIGzHkEKaNv2r77UcrC4ni+XZZNTYiI2XM+lk9IZ0iuiS9N3+ysn0iJ7PNhysmncvBHz5s246mq9yRcz+xM0fDiGIUNRGo5/D8iyjKne1mIiJQXeRYgAEdEGb5LG1FBiEoI7dE92YRztRBiH0NKZuMoPYl3wAsrINHQX/KNNqdh9rUWWZbYcqOabFTmU11rolRDMZZMzyIhv+wZSvtbSkbRGi+zxYMvPw9xsIs7qKu/srD6ZGIYNw5A1rGWdyPHweGSqyhspzqujKL+OihJvpl+lSkFsQjAJKSHN034NpzU+IoyjnQjjEFo6G2fOemy/vo0qfRQBU25tdSp2f9Hi9nhYtb2M73/Lo6HJwdDekVwyKa1NM7D8RUtH0FYtsixjLyzAvHkTjZs3eqf4Ni82NAwbjmHocNShJ89W63S4KS2qbzGSumrvtF9tgIr45BDik73dWm0dHxHG0U6EcQgtXYF92wIcG75CM2Q62pGXtuo9/qbF5nDxy8YiFq4vxOn0MHFwLDPHpxJiOPUaBX/TcjqcjhZZlnGUFNO4eRPmzZtwlJYAzWlPhg3HMGw46vBT76nSZLZTUlDf0q11aAOrwCAtCckhxKeEkpAcQuApdkIUxtFOhHEILV2BLMvYV83FuW8F2ol/RNN30inf469aTE0Oflidx4ptpSgVElOGJXDB6GQMx8kkewh/1dIeOlKLo6y0xUTsRYUAaJNTMGQNxZA1DE1c3ClbELIs01BnPcJI7LbmfdnD9cQne7u14pJC0AYcuX5EGEc7EcYhtHQVsseFddEruEv2oDv/HlQJA056vj9rAe8OhN//ls+63eVoNUrOG5nEuSMSj5sDy9+1tIXO0uKorMS8eRPmrZuw5eYCoI6OxpA1DEPWUAJS05BaMbtNlmWqK8wUF9RTkl9HWXEDLqfHm+03Joj45BCS0sOISwwRxtFehHEILV2J7LBi+eFZPI1V6Gc+hDI88YTn+ruWQ5RUmfluVR6bD1Rh0Kk5f3QSU4YmoD0s6V930dIaukKLq74O89atmLdtwbJvL7jdKIODMQzJwpA1DH3fTCRV61afu90eKkpMXiMpqKOytBGPR+aqW0bQq0+0MI72IIxDaOlqPOZabyp2SeFd4xF4/IHR7qDlcPLKTMxbmcuuvFqCDRpmjE1h4uA4VEpFt9NyMrpai9vSRNPOHZi3bKZp105kux2FTkfgwMEYsoYSOHDgcRccngiH3UVjg42wyECioozCONqDMA6hxRe4qwuw/PgcCmMU+hkPHjcVe3fRcjT7C+v4dmUuB4sbiAgOYNb4VGac1YvaGrOvQ+sQfFkvHqcDy549mLdupmnbNtzmRu+Cw8x+GLKGETh4yEmn+R6N6KpqJ8I4hBZf4SrcgfXnV1AmDEB33t+OScXenbQcjSzL7Mqr5dsVuRRUNJIQZWD6mGSG943y+Ta2p4u/1MuhVevmrVswb92Mq7raO803oxeBQ7IwDB2GJjLqpGUI42gnwjiEFl/i2LMM+2//Rd1vCtpx1x0xg6a7aTkeh7Lw/ri2gKKKRuIjApkxLqVbG4g/1ossyziKi2jcspmmbVuwFxUBoIlPwJCVhWHIULTJKcfM0BLG0U6EcQgtvsa27gucOxaiHX0FmkHnt/y+O2o5EWHhBhb9lsP3v+VRVmMhLiKQmd3UQLpDvTiqKmlqHly3HjwAsowyJATD4CwMWVno+mSiUKs73Dj8I1m8QNAD0I66DLmxCvu6L5AMEajTRvg6pA5HqZAYmRnN8D5RbNpfyQ+r83nn+93Erc73GkifKJ+lFj8T0URGoTn3PELPPQ93Y6N3cH3bFkzr1tCwYhmKwECSH3sKIoM69LrCOASCLkKSFARMvgWLpR7bsvdQBIaijM7wdVidguJEBhIhDKSzUAYFYRw7DuPYcd7B9b17cRQXoQzs+E27Oi79okAgOCWSSoPu3LuQAkOxLHoZd22Rr0PqVA4ZyJN/Gslts/oD8M73u3l0zgY27K1oV/ex4NQo1BoMgwYTdsH0Yzae6pDyO7xEgUBwUhQ6I/oL7kNSqrEu+CfOunJfh9TpKKTjG8gjH6xn9c4yXG6PjyMUtAVhHAKBD1AYI9FdcB+yx0XZp0/gaarzdUhdwtEGolIq+GDBXh58dx1LtxTjcLp9HaKgFXQL4/joo4+YNm0aM2bMYNasWb4ORyDoEJRh8ejPvxe3xYT1p38i286MhXOt4ZCBPP7HEfzt0kGEBGn4+JcD3P/OWhauK8DavLmRwD/x+8HxX375hUWLFvH1119jMBiorq72dUgCQYehjEoj5rLZlH3+NJaF/0Y//X4kdYCvw+oyJElicEYEg9LD2V9Yz4K1+Xy1PIcFawuYOjyBqcMTT5qNV+Ab/L7FMWfOHO644w4MzVsxRkScOoe9QNCd0KUMJODs2/FU52P95TVkl8PXIXU5kiTRNzmUe6/M4pEbhtM3OZQfVudz31tr+PzXg9Q12n0douAw/H4B4IgRI/jTn/7E8uXLcTgcXHnllVx++eW+Dksg6HAadyyn6sfX0fceSfQl/zgmNUlPo6DcxNdLD7JyawkKSWLqyCQumZxBTBt2JBR0Dj43josvvpjS0tLjHluzZg0jRoxgxowZPPbYY9TV1XHVVVfxzDPPMGJE2xZPiZXjQou/crgWx67F2Nd8gqrXOALO+lOrt5/1FzqjXirrrSxaV8BvO8twe2SG94li2qgkUmONHXqdozlT77HW4tcrx+fNm3fS43FxcUyfPh2FQkF4eDhjx45lx44dbTYOgaA7oBlwDrLDgmPTPOwKJdqJN3Y78+hookJ0XD+tLzPGpbJkUxHLt5WwcV8lfZNCmDYqmYFpYW3af1tw+vj9HTl9+nRWrVoFgMViYfPmzfTt29fHUQkEnYd26Cw0WTNw7l+J/beP8PPe5C4jNEjLZZMz+Oft47h8cgYVdVZe+Wo7j87ZINaCdDGnNI6nn376iNdfffXVEa/vvPPOjo3oKG688UbKysq48MILueyyy5gxYwbjxo3r1GsKBL5GM/wPaAZfgHPvMuxrPhbmcRg6rYppo5J44bYx/OnCTAA+WLCXB95Zy6L1hWIqbxdwyjGOoUOHsmXLlpbXI0eOZMOGDSc87q+IMQ6hxV85kRZZlrGv+xznzp9RDzwP7egr/b5Lxhf1IssyO3NrWbS+gH2F9ei0Ss4aEs/U4YmEBrU/3UZPuMdOxmmNcRztK+IvH4Gga5AkCe3oK0H24Nz5M5JCiWbkZX5vHl2NJEkMSg9nUHo4eWUmFq0vZNGGQn7ZWMTo/tGcOyKJxKjjfwEK2scpjePom1TctAJB1yFJEtoxV4PHjWP7TyAp0Iy4RDyHJyA11shfLhpAZb2VxRuKWLWjlNU7y8lMDuWcEYkMSg/vdvuC+COnNA632826detaWhoul+uI1x6PGJASCDoTSZLQjrvWax7b5iN73GhHXS7M4yREhei45tzezJqQysrtpfy6uZjXvt5BdKiOqcMTGTcwhgCNzyeVdltOOcYxZcqUUxaydOnSDguoszh6jEOWZczmBqxWMx7PiROrKRSKM8YchZauQaXSEBoaiVLZui+m1vY/y7IH++qPce5Zirr/VLRjr/E78/DXcQGX28Om/ZUs3lhEXlkjeq2KSUPiOHtYAmHG46d48Vct7UFsHdtOjjaO2tpKJEkiKCgEpVJ1wgdQpVLgcvnnF1RbEVo6H1mWaWoyYbNZiIiIbdV72vJQHzFg3vcstBOu96t1Hv7+ZSvLMjklJn7ZWMjmA1VISAzvG8k5IxJJjws+4lx/19IWzrgFgL7C4bARHZ3gVw+doPsjSRKBgUbM5vpOK187+kokpbq528pJwMQ/ISnEfdwaJEkiIyGYjISBVNdb+XVLMSu3l7JhbyXpcUbOGZHIsD6RKMXneVJ6rHGALExD0Cl0dveRJEloRlwCSjWOzfOwuV0ETP4zkqIHP87tICJExxVTejFzXCq/7SxjyaYi3vl+N2FGLVOGJnDR5F6+DtFvEd+cPZBLL53Bxo3rT3ne+PHDKS5u39amp/NewamRJAntsFloRl6GK2c9tiVvI7udvg6rW6LTqjhneCLP3TKGO/8wkKgQHV8vz+Gmp35hzoK9FJSfGd1VHYn4E0Ug6MZoh1yIpNJgX/MJ1kUvozvnTiSNztdhdUsUComs3pFk9Y6kuMrM2j2V/LqpkN92lpERH8yUYfEM7xOFSin+3hafgEDQzdEMOIeAs/6Mu3QflgUv4rGJv5BPl4RIA7dfOph//3UcV57dC5PFwXs/7OG+t9bw3apc6s09e38Q0eLowezZs4tXX/0XBQV5aLVaJk2awp133oNa/fuOa2vXrubLLz/DYjFzwQUz+ctf7kTRPHA4f/73fPbZR9TU1NCvX3/uv/8hYmJaN5NI0LGoe49D0uqxLnkL6/fPoLvwPhSGcF+H1e3RB6g5d0QiU4cnsCu3lqVbivlhdT4L1hYwrE8kU4clkh5v9Ltp0Z2NaHH0YBQKJXfeeQ/z5y/hnXc+ZPPmjcybd2QSy5Url/HBB//jgw8+YdWqFSxY8AMAq1Yt56OPPuSZZ15i/vzFDBo0hMcff6jrRQhaUCVnobvgH3gsDVi+fwZ3/fH3uRG0HUVzWpO7LxvMc7eO5uxhCezMreXZjzfzxNyNrNpRisN54vVgZxrCOHowfftmMmDAQFQqFbGxccya9Qe2bj0yYeU119yA0RhMTEwMl19+FUuW/AzAd999y3XX3UhKSioqlYrrr7+Jgwf3U15e5gspgmZUsX3Qz5gNHhfW75/FXZnr65DOOKJD9Vx5di/+/ddxXH9eH9wemQ9/2se9b67mq2XZVNdbfR1ipyO6qnowhYUFvPHGy+zbtxebzYbb7aJPn8wjzomKim75f0xMLNXVVQBUVJTx6qv/4o03Xmk5LstQVVUpuqt8jDIiGf3Mh7D89BKW+S+gm/pXVEmDfB3WGYdWo+SsrHgmDYljf2E9v24p5ucNRSxaX8jA9HDOyopnUFo4CsWZ140ljKMH869/PU+vXn14/PFn0OsD+fLLT1m27NcjzqmsrCAtLR2AiopyIiIiAa+hXH/9TZx77vldHrfg1CiCo9HPehjrwpex/vwK2vHXo8k8y9dhnZFIkkTf5FD6JodSa7KxcnspK7aX8trXOwg3apk4JJ6Jg2IJNrQ/xbu/IbqqejAWSxOBgYHodHoKCvKZN+/rY8757LOPMJlMVFSU89VXnzFlyjkAzJp1CR999CG5uTkAmM1mli5d0qXxC06OQh+CfsZslAn9sa+ai33D12JbhE4mzBjARRPSeOkvY7n9ogFEh+mZtzKXf7y1hre+28Xegrozog5Ei6MH89e/3s2LLz7Dp5/+j169+nD22eeyefPGI84ZP34Sf/rTdTQ1mTn//OlMnz4LgEmTJmO1Wnj88f+jvLwcg8HA8OEjmTJlqi+kCE6ApNGhO+9u7L/9D8e2+XjMNQRMuglJqT71mwXtRqVUMLxvFMP7RlFea2H51hJW7yxj075KYsL0nJUVz7iBMQQGdM966LFJDsvLC4iJST7l+/w1mV57EFq6jtbeX9A1yfRkWcaxbQGOjV+jjO2L7tw7kbSBHX6dnp4Y8GQ4nG427qtk+bYSckpMqFUKRmZGMTkrgdTYoE6d0iuSHAoEgjYjSRLarOkogsKxLf8Plu+fQTftbhTGKF+H1mPQqJWMGxjLuIGxFFY0snxrCWt3V7B6ZzlJ0QYmZ8Uzql90t9gnRIxxCAQ9CHXGGO9aD2sDlnlP4irb7+uQeiRJ0UFcP60v/75jHNed2xuPR+a/i/Zz75ur+fiX/RRXmX0d4knxf2sTCAQdiiouk8CLHsG66BWsC170zrjqO8nXYfVIdFoVk4cmcFZWPDklJpZtLWbl9jKWbikhPd7IpMHxjMiMQqtW+jrUIxDGIRD0QBTBMegvegTrr29jX/khnrpStKOuEPt6+Ijf9wkJ5sqzHazZVc7K7aXM+Wkvn/16gNH9Y5g0OI6k6CBfhwp0A+PIy8vj0UcfxWQy4XA4uOCCC7jzzjt9HZZA0O2RtIHopv29ZUdBT30purP/gqTR+zq0Hk2QXsN5I5M4d0QiB4sbWLGtlN92lLFsSwmpsUFMHBzHyMxodFrffX37vXG89NJLnHfeeVx77bU0NTUxffp0Jk2axKBBYiWsQHC6SAolAWOvQREaj/23j7B89zS6c+9CERLj69B6PJIk0TsxhN6JIVx9Ti/W7ipnxfZS/rtoP58vzWZUZjSThsSREtO5M7KOh98bhyRJNDZ6p5HZbDYkSSIsLMzHUQkEZxaazLNQBMdgW/wGTfOeQDf5FlQpWb4OS9BMYICaqcMTOXtYArmlJlZsK2XdHm93VlKUgYlD4hjdLwZ9QNd8pfv9Oo6SkhJuu+026uvrMZlM3H///VxzzTVtLkes4xBauhJ/W8fRWjyN1VgXv4GnOh/N0Jlohl7UpnEPf9Jyuvi7FovNxfo95azYVkphpRmNSsGIzCgmDYknPe7IVO8dvY7D58Zx8cUXU1p6/PTPa9as4dVXX8VoNHLzzTdTWVnJddddx4svvsjgwYNP67q7d+8hLq51D3ZXYjKZmDHjPGbN+gP33HOfr8M5JZs3b8LlcjJq1Bhfh+JXlJYW0L9/P1+H0S48LgfVC9/HvGMpuvQsomb9DaXOPwZlBcciyzLZxfX8vK6AlVuLsdrdJMUEcd7oZCYPSyRIr+nwa/rcOE5FVlYWS5YsITzcuynNY489RmJiIjfffHObyukuLY5vvvmS5ct/JS8vl3nzfjpiU6XTwePxoFYrcbs7tro/+OBdrFYrd9xxd4eWeypEi6NzkWUZ597l2Nd8jBQYhu7cO1GGJ53yff6opb10Ry1Wu4uN+ypZsa2EvLJGtGolT988ir4ZkT1r5XhCQgKrVq3ioosuwmw2s3nzZqZMmeLrsDqNBQt+4Pbb7+Kjj+ayatUKpkyZitls5rnnniAvL5fIyCgiIiIJDQ3jjjvuPumxDz54l7y8XJqazFRUlPOf//yX7du38b//zcFud6BWq7nzznsYMGAgAO+++yZLly7GaAwmK2sYmzdv5IMPPqKmpprHH3+IpqYmHA4HY8eO4/bb/0ZOTjbff/8tHo+HTZs2cPbZ53LddTeydu1vJ7yGoHsgSRKafpNRhidiXfwGlu+eJmDijah7jfV1aIKToNOqmDg4jomD4yisaGR/YT3GwI5vcfi9cTz33HM8/fTTzJkzB5fLxQUXXMCkSR2/WGn1zjJ+23HsJkSS5N1n4nQYP8ibZuBUZGcfxGRqYNiwEdTW1rBgwQ9MmTKVDz98n6AgI59++g0mUwN/+tN1TJrkNc+THQPv9rBz5nxCSEgI5eUlzJ37Af/+9+sEBhrIzc3hH/+4i2+/XcBvv61kzZrfmDv3M7RaLQ8//EBLGQZDEC+88DJ6vR6Xy8U999zBunVrGD16LLNm/eGIFkdJSfEJryHofiijM9D/4Qlsv76Fbdl7uCuy0Y6+EknV8V9Ggo4lKTqo09Z9+L1xDBgwgM8//9zXYXQJ8+d/z7RpFyJJEpMmTebll1+iqqqSrVs3cffd3vEOozGYCRN+N86THQMYM2YcISEhAKxbt5aSkmL++tdbWo673W5qa2vYunUTU6ZMRafTAXD++Rcyd+4HgLeb6623XmXnzh2ATE1NDQcPHmD06GP/+ly//sTXCAsTe2B3RxT6YHQX3od9w9c4dyzCXZGNburtKILFlN2eit8bR1dxKPnY0XRVX7rT6WTJkkWo1RoWLfL+de5yufjppx9Pq1yd7vDFXDKjRo3hkUeebFMZX3zxCY2NJt57by5arZYXXngGh8N+3HNluX3XEPg3kkJFwOgrUcX2xbr8fZq+fZyAiX9EnT7K16EJfIDIL+AnrFq1gsTEZObN+4mvv/6Rr7/+kZdffoOFC+eTlTWsxUwaGxtZtWply/tOduxoRo4cw/r1a1s2XwLYu3d3SznLl/+KzWbD4/Hw888/tZzT2NhIeHgEWq2WqqpKfvttRcuxwMBAmprMh11j9AmvIej+qJKHEHjJkyjCErD9+ja23/6H7HL4OixBFyNaHH7CggU/HLMN64ABg/B4PIwfP4kvv/yMq6++hPDwCPr2zcRg8M52uPHGP/Pss08c99jRJCUl8eijT/H8809ht9txuZwMHDiYzMz+jB8/iZ07d3DDDVdiNBrp339gy8LLyy67kkceeYDrrrucyMhohg0b0VLmxImT+b//u48bb7y6ZXD8RNcQnBkoDOHoZ8zGvuEbnDsWiq6rHojfT8ftKLrLdNzj4XK5cLvdaLVamprM3H77zdxxx98ZMWLUSY8dzam0WCxN6PWBeDwenn/+KSIiIrnllts7U1q78Yd6ORndfTpua3EVbsO67H3wuAmYcANxY87ttlqOpjvXy9GIjZx6II2NJu699y48Hg8Oh51zzpnWYgwnO9ZWnnrqMcrLS7Hb7fTpk8k111zfkTIEZyCqJG/Xle3Xd7AtfZfKyr0w/EqRKPEMR7Q4ToG//2XbFoSWrqOntDgOIXvcOLbNx7H5eyRDGAGTb0UV08vXYZ0WZ0K9HKKjWxxicFwgEJw2kkKJdugs4q5/GpCw/vgs9k3zkD1uX4cm6ASEcQgEgg4jIKEPgZc8iSpjLI4t32P54Vk8pkpfhyXoYIRxCASCDkXS6NBN/jMBU27DU19K0zeP4jywmh7SK94jEMYhEAg6BXXGaAIveQpleBK25e9j+/UtPLYzY8ygpyNmVfkRl146A41Gg0ajBWDo0GHcdde9PosnNzebN954lZKSIjwemT59+nDnnfcQHd118/UPHtxPYWEhZ599zmmVU11dxRNPPMzrr78LeLP6Xn/9TS3Zh//zn3dITU3j7LPPPe2YBb+jCIpAN302ju0LcGz+DnfZfgIm/FFsEtXNEcbhZzz99AukpWV06TU9Hg+SJB2x8YvJZOLuu//K3Xffx5QpUwFv6pF77rmD//73c1Sqzr91XC4XBw8eYM2aVadtHBERkS2mAd7kkFdddV2Lcdx8822nVb7gxEgKBdqsGaiSBmNb/j7WX15F1Xs8AWOvFtN2uynCOPychQvn8+23X+F2uzEYDPzjH7NJSkrh1lv/yN13/4PMzP7885/Ps23bFj7++EtcLhezZp3H11/PR6fT8fHHc1mxYilut5vIyCjuv/8hwsMjjkm5/s47H2I0Gluu+803X5CVNbTFNACuuOIaliz5hSVLfmbatAu5445b6NWrD7t2bcdkMjFlyjnceutfAU56rLi4iJdeepb6+jqUSiW33PLXloSJ48cP549//DNr166mf/8BrFq1AouliRtvvJohQ7K44opruPnm6/j552UAlJWVcvPN17Fgwa8t/5858w+sW7cam83G7NmPMnjwkCPO+9e/XgDgL3+5CUlS8Prr7/Laa/+ib99MLrnkCpxOJ++99xbbtm3G4XCSkZHBvfc+iF6v5/vvv+XLLz9FrdYgyx6efPJ5kpNTuuJW6PYow5PQX/QYji3f49i2gKaSPQRMuglVwgBfhyZoI8I4mnEeWI1z/7F5niRJOu1BPXWfiah7j2vVuQ8//EBLV9XEiWexd+9u3nzzfTQaDWvXrua5557k7bfnMGzYCDZt2khmZn927tyGVqulurqa8vJSkpNT0el0/PzzT5SUlPDuu3NRKBR8//03vPHGKzz22NPAkSnXjyYn5yCDBg055vf9+w8kO/tgy+v8/FzefnsODoeD2277IwMGDGLcuAknPfbEEw8za9bFTJ9+EXl5udxxx5/5+OOvCQ0NBUCr1fKf//wPgD59MlmzZhVPP/0i4DWKk9HQ0MCAAYO49da/8ssvC3nnndd4++05R5xz770PMG/eV7z99hz0+mP/4v3kk/8SGBjI++97Y3jrrdf46KMPufXWv/LWW6/yySffEBERgcPhwOPx37Uk/oikVKEdcQmq5Cxv6+Onf6LuNwXtqMuR1AG+Dk/QSoRx+BmHd1W99darZGcf5JZbbgS8mWcbG00ADB8+kv/+dw7nnjsNozGYIUOGsnnzBsrKSltySf3220r27dvLTTddC4DH4yYwMLDlWoenXD+a1prl+edPR6VSoVKpOPvsc9myZWOLcRzvWFbWULKzD3DBBTMBSE1NIyOjD7t372T8+Ikt72svOp2+5fr9+w/kjTdeaXMZq1evpKmpieXLlwLgdDrIyPAuZhs6dATPPPMY48ZNYMyY8cTHJ7Q71p6MMioN/R+ewL7pW5w7fsZVtJOAs25GFdvH16EJWoEwjmbUvccdt1XgyxXKsgwXXjjzuP3vAwYM4sCBfaxd+xvDho0gK2sYCxb8QGlpScv5sixzww03MX36LOBYLYdSrjc01PO3v3lzUiUlJfPkk8+RkdGb3bt3HnPdPXt2cfHFl3a41sM5MhX8kSiVyiMyADgcR2Zm1Wh+32pXoVDgdrvafH1ZhnvvnX1EMsdDPPvsS+zdu5vNmzdx11238Y9/PMiYMa1rTQqORFJpvKnak7OwLf8P1h+fRz3wXLQj/oCk0vo6PMFJENNx/Zhx4yawaNECKisrAO+GSPv27QVAo9HQu3dfPv74vwwfPor+/QeyY8d2cnKy6d/fu03r+PETmTfva0wmbyvF4XBw8OCBY64THBzC3LmfMnfupzz55HMAXHLJ5WzZspmlS5e0nPfFF59gNjdyzjnTWn73888LcblcWK1Wli5dwtChI056TK8PJCOjNwsXzgcgPz+PnJwDLTEfTWBgIGbz72nbw8LCcblcFBUVArB48aI2fqpe9Poj08EfzvjxE/nii0+w222AN/ljfn4eLpeL0tIS+vUbwHXX3cjIkaM5eHB/u64v+B1VbB8CL30Kdb/JOHf+TNNXD+Mq2ePrsAQnQbQ4/JghQ4Zyyy23M3v2PbjdHlwuJ5MnT6Vv30wAhg8fwb59u8nM7IdSqSQhIYG4uLiWmULTpl1IQ0M9d97p3Y1PlmUuuuhSevXqfcprG43BvPzym7z55iu8887ryDL06tWbf//7jSNmVCUnJ/OXv9zUMgB+qJvoZMcee+xpXnrpWb788lOUSiUPP/xky/jG0QwbNpLPPvuYG264iqysodx993387W/3ctddtxMSEsKYMePb9dleeeU13HXXbWi1AUfMtgK49tob+eCDd7n55utRKBSAxE03/Zm4uHieeeZxzOZGJElBdHQ0t912R7uuLzgSSR1AwPjrUaWNxLbyQ6wLXkTddyLaUVcgaQNPXYCgSxFJDk+BvyfTawsdreWOO27hqquuO8IsWnOsI/D3eulpSQ4P0RFaZJcDx+bvcOxYiKQLJmD8DT5Z99HT60UkORQIBN0GSaVBO+py9Bc9ihQQhPWXV7EueQuP1eTr0ATNiBbHKfD3v2zbgtDSdYgWR8cge1w4tv2EY8sPoNYSMPYaVBljjlis2ln09HoRLQ6BQNAtkRQqtENnor/kCRTBMdiWvYd10ct4zDW+Dq1HI4xDIBD4PcrQePQzH0I79hrcZfto+uohHLsWI4sFmD7BL4zj+++/Z8aMGfTr14+PP/74iGNWq5W7776bc845h2nTprFs2TIfRSkQCHyJpFCgGXAOgZc+gzKmF/Y1n2D57kncVfm+Dq3H4RfGkZmZycsvv8z06ceuGP7ggw8wGAwsXryYd955h4cffpimpiYfRCkQCPwBhTES3bR7CDj7duSmOizfPYFtzafIDquvQ+sx+IVx9O7dm4yMjOY580eycOFCrrjiCgBSUlIYMGAAK1cem1NKIBD0HCRJQp0+ksDLn0WdORnnrsU0ffUQzvwtvg6tR+AXxnEySktLiY+Pb3kdGxtLeXm5DyPqHN577y3++c/nWl6vXr2K8eOHk5ub0/K7+++/m/nzvzthGZdeOoPc3GzAu45i9epVbY6joaGe2267iRtvvJpPP/3fMa8FAn9C0gYSMP569LMeQtLqsf3yGtZfXhOD551Ml6wcv/jiiyktPX5W0zVr1qBUKjs9hqOnlVVWKlCpWuebrT3vdBgxYgT//OcLLdfasWML/fsPYMeOLfTu3Qu3282OHdu45577ThqPUunVJUkSSqV0zLmn0rJly0aMRiP/+c9cABYv/vmI1/5EV9RLe1EoFERGBrX6/Lac6+/4REtkFnLmQBo2zKdu5RdYvn6IsElXYRx+PpKi/d8vol6OT5cYx7x589r93ri4OEpKSggLCwOgrKyMUaNGtbmco9dxeDyeVq0D6Kr1Av36DaSsrJTKyirCwsLZsmUzf/zjLfz0049cdNFl7Nu3F70+EJVKy+zZ/6Ciohy73c7Uqedx/fU3tZTjTU3iQZZl3G75iNgPaXG73bz99uusX78GgFGjxvKXv9zJ9u1bef31V7BYmrj22is566wp/PDDvJbXf//7fQwe7B87t/n7Og6Px9PqefM9fb1Ah5JxNvqoQdhWf0TN4g+p27qUgAl/RBmZ0uaifK6lA+nodRx+n6tq2rRpfPHFFwwcOJD8/Hx27tzJv/71rw6/zvqyzawt23jM7yXJmy31dBgTO4JRscNOeo5WG0BmZn+2bt3MmDHjsFptjBo1htde82rdsmUzWVnDePrpR7nxxpsZMmQoTqeTv/3tL2Rm9mPEiNGtjueHH+Zx8OAB5sz5BIB//OMufvhhHhdffCk333zbEftfREVFH/FaIPB3vIPnf8eVtxH76k+wfPcE6swp3qy7Iu9Vh+AXxjF//nxefPFFTCYTv/76K++99x5z5swhIyODP/3pT8yePZtzzjkHhULBk08+icFwfBfs7mRlDWPr1s3o9YEMGjS4OXFhIrm5OWzbtplRo8by2mv/or6+vuU93syt+W0yjk2b1nPBBdNbkiFecMEMVq5c1unp0gWCrkKSJNRpI1ElDMC+8Vuce37FlbsB7egrUPUa1yUrz89k/MI4pk+fftypuAB6vZ7XXnut02MYFTvsuK2CruwSGTp0OP/61/MEBhoYMsQby+DBQ9m8eSM7dmzj1lvvQJIk/vOf/7V6z+/D99pITk7miSeeO8U7BIIzB0mjJ2Dctaj7TMC2+iNsy/+Dct9KtOOuQxme6Ovwui3+O7rYA+nffyBlZWWsWLGUoUO9xjFkSBbffPMlBkMQ6ekZDB6cxccfz215T0VFOTU11Scs8/C9Np55xrvX9vDho1i4cD4ulwuXy8XChfMZMaLt40YCQXdBGZGMfub/ETDxJjz1ZVi+fUys/TgN/KLFIfCi1Wrp168/1dVVREREApCZ2Z/q6komT54KwKOPPsVrr/2b66/3rm3R6wN58MFHCQ+PaPV1Zs68mOLiIv74x6sBGDlyDDNmXNzBagQC/0KSFKj7TkSVMhT7xm9w7lqMK2c92jFXokofLbqv2oDIjnsK/H32TlsQWroOkR3X/3FX5mJb/RGeqjyUsX3Rjr8OZejva8a6k5ZTIbLjCgQCQQegjEpDP+sRtONvwF1bhOXrR7Gt+wLZafN1aH6P6KoSCAQ9FkmhQNNvMqrUYTg2fI1zx0JcOevQjrkKOWKKr8PzW0SLQyAQ9HgUOiMBk25CP+thpIAgbEveovzTJ3DXlvg6NL9EGIdAIBA0o4zOQH/x42jHXYu9PA/LN49gW/MJsl1k5D4cYRwCgUBwGJJCgab/VBL/8jrqvpNw7lpC0xezcexbgSz774SMrkQYh0AgEBwHpd5IwIQb0P/hcRQhsdhXfojlu6dwV2T7OjSfIwbH/QiXy8Xcuf9hyZJf0Go1KBQKhg4dwV/+cmerV4p3Fh988C7z5n3dsr4E4K233kev7x65fz744F2sVit33HG3r0MRdDOUEcnoZjyIK2cd9nVfYPn+aVS9x6EdeRkKfYivw/MJwjj8iGeffQK73cacOR+h1wficrlYsOAHHA5Hq4zD5XJ1iMF4PB4kSTpmQdS0aReKL15Bj0SSJNQZY1AlZ+HY+iOOHT/jytuMdugs1APOQVL2rK/SnqXWjykqKmTlymV8++1PLX/Fq1QqZs36Azk52fzrX89js1lxOBzMnHkxl1/uXfX9zDOPo1QqKSwswGKxMHfupyxcOJ9vv/0Kt9uNwWDgH/+YTVJSCgAffzyXFSuW4na7iYiI4oEHHiI8PIIPPniXvLxcmprMVFSU8847H2I0Gk8Zd21tDS+99BylpcXIssxVV13H+ed7847l5+fx6qv/pLa25ohjl146gxdffJm0tAyAltcpKWn8+98vsmXLRtRqDXq9jrffnkNdXS2PP/4wdXXezXmGDx/JPffcd1I9ZrOZ559/ktzcHMLCwomOjiY0NLxD60zQ85DUAWhHXubNfbX2M+zrv8C5bwXasdegShzo6/C6DGEczZjWrKbht2O3pJUkidNdXB88fiLGseNOes6BA/tJSEg67pd1bGwsr7zyFhqNBovFwi233MDIkWNISUkF4ODBA7zxxnvodDq2b9/K0qWLefPN99FoNKxdu5rnnnuSt9+ew8KFCygpKeHdd+eiUCiYN+9r3njjFR577GkA9uzZxZw5nxASEnLcGBctWsCmTRsAGDhwMPfe+wCvvPJP0tLSee65f1JdXc2f/nQtffr0JSkphdmz7+WWW25nyhRvupSGhvqTfgbZ2QfYunUTH3/8FQqFApPJBMAvvywkPj6eV199C6Dl9z///NMJ9Xz4obcb7dNPv6G+vp6bbrqGKVPOOen1BYLWogiOQT/t77gKt2Nb+ynWhf9ClZyFdsxVKIxRvg6v0xHG0Q2w2Wy88cbzZGcfQJIUVFdXkZ19oMU4zjrrbHQ6HQCrV68kO/sgt9xyIwCyLNPY6P2i/e23lezZs4ebbroWALfbdUSK+jFjxp3QNOD4XVWbNm1o+V1ERARjxoxjy5ZNgITb7W4xDfAmXDwZcXEJuFwunn/+KYYOHc7YsRMAb/LHL774lDfffJUhQ4YyatSYFj379u09rp6tWzdx993eVklISAiTJonFXIKOR5U0mMD4fjh2Lsax9QdcX/0fmkHnoxkyHUmt9XV4nYYwjmaMY8cdt1XQVTmRevfuQ3FxISaT6ZhWx7vvvklYWDhz5nyCSqXi73//Kw6Ho+W4Xq9r+b8sw4UXzuTmm2875hqyLHPDDTcxffqs48ag0+mBI1OxJyUl8+STHZuKXalUHpE37JAWg8HARx99ydatm9m0aQNvv/06c+Z8zIABg/jww0/YuHE9P//8Ex9/PJf33//wlHoEgq5AUqrRDrkAda8x2Nd/iWPrjzgPrEY76nJU6aPOyOSJYjqun5CYmMS4cRN56aVnsVi8i43cbjc//vgdZnMjUVHRqFQqcnOz2b592wnLGTduAosWLaCysqKljH379gIwYcJE5s37uqWrx+FwcPDggWPKODwV+6lMY/jwkfz443cA1NRUs3btaoYOHUFSUjJKpZKlS5e0nHuoqyo+PpF9+3YD3hZLba137KKurg6bzbvz4W233YHBYKC0tITS0hICAw1MnXoed975d/bv34fH42H8+BPrGTp0BD/99GPLdVeuXHZSHQLB6aIIDEU35VZ0Mx/yrj5f+g6WH57BXZXn69A6HNHi8CMefvgJ5sx5j5tuug61WoUsy4wePY5rr72RZ599ggULvicxMYkhQ0687/eQIUO55ZbbmT37nub9x51MnjyVvn0zOf/86dTW1nHnnbcA3tlTF198Gb169W53zHff/Q9eeulZbrjhSmRZ5rbb7iAtLR2A55//Fy+//CJz576PJCm46qprmTbtQv7859t45pnH+frrLxk2bDjR0TEAVFZW8MILT+N2u3G73YwePZb+/QeycOF8vvjiExQKJbLs4b77HkShUDBt2oU0NNQfV8+NN97Mc889wdVXX0JYWPhJPzOBoCNRxfRCefFjOA+swrHxGyzznkDVezzakZeeMdN3RVr1U+Dv6bvbgtDSdYi06t2fjtAiO6ze6bs7fwGFEk3WdDQDz0NSaTooytYh0qoLBAJBN0HS6NCOupzAy59FldAfx8ZvaPrq/3Dmbjzt2Zq+RBiHQCAQdDIKYxS6c+9Cd+H9SKoAbEvexDr/edzVBb4OrV0I4xAIBIIuQhXfD/0lT6Adfz2e2hIs3z6ObeWHeKwmX4fWJsTguEAgEHQhkkKJpt8U1OmjsG/+HufuX3HmbEA7dGa3SV8iWhwCgUDgAyRtIAFjr0Z/2VMoY3phX/8FTV89hCt/q9+Pf/iFcXz//ffMmDGDfv368fHHHx9x7IknnmDatGnMnDmTK6+8kp07d/ooSoFAIOh4lCFx6M+/B9359yApFFh/eRXrT//EXVvs69BOiF8YR2ZmJi+//DLTp08/5tjEiRP58ccf+eGHH7j11lv5+9//7oMIBQKBoHNRJQ5Cf+lTaMdeg7sqD8s3j2L77SNkm9nXoR2DXxhH7969ycjIQKE4NpzJkyejVqsBGDJkCOXl5Xg8/jt//3RYunQJf/zj1dx449VcffUlPP74Q4B3Lwmn09nh17v00hnk5p7+pjR33HELq1ev6oCIBIKejaRQoRlwDoYrX0SdORnn3qWYv3gAx67FyB6Xr8Nrwf9HYQ7jk08+4ayzzjquwZyKoxeyVFYqUKlaV05rzzsdqqur+Pe/n+e///2U6OgYZFnmwIH9qFQKPvzwfa677gZUqtNPmna0FqWy9Z8DHH/PD0mSUCqlLvmcDqerr9cWFAoFkZFBrT6/Lef6O0JLRxAEibfjGDedmiUfYl3zCZ4DKwifeiP69PZlQehILV1iHBdffDGlpaXHPbZmzRqUSuUpy1iwYAE//vgjn3zySbtiOHrluMfjadXK465aoVxZWYVSqSIw0NhyvfT03rzwgjdX1J//fCOSpOD1199l7drVfPXVZ7hc3lbIX/96N8OHjwS8rYhp0y5k48b11NRUc9VV13LJJVcAsGvXNl580VvekCFDkWW5OS2JhzfeeIVt27bgdDoJCQnhwQcfJSYmlrKyUm6++TrOP38GW7ZsZObMixk8eCjPPvsEVquV9PR07HY7brfcpSu5/X3luMfjafVKXbHa2j/xCy1SKMqpf0dXsA3bus8p//xplImD0I6+EmVoXKuL6eiV411iHPPmzTut9y9evJiXX36ZuXPnEhER0UFRHcn+neXs21F+zO87Yj+OvoNi6DMw5qTnZGT0pl+//lxyyYVkZQ1j0KAhnHfeBdx77wPMm/cVb789B73em7121KjRnHPOeUiSRGFhPn/72+3Mm/dTS1k2m4133/2QsrJSrr/+Cs4/fwYqlYpHHnmQRx7xpiz/9dfFfPvtVy3vufbaG1vSo//443e8/fZrPPGE12QaGhrIzOzXcvymm67lssuu5Pzzp7Nr105uv/1Pp/X5CASCEyNJEqqULAITB+Dc/Sv2Ld9j+fph1P0mox12MVLA8b/cOxO/76patmwZzz33HB9++CEJCQm+DqfTUCgUPPfcv8jNzWbr1i2sWrWcTz/9iP/97/Njzi0pKebxxx+iqqoKlUpFbW0NNTXVhId7TXXq1HMBiI2NIyjISFVVJU6nE602gKFDhwNw9tnn8NJLz7SUuW7dar799iusVgtut/uI62k02pZNkJqazOTl5XDeeRcAMGDAwJad/AQCQechKdVoBk1D1Wssjs3f4dyzFOfBtd7ta/uf3aXrP/zCOObPn8+LL76IyWTi119/5b333mPOnDlkZGTw4IMPolarueuuu1rOnzt3LqGhoR0aQ5+Bx28VdHWXSFpaBmlpGVxyyeVce+1lbN26+ZhzHn/8Ie644+9MnHgWHo+HqVPHH7E/h0bzewI1hUKB232iQTXvPgHl5WW8/vq/ef/9/xEXF8/Ondt54omHW87S6QLOyD0FBILuiEJnJGD89aj7nY193WfY132GY+9SAkZdiTJ5SJc8q35hHNOnTz/uVFyAdevWdXE0vqGqqpKKinIGDBgEeFOM19fXERsbh14fSFOTuaWrymw2Exvr7d9csOCHI0zjRCQlJWO329m+fSuDB2exbNkSzGZvn2dTUxMqlZrw8HA8Hg/ffffNCcsJDDSQlpbB4sWLOO+8C9izZ1eHzMwSCARtQxkWj+78e3EX7cC+7nOsv7yKMi4T7ZirUYYnduq1/cI4BN4Nlz744F3Ky8vQagOQZQ833/wXevfuy5VXXsNdd92GVhvA66+/y1133cP//d8/CAoKYtSosQQHB5+yfI1Gw1NPPcuLLz6HJEkMHpzVsg9GenoGkydP5dprLyc4OIQxY8axffvWE5b18MNP8OyzT/Dxx3NJS8ugb99+HfY5CASC1iNJEqqkwSgT+uPcsxz75nlYvn0UdZ+JaIb/AYX+1N8N7bqu2I/j5Pj77J22ILR0HWI/ju5Pd9Qi28zYt/yAc/evoFKjyZqJZvD5REUZxX4cAoFAIDgWKcBAwNirCbzsaZSxfXFs+BK5sarDryO6qgQCgeAMQxESi37a3cgOC5JG3/Hld3iJAoFAIPALOsM0oEcbh4Qs+28fuaD70kOGDQU9mB5rHBpNAPX11bhcTvGgCzoMWZZpajKhUmlOfbJA0E3psWMcoaGRmM0N1NZW4PG4T3ieQqE4Y7LxCi1dg0qlITQ00tdhCASdRo81DkmSCAoKISgo5KTndccpeSdCaBEIBB1Bj+2qEggEAkH7EMYhEAgEgjbRY7qqFIr2J/46nff6G0KLfyK0+Cc9WcvJzu8xKUcEAoFA0DGIriqBQCAQtAlhHAKBQCBoE8I4BAKBQNAmhHEIBAKBoE0I4xAIBAJBmxDGIRAIBII2IYxDIBAIBG1CGIdAIBAI2oQwDoFAIBC0iR6TcuRk5OXlMXv2bOrr6wkJCeGFF14gJSXliHNef/11Pv30U6KiogAYOnQojz32mA+iPTkvvPACP//8MyUlJfz444/07t37mHPcbjdPP/00q1atQpIkbrnlFi677DIfRHtyWqOlO9RLXV0d999/P4WFhWg0GpKTk3nyyScJCws74jyr1cqDDz7I7t27USqVPPDAA0yePNlHUR+f1mqZPXs2a9asITQ0FIBp06bxl7/8xRchn5Tbb7+d4uJiFAoFer2eRx55hMzMzCPO6S7PS2u0dNjzIgvk6667Tv7uu+9kWZbl7777Tr7uuuuOOee1116Tn3/++a4Orc1s3LhRLi0tlSdPnizv37//uOfMmzdPvummm2S32y3X1NTIEyZMkIuKiro40lPTGi3doV7q6urkdevWtbx+/vnn5QcffPCY815//XX5oYcekmVZlvPy8uSxY8fKZrO5y+JsDa3V8sADD8gfffRRV4bWLkwmU8v/Fy9eLF900UXHnNNdnpfWaOmo56XHd1XV1NSwZ88epk+fDsD06dPZs2cPtbW1Po6sfQwfPpzY2NiTnvPTTz9x2WWXoVAoCAsLY+rUqSxatKiLImw9rdHSHQgJCWHUqFEtr4cMGUJpaekx5y1cuJArrrgCgJSUFAYMGMDKlSu7LM7W0Fot3YWgoKCW/5vNZiTp2MR+3eV5aY2WjqLHd1WVlZURHR2NUqkEQKlUEhUVRVlZ2THN7wULFvDbb78RGRnJnXfeSVZWli9CPm3KysqIi4treR0bG0t5ebkPIzo9ulO9eDwePvvsM6ZMmXLMsdLSUuLj41te+3u9nEwLwIcffsgXX3xBYmIi9957L+np6V0cYet46KGHWL16NbIs85///OeY493peTmVFuiY56XHG0drufLKK7nttttQq9WsXr2a22+/nZ9++qmlD1fgG7pbvTz11FPo9XquvfZaX4dy2pxMy9///nciIyNRKBR899133HzzzSxZsqTlDzR/4plnngHgu+++48UXX+T999/3cUTt51RaOup56fFdVbGxsVRUVOB2e/cdd7vdVFZWHtNFEhkZiVqtBmDcuHHExsZy8ODBLo+3I4iNjT2ie6GsrIyYmBgfRtR+ulO9vPDCCxQUFPDKK6+gUBz76MXFxVFSUtLy2p/r5VRaoqOjW35/0UUXYbFY/Pav9ENcdNFFrF+/nrq6uiN+3x2flxNp6ajnpccbR3h4OJmZmcyfPx+A+fPnk5mZeUw3VUVFRcv/9+7dS0lJCampqV0aa0cxbdo0vvrqKzweD7W1tSxZsoTzzjvP12G1i+5SL//+97/ZtWsXb775JhqN5rjnTJs2jS+++AKA/Px8du7cyYQJE7oyzFbRGi2H18uqVatQKBRER0d3VYitoqmpibKyspbXS5cuJTg4mJCQkCPO6w7PS2u1dNTzIjZyAnJycpg9ezYmkwmj0cgLL7xAWloaf/7zn7nrrrsYOHAgDzzwALt370ahUKBWq7nrrruYNGmSr0M/hqeffppffvmF6upqQkNDCQkJYcGCBUdocbvdPPnkk6xevRqAP//5zy2Dsv5Ea7R0h3o5ePAg06dPJyUlhYCAAAASEhJ48803mTVrFu+99x7R0dFYLBZmz57N3r17USgU3HfffUydOtXH0R9Ja7XceOON1NTUIEkSBoOB+++/nyFDhvg2+KOorq7m9ttvx2q1olAoCA4O5oEHHqB///7d7nlprZaOel6EcQgEAoGgTfT4riqBQCAQtA1hHAKBQCBoE8I4BAKBQNAmhHEIBAKBoE0I4xAIBAJBmxDGIRD4gKysLIqKinwdhkDQLsR0XIGgg5gyZQrV1dUolUp0Oh0TJ07kkUceITAw0NehCQQdimhxCAQdyDvvvMPWrVuZN28eu3bt4u233z7iuMvl8lFkAkHHIYxDIOgEoqOjmTBhAgcPHqRPnz588sknnHvuuZx77rkA9OnTh4KCAgBsNhvPP/88kydPZtiwYVx11VXYbDYAtm3bxpVXXsnw4cOZOXMm69ev95kmgeAQIjuuQNAJlJWVsXLlSs455xyWL1/OkiVL+PLLL1vSdBzOCy+8QHZ2Np9//jkRERFs374dhUJBRUUFt956Ky+++CITJkxg7dq13HXXXSxcuPCYXGoCQVciWhwCQQfy17/+leHDh3P11VczYsQIbrvtNgBuueUWQkJCjjEOj8fDN998w0MPPdSyL8zQoUPRaDR8//33TJw4kUmTJqFQKBg3bhwDBgxgxYoVvpAmELQgWhwCQQfy5ptvMnbs2GN+f6KdDOvq6rDb7SQmJh5zrLS0lEWLFrFs2bKW37lcriN24BMIfIEwDoGgCzjRNp6hoaFotVqKioro27fvEcdiY2OZNWsWTz/9dFeEKBC0GtFVJRD4EIVCwSWXXMJzzz3XsqHY1q1bcTgczJw5k2XLlrFq1Srcbjd2u53169f7/YZIgjMfYRwCgY954IEH6N27N5deeikjR47kn//8Jx6Ph9jYWN566y3effddxowZw6RJk/jggw/weDy+DlnQwxELAAUCgUDQJkSLQyAQCARtQhiHQCAQCNqEMA6BQCAQtAlhHAKBQCBoE8I4BAKBQNAmhHEIBAKBoE0I4xAIBAJBmxDGIRAIBII2IYxDIBAIBG3i/wH0OV9kxGnoNwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.lineplot(data=df_price_elasticity_all, x='Price', y='E', hue='label')\n",
"# df_price_elasticity_all.reset_index(drop=t)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6644df32-084f-44fc-9b70-517dd09e2703",
"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>Price</th>\n",
" <th>Probabilities</th>\n",
" <th>E</th>\n",
" <th>Is_Elastic</th>\n",
" </tr>\n",
" <tr>\n",
" <th>label</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Aggregate</th>\n",
" <td>1.25</td>\n",
" <td>0.657690</td>\n",
" <td>-1.004703</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Career-Focussed</th>\n",
" <td>1.42</td>\n",
" <td>0.568479</td>\n",
" <td>-1.012470</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fewer-Opportunities</th>\n",
" <td>1.26</td>\n",
" <td>0.778088</td>\n",
" <td>-1.003297</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Standard</th>\n",
" <td>1.22</td>\n",
" <td>0.450234</td>\n",
" <td>-1.012326</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Well-off</th>\n",
" <td>1.46</td>\n",
" <td>0.450117</td>\n",
" <td>-1.000156</td>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Price Probabilities E Is_Elastic\n",
"label \n",
"Aggregate 1.25 0.657690 -1.004703 True\n",
"Career-Focussed 1.42 0.568479 -1.012470 True\n",
"Fewer-Opportunities 1.26 0.778088 -1.003297 True\n",
"Standard 1.22 0.450234 -1.012326 True\n",
"Well-off 1.46 0.450117 -1.000156 True"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_price_elasticity_all[df_price_elasticity_all['Is_Elastic']].groupby('label').first()"
]
},
{
"cell_type": "markdown",
"id": "b7200fe5-63e8-4460-a54d-c8bdca8ec0ae",
"metadata": {},
"source": [
"## Price Elasticity & Promotion"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3994189c-bf9f-4926-807e-0a81716c6593",
"metadata": {},
"outputs": [],
"source": [
"def get_elasticity_promo_df(df_segments, segment=None, promo=1):\n",
" label = \"Aggregate\"\n",
" df = df_segments.copy()\n",
" if segment: \n",
" label = segment\n",
" df = df_segments[df_segments['labels'] == segment].copy()\n",
" df['Avg_Price'] = df.filter(regex='Price*').mean(axis=1)\n",
" df['Avg_Promo'] = df.filter(regex='Promotion*').mean(axis=1)\n",
" model_purchase = LogisticRegression()\n",
" model_purchase.fit(df[['Avg_Price', 'Avg_Promo']].values, df.Incidence.values)\n",
" df_price_elasticity = pd.DataFrame()\n",
" df_price_elasticity[\"Price\"]= np.arange(0.5, 3.5,0.01)\n",
" df_price_elasticity[\"Promo\"] = promo\n",
" df_price_elasticity[\"Probabilities\"]= model_purchase.predict_proba(df_price_elasticity[['Price', 'Promo']].values)[:,1]\n",
" df_price_elasticity[\"E\"] = model_purchase.coef_[0][0]*df_price_elasticity[\"Price\"]*(1-df_price_elasticity[\"Probabilities\"])\n",
" df_price_elasticity[\"Is_Elastic\"] = df_price_elasticity[\"E\"].abs() >1 \n",
" df_price_elasticity['label'] = label+ f\"_promo_{promo}\"\n",
" return df_price_elasticity\n",
" # return df"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7c35271c-7217-4889-8676-cce86d01f260",
"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>Price</th>\n",
" <th>Promo</th>\n",
" <th>Probabilities</th>\n",
" <th>E</th>\n",
" <th>Is_Elastic</th>\n",
" <th>label</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.50</td>\n",
" <td>1</td>\n",
" <td>0.831687</td>\n",
" <td>-0.125732</td>\n",
" <td>False</td>\n",
" <td>Aggregate_promo_1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.51</td>\n",
" <td>1</td>\n",
" <td>0.829586</td>\n",
" <td>-0.129848</td>\n",
" <td>False</td>\n",
" <td>Aggregate_promo_1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.52</td>\n",
" <td>1</td>\n",
" <td>0.827463</td>\n",
" <td>-0.134043</td>\n",
" <td>False</td>\n",
" <td>Aggregate_promo_1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.53</td>\n",
" <td>1</td>\n",
" <td>0.825320</td>\n",
" <td>-0.138318</td>\n",
" <td>False</td>\n",
" <td>Aggregate_promo_1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.54</td>\n",
" <td>1</td>\n",
" <td>0.823155</td>\n",
" <td>-0.142674</td>\n",
" <td>False</td>\n",
" <td>Aggregate_promo_1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>295</th>\n",
" <td>3.45</td>\n",
" <td>1</td>\n",
" <td>0.056800</td>\n",
" <td>-4.861622</td>\n",
" <td>True</td>\n",
" <td>Aggregate_promo_1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>296</th>\n",
" <td>3.46</td>\n",
" <td>1</td>\n",
" <td>0.056005</td>\n",
" <td>-4.879824</td>\n",
" <td>True</td>\n",
" <td>Aggregate_promo_1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>297</th>\n",
" <td>3.47</td>\n",
" <td>1</td>\n",
" <td>0.055220</td>\n",
" <td>-4.897996</td>\n",
" <td>True</td>\n",
" <td>Aggregate_promo_1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>298</th>\n",
" <td>3.48</td>\n",
" <td>1</td>\n",
" <td>0.054446</td>\n",
" <td>-4.916137</td>\n",
" <td>True</td>\n",
" <td>Aggregate_promo_1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>299</th>\n",
" <td>3.49</td>\n",
" <td>1</td>\n",
" <td>0.053682</td>\n",
" <td>-4.934247</td>\n",
" <td>True</td>\n",
" <td>Aggregate_promo_1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>300 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" Price Promo Probabilities E Is_Elastic label\n",
"0 0.50 1 0.831687 -0.125732 False Aggregate_promo_1\n",
"1 0.51 1 0.829586 -0.129848 False Aggregate_promo_1\n",
"2 0.52 1 0.827463 -0.134043 False Aggregate_promo_1\n",
"3 0.53 1 0.825320 -0.138318 False Aggregate_promo_1\n",
"4 0.54 1 0.823155 -0.142674 False Aggregate_promo_1\n",
".. ... ... ... ... ... ...\n",
"295 3.45 1 0.056800 -4.861622 True Aggregate_promo_1\n",
"296 3.46 1 0.056005 -4.879824 True Aggregate_promo_1\n",
"297 3.47 1 0.055220 -4.897996 True Aggregate_promo_1\n",
"298 3.48 1 0.054446 -4.916137 True Aggregate_promo_1\n",
"299 3.49 1 0.053682 -4.934247 True Aggregate_promo_1\n",
"\n",
"[300 rows x 6 columns]"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = get_elasticity_promo_df(df_segments)\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1a5ea6a8-33d6-4a3a-b40d-00c6f03ffbc9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[None, 'Fewer-Opportunities', 'Well-off', 'Career-Focussed', 'Standard']"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"segments"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a1917747-e690-45ca-b7fb-99c8fba1824a",
"metadata": {},
"outputs": [],
"source": [
"promo = [0,1]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7ee3b945-d4c9-4ae2-ae0a-75bc64aa4dab",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[(None, 0),\n",
" (None, 1),\n",
" ('Fewer-Opportunities', 0),\n",
" ('Fewer-Opportunities', 1),\n",
" ('Well-off', 0),\n",
" ('Well-off', 1),\n",
" ('Career-Focussed', 0),\n",
" ('Career-Focussed', 1),\n",
" ('Standard', 0),\n",
" ('Standard', 1)]"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(itertools.product(segments, promo))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "390ac17b-f64e-4449-ac37-1fe04239c90b",
"metadata": {},
"outputs": [],
"source": [
"df_price_elasticity_promo_all = pd.concat([get_elasticity_promo_df(df_segments, segment=s, promo=p) for s,p in itertools.product(segments, promo)]).reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d5463c2d-4c90-4d5f-a794-046730ab82e1",
"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>Price</th>\n",
" <th>Promo</th>\n",
" <th>Probabilities</th>\n",
" <th>E</th>\n",
" <th>Is_Elastic</th>\n",
" <th>label</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.50</td>\n",
" <td>0</td>\n",
" <td>0.738098</td>\n",
" <td>-0.195644</td>\n",
" <td>False</td>\n",
" <td>Aggregate_promo_0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.51</td>\n",
" <td>0</td>\n",
" <td>0.735200</td>\n",
" <td>-0.201765</td>\n",
" <td>False</td>\n",
" <td>Aggregate_promo_0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.52</td>\n",
" <td>0</td>\n",
" <td>0.732281</td>\n",
" <td>-0.207989</td>\n",
" <td>False</td>\n",
" <td>Aggregate_promo_0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.53</td>\n",
" <td>0</td>\n",
" <td>0.729342</td>\n",
" <td>-0.214316</td>\n",
" <td>False</td>\n",
" <td>Aggregate_promo_0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.54</td>\n",
" <td>0</td>\n",
" <td>0.726383</td>\n",
" <td>-0.220747</td>\n",
" <td>False</td>\n",
" <td>Aggregate_promo_0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2995</th>\n",
" <td>3.45</td>\n",
" <td>1</td>\n",
" <td>0.230267</td>\n",
" <td>-1.011516</td>\n",
" <td>True</td>\n",
" <td>Standard_promo_1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2996</th>\n",
" <td>3.46</td>\n",
" <td>1</td>\n",
" <td>0.229592</td>\n",
" <td>-1.015337</td>\n",
" <td>True</td>\n",
" <td>Standard_promo_1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2997</th>\n",
" <td>3.47</td>\n",
" <td>1</td>\n",
" <td>0.228919</td>\n",
" <td>-1.019161</td>\n",
" <td>True</td>\n",
" <td>Standard_promo_1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2998</th>\n",
" <td>3.48</td>\n",
" <td>1</td>\n",
" <td>0.228248</td>\n",
" <td>-1.022988</td>\n",
" <td>True</td>\n",
" <td>Standard_promo_1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2999</th>\n",
" <td>3.49</td>\n",
" <td>1</td>\n",
" <td>0.227577</td>\n",
" <td>-1.026819</td>\n",
" <td>True</td>\n",
" <td>Standard_promo_1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3000 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" Price Promo Probabilities E Is_Elastic label\n",
"0 0.50 0 0.738098 -0.195644 False Aggregate_promo_0\n",
"1 0.51 0 0.735200 -0.201765 False Aggregate_promo_0\n",
"2 0.52 0 0.732281 -0.207989 False Aggregate_promo_0\n",
"3 0.53 0 0.729342 -0.214316 False Aggregate_promo_0\n",
"4 0.54 0 0.726383 -0.220747 False Aggregate_promo_0\n",
"... ... ... ... ... ... ...\n",
"2995 3.45 1 0.230267 -1.011516 True Standard_promo_1\n",
"2996 3.46 1 0.229592 -1.015337 True Standard_promo_1\n",
"2997 3.47 1 0.228919 -1.019161 True Standard_promo_1\n",
"2998 3.48 1 0.228248 -1.022988 True Standard_promo_1\n",
"2999 3.49 1 0.227577 -1.026819 True Standard_promo_1\n",
"\n",
"[3000 rows x 6 columns]"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_price_elasticity_promo_all"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "52758c7b-6f66-48cb-a6b9-02a5aec4b80e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Text(0.5, 1.0, 'Price Elasticity with & without promotion')]"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x864 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(12, 12))\n",
"sns.lineplot(data=df_price_elasticity_promo_all, x='Price', y='E', hue='label',ax=ax ).set(title='Price Elasticity with & without promotion')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "61ebaa52-3e37-4773-868a-804c3dc9e6f4",
"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>Price</th>\n",
" <th>Promo</th>\n",
" <th>Probabilities</th>\n",
" <th>E</th>\n",
" <th>Is_Elastic</th>\n",
" </tr>\n",
" <tr>\n",
" <th>label</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Aggregate_promo_0</th>\n",
" <td>1.27</td>\n",
" <td>0</td>\n",
" <td>0.471458</td>\n",
" <td>-1.002863</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Aggregate_promo_1</th>\n",
" <td>1.46</td>\n",
" <td>1</td>\n",
" <td>0.540752</td>\n",
" <td>-1.001749</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Career-Focussed_promo_0</th>\n",
" <td>1.66</td>\n",
" <td>0</td>\n",
" <td>0.397008</td>\n",
" <td>-1.008915</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Career-Focussed_promo_1</th>\n",
" <td>1.85</td>\n",
" <td>1</td>\n",
" <td>0.463423</td>\n",
" <td>-1.000549</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fewer-Opportunities_promo_0</th>\n",
" <td>1.16</td>\n",
" <td>0</td>\n",
" <td>0.666050</td>\n",
" <td>-1.018239</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fewer-Opportunities_promo_1</th>\n",
" <td>1.33</td>\n",
" <td>1</td>\n",
" <td>0.712542</td>\n",
" <td>-1.004932</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Standard_promo_0</th>\n",
" <td>3.02</td>\n",
" <td>0</td>\n",
" <td>0.127973</td>\n",
" <td>-1.003114</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Standard_promo_1</th>\n",
" <td>3.42</td>\n",
" <td>1</td>\n",
" <td>0.232298</td>\n",
" <td>-1.000073</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Well-off_promo_0</th>\n",
" <td>2.16</td>\n",
" <td>0</td>\n",
" <td>0.258809</td>\n",
" <td>-1.006010</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Well-off_promo_1</th>\n",
" <td>2.37</td>\n",
" <td>1</td>\n",
" <td>0.325245</td>\n",
" <td>-1.004877</td>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Price Promo Probabilities E Is_Elastic\n",
"label \n",
"Aggregate_promo_0 1.27 0 0.471458 -1.002863 True\n",
"Aggregate_promo_1 1.46 1 0.540752 -1.001749 True\n",
"Career-Focussed_promo_0 1.66 0 0.397008 -1.008915 True\n",
"Career-Focussed_promo_1 1.85 1 0.463423 -1.000549 True\n",
"Fewer-Opportunities_promo_0 1.16 0 0.666050 -1.018239 True\n",
"Fewer-Opportunities_promo_1 1.33 1 0.712542 -1.004932 True\n",
"Standard_promo_0 3.02 0 0.127973 -1.003114 True\n",
"Standard_promo_1 3.42 1 0.232298 -1.000073 True\n",
"Well-off_promo_0 2.16 0 0.258809 -1.006010 True\n",
"Well-off_promo_1 2.37 1 0.325245 -1.004877 True"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_price_elasticity_promo_all[df_price_elasticity_promo_all['Is_Elastic']].groupby('label').first()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f10d77c5-b945-4913-b4eb-30574c3a7959",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}