Skip to content
Permalink
Branch: master
Find file Copy path
Find file Copy path
Fetching contributors…
Cannot retrieve contributors at this time
1231 lines (1231 sloc) 588 KB
{
"nbformat_minor": 2,
"cells": [
{
"execution_count": null,
"cell_type": "code",
"metadata": {
"scrolled": true,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": "# Export as slides command\n# jupyter nbconvert Jupyter\\ Slides.ipynb --to slides --post serve"
},
{
"source": "# Credit Card Approval\n\n\nHeba El-Shimy \nIBM **Cloud** Developer Advocate\n\n\n<sub>GitHub: HebaNAS</sub> \n<sub>Twitter: @heba_el_shimy</sub>\n\n-------------------\nLink for the notebook: [https://github.com/HebaNAS/Customer-Churn-Prediction/blob/master/notebook/Customer-Churn-Prediction-Pipeline.ipynb](https://github.com/HebaNAS/Customer-Churn-Prediction/blob/master/notebook/Customer-Churn-Prediction-Pipeline.ipynb)",
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
}
},
{
"source": "# Pipeline",
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
}
},
{
"source": "### 1. Loading Libraries",
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
}
},
{
"execution_count": 1,
"cell_type": "code",
"metadata": {
"scrolled": true,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": "import os\nimport pandas as pd\nimport numpy as np\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom sklearn import preprocessing, svm\nfrom itertools import combinations\nfrom sklearn.preprocessing import PolynomialFeatures, LabelEncoder, StandardScaler\nimport sklearn.feature_selection\nfrom sklearn.model_selection import train_test_split\nfrom collections import defaultdict\nfrom sklearn import metrics"
},
{
"source": "### 2. Loading Our Dataset",
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
}
},
{
"source": "![insert-data](https://github.com/HebaNAS/IBM-Watson-Studio-Enablement/blob/master/CreditCardApprovalModel/imgs/insert-dataframe.jpg?raw=true)",
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": 2,
"cell_type": "code",
"metadata": {
"scrolled": true,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": "# The code was removed by DSX for sharing."
},
{
"execution_count": 3,
"cell_type": "code",
"metadata": {
"scrolled": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"execution_count": 3,
"metadata": {},
"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>0</th>\n <th>1</th>\n <th>2</th>\n <th>3</th>\n <th>4</th>\n <th>5</th>\n <th>6</th>\n <th>7</th>\n <th>8</th>\n <th>9</th>\n <th>10</th>\n <th>11</th>\n <th>12</th>\n <th>13</th>\n <th>14</th>\n <th>15</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>b</td>\n <td>30.83</td>\n <td>0.000</td>\n <td>u</td>\n <td>g</td>\n <td>w</td>\n <td>v</td>\n <td>1.250</td>\n <td>t</td>\n <td>t</td>\n <td>1</td>\n <td>f</td>\n <td>g</td>\n <td>00202</td>\n <td>0</td>\n <td>+</td>\n </tr>\n <tr>\n <th>1</th>\n <td>a</td>\n <td>58.67</td>\n <td>4.460</td>\n <td>u</td>\n <td>g</td>\n <td>q</td>\n <td>h</td>\n <td>3.040</td>\n <td>t</td>\n <td>t</td>\n <td>6</td>\n <td>f</td>\n <td>g</td>\n <td>00043</td>\n <td>560</td>\n <td>+</td>\n </tr>\n <tr>\n <th>2</th>\n <td>a</td>\n <td>24.50</td>\n <td>0.500</td>\n <td>u</td>\n <td>g</td>\n <td>q</td>\n <td>h</td>\n <td>1.500</td>\n <td>t</td>\n <td>f</td>\n <td>0</td>\n <td>f</td>\n <td>g</td>\n <td>00280</td>\n <td>824</td>\n <td>+</td>\n </tr>\n <tr>\n <th>3</th>\n <td>b</td>\n <td>27.83</td>\n <td>1.540</td>\n <td>u</td>\n <td>g</td>\n <td>w</td>\n <td>v</td>\n <td>3.750</td>\n <td>t</td>\n <td>t</td>\n <td>5</td>\n <td>t</td>\n <td>g</td>\n <td>00100</td>\n <td>3</td>\n <td>+</td>\n </tr>\n <tr>\n <th>4</th>\n <td>b</td>\n <td>20.17</td>\n <td>5.625</td>\n <td>u</td>\n <td>g</td>\n <td>w</td>\n <td>v</td>\n <td>1.710</td>\n <td>t</td>\n <td>f</td>\n <td>0</td>\n <td>f</td>\n <td>s</td>\n <td>00120</td>\n <td>0</td>\n <td>+</td>\n </tr>\n <tr>\n <th>5</th>\n <td>b</td>\n <td>32.08</td>\n <td>4.000</td>\n <td>u</td>\n <td>g</td>\n <td>m</td>\n <td>v</td>\n <td>2.500</td>\n <td>t</td>\n <td>f</td>\n <td>0</td>\n <td>t</td>\n <td>g</td>\n <td>00360</td>\n <td>0</td>\n <td>+</td>\n </tr>\n <tr>\n <th>6</th>\n <td>b</td>\n <td>33.17</td>\n <td>1.040</td>\n <td>u</td>\n <td>g</td>\n <td>r</td>\n <td>h</td>\n <td>6.500</td>\n <td>t</td>\n <td>f</td>\n <td>0</td>\n <td>t</td>\n <td>g</td>\n <td>00164</td>\n <td>31285</td>\n <td>+</td>\n </tr>\n <tr>\n <th>7</th>\n <td>a</td>\n <td>22.92</td>\n <td>11.585</td>\n <td>u</td>\n <td>g</td>\n <td>cc</td>\n <td>v</td>\n <td>0.040</td>\n <td>t</td>\n <td>f</td>\n <td>0</td>\n <td>f</td>\n <td>g</td>\n <td>00080</td>\n <td>1349</td>\n <td>+</td>\n </tr>\n <tr>\n <th>8</th>\n <td>b</td>\n <td>54.42</td>\n <td>0.500</td>\n <td>y</td>\n <td>p</td>\n <td>k</td>\n <td>h</td>\n <td>3.960</td>\n <td>t</td>\n <td>f</td>\n <td>0</td>\n <td>f</td>\n <td>g</td>\n <td>00180</td>\n <td>314</td>\n <td>+</td>\n </tr>\n <tr>\n <th>9</th>\n <td>b</td>\n <td>42.50</td>\n <td>4.915</td>\n <td>y</td>\n <td>p</td>\n <td>w</td>\n <td>v</td>\n <td>3.165</td>\n <td>t</td>\n <td>f</td>\n <td>0</td>\n <td>t</td>\n <td>g</td>\n <td>00052</td>\n <td>1442</td>\n <td>+</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15\n0 b 30.83 0.000 u g w v 1.250 t t 1 f g 00202 0 +\n1 a 58.67 4.460 u g q h 3.040 t t 6 f g 00043 560 +\n2 a 24.50 0.500 u g q h 1.500 t f 0 f g 00280 824 +\n3 b 27.83 1.540 u g w v 3.750 t t 5 t g 00100 3 +\n4 b 20.17 5.625 u g w v 1.710 t f 0 f s 00120 0 +\n5 b 32.08 4.000 u g m v 2.500 t f 0 t g 00360 0 +\n6 b 33.17 1.040 u g r h 6.500 t f 0 t g 00164 31285 +\n7 a 22.92 11.585 u g cc v 0.040 t f 0 f g 00080 1349 +\n8 b 54.42 0.500 y p k h 3.960 t f 0 f g 00180 314 +\n9 b 42.50 4.915 y p w v 3.165 t f 0 t g 00052 1442 +"
},
"output_type": "execute_result"
}
],
"source": "# Checking that everything is correct\npd.set_option('display.max_columns', 30)\napplicants.head(10)"
},
{
"source": "### 3. Get some info about our Dataset and whether we have missing values",
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
}
},
{
"execution_count": 4,
"cell_type": "code",
"metadata": {
"scrolled": true,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 690 entries, 0 to 689\nData columns (total 16 columns):\n0 690 non-null object\n1 690 non-null object\n2 690 non-null float64\n3 690 non-null object\n4 690 non-null object\n5 690 non-null object\n6 690 non-null object\n7 690 non-null float64\n8 690 non-null object\n9 690 non-null object\n10 690 non-null int64\n11 690 non-null object\n12 690 non-null object\n13 690 non-null object\n14 690 non-null int64\n15 690 non-null object\ndtypes: float64(2), int64(2), object(12)\nmemory usage: 86.3+ KB\n"
}
],
"source": "# After running this cell we will see that we have no missing values\napplicants.info()"
},
{
"execution_count": 5,
"cell_type": "code",
"metadata": {
"scrolled": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"execution_count": 5,
"metadata": {},
"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>0</th>\n <th>1</th>\n <th>2</th>\n <th>3</th>\n <th>4</th>\n <th>5</th>\n <th>6</th>\n <th>7</th>\n <th>8</th>\n <th>9</th>\n <th>10</th>\n <th>11</th>\n <th>12</th>\n <th>13</th>\n <th>14</th>\n <th>15</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>b</td>\n <td>30.83</td>\n <td>0.000</td>\n <td>u</td>\n <td>g</td>\n <td>w</td>\n <td>v</td>\n <td>1.250</td>\n <td>t</td>\n <td>t</td>\n <td>1</td>\n <td>f</td>\n <td>g</td>\n <td>202.0</td>\n <td>0</td>\n <td>+</td>\n </tr>\n <tr>\n <th>1</th>\n <td>a</td>\n <td>58.67</td>\n <td>4.460</td>\n <td>u</td>\n <td>g</td>\n <td>q</td>\n <td>h</td>\n <td>3.040</td>\n <td>t</td>\n <td>t</td>\n <td>6</td>\n <td>f</td>\n <td>g</td>\n <td>43.0</td>\n <td>560</td>\n <td>+</td>\n </tr>\n <tr>\n <th>2</th>\n <td>a</td>\n <td>24.50</td>\n <td>0.500</td>\n <td>u</td>\n <td>g</td>\n <td>q</td>\n <td>h</td>\n <td>1.500</td>\n <td>t</td>\n <td>f</td>\n <td>0</td>\n <td>f</td>\n <td>g</td>\n <td>280.0</td>\n <td>824</td>\n <td>+</td>\n </tr>\n <tr>\n <th>3</th>\n <td>b</td>\n <td>27.83</td>\n <td>1.540</td>\n <td>u</td>\n <td>g</td>\n <td>w</td>\n <td>v</td>\n <td>3.750</td>\n <td>t</td>\n <td>t</td>\n <td>5</td>\n <td>t</td>\n <td>g</td>\n <td>100.0</td>\n <td>3</td>\n <td>+</td>\n </tr>\n <tr>\n <th>4</th>\n <td>b</td>\n <td>20.17</td>\n <td>5.625</td>\n <td>u</td>\n <td>g</td>\n <td>w</td>\n <td>v</td>\n <td>1.710</td>\n <td>t</td>\n <td>f</td>\n <td>0</td>\n <td>f</td>\n <td>s</td>\n <td>120.0</td>\n <td>0</td>\n <td>+</td>\n </tr>\n <tr>\n <th>5</th>\n <td>b</td>\n <td>32.08</td>\n <td>4.000</td>\n <td>u</td>\n <td>g</td>\n <td>m</td>\n <td>v</td>\n <td>2.500</td>\n <td>t</td>\n <td>f</td>\n <td>0</td>\n <td>t</td>\n <td>g</td>\n <td>360.0</td>\n <td>0</td>\n <td>+</td>\n </tr>\n <tr>\n <th>6</th>\n <td>b</td>\n <td>33.17</td>\n <td>1.040</td>\n <td>u</td>\n <td>g</td>\n <td>r</td>\n <td>h</td>\n <td>6.500</td>\n <td>t</td>\n <td>f</td>\n <td>0</td>\n <td>t</td>\n <td>g</td>\n <td>164.0</td>\n <td>31285</td>\n <td>+</td>\n </tr>\n <tr>\n <th>7</th>\n <td>a</td>\n <td>22.92</td>\n <td>11.585</td>\n <td>u</td>\n <td>g</td>\n <td>cc</td>\n <td>v</td>\n <td>0.040</td>\n <td>t</td>\n <td>f</td>\n <td>0</td>\n <td>f</td>\n <td>g</td>\n <td>80.0</td>\n <td>1349</td>\n <td>+</td>\n </tr>\n <tr>\n <th>8</th>\n <td>b</td>\n <td>54.42</td>\n <td>0.500</td>\n <td>y</td>\n <td>p</td>\n <td>k</td>\n <td>h</td>\n <td>3.960</td>\n <td>t</td>\n <td>f</td>\n <td>0</td>\n <td>f</td>\n <td>g</td>\n <td>180.0</td>\n <td>314</td>\n <td>+</td>\n </tr>\n <tr>\n <th>9</th>\n <td>b</td>\n <td>42.50</td>\n <td>4.915</td>\n <td>y</td>\n <td>p</td>\n <td>w</td>\n <td>v</td>\n <td>3.165</td>\n <td>t</td>\n <td>f</td>\n <td>0</td>\n <td>t</td>\n <td>g</td>\n <td>52.0</td>\n <td>1442</td>\n <td>+</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15\n0 b 30.83 0.000 u g w v 1.250 t t 1 f g 202.0 0 +\n1 a 58.67 4.460 u g q h 3.040 t t 6 f g 43.0 560 +\n2 a 24.50 0.500 u g q h 1.500 t f 0 f g 280.0 824 +\n3 b 27.83 1.540 u g w v 3.750 t t 5 t g 100.0 3 +\n4 b 20.17 5.625 u g w v 1.710 t f 0 f s 120.0 0 +\n5 b 32.08 4.000 u g m v 2.500 t f 0 t g 360.0 0 +\n6 b 33.17 1.040 u g r h 6.500 t f 0 t g 164.0 31285 +\n7 a 22.92 11.585 u g cc v 0.040 t f 0 f g 80.0 1349 +\n8 b 54.42 0.500 y p k h 3.960 t f 0 f g 180.0 314 +\n9 b 42.50 4.915 y p w v 3.165 t f 0 t g 52.0 1442 +"
},
"output_type": "execute_result"
}
],
"source": "# Convert columns with numbers as values but object as datatype into numeric\ncols = [1, 13]\n\n# Set error level to coerce so any string value will be replaced with NaN\napplicants[cols] = applicants[cols].apply(pd.to_numeric, errors='coerce')\napplicants.head(10)"
},
{
"execution_count": 6,
"cell_type": "code",
"metadata": {
"scrolled": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"execution_count": 6,
"metadata": {},
"data": {
"text/plain": "True"
},
"output_type": "execute_result"
}
],
"source": "# Check if we have any NaN values\napplicants.isnull().values.any()"
},
{
"execution_count": 7,
"cell_type": "code",
"metadata": {
"scrolled": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"execution_count": 7,
"metadata": {},
"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>0</th>\n <th>1</th>\n <th>2</th>\n <th>3</th>\n <th>4</th>\n <th>5</th>\n <th>6</th>\n <th>7</th>\n <th>8</th>\n <th>9</th>\n <th>10</th>\n <th>11</th>\n <th>12</th>\n <th>13</th>\n <th>14</th>\n <th>15</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>b</td>\n <td>30.83</td>\n <td>0.000</td>\n <td>u</td>\n <td>g</td>\n <td>w</td>\n <td>v</td>\n <td>1.250</td>\n <td>t</td>\n <td>t</td>\n <td>1</td>\n <td>f</td>\n <td>g</td>\n <td>202.0</td>\n <td>0</td>\n <td>+</td>\n </tr>\n <tr>\n <th>1</th>\n <td>a</td>\n <td>58.67</td>\n <td>4.460</td>\n <td>u</td>\n <td>g</td>\n <td>q</td>\n <td>h</td>\n <td>3.040</td>\n <td>t</td>\n <td>t</td>\n <td>6</td>\n <td>f</td>\n <td>g</td>\n <td>43.0</td>\n <td>560</td>\n <td>+</td>\n </tr>\n <tr>\n <th>2</th>\n <td>a</td>\n <td>24.50</td>\n <td>0.500</td>\n <td>u</td>\n <td>g</td>\n <td>q</td>\n <td>h</td>\n <td>1.500</td>\n <td>t</td>\n <td>f</td>\n <td>0</td>\n <td>f</td>\n <td>g</td>\n <td>280.0</td>\n <td>824</td>\n <td>+</td>\n </tr>\n <tr>\n <th>3</th>\n <td>b</td>\n <td>27.83</td>\n <td>1.540</td>\n <td>u</td>\n <td>g</td>\n <td>w</td>\n <td>v</td>\n <td>3.750</td>\n <td>t</td>\n <td>t</td>\n <td>5</td>\n <td>t</td>\n <td>g</td>\n <td>100.0</td>\n <td>3</td>\n <td>+</td>\n </tr>\n <tr>\n <th>4</th>\n <td>b</td>\n <td>20.17</td>\n <td>5.625</td>\n <td>u</td>\n <td>g</td>\n <td>w</td>\n <td>v</td>\n <td>1.710</td>\n <td>t</td>\n <td>f</td>\n <td>0</td>\n <td>f</td>\n <td>s</td>\n <td>120.0</td>\n <td>0</td>\n <td>+</td>\n </tr>\n <tr>\n <th>5</th>\n <td>b</td>\n <td>32.08</td>\n <td>4.000</td>\n <td>u</td>\n <td>g</td>\n <td>m</td>\n <td>v</td>\n <td>2.500</td>\n <td>t</td>\n <td>f</td>\n <td>0</td>\n <td>t</td>\n <td>g</td>\n <td>360.0</td>\n <td>0</td>\n <td>+</td>\n </tr>\n <tr>\n <th>6</th>\n <td>b</td>\n <td>33.17</td>\n <td>1.040</td>\n <td>u</td>\n <td>g</td>\n <td>r</td>\n <td>h</td>\n <td>6.500</td>\n <td>t</td>\n <td>f</td>\n <td>0</td>\n <td>t</td>\n <td>g</td>\n <td>164.0</td>\n <td>31285</td>\n <td>+</td>\n </tr>\n <tr>\n <th>7</th>\n <td>a</td>\n <td>22.92</td>\n <td>11.585</td>\n <td>u</td>\n <td>g</td>\n <td>cc</td>\n <td>v</td>\n <td>0.040</td>\n <td>t</td>\n <td>f</td>\n <td>0</td>\n <td>f</td>\n <td>g</td>\n <td>80.0</td>\n <td>1349</td>\n <td>+</td>\n </tr>\n <tr>\n <th>8</th>\n <td>b</td>\n <td>54.42</td>\n <td>0.500</td>\n <td>y</td>\n <td>p</td>\n <td>k</td>\n <td>h</td>\n <td>3.960</td>\n <td>t</td>\n <td>f</td>\n <td>0</td>\n <td>f</td>\n <td>g</td>\n <td>180.0</td>\n <td>314</td>\n <td>+</td>\n </tr>\n <tr>\n <th>9</th>\n <td>b</td>\n <td>42.50</td>\n <td>4.915</td>\n <td>y</td>\n <td>p</td>\n <td>w</td>\n <td>v</td>\n <td>3.165</td>\n <td>t</td>\n <td>f</td>\n <td>0</td>\n <td>t</td>\n <td>g</td>\n <td>52.0</td>\n <td>1442</td>\n <td>+</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15\n0 b 30.83 0.000 u g w v 1.250 t t 1 f g 202.0 0 +\n1 a 58.67 4.460 u g q h 3.040 t t 6 f g 43.0 560 +\n2 a 24.50 0.500 u g q h 1.500 t f 0 f g 280.0 824 +\n3 b 27.83 1.540 u g w v 3.750 t t 5 t g 100.0 3 +\n4 b 20.17 5.625 u g w v 1.710 t f 0 f s 120.0 0 +\n5 b 32.08 4.000 u g m v 2.500 t f 0 t g 360.0 0 +\n6 b 33.17 1.040 u g r h 6.500 t f 0 t g 164.0 31285 +\n7 a 22.92 11.585 u g cc v 0.040 t f 0 f g 80.0 1349 +\n8 b 54.42 0.500 y p k h 3.960 t f 0 f g 180.0 314 +\n9 b 42.50 4.915 y p w v 3.165 t f 0 t g 52.0 1442 +"
},
"output_type": "execute_result"
}
],
"source": "# Handle missing values using scikit learn Imputer\nfrom sklearn.preprocessing import Imputer\n\n# Define the values to replce and the strategy of choosing the replacement value\nimp = Imputer(missing_values=\"NaN\", strategy=\"mean\")\n\napplicants[cols] = imp.fit_transform(applicants[cols])\napplicants.head(10)"
},
{
"execution_count": 8,
"cell_type": "code",
"metadata": {
"scrolled": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"execution_count": 8,
"metadata": {},
"data": {
"text/plain": "False"
},
"output_type": "execute_result"
}
],
"source": "# Check if we have any NaN values\napplicants.isnull().values.any()"
},
{
"execution_count": 9,
"cell_type": "code",
"metadata": {
"scrolled": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 690 entries, 0 to 689\nData columns (total 16 columns):\n0 690 non-null object\n1 690 non-null float64\n2 690 non-null float64\n3 690 non-null object\n4 690 non-null object\n5 690 non-null object\n6 690 non-null object\n7 690 non-null float64\n8 690 non-null object\n9 690 non-null object\n10 690 non-null int64\n11 690 non-null object\n12 690 non-null object\n13 690 non-null float64\n14 690 non-null int64\n15 690 non-null object\ndtypes: float64(4), int64(2), object(10)\nmemory usage: 86.3+ KB\n"
}
],
"source": "applicants.info()"
},
{
"source": "### 4. Descriptive analytics for our data",
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
}
},
{
"execution_count": 10,
"cell_type": "code",
"metadata": {
"scrolled": true,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"execution_count": 10,
"metadata": {},
"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>1</th>\n <th>2</th>\n <th>7</th>\n <th>10</th>\n <th>13</th>\n <th>14</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>count</th>\n <td>690.000</td>\n <td>690.000</td>\n <td>690.000</td>\n <td>690.000</td>\n <td>690.000</td>\n <td>690.000</td>\n </tr>\n <tr>\n <th>mean</th>\n <td>31.568</td>\n <td>4.759</td>\n <td>2.223</td>\n <td>2.400</td>\n <td>184.015</td>\n <td>1017.386</td>\n </tr>\n <tr>\n <th>std</th>\n <td>11.853</td>\n <td>4.978</td>\n <td>3.347</td>\n <td>4.863</td>\n <td>172.159</td>\n <td>5210.103</td>\n </tr>\n <tr>\n <th>min</th>\n <td>13.750</td>\n <td>0.000</td>\n <td>0.000</td>\n <td>0.000</td>\n <td>0.000</td>\n <td>0.000</td>\n </tr>\n <tr>\n <th>25%</th>\n <td>22.670</td>\n <td>1.000</td>\n <td>0.165</td>\n <td>0.000</td>\n <td>80.000</td>\n <td>0.000</td>\n </tr>\n <tr>\n <th>50%</th>\n <td>28.625</td>\n <td>2.750</td>\n <td>1.000</td>\n <td>0.000</td>\n <td>160.000</td>\n <td>5.000</td>\n </tr>\n <tr>\n <th>75%</th>\n <td>37.707</td>\n <td>7.207</td>\n <td>2.625</td>\n <td>3.000</td>\n <td>272.000</td>\n <td>395.500</td>\n </tr>\n <tr>\n <th>max</th>\n <td>80.250</td>\n <td>28.000</td>\n <td>28.500</td>\n <td>67.000</td>\n <td>2000.000</td>\n <td>100000.000</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " 1 2 7 10 13 14\ncount 690.000 690.000 690.000 690.000 690.000 690.000\nmean 31.568 4.759 2.223 2.400 184.015 1017.386\nstd 11.853 4.978 3.347 4.863 172.159 5210.103\nmin 13.750 0.000 0.000 0.000 0.000 0.000\n25% 22.670 1.000 0.165 0.000 80.000 0.000\n50% 28.625 2.750 1.000 0.000 160.000 5.000\n75% 37.707 7.207 2.625 3.000 272.000 395.500\nmax 80.250 28.000 28.500 67.000 2000.000 100000.000"
},
"output_type": "execute_result"
}
],
"source": "# Describe columns with numerical values\npd.set_option('precision', 3)\napplicants.describe()"
},
{
"execution_count": 11,
"cell_type": "code",
"metadata": {
"scrolled": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"execution_count": 11,
"metadata": {},
"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>1</th>\n <th>2</th>\n <th>7</th>\n <th>10</th>\n <th>13</th>\n <th>14</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>1</th>\n <td>1.000</td>\n <td>0.201</td>\n <td>0.393</td>\n <td>0.186</td>\n <td>-0.077</td>\n <td>0.019</td>\n </tr>\n <tr>\n <th>2</th>\n <td>0.201</td>\n <td>1.000</td>\n <td>0.299</td>\n <td>0.271</td>\n <td>-0.222</td>\n <td>0.123</td>\n </tr>\n <tr>\n <th>7</th>\n <td>0.393</td>\n <td>0.299</td>\n <td>1.000</td>\n <td>0.322</td>\n <td>-0.076</td>\n <td>0.051</td>\n </tr>\n <tr>\n <th>10</th>\n <td>0.186</td>\n <td>0.271</td>\n <td>0.322</td>\n <td>1.000</td>\n <td>-0.120</td>\n <td>0.064</td>\n </tr>\n <tr>\n <th>13</th>\n <td>-0.077</td>\n <td>-0.222</td>\n <td>-0.076</td>\n <td>-0.120</td>\n <td>1.000</td>\n <td>0.066</td>\n </tr>\n <tr>\n <th>14</th>\n <td>0.019</td>\n <td>0.123</td>\n <td>0.051</td>\n <td>0.064</td>\n <td>0.066</td>\n <td>1.000</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " 1 2 7 10 13 14\n1 1.000 0.201 0.393 0.186 -0.077 0.019\n2 0.201 1.000 0.299 0.271 -0.222 0.123\n7 0.393 0.299 1.000 0.322 -0.076 0.051\n10 0.186 0.271 0.322 1.000 -0.120 0.064\n13 -0.077 -0.222 -0.076 -0.120 1.000 0.066\n14 0.019 0.123 0.051 0.064 0.066 1.000"
},
"output_type": "execute_result"
}
],
"source": "# Find correlations\napplicants.corr(method='pearson')"
},
{
"source": "### 5. Visualize our Data to understand it better",
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
}
},
{
"source": "#### Plot Relationships",
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
}
},
{
"execution_count": 12,
"cell_type": "code",
"metadata": {
"scrolled": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": "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\n",
"text/plain": "<matplotlib.figure.Figure at 0x2ac273f3add8>"
},
"metadata": {}
}
],
"source": "# Create Grid for pairwise relationships\ngr = sns.PairGrid(applicants, size=5, hue=15)\ngr = gr.map_diag(plt.hist)\ngr = gr.map_offdiag(plt.scatter)\ngr = gr.add_legend()"
},
{
"source": "#### Understand Data Distribution",
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
}
},
{
"execution_count": 13,
"cell_type": "code",
"metadata": {
"scrolled": false,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": "/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/seaborn/categorical.py:462: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n box_data = remove_na(group_data)\n"
},
{
"output_type": "display_data",
"data": {
"image/png": "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\n",
"text/plain": "<matplotlib.figure.Figure at 0x2ac27d2a3f98>"
},
"metadata": {}
}
],
"source": "# Set up plot size\nfig, ax = plt.subplots(figsize=(20,10))\n\n# Attributes destribution\na = sns.boxplot(orient=\"v\", palette=\"hls\", data=applicants.iloc[:, :13], fliersize=14)"
},
{
"execution_count": 14,
"cell_type": "code",
"metadata": {
"scrolled": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": "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\n",
"text/plain": "<matplotlib.figure.Figure at 0x2ac27e8f9828>"
},
"metadata": {}
}
],
"source": "# Tenure data distribution\nhistogram = sns.distplot(applicants.iloc[:, 1], hist=True)\nplt.show()"
},
{
"source": "### 6. Encode string values in data into numerical values",
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
}
},
{
"execution_count": 15,
"cell_type": "code",
"metadata": {
"scrolled": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"execution_count": 15,
"metadata": {},
"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>1</th>\n <th>2</th>\n <th>7</th>\n <th>10</th>\n <th>13</th>\n <th>14</th>\n <th>0_?</th>\n <th>0_a</th>\n <th>0_b</th>\n <th>3_?</th>\n <th>3_l</th>\n <th>3_u</th>\n <th>3_y</th>\n <th>4_?</th>\n <th>4_g</th>\n <th>...</th>\n <th>6_n</th>\n <th>6_o</th>\n <th>6_v</th>\n <th>6_z</th>\n <th>8_f</th>\n <th>8_t</th>\n <th>9_f</th>\n <th>9_t</th>\n <th>11_f</th>\n <th>11_t</th>\n <th>12_g</th>\n <th>12_p</th>\n <th>12_s</th>\n <th>15_+</th>\n <th>15_-</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>30.83</td>\n <td>0.000</td>\n <td>1.250</td>\n <td>1</td>\n <td>202.0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\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>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>58.67</td>\n <td>4.460</td>\n <td>3.040</td>\n <td>6</td>\n <td>43.0</td>\n <td>560</td>\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>0</td>\n <td>1</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>1</td>\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>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>24.50</td>\n <td>0.500</td>\n <td>1.500</td>\n <td>0</td>\n <td>280.0</td>\n <td>824</td>\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>0</td>\n <td>1</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>1</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>0</td>\n <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>27.83</td>\n <td>1.540</td>\n <td>3.750</td>\n <td>5</td>\n <td>100.0</td>\n <td>3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\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>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>20.17</td>\n <td>5.625</td>\n <td>1.710</td>\n <td>0</td>\n <td>120.0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\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>0</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>5</th>\n <td>32.08</td>\n <td>4.000</td>\n <td>2.500</td>\n <td>0</td>\n <td>360.0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>6</th>\n <td>33.17</td>\n <td>1.040</td>\n <td>6.500</td>\n <td>0</td>\n <td>164.0</td>\n <td>31285</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</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>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>7</th>\n <td>22.92</td>\n <td>11.585</td>\n <td>0.040</td>\n <td>0</td>\n <td>80.0</td>\n <td>1349</td>\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>0</td>\n <td>1</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\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>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>8</th>\n <td>54.42</td>\n <td>0.500</td>\n <td>3.960</td>\n <td>0</td>\n <td>180.0</td>\n <td>314</td>\n <td>0</td>\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>0</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>1</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>0</td>\n <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>9</th>\n <td>42.50</td>\n <td>4.915</td>\n <td>3.165</td>\n <td>0</td>\n <td>52.0</td>\n <td>1442</td>\n <td>0</td>\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>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n </tr>\n </tbody>\n</table>\n<p>10 rows \u00d7 53 columns</p>\n</div>",
"text/plain": " 1 2 7 10 13 14 0_? 0_a 0_b 3_? 3_l 3_u 3_y \\\n0 30.83 0.000 1.250 1 202.0 0 0 0 1 0 0 1 0 \n1 58.67 4.460 3.040 6 43.0 560 0 1 0 0 0 1 0 \n2 24.50 0.500 1.500 0 280.0 824 0 1 0 0 0 1 0 \n3 27.83 1.540 3.750 5 100.0 3 0 0 1 0 0 1 0 \n4 20.17 5.625 1.710 0 120.0 0 0 0 1 0 0 1 0 \n5 32.08 4.000 2.500 0 360.0 0 0 0 1 0 0 1 0 \n6 33.17 1.040 6.500 0 164.0 31285 0 0 1 0 0 1 0 \n7 22.92 11.585 0.040 0 80.0 1349 0 1 0 0 0 1 0 \n8 54.42 0.500 3.960 0 180.0 314 0 0 1 0 0 0 1 \n9 42.50 4.915 3.165 0 52.0 1442 0 0 1 0 0 0 1 \n\n 4_? 4_g ... 6_n 6_o 6_v 6_z 8_f 8_t 9_f 9_t 11_f 11_t 12_g \\\n0 0 1 ... 0 0 1 0 0 1 0 1 1 0 1 \n1 0 1 ... 0 0 0 0 0 1 0 1 1 0 1 \n2 0 1 ... 0 0 0 0 0 1 1 0 1 0 1 \n3 0 1 ... 0 0 1 0 0 1 0 1 0 1 1 \n4 0 1 ... 0 0 1 0 0 1 1 0 1 0 0 \n5 0 1 ... 0 0 1 0 0 1 1 0 0 1 1 \n6 0 1 ... 0 0 0 0 0 1 1 0 0 1 1 \n7 0 1 ... 0 0 1 0 0 1 1 0 1 0 1 \n8 0 0 ... 0 0 0 0 0 1 1 0 1 0 1 \n9 0 0 ... 0 0 1 0 0 1 1 0 0 1 1 \n\n 12_p 12_s 15_+ 15_- \n0 0 0 1 0 \n1 0 0 1 0 \n2 0 0 1 0 \n3 0 0 1 0 \n4 0 1 1 0 \n5 0 0 1 0 \n6 0 0 1 0 \n7 0 0 1 0 \n8 0 0 1 0 \n9 0 0 1 0 \n\n[10 rows x 53 columns]"
},
"output_type": "execute_result"
}
],
"source": "# Use pandas get_dummies\napplicants_encoded = pd.get_dummies(applicants)\napplicants_encoded.head(10)"
},
{
"source": "### 7. Create Training Set and Labels ",
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
}
},
{
"execution_count": 16,
"cell_type": "code",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"execution_count": 16,
"metadata": {},
"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>1</th>\n <th>2</th>\n <th>7</th>\n <th>10</th>\n <th>13</th>\n <th>14</th>\n <th>0_?</th>\n <th>0_a</th>\n <th>0_b</th>\n <th>3_?</th>\n <th>3_l</th>\n <th>3_u</th>\n <th>3_y</th>\n <th>4_?</th>\n <th>4_g</th>\n <th>...</th>\n <th>6_h</th>\n <th>6_j</th>\n <th>6_n</th>\n <th>6_o</th>\n <th>6_v</th>\n <th>6_z</th>\n <th>8_f</th>\n <th>8_t</th>\n <th>9_f</th>\n <th>9_t</th>\n <th>11_f</th>\n <th>11_t</th>\n <th>12_g</th>\n <th>12_p</th>\n <th>12_s</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>30.83</td>\n <td>0.000</td>\n <td>1.250</td>\n <td>1</td>\n <td>202.0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\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>1</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>58.67</td>\n <td>4.460</td>\n <td>3.040</td>\n <td>6</td>\n <td>43.0</td>\n <td>560</td>\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>0</td>\n <td>1</td>\n <td>...</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>0</td>\n <td>1</td>\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 </tr>\n <tr>\n <th>2</th>\n <td>24.50</td>\n <td>0.500</td>\n <td>1.500</td>\n <td>0</td>\n <td>280.0</td>\n <td>824</td>\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>0</td>\n <td>1</td>\n <td>...</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>0</td>\n <td>1</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>0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>27.83</td>\n <td>1.540</td>\n <td>3.750</td>\n <td>5</td>\n <td>100.0</td>\n <td>3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\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>1</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>20.17</td>\n <td>5.625</td>\n <td>1.710</td>\n <td>0</td>\n <td>120.0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\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>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>5</th>\n <td>32.08</td>\n <td>4.000</td>\n <td>2.500</td>\n <td>0</td>\n <td>360.0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>6</th>\n <td>33.17</td>\n <td>1.040</td>\n <td>6.500</td>\n <td>0</td>\n <td>164.0</td>\n <td>31285</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</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>0</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>7</th>\n <td>22.92</td>\n <td>11.585</td>\n <td>0.040</td>\n <td>0</td>\n <td>80.0</td>\n <td>1349</td>\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>0</td>\n <td>1</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\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>1</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>8</th>\n <td>54.42</td>\n <td>0.500</td>\n <td>3.960</td>\n <td>0</td>\n <td>180.0</td>\n <td>314</td>\n <td>0</td>\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>0</td>\n <td>...</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>0</td>\n <td>1</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>0</td>\n </tr>\n <tr>\n <th>9</th>\n <td>42.50</td>\n <td>4.915</td>\n <td>3.165</td>\n <td>0</td>\n <td>52.0</td>\n <td>1442</td>\n <td>0</td>\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>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n </tr>\n </tbody>\n</table>\n<p>10 rows \u00d7 51 columns</p>\n</div>",
"text/plain": " 1 2 7 10 13 14 0_? 0_a 0_b 3_? 3_l 3_u 3_y \\\n0 30.83 0.000 1.250 1 202.0 0 0 0 1 0 0 1 0 \n1 58.67 4.460 3.040 6 43.0 560 0 1 0 0 0 1 0 \n2 24.50 0.500 1.500 0 280.0 824 0 1 0 0 0 1 0 \n3 27.83 1.540 3.750 5 100.0 3 0 0 1 0 0 1 0 \n4 20.17 5.625 1.710 0 120.0 0 0 0 1 0 0 1 0 \n5 32.08 4.000 2.500 0 360.0 0 0 0 1 0 0 1 0 \n6 33.17 1.040 6.500 0 164.0 31285 0 0 1 0 0 1 0 \n7 22.92 11.585 0.040 0 80.0 1349 0 1 0 0 0 1 0 \n8 54.42 0.500 3.960 0 180.0 314 0 0 1 0 0 0 1 \n9 42.50 4.915 3.165 0 52.0 1442 0 0 1 0 0 0 1 \n\n 4_? 4_g ... 6_h 6_j 6_n 6_o 6_v 6_z 8_f 8_t 9_f 9_t 11_f \\\n0 0 1 ... 0 0 0 0 1 0 0 1 0 1 1 \n1 0 1 ... 1 0 0 0 0 0 0 1 0 1 1 \n2 0 1 ... 1 0 0 0 0 0 0 1 1 0 1 \n3 0 1 ... 0 0 0 0 1 0 0 1 0 1 0 \n4 0 1 ... 0 0 0 0 1 0 0 1 1 0 1 \n5 0 1 ... 0 0 0 0 1 0 0 1 1 0 0 \n6 0 1 ... 1 0 0 0 0 0 0 1 1 0 0 \n7 0 1 ... 0 0 0 0 1 0 0 1 1 0 1 \n8 0 0 ... 1 0 0 0 0 0 0 1 1 0 1 \n9 0 0 ... 0 0 0 0 1 0 0 1 1 0 0 \n\n 11_t 12_g 12_p 12_s \n0 0 1 0 0 \n1 0 1 0 0 \n2 0 1 0 0 \n3 1 1 0 0 \n4 0 0 0 1 \n5 1 1 0 0 \n6 1 1 0 0 \n7 0 1 0 0 \n8 0 1 0 0 \n9 1 1 0 0 \n\n[10 rows x 51 columns]"
},
"output_type": "execute_result"
}
],
"source": "# Create training data for non-preprocessed approach\nX_npp = applicants_encoded.iloc[:, :-2]\npd.DataFrame(X_npp).head(10)"
},
{
"execution_count": 47,
"cell_type": "code",
"metadata": {
"scrolled": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"execution_count": 47,
"metadata": {},
"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>1</th>\n <th>2</th>\n <th>7</th>\n <th>10</th>\n <th>13</th>\n <th>14</th>\n <th>0_?</th>\n <th>0_a</th>\n <th>0_b</th>\n <th>3_?</th>\n <th>3_l</th>\n <th>3_u</th>\n <th>3_y</th>\n <th>4_?</th>\n <th>4_g</th>\n <th>...</th>\n <th>6_h</th>\n <th>6_j</th>\n <th>6_n</th>\n <th>6_o</th>\n <th>6_v</th>\n <th>6_z</th>\n <th>8_f</th>\n <th>8_t</th>\n <th>9_f</th>\n <th>9_t</th>\n <th>11_f</th>\n <th>11_t</th>\n <th>12_g</th>\n <th>12_p</th>\n <th>12_s</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>30.83</td>\n <td>0.000</td>\n <td>1.25</td>\n <td>1</td>\n <td>202.0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\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>1</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>58.67</td>\n <td>4.460</td>\n <td>3.04</td>\n <td>6</td>\n <td>43.0</td>\n <td>560</td>\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>0</td>\n <td>1</td>\n <td>...</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>0</td>\n <td>1</td>\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 </tr>\n <tr>\n <th>2</th>\n <td>24.50</td>\n <td>0.500</td>\n <td>1.50</td>\n <td>0</td>\n <td>280.0</td>\n <td>824</td>\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>0</td>\n <td>1</td>\n <td>...</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>0</td>\n <td>1</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>0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>27.83</td>\n <td>1.540</td>\n <td>3.75</td>\n <td>5</td>\n <td>100.0</td>\n <td>3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\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>1</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>20.17</td>\n <td>5.625</td>\n <td>1.71</td>\n <td>0</td>\n <td>120.0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\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>0</td>\n <td>1</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows \u00d7 51 columns</p>\n</div>",
"text/plain": " 1 2 7 10 13 14 0_? 0_a 0_b 3_? 3_l 3_u 3_y 4_? \\\n0 30.83 0.000 1.25 1 202.0 0 0 0 1 0 0 1 0 0 \n1 58.67 4.460 3.04 6 43.0 560 0 1 0 0 0 1 0 0 \n2 24.50 0.500 1.50 0 280.0 824 0 1 0 0 0 1 0 0 \n3 27.83 1.540 3.75 5 100.0 3 0 0 1 0 0 1 0 0 \n4 20.17 5.625 1.71 0 120.0 0 0 0 1 0 0 1 0 0 \n\n 4_g ... 6_h 6_j 6_n 6_o 6_v 6_z 8_f 8_t 9_f 9_t 11_f 11_t \\\n0 1 ... 0 0 0 0 1 0 0 1 0 1 1 0 \n1 1 ... 1 0 0 0 0 0 0 1 0 1 1 0 \n2 1 ... 1 0 0 0 0 0 0 1 1 0 1 0 \n3 1 ... 0 0 0 0 1 0 0 1 0 1 0 1 \n4 1 ... 0 0 0 0 1 0 0 1 1 0 1 0 \n\n 12_g 12_p 12_s \n0 1 0 0 \n1 1 0 0 \n2 1 0 0 \n3 1 0 0 \n4 0 0 1 \n\n[5 rows x 51 columns]"
},
"output_type": "execute_result"
}
],
"source": "# Create training data for that will undergo preprocessing\nX = applicants_encoded.iloc[:, :-2]\nX.head()"
},
{
"execution_count": 18,
"cell_type": "code",
"metadata": {
"scrolled": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"execution_count": 18,
"metadata": {},
"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>0</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>0</td>\n </tr>\n <tr>\n <th>5</th>\n <td>0</td>\n </tr>\n <tr>\n <th>6</th>\n <td>0</td>\n </tr>\n <tr>\n <th>7</th>\n <td>0</td>\n </tr>\n <tr>\n <th>8</th>\n <td>0</td>\n </tr>\n <tr>\n <th>9</th>\n <td>0</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " 0\n0 0\n1 0\n2 0\n3 0\n4 0\n5 0\n6 0\n7 0\n8 0\n9 0"
},
"output_type": "execute_result"
}
],
"source": "# Extract labels\nfrom sklearn.preprocessing import LabelEncoder\n\n# Split last column from original dataset as the labels column\ny = applicants[15]\n\n# Apply encoder to transform strings to numeric values 0 and 1\nle = LabelEncoder().fit(y)\n\ny_enc = le.transform(y)\npd.DataFrame(y_enc).head(10)"
},
{
"source": "### 8. Detect outliers in numerical values",
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
}
},
{
"execution_count": 41,
"cell_type": "code",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": "# Detect outlier using interquartile method and remove them\ndef find_outliers(df):\n quartile_1, quartile_3 = np.percentile(df, [25, 75])\n iqr = quartile_3 - quartile_1\n lower_bound = quartile_1 - (iqr * 1.5)\n upper_bound = quartile_3 + (iqr * 1.5)\n\n outlier_indices = list(df.index[(df < lower_bound)|(df > upper_bound)])\n outlier_values = list(df[outlier_indices])\n \n df[outlier_indices] = np.NaN\n \n return df"
},
{
"execution_count": 48,
"cell_type": "code",
"metadata": {
"scrolled": true
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "0 30.83\n1 58.67\n2 24.50\n3 27.83\n4 20.17\n5 32.08\n6 33.17\n7 22.92\n8 54.42\n9 42.50\n10 22.08\n11 29.92\n12 38.25\n13 48.08\n14 45.83\n15 36.67\n16 28.25\n17 23.25\n18 21.83\n19 19.17\n20 25.00\n21 23.25\n22 47.75\n23 27.42\n24 41.17\n25 15.83\n26 47.00\n27 56.58\n28 57.42\n29 42.08\n ... \n660 22.25\n661 29.83\n662 23.50\n663 32.08\n664 31.08\n665 31.83\n666 21.75\n667 17.92\n668 30.33\n669 51.83\n670 47.17\n671 25.83\n672 50.25\n673 29.50\n674 37.33\n675 41.58\n676 30.58\n677 19.42\n678 17.92\n679 20.08\n680 19.50\n681 27.83\n682 17.08\n683 36.42\n684 40.58\n685 21.08\n686 22.67\n687 25.25\n688 17.92\n689 35.00\nName: 1, Length: 690, dtype: float64\n"
},
{
"output_type": "stream",
"name": "stderr",
"text": "/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/ipykernel/__main__.py:11: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
}
],
"source": "# Find outliers in first column (continuous values)\nprint(find_outliers(X[1]))"
},
{
"execution_count": 49,
"cell_type": "code",
"metadata": {
"scrolled": true
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "0 0.000\n1 4.460\n2 0.500\n3 1.540\n4 5.625\n5 4.000\n6 1.040\n7 11.585\n8 0.500\n9 4.915\n10 0.830\n11 1.835\n12 6.000\n13 6.040\n14 10.500\n15 4.415\n16 0.875\n17 5.875\n18 0.250\n19 8.585\n20 11.250\n21 1.000\n22 8.000\n23 14.500\n24 6.500\n25 0.585\n26 13.000\n27 NaN\n28 8.500\n29 1.040\n ... \n660 9.000\n661 3.500\n662 1.500\n663 4.000\n664 1.500\n665 0.040\n666 11.750\n667 0.540\n668 0.500\n669 2.040\n670 5.835\n671 12.835\n672 0.835\n673 2.000\n674 2.500\n675 1.040\n676 10.665\n677 7.250\n678 10.210\n679 1.250\n680 0.290\n681 1.000\n682 3.290\n683 0.750\n684 3.290\n685 10.085\n686 0.750\n687 13.500\n688 0.205\n689 3.375\nName: 2, Length: 690, dtype: float64\n"
},
{
"output_type": "stream",
"name": "stderr",
"text": "/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/ipykernel/__main__.py:11: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
}
],
"source": "# Find outliers in first column (continuous values)\nprint(find_outliers(X[2]))"
},
{
"execution_count": 50,
"cell_type": "code",
"metadata": {
"scrolled": true
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "0 1.250\n1 3.040\n2 1.500\n3 3.750\n4 1.710\n5 2.500\n6 NaN\n7 0.040\n8 3.960\n9 3.165\n10 2.165\n11 4.335\n12 1.000\n13 0.040\n14 5.000\n15 0.250\n16 0.960\n17 3.170\n18 0.665\n19 0.750\n20 2.500\n21 0.835\n22 NaN\n23 3.085\n24 0.500\n25 1.500\n26 5.165\n27 NaN\n28 NaN\n29 5.000\n ... \n660 0.085\n661 0.165\n662 0.875\n663 1.500\n664 0.040\n665 0.040\n666 0.250\n667 1.750\n668 0.085\n669 1.500\n670 5.500\n671 0.500\n672 0.500\n673 2.000\n674 0.210\n675 0.665\n676 0.085\n677 0.040\n678 0.000\n679 0.000\n680 0.290\n681 3.000\n682 0.335\n683 0.585\n684 3.500\n685 1.250\n686 2.000\n687 2.000\n688 0.040\n689 NaN\nName: 7, Length: 690, dtype: float64\n"
},
{
"output_type": "stream",
"name": "stderr",
"text": "/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/ipykernel/__main__.py:11: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
}
],
"source": "# Find outliers in first column (continuous values)\nprint(find_outliers(X[7]))"
},
{
"execution_count": 51,
"cell_type": "code",
"metadata": {
"scrolled": true
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "0 1.0\n1 6.0\n2 0.0\n3 5.0\n4 0.0\n5 0.0\n6 0.0\n7 0.0\n8 0.0\n9 0.0\n10 0.0\n11 0.0\n12 0.0\n13 0.0\n14 7.0\n15 NaN\n16 3.0\n17 NaN\n18 0.0\n19 7.0\n20 NaN\n21 0.0\n22 6.0\n23 1.0\n24 3.0\n25 2.0\n26 NaN\n27 NaN\n28 3.0\n29 6.0\n ... \n660 0.0\n661 0.0\n662 0.0\n663 0.0\n664 0.0\n665 0.0\n666 0.0\n667 1.0\n668 0.0\n669 0.0\n670 0.0\n671 0.0\n672 0.0\n673 0.0\n674 0.0\n675 0.0\n676 NaN\n677 1.0\n678 0.0\n679 0.0\n680 0.0\n681 0.0\n682 0.0\n683 0.0\n684 0.0\n685 0.0\n686 2.0\n687 1.0\n688 0.0\n689 0.0\nName: 10, Length: 690, dtype: float64\n"
},
{
"output_type": "stream",
"name": "stderr",
"text": "/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/ipykernel/__main__.py:11: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
}
],
"source": "# Find outliers in first column (continuous values)\nprint(find_outliers(X[10]))"
},
{
"execution_count": 52,
"cell_type": "code",
"metadata": {
"scrolled": true
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "0 202.0\n1 43.0\n2 280.0\n3 100.0\n4 120.0\n5 360.0\n6 164.0\n7 80.0\n8 180.0\n9 52.0\n10 128.0\n11 260.0\n12 0.0\n13 0.0\n14 0.0\n15 320.0\n16 396.0\n17 120.0\n18 0.0\n19 96.0\n20 200.0\n21 300.0\n22 0.0\n23 120.0\n24 145.0\n25 100.0\n26 0.0\n27 0.0\n28 0.0\n29 500.0\n ... \n660 0.0\n661 216.0\n662 160.0\n663 120.0\n664 160.0\n665 0.0\n666 180.0\n667 80.0\n668 252.0\n669 120.0\n670 465.0\n671 0.0\n672 240.0\n673 256.0\n674 260.0\n675 240.0\n676 129.0\n677 100.0\n678 0.0\n679 0.0\n680 280.0\n681 176.0\n682 140.0\n683 240.0\n684 400.0\n685 260.0\n686 200.0\n687 200.0\n688 280.0\n689 0.0\nName: 13, Length: 690, dtype: float64\n"
},
{
"output_type": "stream",
"name": "stderr",
"text": "/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/ipykernel/__main__.py:11: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
}
],
"source": "# Find outliers in first column (continuous values)\nprint(find_outliers(X[13]))"
},
{
"execution_count": 54,
"cell_type": "code",
"metadata": {},
"outputs": [
{
"execution_count": 54,
"metadata": {},
"data": {
"text/plain": "True"
},
"output_type": "execute_result"
}
],
"source": "# Check for null values\nX.isnull().values.any()"
},
{
"execution_count": 53,
"cell_type": "code",
"metadata": {
"scrolled": true
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "0 0.0\n1 560.0\n2 824.0\n3 3.0\n4 0.0\n5 0.0\n6 NaN\n7 NaN\n8 314.0\n9 NaN\n10 0.0\n11 200.0\n12 0.0\n13 NaN\n14 0.0\n15 0.0\n16 0.0\n17 245.0\n18 0.0\n19 0.0\n20 NaN\n21 0.0\n22 NaN\n23 11.0\n24 0.0\n25 0.0\n26 0.0\n27 0.0\n28 0.0\n29 NaN\n ... \n660 0.0\n661 0.0\n662 0.0\n663 0.0\n664 0.0\n665 0.0\n666 0.0\n667 5.0\n668 0.0\n669 1.0\n670 150.0\n671 2.0\n672 117.0\n673 17.0\n674 246.0\n675 237.0\n676 3.0\n677 1.0\n678 50.0\n679 0.0\n680 364.0\n681 537.0\n682 2.0\n683 3.0\n684 0.0\n685 0.0\n686 394.0\n687 1.0\n688 750.0\n689 0.0\nName: 14, Length: 690, dtype: float64\n"
},
{
"output_type": "stream",
"name": "stderr",
"text": "/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/ipykernel/__main__.py:11: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
}
],
"source": "# Find outliers in first column (continuous values)\nprint(find_outliers(X[14]))"
},
{
"execution_count": 56,
"cell_type": "code",
"metadata": {},
"outputs": [
{
"execution_count": 56,
"metadata": {},
"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>1</th>\n <th>2</th>\n <th>7</th>\n <th>10</th>\n <th>13</th>\n <th>14</th>\n <th>0_?</th>\n <th>0_a</th>\n <th>0_b</th>\n <th>3_?</th>\n <th>3_l</th>\n <th>3_u</th>\n <th>3_y</th>\n <th>4_?</th>\n <th>4_g</th>\n <th>...</th>\n <th>6_h</th>\n <th>6_j</th>\n <th>6_n</th>\n <th>6_o</th>\n <th>6_v</th>\n <th>6_z</th>\n <th>8_f</th>\n <th>8_t</th>\n <th>9_f</th>\n <th>9_t</th>\n <th>11_f</th>\n <th>11_t</th>\n <th>12_g</th>\n <th>12_p</th>\n <th>12_s</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>30.83</td>\n <td>0.000</td>\n <td>1.250</td>\n <td>1.0</td>\n <td>202.0</td>\n <td>0.000</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\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>1</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>58.67</td>\n <td>4.460</td>\n <td>3.040</td>\n <td>6.0</td>\n <td>43.0</td>\n <td>560.000</td>\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>0</td>\n <td>1</td>\n <td>...</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>0</td>\n <td>1</td>\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 </tr>\n <tr>\n <th>2</th>\n <td>24.50</td>\n <td>0.500</td>\n <td>1.500</td>\n <td>0.0</td>\n <td>280.0</td>\n <td>824.000</td>\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>0</td>\n <td>1</td>\n <td>...</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>0</td>\n <td>1</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>0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>27.83</td>\n <td>1.540</td>\n <td>3.750</td>\n <td>5.0</td>\n <td>100.0</td>\n <td>3.000</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\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>1</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>20.17</td>\n <td>5.625</td>\n <td>1.710</td>\n <td>0.0</td>\n <td>120.0</td>\n <td>0.000</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\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>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>5</th>\n <td>32.08</td>\n <td>4.000</td>\n <td>2.500</td>\n <td>0.0</td>\n <td>360.0</td>\n <td>0.000</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>6</th>\n <td>33.17</td>\n <td>1.040</td>\n <td>1.362</td>\n <td>0.0</td>\n <td>164.0</td>\n <td>101.047</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</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>0</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>7</th>\n <td>22.92</td>\n <td>11.585</td>\n <td>0.040</td>\n <td>0.0</td>\n <td>80.0</td>\n <td>101.047</td>\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>0</td>\n <td>1</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\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>1</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>8</th>\n <td>54.42</td>\n <td>0.500</td>\n <td>3.960</td>\n <td>0.0</td>\n <td>180.0</td>\n <td>314.000</td>\n <td>0</td>\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>0</td>\n <td>...</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>0</td>\n <td>1</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>0</td>\n </tr>\n <tr>\n <th>9</th>\n <td>42.50</td>\n <td>4.915</td>\n <td>3.165</td>\n <td>0.0</td>\n <td>52.0</td>\n <td>101.047</td>\n <td>0</td>\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>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n </tr>\n </tbody>\n</table>\n<p>10 rows \u00d7 51 columns</p>\n</div>",
"text/plain": " 1 2 7 10 13 14 0_? 0_a 0_b 3_? 3_l 3_u \\\n0 30.83 0.000 1.250 1.0 202.0 0.000 0 0 1 0 0 1 \n1 58.67 4.460 3.040 6.0 43.0 560.000 0 1 0 0 0 1 \n2 24.50 0.500 1.500 0.0 280.0 824.000 0 1 0 0 0 1 \n3 27.83 1.540 3.750 5.0 100.0 3.000 0 0 1 0 0 1 \n4 20.17 5.625 1.710 0.0 120.0 0.000 0 0 1 0 0 1 \n5 32.08 4.000 2.500 0.0 360.0 0.000 0 0 1 0 0 1 \n6 33.17 1.040 1.362 0.0 164.0 101.047 0 0 1 0 0 1 \n7 22.92 11.585 0.040 0.0 80.0 101.047 0 1 0 0 0 1 \n8 54.42 0.500 3.960 0.0 180.0 314.000 0 0 1 0 0 0 \n9 42.50 4.915 3.165 0.0 52.0 101.047 0 0 1 0 0 0 \n\n 3_y 4_? 4_g ... 6_h 6_j 6_n 6_o 6_v 6_z 8_f 8_t 9_f 9_t \\\n0 0 0 1 ... 0 0 0 0 1 0 0 1 0 1 \n1 0 0 1 ... 1 0 0 0 0 0 0 1 0 1 \n2 0 0 1 ... 1 0 0 0 0 0 0 1 1 0 \n3 0 0 1 ... 0 0 0 0 1 0 0 1 0 1 \n4 0 0 1 ... 0 0 0 0 1 0 0 1 1 0 \n5 0 0 1 ... 0 0 0 0 1 0 0 1 1 0 \n6 0 0 1 ... 1 0 0 0 0 0 0 1 1 0 \n7 0 0 1 ... 0 0 0 0 1 0 0 1 1 0 \n8 1 0 0 ... 1 0 0 0 0 0 0 1 1 0 \n9 1 0 0 ... 0 0 0 0 1 0 0 1 1 0 \n\n 11_f 11_t 12_g 12_p 12_s \n0 1 0 1 0 0 \n1 1 0 1 0 0 \n2 1 0 1 0 0 \n3 0 1 1 0 0 \n4 1 0 0 0 1 \n5 0 1 1 0 0 \n6 0 1 1 0 0 \n7 1 0 1 0 0 \n8 1 0 1 0 0 \n9 0 1 1 0 0 \n\n[10 rows x 51 columns]"
},
"output_type": "execute_result"
}
],
"source": "# Define the values to replce and the strategy of choosing the replacement value\nsuspected_cols = [1, 2, 7, 10, 13, 14]\nimp = Imputer(missing_values=\"NaN\", strategy=\"mean\")\n\npd.DataFrame(X)[suspected_cols] = imp.fit_transform(pd.DataFrame(X)[suspected_cols])\npd.DataFrame(X).head(10)"
},
{
"execution_count": 57,
"cell_type": "code",
"metadata": {},
"outputs": [
{
"execution_count": 57,
"metadata": {},
"data": {
"text/plain": "False"
},
"output_type": "execute_result"
}
],
"source": "# Check for null values\npd.DataFrame(X).isnull().values.any()"
},
{
"source": "### 9. Feature Engineering",
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
}
},
{
"execution_count": 108,
"cell_type": "code",
"metadata": {
"scrolled": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"execution_count": 108,
"metadata": {},
"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>1</th>\n <th>2</th>\n <th>7</th>\n <th>10</th>\n <th>14</th>\n <th>3_u</th>\n <th>3_y</th>\n <th>4_g</th>\n <th>4_p</th>\n <th>5_cc</th>\n <th>5_ff</th>\n <th>5_i</th>\n <th>5_q</th>\n <th>5_x</th>\n <th>6_ff</th>\n <th>6_h</th>\n <th>8_f</th>\n <th>8_t</th>\n <th>9_f</th>\n <th>9_t</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>30.83</td>\n <td>0.000</td>\n <td>1.250</td>\n <td>1.0</td>\n <td>0.000</td>\n <td>1</td>\n <td>0</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>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>1</th>\n <td>58.67</td>\n <td>4.460</td>\n <td>3.040</td>\n <td>6.0</td>\n <td>560.000</td>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\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 </tr>\n <tr>\n <th>2</th>\n <td>24.50</td>\n <td>0.500</td>\n <td>1.500</td>\n <td>0.0</td>\n <td>824.000</td>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\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 </tr>\n <tr>\n <th>3</th>\n <td>27.83</td>\n <td>1.540</td>\n <td>3.750</td>\n <td>5.0</td>\n <td>3.000</td>\n <td>1</td>\n <td>0</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>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>4</th>\n <td>20.17</td>\n <td>5.625</td>\n <td>1.710</td>\n <td>0.0</td>\n <td>0.000</td>\n <td>1</td>\n <td>0</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>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>5</th>\n <td>32.08</td>\n <td>4.000</td>\n <td>2.500</td>\n <td>0.0</td>\n <td>0.000</td>\n <td>1</td>\n <td>0</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>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>6</th>\n <td>33.17</td>\n <td>1.040</td>\n <td>1.362</td>\n <td>0.0</td>\n <td>101.047</td>\n <td>1</td>\n <td>0</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>0</td>\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 </tr>\n <tr>\n <th>7</th>\n <td>22.92</td>\n <td>11.585</td>\n <td>0.040</td>\n <td>0.0</td>\n <td>101.047</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>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>8</th>\n <td>54.42</td>\n <td>0.500</td>\n <td>3.960</td>\n <td>0.0</td>\n <td>314.000</td>\n <td>0</td>\n <td>1</td>\n <td>0</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>0</td>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>9</th>\n <td>42.50</td>\n <td>4.915</td>\n <td>3.165</td>\n <td>0.0</td>\n <td>101.047</td>\n <td>0</td>\n <td>1</td>\n <td>0</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>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " 1 2 7 10 14 3_u 3_y 4_g 4_p 5_cc 5_ff 5_i \\\n0 30.83 0.000 1.250 1.0 0.000 1 0 1 0 0 0 0 \n1 58.67 4.460 3.040 6.0 560.000 1 0 1 0 0 0 0 \n2 24.50 0.500 1.500 0.0 824.000 1 0 1 0 0 0 0 \n3 27.83 1.540 3.750 5.0 3.000 1 0 1 0 0 0 0 \n4 20.17 5.625 1.710 0.0 0.000 1 0 1 0 0 0 0 \n5 32.08 4.000 2.500 0.0 0.000 1 0 1 0 0 0 0 \n6 33.17 1.040 1.362 0.0 101.047 1 0 1 0 0 0 0 \n7 22.92 11.585 0.040 0.0 101.047 1 0 1 0 1 0 0 \n8 54.42 0.500 3.960 0.0 314.000 0 1 0 1 0 0 0 \n9 42.50 4.915 3.165 0.0 101.047 0 1 0 1 0 0 0 \n\n 5_q 5_x 6_ff 6_h 8_f 8_t 9_f 9_t \n0 0 0 0 0 0 1 0 1 \n1 1 0 0 1 0 1 0 1 \n2 1 0 0 1 0 1 1 0 \n3 0 0 0 0 0 1 0 1 \n4 0 0 0 0 0 1 1 0 \n5 0 0 0 0 0 1 1 0 \n6 0 0 0 1 0 1 1 0 \n7 0 0 0 0 0 1 1 0 \n8 0 0 0 1 0 1 1 0 \n9 0 0 0 0 0 1 1 0 "
},
"output_type": "execute_result"
}
],
"source": "# Select best features\nselect = sklearn.feature_selection.SelectKBest(k=20)\nselected_features = select.fit(X, y_enc)\nindexes = selected_features.get_support(indices=True)\ncol_names_selected = [pd.DataFrame(X).columns[i] for i in indexes]\n\nX_selected = pd.DataFrame(X)[col_names_selected]\npd.DataFrame(X_selected).head(10)"
},
{
"source": "### 10. Split our dataset into train and test datasets",
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
}
},
{
"source": "#### Split non-preprocessed data",
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
}
},
{
"execution_count": 59,
"cell_type": "code",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "(483, 51) (483,)\n(207, 51) (207,)\n"
}
],
"source": "X_train_npp, X_test_npp, y_train_npp, y_test_npp = train_test_split(X_npp, y_enc,\\\n test_size=0.3, random_state=42)\nprint(X_train_npp.shape, y_train_npp.shape)\nprint(X_test_npp.shape, y_test_npp.shape)"
},
{
"execution_count": 109,
"cell_type": "code",
"metadata": {
"scrolled": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "(483, 20) (483,)\n(207, 20) (207,)\n"
}
],
"source": "X_train, X_test, y_train, y_test = train_test_split(X_selected, y_enc,\\\n test_size=0.3, random_state=42)\nprint(X_train.shape, y_train.shape)\nprint(X_test.shape, y_test.shape)"
},
{
"source": "### 11. Scale our data",
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
}
},
{
"execution_count": 110,
"cell_type": "code",
"metadata": {
"scrolled": true,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"execution_count": 110,
"metadata": {},
"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>1</th>\n <th>2</th>\n <th>7</th>\n <th>10</th>\n <th>14</th>\n <th>3_u</th>\n <th>3_y</th>\n <th>4_g</th>\n <th>4_p</th>\n <th>5_cc</th>\n <th>5_ff</th>\n <th>5_i</th>\n <th>5_q</th>\n <th>5_x</th>\n <th>6_ff</th>\n <th>6_h</th>\n <th>8_f</th>\n <th>8_t</th>\n <th>9_f</th>\n <th>9_t</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>-1.184</td>\n <td>1.234</td>\n <td>-0.084</td>\n <td>1.625</td>\n <td>2.406</td>\n <td>0.600</td>\n <td>-0.578</td>\n <td>0.600</td>\n <td>-0.578</td>\n <td>-0.234</td>\n <td>-0.3</td>\n <td>-0.305</td>\n <td>2.790</td>\n <td>-0.224</td>\n <td>-0.313</td>\n <td>-0.492</td>\n <td>-0.977</td>\n <td>0.977</td>\n <td>-1.135</td>\n <td>1.135</td>\n </tr>\n <tr>\n <th>1</th>\n <td>-1.314</td>\n <td>-1.034</td>\n <td>-0.881</td>\n <td>-0.607</td>\n <td>3.407</td>\n <td>-1.665</td>\n <td>1.730</td>\n <td>-1.665</td>\n <td>1.730</td>\n <td>-0.234</td>\n <td>-0.3</td>\n <td>-0.305</td>\n <td>-0.358</td>\n <td>-0.224</td>\n <td>-0.313</td>\n <td>-0.492</td>\n <td>1.023</td>\n <td>-1.023</td>\n <td>0.881</td>\n <td>-0.881</td>\n </tr>\n <tr>\n <th>2</th>\n <td>-0.785</td>\n <td>1.790</td>\n <td>0.113</td>\n <td>-0.607</td>\n <td>-0.009</td>\n <td>-1.665</td>\n <td>1.730</td>\n <td>-1.665</td>\n <td>1.730</td>\n <td>-0.234</td>\n <td>-0.3</td>\n <td>-0.305</td>\n <td>-0.358</td>\n <td>-0.224</td>\n <td>-0.313</td>\n <td>-0.492</td>\n <td>1.023</td>\n <td>-1.023</td>\n <td>0.881</td>\n <td>-0.881</td>\n </tr>\n <tr>\n <th>3</th>\n <td>1.240</td>\n <td>0.017</td>\n <td>-0.765</td>\n <td>-0.049</td>\n <td>-0.069</td>\n <td>0.600</td>\n <td>-0.578</td>\n <td>0.600</td>\n <td>-0.578</td>\n <td>-0.234</td>\n <td>-0.3</td>\n <td>-0.305</td>\n <td>2.790</td>\n <td>-0.224</td>\n <td>-0.313</td>\n <td>2.034</td>\n <td>-0.977</td>\n <td>0.977</td>\n <td>-1.135</td>\n <td>1.135</td>\n </tr>\n <tr>\n <th>4</th>\n <td>-1.314</td>\n <td>-0.993</td>\n <td>-0.680</td>\n <td>1.625</td>\n <td>-0.521</td>\n <td>0.600</td>\n <td>-0.578</td>\n <td>0.600</td>\n <td>-0.578</td>\n <td>-0.234</td>\n <td>-0.3</td>\n <td>-0.305</td>\n <td>2.790</td>\n <td>-0.224</td>\n <td>-0.313</td>\n <td>-0.492</td>\n <td>1.023</td>\n <td>-1.023</td>\n <td>-1.135</td>\n <td>1.135</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " 1 2 7 10 14 3_u 3_y 4_g 4_p 5_cc 5_ff \\\n0 -1.184 1.234 -0.084 1.625 2.406 0.600 -0.578 0.600 -0.578 -0.234 -0.3 \n1 -1.314 -1.034 -0.881 -0.607 3.407 -1.665 1.730 -1.665 1.730 -0.234 -0.3 \n2 -0.785 1.790 0.113 -0.607 -0.009 -1.665 1.730 -1.665 1.730 -0.234 -0.3 \n3 1.240 0.017 -0.765 -0.049 -0.069 0.600 -0.578 0.600 -0.578 -0.234 -0.3 \n4 -1.314 -0.993 -0.680 1.625 -0.521 0.600 -0.578 0.600 -0.578 -0.234 -0.3 \n\n 5_i 5_q 5_x 6_ff 6_h 8_f 8_t 9_f 9_t \n0 -0.305 2.790 -0.224 -0.313 -0.492 -0.977 0.977 -1.135 1.135 \n1 -0.305 -0.358 -0.224 -0.313 -0.492 1.023 -1.023 0.881 -0.881 \n2 -0.305 -0.358 -0.224 -0.313 -0.492 1.023 -1.023 0.881 -0.881 \n3 -0.305 2.790 -0.224 -0.313 2.034 -0.977 0.977 -1.135 1.135 \n4 -0.305 2.790 -0.224 -0.313 -0.492 1.023 -1.023 -1.135 1.135 "
},
"output_type": "execute_result"
}
],
"source": "# Use StandardScaler\nscaler = preprocessing.StandardScaler().fit(X_train, y_train)\nX_train_scaled = scaler.transform(X_train)\n\npd.DataFrame(X_train_scaled, columns=pd.DataFrame(X_train).columns).head()"
},
{
"execution_count": 111,
"cell_type": "code",
"metadata": {
"scrolled": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"execution_count": 111,
"metadata": {},
"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>0</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1</td>\n </tr>\n <tr>\n <th>2</th>\n <td>1</td>\n </tr>\n <tr>\n <th>3</th>\n <td>1</td>\n </tr>\n <tr>\n <th>4</th>\n <td>1</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " 0\n0 0\n1 1\n2 1\n3 1\n4 1"
},
"output_type": "execute_result"
}
],
"source": "pd.DataFrame(y_train).head()"
},
{
"source": "### 12. Start building a classifier",
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
}
},
{
"source": "#### Logestic Regression on non-preprocessed data",
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
}
},
{
"execution_count": 64,
"cell_type": "code",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"execution_count": 64,
"metadata": {},
"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)"
},
"output_type": "execute_result"
}
],
"source": "from sklearn.linear_model import LogisticRegression\n\nclf_lr_npp = LogisticRegression()\nclf_lr_npp.fit(X_train_npp, y_train_npp)"
},
{
"source": "#### Logestic Regression on preprocessed data",
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
}
},
{
"execution_count": 112,
"cell_type": "code",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"execution_count": 112,
"metadata": {},
"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)"
},
"output_type": "execute_result"
}
],
"source": "from sklearn.linear_model import LogisticRegression\n\nclf_lr = LogisticRegression()\nmodel = clf_lr.fit(X_train_scaled, y_train)\nmodel"
},
{
"source": "### 13. Evaluate our model",
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
}
},
{
"execution_count": 116,
"cell_type": "code",
"metadata": {
"scrolled": true,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"execution_count": 116,
"metadata": {},
"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>1</th>\n <th>2</th>\n <th>7</th>\n <th>10</th>\n <th>14</th>\n <th>3_u</th>\n <th>3_y</th>\n <th>4_g</th>\n <th>4_p</th>\n <th>5_cc</th>\n <th>5_ff</th>\n <th>5_i</th>\n <th>5_q</th>\n <th>5_x</th>\n <th>6_ff</th>\n <th>6_h</th>\n <th>8_f</th>\n <th>8_t</th>\n <th>9_f</th>\n <th>9_t</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>0.100</td>\n <td>-0.684</td>\n <td>-0.908</td>\n <td>0.509</td>\n <td>0.013</td>\n <td>0.600</td>\n <td>-0.578</td>\n <td>0.600</td>\n <td>-0.578</td>\n <td>-0.234</td>\n <td>3.328</td>\n <td>-0.305</td>\n <td>-0.358</td>\n <td>-0.224</td>\n <td>3.199</td>\n <td>-0.492</td>\n <td>1.023</td>\n <td>-1.023</td>\n <td>-1.135</td>\n <td>1.135</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1.508</td>\n <td>-0.065</td>\n <td>-0.908</td>\n <td>-0.607</td>\n <td>4.717</td>\n <td>0.600</td>\n <td>-0.578</td>\n <td>0.600</td>\n <td>-0.578</td>\n <td>-0.234</td>\n <td>-0.300</td>\n <td>-0.305</td>\n <td>-0.358</td>\n <td>-0.224</td>\n <td>-0.313</td>\n <td>-0.492</td>\n <td>-0.977</td>\n <td>0.977</td>\n <td>0.881</td>\n <td>-0.881</td>\n </tr>\n <tr>\n <th>2</th>\n <td>-1.029</td>\n <td>-1.055</td>\n <td>-0.568</td>\n <td>-0.607</td>\n <td>-0.565</td>\n <td>0.600</td>\n <td>-0.578</td>\n <td>0.600</td>\n <td>-0.578</td>\n <td>-0.234</td>\n <td>-0.300</td>\n <td>-0.305</td>\n <td>-0.358</td>\n <td>-0.224</td>\n <td>-0.313</td>\n <td>-0.492</td>\n <td>1.023</td>\n <td>-1.023</td>\n <td>0.881</td>\n <td>-0.881</td>\n </tr>\n <tr>\n <th>3</th>\n <td>1.638</td>\n <td>0.553</td>\n <td>-0.227</td>\n <td>-0.607</td>\n <td>0.690</td>\n <td>0.600</td>\n <td>-0.578</td>\n <td>0.600</td>\n <td>-0.578</td>\n <td>-0.234</td>\n <td>-0.300</td>\n <td>-0.305</td>\n <td>-0.358</td>\n <td>-0.224</td>\n <td>-0.313</td>\n <td>-0.492</td>\n <td>1.023</td>\n <td>-1.023</td>\n <td>0.881</td>\n <td>-0.881</td>\n </tr>\n <tr>\n <th>4</th>\n <td>-1.110</td>\n <td>-1.055</td>\n <td>-0.908</td>\n <td>-0.607</td>\n <td>-0.559</td>\n <td>-1.665</td>\n <td>1.730</td>\n <td>-1.665</td>\n <td>1.730</td>\n <td>-0.234</td>\n <td>-0.300</td>\n <td>-0.305</td>\n <td>-0.358</td>\n <td>-0.224</td>\n <td>-0.313</td>\n <td>-0.492</td>\n <td>1.023</td>\n <td>-1.023</td>\n <td>0.881</td>\n <td>-0.881</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " 1 2 7 10 14 3_u 3_y 4_g 4_p 5_cc \\\n0 0.100 -0.684 -0.908 0.509 0.013 0.600 -0.578 0.600 -0.578 -0.234 \n1 1.508 -0.065 -0.908 -0.607 4.717 0.600 -0.578 0.600 -0.578 -0.234 \n2 -1.029 -1.055 -0.568 -0.607 -0.565 0.600 -0.578 0.600 -0.578 -0.234 \n3 1.638 0.553 -0.227 -0.607 0.690 0.600 -0.578 0.600 -0.578 -0.234 \n4 -1.110 -1.055 -0.908 -0.607 -0.559 -1.665 1.730 -1.665 1.730 -0.234 \n\n 5_ff 5_i 5_q 5_x 6_ff 6_h 8_f 8_t 9_f 9_t \n0 3.328 -0.305 -0.358 -0.224 3.199 -0.492 1.023 -1.023 -1.135 1.135 \n1 -0.300 -0.305 -0.358 -0.224 -0.313 -0.492 -0.977 0.977 0.881 -0.881 \n2 -0.300 -0.305 -0.358 -0.224 -0.313 -0.492 1.023 -1.023 0.881 -0.881 \n3 -0.300 -0.305 -0.358 -0.224 -0.313 -0.492 1.023 -1.023 0.881 -0.881 \n4 -0.300 -0.305 -0.358 -0.224 -0.313 -0.492 1.023 -1.023 0.881 -0.881 "
},
"output_type": "execute_result"
}
],
"source": "# Use the scaler fit on trained data to scale our test data\nX_test_scaled = scaler.transform(X_test)\npd.DataFrame(X_test_scaled, columns=pd.DataFrame(X_train).columns).head()"
},
{
"source": "#### Evaluate Logistic Regression on non-preprocessed data",
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
}
},
{
"execution_count": 113,
"cell_type": "code",
"metadata": {
"scrolled": true,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"execution_count": 113,
"metadata": {},
"data": {
"text/plain": "array([ 3.79114351e+00, -1.53289391e+00, 3.59750686e+00,\n 2.63899790e+00, 5.34015063e+00, 1.91329086e-01,\n 2.98874665e+00, 8.67871930e-02, 3.58419649e+00,\n 3.70771837e+00, 2.70176737e+00, -1.66358540e+00,\n 3.30947758e+00, -2.16630789e-01, 1.65493314e+00,\n -9.88884781e-01, 3.08195370e+00, 4.06404098e+00,\n 1.72760092e+00, 2.24916855e+00, 2.84404228e+00,\n 3.23920898e+00, 3.90750984e+00, -9.35919037e-01,\n 2.75681502e+00, 4.32138435e+00, -3.96619809e+00,\n -2.43401740e+00, 3.17308702e+00, 3.08414031e+00,\n -1.36637422e+00, -4.38155190e+00, -1.47865022e+00,\n -1.53280871e+00, -5.89477010e-03, -3.27439787e+00,\n -3.23184603e+00, -1.70721642e+00, -1.24822963e+00,\n -3.89545810e+01, -2.22081486e+00, -2.03776823e+00,\n 3.28656327e+00, -1.41177687e+00, 3.87553319e+00,\n -5.86624065e+00, 2.55288734e+00, 5.04640615e+00,\n 3.70524984e-01, 3.67618059e+00, 4.14496605e+00,\n -1.06645262e+00, 3.26419631e+00, 2.94093624e+00,\n -1.38959553e+00, 3.30948662e+00, -2.59556398e+00,\n 1.88674146e+00, -1.95916853e+00, 3.09434849e+00,\n -4.07223521e+00, -1.51584072e+00, -1.98688188e+00,\n 4.26446591e-01, 3.34786968e+00, -2.91758212e+00,\n -3.96169228e+00, 9.55569194e-02, -1.08368095e+00,\n 2.97308040e+00, 2.48778124e+00, -1.32618683e+00,\n 3.27962750e+00, -5.24952660e-01, 3.50150442e+00,\n 3.77807493e+00, 2.89412552e+00, 6.84010685e-01,\n -1.24066814e+00, 2.65344525e+00, -2.24530489e+00,\n -2.54550656e+00, 2.97594603e+00, 2.86170658e+00,\n -1.30793237e+00, 2.99635702e+00, -1.52276649e+00,\n -3.55282890e+00, -1.00235837e+00, -1.97330036e+00,\n -7.45244467e-01, 1.41793056e-01, -5.87737322e+00,\n -1.99269067e+00, 5.69623798e-01, -1.21480780e+00,\n -9.50015972e+00, -2.38241709e-01, 3.17514825e+00,\n 5.36445225e+00, 3.62110744e+00, 2.03296351e+00,\n -1.29695567e+00, 1.04823057e+00, -3.82401680e+01,\n -9.51642550e-01, 3.26862368e+00, -3.91844392e+00,\n 4.35637354e+00, -2.12894225e+00, 5.03956329e+00,\n -2.22947647e+00, -2.41773267e+01, 1.22531146e-01,\n -8.84799954e-01, 2.97010078e+00, -5.24369939e+00,\n -2.80920674e+00, -1.24332530e+00, -4.51715884e+00,\n 3.18712038e+00, 2.09019214e+00, -2.05962173e+00,\n 1.88170191e+00, 4.75400258e+00, 5.29170997e-01,\n 3.28973175e+00, 2.73475574e+00, -1.97119683e+00,\n -3.95391398e+00, -2.46562994e+00, 3.52438955e+00,\n -1.34915306e+00, 2.94900993e+00, -3.33782465e+00,\n -1.49319261e+00, -1.31290712e+00, -1.62866105e+00,\n 6.08706078e+00, -1.68378989e+00, -4.86912903e-01,\n -9.23741589e-02, -2.95888858e+00, -2.26403654e+00,\n 3.46684559e+00, 1.90960822e+00, 4.23758334e+00,\n -3.53997054e-01, -4.38350526e-01, 3.63497785e+00,\n 4.36102280e+00, 2.02311100e+00, 3.89632321e+00,\n -1.68652434e+00, 2.09286610e+00, -3.19852344e+00,\n -9.55022951e+00, -6.46570592e-01, 3.15298232e+00,\n 1.11557156e+00, -2.78222787e+00, 3.22151345e+00,\n 3.26719016e+00, 4.48931011e+00, 3.58667747e+00,\n 3.91830889e+00, 3.27661986e+00, -3.83761354e+00,\n 3.63008882e+00, -7.53135970e-01, 2.66739640e+00,\n 3.94169192e+00, -1.72347787e-01, 3.86725662e+00,\n -9.75240767e-02, 3.81199897e+00, 3.63741719e+00,\n 4.16426730e+00, 2.84012076e+00, 3.82328514e+00,\n 2.21065287e+00, -1.31928430e+00, -1.06864401e+00,\n 2.84181981e+00, -2.58833779e+00, 2.79809424e+00,\n -4.29753873e-01, -2.08858490e+00, -9.42408558e-01,\n -1.42794764e+00, 2.96748129e+00, -2.08301485e+00,\n 3.85207430e+00, -2.90721949e+00, -2.14365352e+00,\n -3.11438657e+00, -3.09122742e+00, -2.51190272e+00,\n -1.92046333e+00, -1.49322540e+00, -9.52598854e+00,\n -3.04087652e+00, 1.99685060e+00, -3.01345380e+00,\n -1.13705121e+00, 2.48054301e+00, -3.68041180e+00])"
},
"output_type": "execute_result"
}
],
"source": "y_score_lr_npp = clf_lr_npp.decision_function(X_test_npp)\ny_score_lr_npp"
},
{
"execution_count": 114,
"cell_type": "code",
"metadata": {
"scrolled": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "0.840579710145\n"
}
],
"source": "# Get accuracy score\nfrom sklearn.metrics import accuracy_score\n\ny_pred_lr_npp = clf_lr_npp.predict(X_test_npp)\nacc_lr_npp = accuracy_score(y_test_npp, y_pred_lr_npp)\nprint(acc_lr_npp)"
},
{
"execution_count": 69,
"cell_type": "code",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "Average precision-recall score: 0.90\n"
}
],
"source": "# Get Precision vs. Recall score\nfrom sklearn.metrics import average_precision_score\n\naverage_precision_lr_npp = average_precision_score(y_test_npp, y_score_lr_npp)\n\nprint('Average precision-recall score: {0:0.2f}'.format(\n average_precision_lr_npp))"
},
{
"source": "#### Evaluate Logistic Regression on preprocessed data",
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
}
},
{
"execution_count": 117,
"cell_type": "code",
"metadata": {
"scrolled": true,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"execution_count": 117,
"metadata": {},
"data": {
"text/plain": "array([ 3.56748043, -2.73442698, 3.8015596 , 2.35479139, 4.59269848,\n -0.8607685 , 2.88428431, -0.91354643, 3.84927625, 3.22768285,\n 3.58946094, -2.71929997, 3.25251482, -1.07305078, 2.51828729,\n -1.64178561, 2.13600714, 3.95220877, 0.79395167, 1.71794504,\n 2.59317613, 1.94079023, 4.50506087, -2.90542169, 2.82553382,\n 3.81591255, -2.09719645, -2.16097604, 4.36303526, 3.27114743,\n -3.09495293, -5.35336695, -1.22127421, -1.27589192, -0.27360866,\n -1.57714257, -1.38991232, -3.80568844, -1.46912589, -2.48294848,\n -3.77811265, -2.14891293, 3.48721316, -1.34557765, 2.97620065,\n -4.42509853, 2.46208432, 4.43784814, -0.26272097, 4.17192989,\n 4.59461887, -1.11900003, 3.50150034, 3.13145608, -1.05864704,\n 3.72408428, -2.92307375, 3.94432024, -1.03795131, 3.72260601,\n -4.55291223, -2.54102105, -1.77485624, 0.70497922, 4.02256182,\n -1.85814971, -3.46653777, -0.3313209 , -2.06875843, 3.46391108,\n 1.29383252, -2.0739646 , 2.70434366, -0.54400562, 3.701686 ,\n 3.90699329, 3.45530915, -0.05884862, -1.90125582, 2.00635753,\n -1.05812054, -4.12532071, 2.69824479, 2.85481591, -1.92293673,\n 2.09366194, -2.44854461, -2.79414406, -2.87179815, -5.54263547,\n -1.76406601, 0.08337258, -1.66297942, -0.63680903, -0.63439523,\n -0.78314549, -0.58262445, -0.40301226, 3.34660656, 5.82457794,\n 4.93722522, -0.4362406 , -3.5896711 , 1.42747008, -3.40840264,\n -1.11702216, 3.78606668, -2.72809978, 4.51490872, -1.64363597,\n 4.24570329, -4.84659164, -0.88499044, -0.86634338, -0.49400349,\n 2.64660804, -4.70446673, -4.48724374, 2.23548011, -4.02540764,\n 3.06718513, 4.91616963, -3.27630201, 1.22469856, 4.68838084,\n -0.31590368, 4.01015405, 1.94124362, -1.62680525, -3.21102093,\n -2.64277405, 3.12815903, -1.34116763, 3.11350676, -2.5876485 ,\n -1.42301576, -2.46891388, -1.06354279, 5.6124747 , -2.21935447,\n -0.35202978, -0.11024982, -2.40132195, -3.29498563, 3.97879929,\n 1.57676641, 4.5991717 , -0.48738402, -0.16322052, 3.48981937,\n 3.14407624, 3.1549296 , 3.11965969, -3.97812189, 2.18259764,\n -3.88261848, -3.24564837, -0.62834504, 4.42187855, 2.22448123,\n -4.28131873, 4.17346056, 3.83862932, 4.56658714, 3.65156776,\n 3.9966952 , 3.73771057, -1.75206628, 4.25401689, -1.49548997,\n 3.4709233 , 3.68178493, -0.20239066, 3.63625129, 0.36854924,\n 3.25546421, 4.21500234, 3.84720572, 2.58168781, 3.74202326,\n 2.22529624, -1.70873245, -1.39653903, 1.78747374, -2.98883551,\n 1.86003179, 0.11802022, -1.72926967, -0.58968377, -3.25734618,\n 3.44094246, -3.01745767, 4.02030767, -1.07055508, -0.9793049 ,\n -3.03175641, -2.55925347, -2.13823871, -1.76792699, -0.76730825,\n -1.80128357, -2.07730882, 3.99216771, -2.94769407, -1.86277713,\n 3.27253696, -1.90516603])"
},
"output_type": "execute_result"
}
],
"source": "y_score_lr = clf_lr.decision_function(X_test_scaled)\ny_score_lr"
},
{
"execution_count": 118,
"cell_type": "code",
"metadata": {
"scrolled": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "0.850241545894\n"
}
],
"source": "y_pred_lr = clf_lr.predict(X_test_scaled)\nacc_lr = accuracy_score(y_test, y_pred_lr)\nprint(acc_lr)"
},
{
"execution_count": 119,
"cell_type": "code",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "Average precision-recall score: 0.90\n"
}
],
"source": "average_precision_lr = average_precision_score(y_test, y_score_lr)\n\nprint('Average precision-recall score: {0:0.2f}'.format(\n average_precision_lr))"
},
{
"source": "### 14. ROC Curve and models comparisons",
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
}
},
{
"execution_count": 122,
"cell_type": "code",
"metadata": {
"scrolled": true,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"execution_count": 122,
"metadata": {},
"data": {
"text/plain": "Text(0,0.5,'True Positives')"
},
"output_type": "execute_result"
},
{
"output_type": "display_data",
"data": {
"image/png": "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\n",
"text/plain": "<matplotlib.figure.Figure at 0x2ac2845384e0>"
},
"metadata": {}
}
],
"source": "# Plot SVC ROC Curve\nplt.figure(0, figsize=(15,10)).clf()\n\nfpr_lr_npp, tpr_lr_npp, thresh_lr_npp = metrics.roc_curve(y_test_npp, y_score_lr_npp)\nauc_lr_npp = metrics.roc_auc_score(y_test_npp, y_score_lr_npp)\nplt.plot(fpr_lr_npp, tpr_lr_npp, label=\"Logistic Regression on Non-preprocessed Data, auc=\" + str(auc_lr_npp))\n\nfpr_lr, tpr_lr, thresh_lr = metrics.roc_curve(y_test, y_score_lr)\nauc_lr = metrics.roc_auc_score(y_test, y_score_lr)\nplt.plot(fpr_lr, tpr_lr, label=\"Logistic Regression on Preprocessed Data, auc=\" + str(auc_lr))\n\nplt.legend(loc=0)\nplt.xlabel('False Positives')\nplt.ylabel('True Positives')"
},
{
"source": "#### Bonus: Deploy model on the cloud using IBM Watson Machine Learning\n\nWe have our model, but we want to use it through multiple apps. A solution is to deploy it on the cloud as an endpoint (url) and send data collected from a web/mobile app as a REST API call with data sent in the form of a JSON request.",
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
}
},
{
"execution_count": 123,
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": "# The code was removed by DSX for sharing."
},
{
"execution_count": 132,
"cell_type": "code",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "Bearer eyJhbGciOiJSUzUxMiIsInR5cCI6IkpXVCJ9.eyJ0ZW5hbnRJZCI6IjQ2MWViYWMyLWNlOGUtNDRlNi1iM2I5LWI2ZjQyYTVmMzFiNiIsImluc3RhbmNlSWQiOiI0NjFlYmFjMi1jZThlLTQ0ZTYtYjNiOS1iNmY0MmE1ZjMxYjYiLCJwbGFuSWQiOiIzZjZhY2Y0My1lZGU4LTQxM2EtYWM2OS1mOGFmM2JiMGNiZmUiLCJyZWdpb24iOiJ1cy1zb3V0aCIsInVzZXJJZCI6IjM1MDRlODgyLWI1NDktNGQwNi04ZWM5LTYxNmI2MjRiYjljYiIsImlzcyI6Imh0dHBzOi8vaWJtLXdhdHNvbi1tbC5teWJsdWVtaXgubmV0L3YzL2lkZW50aXR5IiwiaWF0IjoxNTI1OTc3MTE4LCJleHAiOjE1MjYwMDU5MTh9.ogsHrN01ijtqnIlvpFNu4naVPXqz6ByMik3umBqAToVC9VG3ccMGNniSoKwnQoIPwHYiplr319r5Ey09ciADx_ri4-sBaHR3KIspQuI8o_GMX5IFikgn-JXFKZNMffVAcsMMiDq3cmnKxtxc-cxXKe4vmvr7anxpEAXiViZbkbJNRLaYJbp4JTB8eSrllXSiCAAmnFTQjNaJSbuXEYu7IXlbMRcp20X0iq56L4snKhsAI_A5qmLkjNi6FNlOc1dNifktj3GOT0BnDR6-QSQ9o-Rngwdik8kGUxpg6Mv4JIp_I7kFDevoz4WQ68CIToMQouMkILK0tx6mbUx-ObeY2A\n"
}
],
"source": "# To work with the Watson Machine Learning REST API you must generate a Bearer access token\nimport urllib3, requests, json\n\nheaders = urllib3.util.make_headers(basic_auth='{}:{}'.format(credentials['username'], credentials['password']))\nurl = '{}/v3/identity/token'.format(credentials['url'])\nresponse = requests.get(url, headers=headers)\nml_token = 'Bearer ' + json.loads(response.text).get('token')\nprint(ml_token)"
},
{
"execution_count": 125,
"cell_type": "code",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "<Response [200]>\n{\"metadata\":{\"guid\":\"461ebac2-ce8e-44e6-b3b9-b6f42a5f31b6\",\"url\":\"https://instances/v3/wml_instances/461ebac2-ce8e-44e6-b3b9-b6f42a5f31b6\",\"created_at\":\"2018-03-29T16:59:06.075Z\",\"modified_at\":\"2018-03-29T16:59:06.075Z\"},\"entity\":{\"source\":\"Bluemix\",\"published_models\":{\"url\":\"https://ibm-watson-ml.mybluemix.net/v3/wml_instances/461ebac2-ce8e-44e6-b3b9-b6f42a5f31b6/published_models\"},\"usage\":{\"expiration_date\":\"2018-06-01T00:00:00.000Z\",\"computation_time\":{\"limit\":180000,\"current\":0},\"model_count\":{\"limit\":200,\"current\":4},\"prediction_count\":{\"limit\":5000,\"current\":3},\"gpu_count\":{\"limit\":8,\"current\":0},\"capacity_units\":{\"limit\":180000000,\"current\":17},\"deployment_count\":{\"limit\":5,\"current\":5}},\"plan_id\":\"3f6acf43-ede8-413a-ac69-f8af3bb0cbfe\",\"status\":\"Active\",\"organization_guid\":\"acec7554-82ac-49c0-a1d1-2f6803ce2b02\",\"region\":\"us-south\",\"account\":{\"id\":\"13bdb8509a2f1e6aa4bf611f8673a191\",\"name\":\"Heba El-Shimy's Account\",\"type\":\"TRIAL\"},\"owner\":{\"ibm_id\":\"50RX9K19A7\",\"email\":\"Heba.Elshimy1@ibm.com\",\"user_id\":\"45e8c98a-51f4-420b-9202-ecc25050fbb9\",\"country_code\":\"ARE\",\"beta_user\":true},\"deployments\":{\"url\":\"https://ibm-watson-ml.mybluemix.net/v3/wml_instances/461ebac2-ce8e-44e6-b3b9-b6f42a5f31b6/deployments\"},\"space_guid\":\"689631fe-8bef-4f8a-a515-96f7ce010036\",\"plan\":\"lite\"}}\n"
}
],
"source": "# Create an online scoring endpoint\n\nendpoint_instance = credentials['url'] + \"/v3/wml_instances/\" + credentials['instance_id']\nheader = {'Content-Type': 'application/json', 'Authorization': ml_token}\n\nresponse_get_instance = requests.get(endpoint_instance, headers=header)\nprint(response_get_instance)\nprint(response_get_instance.text)"
},
{
"execution_count": 126,
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": "# Create API client\n\nfrom watson_machine_learning_client import WatsonMachineLearningAPIClient\n\nclient = WatsonMachineLearningAPIClient(credentials)"
},
{
"execution_count": 127,
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": "# Publish model in Watson Machine Learning repository on Cloud\n\nmodel_props = {client.repository.ModelMetaNames.AUTHOR_NAME: \"Heba El-Shimy\", \n client.repository.ModelMetaNames.NAME: \"Credit Card Approval Model\"}"
},
{
"execution_count": 128,
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": "published_model = client.repository.store_model(model=model, meta_props=model_props, \\\n training_data=X_train_scaled, training_target=y_train)"
},
{
"execution_count": 129,
"cell_type": "code",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "\n\n#######################################################################################\n\nSynchronous deployment creation for uid: 'b76d078b-667f-4333-96a6-a63a42befecf' started\n\n#######################################################################################\n\n\nINITIALIZING\nDEPLOY_SUCCESS\n\n\n------------------------------------------------------------------------------------------------\nSuccessfully finished deployment creation, deployment_uid='b76d078b-667f-4333-96a6-a63a42befecf'\n------------------------------------------------------------------------------------------------\n\n\n"
}
],
"source": "# Create model deployment\n\npublished_model_uid = client.repository.get_model_uid(published_model)\ncreated_deployment = client.deployments.create(published_model_uid, \"Deployment of Credit Card Approval Model\")"
},
{
"execution_count": 130,
"cell_type": "code",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "https://ibm-watson-ml.mybluemix.net/v3/wml_instances/461ebac2-ce8e-44e6-b3b9-b6f42a5f31b6/published_models/c76ac134-1259-440b-aab6-1009369a1967/deployments/b76d078b-667f-4333-96a6-a63a42befecf/online\n"
}
],
"source": "# Get Scoring URL\nscoring_endpoint = client.deployments.get_scoring_url(created_deployment)\n\nprint(scoring_endpoint)"
},
{
"execution_count": 131,
"cell_type": "code",
"metadata": {
"scrolled": true
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "{\n \"entity\": {\n \"author\": {\n \"name\": \"Heba El-Shimy\"\n },\n \"evaluation_metrics_url\": \"https://ibm-watson-ml.mybluemix.net/v3/wml_instances/461ebac2-ce8e-44e6-b3b9-b6f42a5f31b6/published_models/c76ac134-1259-440b-aab6-1009369a1967/evaluation_metrics\",\n \"learning_iterations_url\": \"https://ibm-watson-ml.mybluemix.net/v3/wml_instances/461ebac2-ce8e-44e6-b3b9-b6f42a5f31b6/published_models/c76ac134-1259-440b-aab6-1009369a1967/learning_iterations\",\n \"training_data_schema\": {\n \"labels\": {\n \"type\": \"ndarray\",\n \"fields\": [\n {\n \"type\": \"int\",\n \"name\": \"l1\"\n }\n ]\n },\n \"features\": {\n \"type\": \"ndarray\",\n \"fields\": [\n {\n \"type\": \"float\",\n \"name\": \"f0\"\n },\n {\n \"type\": \"float\",\n \"name\": \"f1\"\n },\n {\n \"type\": \"float\",\n \"name\": \"f2\"\n },\n {\n \"type\": \"float\",\n \"name\": \"f3\"\n },\n {\n \"type\": \"float\",\n \"name\": \"f4\"\n },\n {\n \"type\": \"float\",\n \"name\": \"f5\"\n },\n {\n \"type\": \"float\",\n \"name\": \"f6\"\n },\n {\n \"type\": \"float\",\n \"name\": \"f7\"\n },\n {\n \"type\": \"float\",\n \"name\": \"f8\"\n },\n {\n \"type\": \"float\",\n \"name\": \"f9\"\n },\n {\n \"type\": \"float\",\n \"name\": \"f10\"\n },\n {\n \"type\": \"float\",\n \"name\": \"f11\"\n },\n {\n \"type\": \"float\",\n \"name\": \"f12\"\n },\n {\n \"type\": \"float\",\n \"name\": \"f13\"\n },\n {\n \"type\": \"float\",\n \"name\": \"f14\"\n },\n {\n \"type\": \"float\",\n \"name\": \"f15\"\n },\n {\n \"type\": \"float\",\n \"name\": \"f16\"\n },\n {\n \"type\": \"float\",\n \"name\": \"f17\"\n },\n {\n \"type\": \"float\",\n \"name\": \"f18\"\n },\n {\n \"type\": \"float\",\n \"name\": \"f19\"\n }\n ]\n }\n },\n \"name\": \"Credit Card Approval Model\",\n \"deployments\": {\n \"count\": 1,\n \"url\": \"https://ibm-watson-ml.mybluemix.net/v3/wml_instances/461ebac2-ce8e-44e6-b3b9-b6f42a5f31b6/published_models/c76ac134-1259-440b-aab6-1009369a1967/deployments\"\n },\n \"label_col\": \"l1\",\n \"learning_configuration_url\": \"https://ibm-watson-ml.mybluemix.net/v3/wml_instances/461ebac2-ce8e-44e6-b3b9-b6f42a5f31b6/published_models/c76ac134-1259-440b-aab6-1009369a1967/learning_configuration\",\n \"model_type\": \"scikit-learn-0.19\",\n \"deployed_version\": {\n \"guid\": \"8fbde70a-56af-423b-a38e-f68c5f4731a8\",\n \"url\": \"https://ibm-watson-ml.mybluemix.net/v3/ml_assets/models/c76ac134-1259-440b-aab6-1009369a1967/versions/8fbde70a-56af-423b-a38e-f68c5f4731a8\"\n },\n \"runtime_environment\": \"python-3.5\",\n \"feedback_url\": \"https://ibm-watson-ml.mybluemix.net/v3/wml_instances/461ebac2-ce8e-44e6-b3b9-b6f42a5f31b6/published_models/c76ac134-1259-440b-aab6-1009369a1967/feedback\",\n \"input_data_schema\": {\n \"labels\": {\n \"type\": \"ndarray\",\n \"fields\": [\n {\n \"type\": \"int\",\n \"name\": \"l1\"\n }\n ]\n },\n \"features\": {\n \"type\": \"ndarray\",\n \"fields\": [\n {\n \"type\": \"float\",\n \"name\": \"f0\"\n },\n {\n \"type\": \"float\",\n \"name\": \"f1\"\n },\n {\n \"type\": \"float\",\n \"name\": \"f2\"\n },\n {\n \"type\": \"float\",\n \"name\": \"f3\"\n },\n {\n \"type\": \"float\",\n \"name\": \"f4\"\n },\n {\n \"type\": \"float\",\n \"name\": \"f5\"\n },\n {\n \"type\": \"float\",\n \"name\": \"f6\"\n },\n {\n \"type\": \"float\",\n \"name\": \"f7\"\n },\n {\n \"type\": \"float\",\n \"name\": \"f8\"\n },\n {\n \"type\": \"float\",\n \"name\": \"f9\"\n },\n {\n \"type\": \"float\",\n \"name\": \"f10\"\n },\n {\n \"type\": \"float\",\n \"name\": \"f11\"\n },\n {\n \"type\": \"float\",\n \"name\": \"f12\"\n },\n {\n \"type\": \"float\",\n \"name\": \"f13\"\n },\n {\n \"type\": \"float\",\n \"name\": \"f14\"\n },\n {\n \"type\": \"float\",\n \"name\": \"f15\"\n },\n {\n \"type\": \"float\",\n \"name\": \"f16\"\n },\n {\n \"type\": \"float\",\n \"name\": \"f17\"\n },\n {\n \"type\": \"float\",\n \"name\": \"f18\"\n },\n {\n \"type\": \"float\",\n \"name\": \"f19\"\n }\n ]\n }\n },\n \"latest_version\": {\n \"created_at\": \"2018-05-10T18:30:13.631Z\",\n \"guid\": \"8fbde70a-56af-423b-a38e-f68c5f4731a8\",\n \"url\": \"https://ibm-watson-ml.mybluemix.net/v3/ml_assets/models/c76ac134-1259-440b-aab6-1009369a1967/versions/8fbde70a-56af-423b-a38e-f68c5f4731a8\"\n }\n },\n \"metadata\": {\n \"created_at\": \"2018-05-10T18:30:13.578Z\",\n \"guid\": \"c76ac134-1259-440b-aab6-1009369a1967\",\n \"modified_at\": \"2018-05-10T18:30:42.254Z\",\n \"url\": \"https://ibm-watson-ml.mybluemix.net/v3/wml_instances/461ebac2-ce8e-44e6-b3b9-b6f42a5f31b6/published_models/c76ac134-1259-440b-aab6-1009369a1967\"\n }\n}\n"
}
],
"source": "# Get model details and expected input\nmodel_details = client.repository.get_details(published_model_uid)\nprint(json.dumps(model_details, indent=2))"
},
{
"source": "### Sending data to the model\nSending new data (may be collected from web/mobile app) in the format the model is excpecting as shown above.\nWe get back a response with the predicted class (0 - Credit Card Application will be rejected)\nand probabilities of both classes (0 or Application Rejection has a probability of 1 which is very high, 1 or Application Acceptance has a probability of 5.096701256722081e-98 which is very low. This gives us an idea about the model's confidence of its predictions.",
"cell_type": "markdown",
"metadata": {}
},
{
"source": "![postman](https://github.com/HebaNAS/IBM-Watson-Studio-Enablement/blob/master/CreditCardApprovalModel/imgs/API-Call.jpg?raw=true)",
"cell_type": "markdown",
"metadata": {}
},
{
"source": "## References:\n\n#### <a name=\"first\" id=\"first\"></a><sub>[1] https://www.sciencedirect.com/science/article/abs/pii/S0148296318301231 \"Customer churn prediction in telecommunication industry using data certainty\"</sub> \n#### <a name=\"second\" id=\"second\"></a><sub>[2] https://www.signal.co/blog/understanding-customer-churn/ \"10 Stats Expose the Real Connection Between Customer Experience and Customer Churn\"</sub> \n#### <a name=\"third\" id=\"third\"></a><sub>[3] https://www.pinterest.com/pin/456904324667676431/ \"Mobile Telco Churn Infographic\"</sub> \n#### <sub>[4] https://pandas.pydata.org/pandas-docs/stable/ \"Pandas Documentation\"</sub> \n#### <sub>[5] http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Imputer.html \"Scikit-Learn Imputer\"</sub> \n#### <sub>[6] https://github.com/ibm-watson-data-lab/pixiedust/wiki/Tutorial:-Extending-the-PixieDust-Visualization \"PixieDust Documentation\"</sub>\n#### <sub>[7] https://seaborn.pydata.org/ \"Seaborn Documentation\"</sub>\n#### <sub>[8] http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html#sklearn.preprocessing.LabelEncoder \"Scikit-Learn LabelEncoder\"</sub>\n#### <sub>[9] http://colingorrie.github.io/outlier-detection.html \"Outlier Detection Methods\"</sub>\n#### <sub>[10] http://scikit-learn.org/stable/auto_examples/linear_model/plot_polynomial_interpolation.html#sphx-glr-auto-examples-linear-model-plot-polynomial-interpolation-py \"Scikit-Learn Polynomial\"</sub>\n#### <sub>[11] http://scikit-learn.org/stable/modules/feature_selection.html \"Scikit-Learn Feature Selection\"</sub>\n#### <sub>[12] http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler \"Scikit-Learn StandardScaler\"</sub>\n#### <sub>[13] http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC \"Scikit-Learn SVC\"</sub>\n#### <sub>[14] http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression \"Scikit-Learn Logistic Regression\"</sub>\n#### <sub>[15] http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html \"Scikit-Learn MLP Classifier\"</sub>\n#### <sub>[16] http://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html#sklearn.metrics.accuracy_score \"Scikit-Learn Accuracy Score\"</sub>\n#### <sub>[17] http://scikit-learn.org/stable/modules/generated/sklearn.metrics.average_precision_score.html#sklearn.metrics.average_precision_score \"Scikit-Learn Average Precision Score\"</sub>\n#### <sub>[18] https://www.sciencedirect.com/science/article/pii/S016786550500303X \"An introduction to ROC analysis\"</sub>\n#### <sub>[19] https://wml-api-pyclient.mybluemix.net/ \"Watson Machine Learning Client Documentation\"</sub>\n#### <sub>[20] https://dataplatform.ibm.com/docs/content/analyze-data/ml-deploy-notebook.html?context=analytics \"IBM Watson Studio Documentation-Deploy a model from a notebook\"</sub>",
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
}
},
{
"execution_count": null,
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": ""
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.5",
"name": "python3",
"language": "python"
},
"language_info": {
"mimetype": "text/x-python",
"nbconvert_exporter": "python",
"version": "3.5.4",
"name": "python",
"file_extension": ".py",
"pygments_lexer": "ipython3",
"codemirror_mode": {
"version": 3,
"name": "ipython"
}
},
"celltoolbar": "Slideshow"
},
"nbformat": 4
}