{
"cells": [
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"cell_type": "markdown",
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"source": [
"### My first Kaggle competition\n",
"\n",
"It's time! After embarking on a Machine Learning class this semester, and with a Saturday in which I don't have much planned, I wanted to put this class and training to work. It's my first competition submission. I want to walk you guys through how I'm approaching this problem, because I thought it would be really neat. The competition is Banco Santander's [Santander Customer Satisfaction][1] competition. It seemed like an easy enough problem I could actually make decent progress on it.\n",
"\n",
"# Data Exploration\n",
"\n",
"First up: we need to load our data and do some exploratory work. Because we're going to be using this data for model selection prior to testing, we need to make a further split. I've already gone ahead and done this work, please see the code in the [appendix below](#Appendix).\n",
"\n",
"[1]: https://www.kaggle.com/c/santander-customer-satisfaction"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"# Record how long it takes to run the notebook - I'm curious.\n",
"from datetime import datetime\n",
"start = datetime.now()\n",
"\n",
"dataset = pd.read_csv('split_train.csv')\n",
"dataset.index = dataset.ID\n",
"X = dataset.drop(['TARGET', 'ID', 'ID.1'], 1)\n",
"y = dataset.TARGET"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 1], dtype=int64)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y.unique()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"369"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(X.columns)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Okay, so there are only [two classes we're predicting][2]: 1 for unsatisfied customers, 0 for satisfied customers. I would have preferred this to be something more like a regression, or predicting multiple classes: maybe the customer isn't the most happy, but is nowhere near closing their accounts. For now though, that's just the data we're working with.\n",
"\n",
"Now, I'd like to make a scatter matrix of everything going on. Unfortunately as noted above, we have 369 different features. There's no way I can graphically make sense of that much data to start with.\n",
"\n",
"We're also not told what the data actually represents: Are these survey results? Average time between contact with a customer care person? Frequency of contacting a customer care person? The idea is that I need to reduce the number of dimensions we're predicting across.\n",
"\n",
"## Dimensionality Reduction pt. 1 - Binary Classifiers\n",
"\n",
"My first attempt to reduce the data dimensionality is to find all the binary classifiers in the dataset \\(i.e. 0 or 1 values\\) and see if any of those are good \\(or anti-good\\) predictors of the final data.\n",
"\n",
"[2]: https://www.kaggle.com/c/santander-customer-satisfaction/data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"111"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cols = X.columns\n",
"b_class = []\n",
"for c in cols:\n",
" if len(X[c].unique()) == 2:\n",
" b_class.append(c)\n",
" \n",
"len(b_class)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So there are 111 features in the dataset that are a binary label. Let's see if any of them are good at predicting the users satisfaction!"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"\n",
"
\n",
" \n",
" \n",
" | \n",
" Accuracy | \n",
"
\n",
" \n",
" \n",
" \n",
" | count | \n",
" 111.000000 | \n",
"
\n",
" \n",
" | mean | \n",
" 0.905159 | \n",
"
\n",
" \n",
" | std | \n",
" 0.180602 | \n",
"
\n",
" \n",
" | min | \n",
" 0.043598 | \n",
"
\n",
" \n",
" | 25% | \n",
" 0.937329 | \n",
"
\n",
" \n",
" | 50% | \n",
" 0.959372 | \n",
"
\n",
" \n",
" | 75% | \n",
" 0.960837 | \n",
"
\n",
" \n",
" | max | \n",
" 0.960837 | \n",
"
\n",
" \n",
"
\n",
"