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@h5li
Last active February 11, 2019 18:40
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data Exploration Analysis\n",
"#### Hello! Today we are going to show how to do data exploration analysis. Data exploration analysis is a critical part in participating Kaggle competitions and also successfully fitting a model. Not only it can give you an idea on how to clean the data but also tells you how data are distributed and which model are probably the best one for this kind of dataset. So, let's get started!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Note that this dataset is a multiclass dataset. Lower Labels mean they are more poor and higher labels means they are richer."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib import rc"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Read in all the data we have\n",
"train_data = pd.read_csv('train.csv')\n",
"test_data = pd.read_csv('test.csv')\n",
"data = train_data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### First, let's take a look at the Monthly Payment. If a family rent a house, they are going to pay monthly. If a family owns a house, then their monthly payment would be 0."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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1wPfm+8aTFpbHhGvMEZ4SwhqHcKktfuJycfv2JWujMWb5mU9P+DRgm6o+oapF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\nGFNHFsLGGFNHFsLGGFNH/z8hU1MTZIkvoQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11416ceb8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"p = sns.FacetGrid(data = train_data, hue = 'Target', size = 5, legend_out=True)\n",
"p = p.map(sns.kdeplot, 'v2a1')\n",
"plt.legend()\n",
"plt.title(\"Monthly Payment\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### We can see that only target 4 have monthly payments over 300000$, which is an indicative feature for target 4. Other three labels have a strong overlap, which means that we need more features to classify.\n",
"\n",
"#### Before taking a look at other features, let's take a look at how monthly payment is correlated with target labels"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x110948f98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Let's plot a regression plot.\n",
"sns.regplot(x = train_data['Target'], y = train_data['v2a1'])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Clearly, the almost horizontal line shows the uncorrelation. So we might need to remove this feature to avoid causing confusion.\n",
"#### Next, let's take a look at the walls of houses."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Wall Meterial Features\n",
"wall_material = ['paredblolad','paredzocalo','paredpreb','pareddes','paredmad','paredzinc','paredfibras','paredother']"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x1a19106be0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x116eee748>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x1a19039080>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x117622550>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x1173496a0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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/21+OiIWI+DnwM5KQ6FSNHPPbgc8BRMQPgEGSGkGbVUP/3k/XZgyFHwIXS3qm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\nh4KZmWUcCmZmlnEomJlZxqFgZmaZ/w96ptJewDKT/wAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x114183f60>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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OkFy86z+vTteaZrn/35els1FvtIZU+42/colVPW1aSd3nI+nlwCjw+7n2KH+L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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1174593c8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x1174831d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Wall Regression plot\n",
"for f in wall_material:\n",
" sns.regplot(x = train_data[f], y = train_data['Target'])\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Given that there are so many types of wall. we need to make an order of wall materials to implicitly indicate the price of houses. We are going to construct the order based on how each material is correlated with four target labesl and sort them by the correlation coefficients."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# This is final comparision of wall price results\n",
"def wall_price(data):\n",
" if data['paredblolad'] == 1:\n",
" return 12\n",
" elif data['paredpreb'] == 1:\n",
" return 10\n",
" elif data['paredzocalo'] == 1:\n",
" return 9\n",
" elif data['paredother'] == 1:\n",
" return 8\n",
" elif data['paredmad'] == 1:\n",
" return 6\n",
" elif data['paredzinc'] == 1:\n",
" return 4\n",
" elif data['pareddes'] == 1:\n",
" return 2\n",
" elif data['paredfibras'] == 1:\n",
" return 1\n",
" else:\n",
" print('Exceptions')\n",
"# We are going to use the conditions of wall to further estimate the current value of house\n",
"def wall_condition(data):\n",
" if data['epared1'] == 1:\n",
" return 1\n",
" elif data['epared2'] == 1:\n",
" return 2\n",
" elif data['epared3'] == 1:\n",
" return 3\n",
" else:\n",
" print('Exceptions!')\n",
"# Generate a feature called wall\n",
"def wall (data):\n",
" a = data['wall_materials'] * data['wall_conditions']\n",
" return a"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data['wall_conditions'] = data.apply(lambda row: wall_condition(row),axis=1)\n",
"data['wall_materials'] = data.apply(lambda row: wall_price(row),axis = 1)\n",
"data['wall'] = data.apply(lambda row: wall(row),axis = 1)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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FWke+xuSjlmL55z+6e0YAzPfso3L6W8mHYtWx2F6nmIr9nsx9xiH8stbR0eGd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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1a1914a4e0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.regplot(x = data['wall'], y = train_data['Target'])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Now our newly generated wall feature have a high correlation with target labels right now. Wall prices high indicate high target labels."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Now I am going to apply the same process on the floor and roof of houses."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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PmVkZsN/MHnX3gxPavAfYnPi6Afi7xPcF96EvNbHneE/wfOeGSh78aOO8jlF3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\nmk0+AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1a18a2fa90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"floor_material = ['pisomoscer','pisocemento','pisoother','pisonatur','pisonotiene','pisomadera']\n",
"def floor_price(data):\n",
" if data['pisoother'] == 1:\n",
" return 6\n",
" elif data['pisomoscer'] == 1:\n",
" return 5\n",
" elif data['pisocemento'] == 1:\n",
" return 4\n",
" elif data['pisomadera'] == 1:\n",
" return 3\n",
" elif data['pisonotiene'] == 1:\n",
" return 2\n",
" elif data['pisonatur'] == 1:\n",
" return 1\n",
" else:\n",
" print('Exceptions!')\n",
"data['floor_materials'] = data.apply(lambda row: floor_price(row),axis = 1)\n",
"def floor_condition(data):\n",
" if data['etecho1'] == 1:\n",
" return 1\n",
" elif data['etecho2'] == 1:\n",
" return 2\n",
" elif data['etecho3'] == 1:\n",
" return 3\n",
" else:\n",
" print('Exceptions!')\n",
"data['floor_conditions'] = data.apply(lambda row: floor_condition(row),axis=1)\n",
"def floor (data):\n",
" a = data['floor_materials'] * data['floor_conditions']\n",
" return a\n",
"data['floor'] = data.apply(lambda row: floor(row),axis = 1)\n",
"# Draw the correlation plot for housing floor materials\n",
"sns.regplot(x = data['floor'], y = train_data['Target'])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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JZYU6aSxDoqQgkmPcnTdb2nj4pX00bG3u07xmxeJqPlo/l4tnVOiksQybkoJIjujojvDU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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1a18a23400>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"roof_material = ['techozinc','techoentrepiso','techocane','techootro']\n",
"def roof_price(data):\n",
" if data['techootro'] == 1:\n",
" return 8\n",
" elif data['techoentrepiso'] == 1:\n",
" return 6\n",
" elif data['techozinc'] == 1:\n",
" return 4\n",
" elif data['techocane'] == 1:\n",
" return 2\n",
" else:\n",
" return 0\n",
"train_data['roof_materials'] = train_data.apply(lambda row: roof_price(row),axis = 1)\n",
"def roof_condition(data):\n",
" if data['eviv1'] == 1:\n",
" return 1\n",
" elif data['eviv2'] == 1:\n",
" return 2\n",
" elif data['eviv3'] == 1:\n",
" return 3\n",
" else:\n",
" print('Exceptions!')\n",
"train_data['roof_conditions'] = train_data.apply(lambda row: roof_condition(row),axis=1)\n",
"def roof (data):\n",
" a = data['roof_materials'] * data['roof_conditions']\n",
" return a\n",
"train_data['roof'] = train_data.apply(lambda row: roof(row),axis = 1)\n",
"sns.regplot(x = data['roof'], y = train_data['Target'])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### We have evaluated each part of houses. Let's take a look at the how estimated housing price correlated with target labels."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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m6hbMilcKDZaSlhT48Pt0VCAi0yOVbi7CZlYGXAEUJMx6Y5yn3gT8IfC+me2O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\nXTP7RexxtsZ5xMzeN7PdZtY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"text/plain": [
"<matplotlib.figure.Figure at 0x1a18841c50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.regplot(x = train_data['floor'] + train_data['wall'] + train_data['roof'], y = train_data['Target'])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Looks Good, let's compare with other feature and check if we have generated a more indicative feature!"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/androidap/anaconda3/lib/python3.6/site-packages/seaborn/categorical.py:1460: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
" stat_data = remove_na(group_data)\n"
]
},
{
"data": {
"image/png": 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qOmmE9UmSJEmSJGmMrbS5lGQ94A3ACVV1MnDyyKuSJEmSJEnSRFjp1eKaUUtv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\na38B3+rNsVx32RcAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1a18a2fd68>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"train_data['Estimated_house'] = (train_data['floor'] + train_data['wall'] + train_data['roof'])\n",
"corrmat = train_data.dropna().corr().abs()['Target'].sort_values(ascending=False).drop('Target')\n",
"f, ax = plt.subplots(figsize=(20, 6))\n",
"plt.xticks(rotation='90')\n",
"sns.barplot(x=corrmat.head(50).index, y=corrmat.head(50))\n",
"plt.xlabel('Features', fontsize=15)\n",
"plt.ylabel('Abs correlation with Target variable', fontsize=15)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Compared with previous sparse and one hot encoding features of houses, our newly generated house have a much better correlation with other features!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Challenge\n",
"#### Try to explore features of education and generate a more better one!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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