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jam1245jam1245
committedJun 21, 2017
data mining
1 parent c7d3b67 commit 344f3ed

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‎Association_Rules_and_Frequent_Pattern_Mining.ipynb

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‎Exercises_Auto_Sales.ipynb

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‎Practice_crime.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Module 2 practice - Minneapolis crime data\n",
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"\n",
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"The dataset used in the notebook (curtesy of Open Data Minneapolis) includes information about 311 calls and crimes committed between 2010 to 2016. \n",
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"\n",
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"We will use the data to do some association rule mining for finding frequent patterns. Read the data from /dsa/data/DSA-8630/minneapolis_crimedata/crimes.csv"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"crimes_data = read.csv('/dsa/data/DSA-8630/minneapolis_crimedata/crimes.csv')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"head(crimes_data,2)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false,
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"dim(crimes_data)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The columns \n",
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"- `controlnbr`\n",
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"- `CCN`\n",
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"- `Time`\n",
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"- `ReportedDate`\n",
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"- `Offense`\n",
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"- `UCRCode`\n",
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"- `EnteredDate`\n",
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"- `x`\n",
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"- `y`\n",
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"- `lastchanged`\n",
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"- `LastUpdateDate`\n",
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"- `OBJECTID`\n",
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"- `ESRI_OID` \n",
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"\n",
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"are not helpful or interpretable anyway. So lets just delete them from dataset."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Activity 1: ** Remove the columns listed avove from dataframe. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# Your code for activity 1 goes here..\n",
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"\n",
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"crimes_data = crimes_data[,-c(2, 3, 5, 7, 8, 10, 11,12, 13, 14, 15, 17, 18, 19, 20)]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Activity 2: ** The BeginDate columnis of type factor. Convert its type to POSIXlt using strptime() function,. Also, replace the character \"T\" in the column with a white space \" \". \n",
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"\n",
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"We are trying to format the date and time values appropriately using strptime() function."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# Your code for activity 2 goes here..\n",
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"\n",
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"crimes_data$BeginDate = strptime(sub(\"T\",\" \", crimes_data$BeginDate), format = \"%Y-%m-%d %X\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The first 6 characters in address doesn't make any sense. \n",
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"\n",
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"**Activity 3: ** Strip the first 7 characters or extract rest of the characters from the publicaddress column."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# Your code for activity 3 goes here..\n",
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"\n",
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"crimes_data$publicaddress = substr(crimes_data$publicaddress,7,(length(crimes_data$publicaddress)-7))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Activity 4:** Extract the date from BeginDate column and stored it as a new column called date. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# Your code for activity 4 goes here..\n",
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"\n",
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"library(lubridate)\n",
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"crimes_data$date = as.Date(format(crimes_data$BeginDate,\"%Y-%m-%d\"))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"class(crimes_data$date)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Activity 5:** Extract weekday from date and hour from from BeginDate column and stored them as new columns called weekday and hour respectively. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# Your code for activity 5 goes here..\n",
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"\n",
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"crimes_data$weekday = weekdays(crimes_data$date)\n",
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"crimes_data$hour = hour(crimes_data$BeginDate)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We dont need the BeginDate column any more. So lets just delete it from dataframe."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"crimes_data = crimes_data[,names(crimes_data)!=\"BeginDate\"]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Convert the hour variable into an ordered factor with levels \"mid night\", \"morning\", \"noon\",\"night\" for different hours of the day. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"crimes_data[[\"hour\"]] <- ordered(cut(crimes_data[[\"hour\"]], c(1,6,12,18,24)), labels = c(\"mid night\", \"morning\", \"noon\",\"night\"))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": false
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},
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"source": [
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"**Activity 6: ** Convert the columns \"publicaddress\", \"Precinct\", \"weekday\", \"date\" into factor type. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# Your code for activity 6 goes here..\n",
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"\n",
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"crimes_data[\"publicaddress\"] = as.factor(crimes_data[[\"publicaddress\"]])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"crimes_data[\"Precinct\"] = as.factor(crimes_data[[\"Precinct\"]])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"crimes_data[\"weekday\"] = as.factor(crimes_data[[\"weekday\"]])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"crimes_data[\"date\"] = as.factor(crimes_data[[\"date\"]])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"library(\"arules\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"str(crimes_data)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Activity 7: ** Now, coerce the data set into transactions. Save this transactions to crimes_trans variable."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# Your code for activity 7 goes here..\n",
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"\n",
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"crimes_trans <- as(crimes_data, \"transactions\")\n",
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"crimes_trans"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"summary(crimes_trans)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Activity 8: ** Generate association rules for the transactions in crimes_trans with support of 0.01 and confidence of 0.6"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# Your code for activity 8 goes here..\n",
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"\n",
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"rules <- apriori(crimes_trans, parameter = list(support = 0.01, confidence = 0.6))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Activity 9: ** Display the generated rules using inspect(). "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# Your code for activity 9 goes here..\n",
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"\n",
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"inspect(rules)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "R",
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"language": "R",
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"name": "ir"
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},
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"language_info": {
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"codemirror_mode": "r",
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"file_extension": ".r",
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"mimetype": "text/x-r-source",
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"name": "R",
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"pygments_lexer": "r",
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"version": "3.3.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎Practice_glass.ipynb

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‎Practice_groceries.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"groceries_data = read.csv(\"../../../datasets/groceries.csv\",head=FALSE,col.names=c(\"item1\",\"item2\",\"item3\",\"item4\"))\n",
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"head(groceries_data)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"summary(groceries_data)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"library(\"arules\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"groceries_trans <- as(groceries_data, \"transactions\")\n",
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"groceries_trans"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"summary(groceries_trans)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"itemFrequencyPlot(groceries_trans, support = 0.02, cex.names=0.8)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"rules <- apriori(groceries_trans, parameter = list(support = 0.01, confidence = 0.01))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"summary(rules)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"inspect(head(rules, n = 3, by = \"confidence\"))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"write(rules, file = \"groceries_rules.csv\", sep = \",\", col.names = NA)"
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]
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}
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],
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"metadata": {
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"anaconda-cloud": {},
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"kernelspec": {
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"display_name": "R",
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"language": "R",
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"name": "ir"
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},
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"language_info": {
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"codemirror_mode": "r",
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"file_extension": ".r",
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"mimetype": "text/x-r-source",
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"name": "R",
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"pygments_lexer": "r",
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"version": "3.3.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}

‎Visualizing_Association_Rules.ipynb

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‎data.csv

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‎glass.txt

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211,1.51685,14.92,0.00,1.99,73.06,0.00,8.40,1.59,0.00,7
212+
212,1.52065,14.36,0.00,2.02,73.42,0.00,8.44,1.64,0.00,7
213+
213,1.51651,14.38,0.00,1.94,73.61,0.00,8.48,1.57,0.00,7
214+
214,1.51711,14.23,0.00,2.08,73.36,0.00,8.62,1.67,0.00,7

‎header.txt

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1. Id number: 1 to 214
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2. RI: refractive index
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3. Na: Sodium (unit measurement: weight percent in corresponding oxide, as
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are attributes 4-10)
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4. Mg: Magnesium
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5. Al: Aluminum
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6. Si: Silicon
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7. K: Potassium
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8. Ca: Calcium
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9. Ba: Barium
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10. Fe: Iron
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11. Type of glass: (class attribute)
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-- 1 building_windows_float_processed
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-- 2 building_windows_non_float_processed
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-- 3 vehicle_windows_float_processed
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-- 4 vehicle_windows_non_float_processed (none in this database)
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-- 5 containers
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-- 6 tableware
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-- 7 headlamps

‎market

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Bread, Milk
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Bread, Diapers, Beer, Eggs
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Milk, Diapers, Beer, Cola
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Bread, Milk, Diapers, Beer
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Bread, Milk, Diapers, Cola

‎readme.txt

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1. Title: Glass Identification Database
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3+
2. Sources:
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(a) Creator: B. German
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-- Central Research Establishment
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Home Office Forensic Science Service
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Aldermaston, Reading, Berkshire RG7 4PN
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(b) Donor: Vina Spiehler, Ph.D., DABFT
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Diagnostic Products Corporation
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(213) 776-0180 (ext 3014)
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(c) Date: September, 1987
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13+
3. Past Usage:
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-- Rule Induction in Forensic Science
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-- Ian W. Evett and Ernest J. Spiehler
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-- Central Research Establishment
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Home Office Forensic Science Service
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Aldermaston, Reading, Berkshire RG7 4PN
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-- Unknown technical note number (sorry, not listed here)
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-- General Results: nearest neighbor held its own with respect to the
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rule-based system
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4. Relevant Information:n
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Vina conducted a comparison test of her rule-based system, BEAGLE, the
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nearest-neighbor algorithm, and discriminant analysis. BEAGLE is
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a product available through VRS Consulting, Inc.; 4676 Admiralty Way,
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Suite 206; Marina Del Ray, CA 90292 (213) 827-7890 and FAX: -3189.
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In determining whether the glass was a type of "float" glass or not,
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the following results were obtained (# incorrect answers):
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Type of Sample Beagle NN DA
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Windows that were float processed (87) 10 12 21
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Windows that were not: (76) 19 16 22
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The study of classification of types of glass was motivated by
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criminological investigation. At the scene of the crime, the glass left
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can be used as evidence...if it is correctly identified!
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5. Number of Instances: 214
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6. Number of Attributes: 10 (including an Id#) plus the class attribute
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-- all attributes are continuously valued
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44+
7. Attribute Information:
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1. Id number: 1 to 214
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2. RI: refractive index
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3. Na: Sodium (unit measurement: weight percent in corresponding oxide, as
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are attributes 4-10)
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4. Mg: Magnesium
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5. Al: Aluminum
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6. Si: Silicon
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7. K: Potassium
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8. Ca: Calcium
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9. Ba: Barium
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10. Fe: Iron
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11. Type of glass: (class attribute)
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-- 1 building_windows_float_processed
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-- 2 building_windows_non_float_processed
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-- 3 vehicle_windows_float_processed
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-- 4 vehicle_windows_non_float_processed (none in this database)
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-- 5 containers
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-- 6 tableware
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-- 7 headlamps
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8. Missing Attribute Values: None
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Summary Statistics:
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Attribute: Min Max Mean SD Correlation with class
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2. RI: 1.5112 1.5339 1.5184 0.0030 -0.1642
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3. Na: 10.73 17.38 13.4079 0.8166 0.5030
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4. Mg: 0 4.49 2.6845 1.4424 -0.7447
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5. Al: 0.29 3.5 1.4449 0.4993 0.5988
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6. Si: 69.81 75.41 72.6509 0.7745 0.1515
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7. K: 0 6.21 0.4971 0.6522 -0.0100
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8. Ca: 5.43 16.19 8.9570 1.4232 0.0007
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9. Ba: 0 3.15 0.1750 0.4972 0.5751
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10. Fe: 0 0.51 0.0570 0.0974 -0.1879
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9. Class Distribution: (out of 214 total instances)
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-- 163 Window glass (building windows and vehicle windows)
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-- 87 float processed
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-- 70 building windows
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-- 17 vehicle windows
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-- 76 non-float processed
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-- 76 building windows
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-- 0 vehicle windows
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-- 51 Non-window glass
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-- 13 containers
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-- 9 tableware
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-- 29 headlamps
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92+
93+

‎rules.graphml

+15,918
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