diff --git a/G2M Case Study Virtual Internship.pdf b/G2M Case Study Virtual Internship.pdf new file mode 100644 index 00000000..740aa1d4 Binary files /dev/null and b/G2M Case Study Virtual Internship.pdf differ diff --git a/G2M Case Study Virtual Internship.pptx b/G2M Case Study Virtual Internship.pptx new file mode 100644 index 00000000..f68cea75 Binary files /dev/null and b/G2M Case Study Virtual Internship.pptx differ diff --git a/G2M insight for Cab Investment firm .ipynb b/G2M insight for Cab Investment firm .ipynb new file mode 100644 index 00000000..3184b41a --- /dev/null +++ b/G2M insight for Cab Investment firm .ipynb @@ -0,0 +1,1064 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "dd6035d3", + "metadata": {}, + "source": [ + "# 1. Introduccion\n", + "\n", + "XYZ is a private firm in US. Due to remarkable growth in the Cab Industry in last few years and multiple key players in the market, it is planning for an investment in Cab industry and as per their Go-to-Market(G2M) strategy they want to understand the market before taking final decision.\n", + "\n", + "Before choosing the problem above described, a little research has been conducted in order to decide the following items:\n", + "\n", + "* Which company has maximum cab users at a particular time period?\n", + "\n", + "* Does margin proportionally increase with increase in number of customers?\n", + "\n", + "* What are the attributes of these customer segments?These questions will be answered in the following section.\n", + "\n", + "The business problem that can be proposed is, given an investor interested in the cab industry , which cab company would be a good choice according to the methodology described below.\n" + ] + }, + { + "cell_type": "markdown", + "id": "8a7fc710", + "metadata": {}, + "source": [ + "# 2. Data\n", + "\n", + "Below are the list of datasets which are provided for the analysis:\n", + "\n", + "* Cab_Data.csv – this file includes details of transaction for 2 cab companies\n", + "\n", + "* Customer_ID.csv – this is a mapping table that contains a unique identifier which links the customer’s demographic details\n", + "\n", + "* Transaction_ID.csv – this is a mapping table that contains transaction to customer mapping and payment mode\n", + "\n", + "* City.csv – this file contains list of US cities, their population and number of cab users\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "id": "9d64f3d8", + "metadata": {}, + "outputs": [], + "source": [ + "library(forecast)\n", + "library(ggplot2)\n", + "theme_set(theme_bw())\n", + "library(gridExtra)\n", + "library(grid)\n", + "library(tseries)\n", + "library(aod)\n", + "library(seasonal)\n", + "library(lubridate)\n", + "library(dplyr)\n", + "library(readxl)\n", + "library(ggplot2)\n", + "library(GGally)\n", + "library(Hmisc)\n", + "library(corrplot)\n", + "library(PerformanceAnalytics)\n", + "library(readr)" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "id": "0eb6701d", + "metadata": {}, + "outputs": [], + "source": [ + "Cab <- read_csv(\"Cab_Data1.csv\", col_types = cols(`Date of Travel` = col_date(format = \"%d/%m/%Y\"),\n", + " Company = col_factor(levels = c(\"Pink Cab\",\"Yellow Cab\")), \n", + " City = col_factor(levels = c(\"NEW YORK NY\", \n", + " \"CHICAGO IL\", \"LOS ANGELES CA\", \"MIAMI FL\", \n", + " \"SILICON VALLEY\", \"ORANGE COUNTY\", \"SAN DIEGO CA\", \n", + " \"PHOENIX AZ\", \"DALLAS TX\", \"ATLANTA GA\", \n", + " \"DENVER CO\", \"AUSTIN TX\", \"SEATTLE WA\", \n", + " \"TUCSON AZ\", \"SAN FRANCISCO CA\", \"SACRAMENTO CA\", \n", + " \"PITTSBURGH PA\", \"WASHINGTON DC\", \"NASHVILLE TN\", \n", + " \"BOSTON MA\")),\n", + " `KM Travelled` = col_number(), \n", + " `Price Charged` = col_number(), \n", + " `Cost of Trip` = col_number()))\n", + "\n", + "City <- read_csv(\"City.csv\", col_types = cols(City = col_factor(levels = c(\"NEW YORK NY\", \n", + " \"CHICAGO IL\", \"LOS ANGELES CA\", \"MIAMI FL\", \n", + " \"SILICON VALLEY\", \"ORANGE COUNTY\", \"SAN DIEGO CA\", \n", + " \"PHOENIX AZ\", \"DALLAS TX\", \"ATLANTA GA\", \n", + " \"DENVER CO\", \"AUSTIN TX\", \"SEATTLE WA\", \n", + " \"TUCSON AZ\", \"SAN FRANCISCO CA\", \"SACRAMENTO CA\", \n", + " \"PITTSBURGH PA\", \"WASHINGTON DC\", \"NASHVILLE TN\", \n", + " \"BOSTON MA\")), \n", + " Population = col_number(), Users = col_number()))\n", + "\n", + "\n", + "Customer <- read_csv(\"Customer_ID.csv\", col_types = cols(Gender = col_factor(levels = c(\"Male\", \n", + " \"Female\")), \n", + " Age = col_number(),\n", + " `Income (USD/Month)` = col_number()))\n", + "\n", + "Transaction<- read_csv(\"Transaction_ID.csv\", \n", + " col_types = cols(Payment_Mode = col_factor(levels = c(\"Cash\",\"Card\"))))\n" + ] + }, + { + "cell_type": "markdown", + "id": "9e69aac4", + "metadata": {}, + "source": [ + "After exloring the four datasets understanding the names of the columns and identifing the relationships between them, we proceed to create a master dataset joining and ordering all the four previous dataset we have. The final result would be like:" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "id": "d7e1f751", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 15
Transaction IDCustomer IDGenderAgeIncome (USD/Month)CompanyCityPopulationUsersDate of TravelKM TravelledPrice ChargedCost of TripPayment_ModeProfit
<dbl><dbl><fct><dbl><dbl><fct><fct><dbl><dbl><date><dbl><dbl><dbl><fct><dbl>
11040753527001Female4511054Pink Cab ATLANTA GA814885247012018-11-1636.86497.40438.634Card 58.766
21029502927002Male 2516275Yellow CabATLANTA GA814885247012018-01-1921.66463.01272.916Card190.094
31014994827002Male 2516275Yellow CabATLANTA GA814885247012017-03-02 3.33 79.05 47.952Card 31.098
41034366527002Male 2516275Yellow CabATLANTA GA814885247012018-06-2943.29781.18 5.506Card775.674
51005650927003Female2618450Pink Cab ATLANTA GA814885247012016-07-2825.52363.44275.616Card 87.824
61033410927003Female2618450Yellow CabATLANTA GA814885247012018-05-2916.49289.83197.880Cash 91.950
\n" + ], + "text/latex": [ + "A data.frame: 6 × 15\n", + "\\begin{tabular}{r|lllllllllllllll}\n", + " & Transaction ID & Customer ID & Gender & Age & Income (USD/Month) & Company & City & Population & Users & Date of Travel & KM Travelled & Price Charged & Cost of Trip & Payment\\_Mode & Profit\\\\\n", + " & & & & & & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t1 & 10407535 & 27001 & Female & 45 & 11054 & Pink Cab & ATLANTA GA & 814885 & 24701 & 2018-11-16 & 36.86 & 497.40 & 438.634 & Card & 58.766\\\\\n", + "\t2 & 10295029 & 27002 & Male & 25 & 16275 & Yellow Cab & ATLANTA GA & 814885 & 24701 & 2018-01-19 & 21.66 & 463.01 & 272.916 & Card & 190.094\\\\\n", + "\t3 & 10149948 & 27002 & Male & 25 & 16275 & Yellow Cab & ATLANTA GA & 814885 & 24701 & 2017-03-02 & 3.33 & 79.05 & 47.952 & Card & 31.098\\\\\n", + "\t4 & 10343665 & 27002 & Male & 25 & 16275 & Yellow Cab & ATLANTA GA & 814885 & 24701 & 2018-06-29 & 43.29 & 781.18 & 5.506 & Card & 775.674\\\\\n", + "\t5 & 10056509 & 27003 & Female & 26 & 18450 & Pink Cab & ATLANTA GA & 814885 & 24701 & 2016-07-28 & 25.52 & 363.44 & 275.616 & Card & 87.824\\\\\n", + "\t6 & 10334109 & 27003 & Female & 26 & 18450 & Yellow Cab & ATLANTA GA & 814885 & 24701 & 2018-05-29 & 16.49 & 289.83 & 197.880 & Cash & 91.950\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 15\n", + "\n", + "| | Transaction ID <dbl> | Customer ID <dbl> | Gender <fct> | Age <dbl> | Income (USD/Month) <dbl> | Company <fct> | City <fct> | Population <dbl> | Users <dbl> | Date of Travel <date> | KM Travelled <dbl> | Price Charged <dbl> | Cost of Trip <dbl> | Payment_Mode <fct> | Profit <dbl> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| 1 | 10407535 | 27001 | Female | 45 | 11054 | Pink Cab | ATLANTA GA | 814885 | 24701 | 2018-11-16 | 36.86 | 497.40 | 438.634 | Card | 58.766 |\n", + "| 2 | 10295029 | 27002 | Male | 25 | 16275 | Yellow Cab | ATLANTA GA | 814885 | 24701 | 2018-01-19 | 21.66 | 463.01 | 272.916 | Card | 190.094 |\n", + "| 3 | 10149948 | 27002 | Male | 25 | 16275 | Yellow Cab | ATLANTA GA | 814885 | 24701 | 2017-03-02 | 3.33 | 79.05 | 47.952 | Card | 31.098 |\n", + "| 4 | 10343665 | 27002 | Male | 25 | 16275 | Yellow Cab | ATLANTA GA | 814885 | 24701 | 2018-06-29 | 43.29 | 781.18 | 5.506 | Card | 775.674 |\n", + "| 5 | 10056509 | 27003 | Female | 26 | 18450 | Pink Cab | ATLANTA GA | 814885 | 24701 | 2016-07-28 | 25.52 | 363.44 | 275.616 | Card | 87.824 |\n", + "| 6 | 10334109 | 27003 | Female | 26 | 18450 | Yellow Cab | ATLANTA GA | 814885 | 24701 | 2018-05-29 | 16.49 | 289.83 | 197.880 | Cash | 91.950 |\n", + "\n" + ], + "text/plain": [ + " Transaction ID Customer ID Gender Age Income (USD/Month) Company \n", + "1 10407535 27001 Female 45 11054 Pink Cab \n", + "2 10295029 27002 Male 25 16275 Yellow Cab\n", + "3 10149948 27002 Male 25 16275 Yellow Cab\n", + "4 10343665 27002 Male 25 16275 Yellow Cab\n", + "5 10056509 27003 Female 26 18450 Pink Cab \n", + "6 10334109 27003 Female 26 18450 Yellow Cab\n", + " City Population Users Date of Travel KM Travelled Price Charged\n", + "1 ATLANTA GA 814885 24701 2018-11-16 36.86 497.40 \n", + "2 ATLANTA GA 814885 24701 2018-01-19 21.66 463.01 \n", + "3 ATLANTA GA 814885 24701 2017-03-02 3.33 79.05 \n", + "4 ATLANTA GA 814885 24701 2018-06-29 43.29 781.18 \n", + "5 ATLANTA GA 814885 24701 2016-07-28 25.52 363.44 \n", + "6 ATLANTA GA 814885 24701 2018-05-29 16.49 289.83 \n", + " Cost of Trip Payment_Mode Profit \n", + "1 438.634 Card 58.766\n", + "2 272.916 Card 190.094\n", + "3 47.952 Card 31.098\n", + "4 5.506 Card 775.674\n", + "5 275.616 Card 87.824\n", + "6 197.880 Cash 91.950" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Cab_Transaction<-merge(Cab,Transaction,by=\"Transaction ID\")\n", + "Cab_Transaction_Customer<-merge(Cab_Transaction,Customer,by=\"Customer ID\")\n", + "Data<-merge(Cab_Transaction_Customer,City, by=\"City\")\n", + "Data2<-Data[,c(3,2,10,11,12,5,1,13,14,4,6,7,8,9)]\n", + "Profit<-Data2$`Price Charged`-Data2$`Cost of Trip`\n", + "Data2<-cbind(Data2,Profit)\n", + "write.csv(Data2, file=\"Data2.csv\")\n", + "head(Data2)" + ] + }, + { + "cell_type": "markdown", + "id": "5429aa84", + "metadata": {}, + "source": [ + "Now we proceed to identify each one of the columns, and what type of data they contain (quantitative, categorical):\n", + "\n", + "* Transacction ID and Curtomer ID: variables that are only used to identify each record, and that almost certainly will never be used in any statistical analysis (name or ID number of a person, for example).\n", + "\n", + "* Gender: Qualitative Binary variable with two levels of the factor (Male/Female).\n", + "\n", + "* Age: Quantitative Discrete variable\n", + "\n", + "* Income: Quantitative Continuous variable. Income per month of each client in USD\n", + "\n", + "* Company: Qualitative Variable (Binary). The Pink cab company and yeloow cab company.\n", + "\n", + "* City: Qualitative Nominal Variable. The citys where the two cab companys operate. 20 factorqs for this variable.\n", + "\n", + "* Population: Quantitative Discrete variable. Number of people of each city.\n", + "\n", + "* Users: Quantitative Discrete variable. Number of Users os the cab companys of each city.\n", + "\n", + "* Date of travel: Quantitative continuos variable\n", + "\n", + "* Km travelled: Quantitative continuous variable. KM travelled in each trip made.\n", + "\n", + "* Price charged: Quantitative continuous variable. Price in USD charged in each Trip.\n", + "\n", + "* Cost of Trip: Quantitative continuous variable. Cost in USD of each Trip.\n", + "\n", + "* Payment Mode: Qualitative Binary variable. Method of payement (Cach/Credit card)" + ] + }, + { + "cell_type": "markdown", + "id": "07af494d", + "metadata": {}, + "source": [ + "## 2.1.Identification and treatment of outliers and NA\n" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "id": "09f50199", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " Transaction ID Customer ID Gender Age \n", + " Min. :10000011 Min. : 1 Male :205912 Min. :18.00 \n", + " 1st Qu.:10110810 1st Qu.: 2705 Female:153480 1st Qu.:25.00 \n", + " Median :10221036 Median : 7459 Median :33.00 \n", + " Mean :10220761 Mean :19192 Mean :35.34 \n", + " 3rd Qu.:10330937 3rd Qu.:36078 3rd Qu.:42.00 \n", + " Max. :10440107 Max. :60000 Max. :65.00 \n", + " \n", + " Income (USD/Month) Company City \n", + " Min. : 2000 Pink Cab : 84711 NEW YORK NY :99885 \n", + " 1st Qu.: 8424 Yellow Cab:274681 CHICAGO IL :56625 \n", + " Median :14685 LOS ANGELES CA:48033 \n", + " Mean :15049 WASHINGTON DC :43737 \n", + " 3rd Qu.:21035 BOSTON MA :29692 \n", + " Max. :35000 SAN DIEGO CA :20488 \n", + " (Other) :60932 \n", + " Population Users Date of Travel KM Travelled \n", + " Min. : 248968 Min. : 3643 Min. :2016-01-02 Min. : 1.90 \n", + " 1st Qu.: 671238 1st Qu.: 80021 1st Qu.:2016-11-23 1st Qu.:12.00 \n", + " Median :1595037 Median :144132 Median :2017-09-10 Median :22.44 \n", + " Mean :3132198 Mean :158366 Mean :2017-08-17 Mean :22.57 \n", + " 3rd Qu.:8405837 3rd Qu.:302149 3rd Qu.:2018-05-12 3rd Qu.:32.96 \n", + " Max. :8405837 Max. :302149 Max. :2018-12-31 Max. :48.00 \n", + " \n", + " Price Charged Cost of Trip Payment_Mode Profit \n", + " Min. : 15.6 Min. : 1.001 Cash:143888 Min. :-909.2 \n", + " 1st Qu.: 206.4 1st Qu.: 4.833 Card:215504 1st Qu.: 33.4 \n", + " Median : 386.4 Median :178.542 Median : 136.7 \n", + " Mean : 423.4 Mean :214.311 Mean : 209.1 \n", + " 3rd Qu.: 583.7 3rd Qu.:367.710 3rd Qu.: 370.8 \n", + " Max. :2048.0 Max. :998.784 Max. :1975.5 \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "summary(Data2)\n", + "par(mfrow=c(2,2))\n", + "boxplot(Data2[,c(12:13,15)])\n", + "boxplot(Data2[,5])\n", + "boxplot(Data2[,8])\n", + "boxplot(Data2[,9])" + ] + }, + { + "cell_type": "markdown", + "id": "c33f231f", + "metadata": { + "scrolled": true + }, + "source": [ + "In this case we see that there are outliers for the variables Price charged, cost of the trip and profits. we are going to identify the outliers, and in this case we will substitute their value for the mean of the variable.\n", + "In this case we know that these outliers are due to human errors, since charging a trip for less than it costs is clearly an error.\n", + "Once we treat the outliers, our data will be as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "id": "7e0e0c25", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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CBIgAGCBBggSIABggQYIEiAAYIEGCBIgAGCBBggSIABggQYIEiAAYIEGCBIgAGC\nBBggSIABggQYIEiAAYIEGCBIgAGCBBggSIABggQY+D+lrmjXldfe7QAAAABJRU5ErkJggg==", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "m1<-which(Data2$`Price Charged`<=(206.4-1.5*IQR(Data2$`Price Charged`)))\n", + "m2<-which(Data2$`Price Charged`>=(583.7+1.5*IQR(Data2$`Price Charged`)))\n", + "Data2$`Price Charged`[m1]<-mean(Data2$`Price Charged`)\n", + "Data2$`Price Charged`[m2]<-mean(Data2$`Price Charged`)\n", + "\n", + "m3<-which(Data2$`Cost of Trip`<=(4.833-1.5*IQR(Data2$`Cost of Trip`)))\n", + "m4<-which(Data2$`Cost of Trip`>=(367.710+1.5*IQR(Data2$`Cost of Trip`)))\n", + "Data2$`Cost of Trip`[m3]<-mean(Data2$`Cost of Trip`) \n", + "Data2$`Cost of Trip`[m4]<-mean(Data2$`Cost of Trip`) \n", + "\n", + "\n", + "m5<-which(Data2$Profit<=(33.4 -1.5*IQR(Data2$Profit)))\n", + "m6<-which(Data2$Profit>=(370.8+1.5*IQR(Data2$Profit)))\n", + "Data2$Profit[m5]<-mean(Data2$Profit) \n", + "Data2$Profit[m6]<-mean(Data2$Profit) \n", + "boxplot(Data2[,c(12:13,15)])" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "id": "10132edd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [], + "text/latex": [], + "text/markdown": [], + "text/plain": [ + "integer(0)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "FALSE" + ], + "text/latex": [ + "FALSE" + ], + "text/markdown": [ + "FALSE" + ], + "text/plain": [ + "[1] FALSE" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "which(is.na(Data2))\n", + "anyNA(Data2)" + ] + }, + { + "cell_type": "markdown", + "id": "0c502cde", + "metadata": {}, + "source": [ + "As we can observe, no missing values has been identified." + ] + }, + { + "cell_type": "markdown", + "id": "11755fc7", + "metadata": {}, + "source": [ + "## 2.2 Graphic descriptive\n" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "id": "c523657f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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IZ25Nw6K7y7Z+30AqQYwXMVDkiANKA9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\n6v0JkN4FStdSbaqN9ucnnYCkO6cLl5y9Je3hpFMXIOk+vkR4LuI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SZq+mB0l5tTesbEDKgxIg\n9Wx8YAl3VlU2kgWjMUDqeqkHkNalBinZCs4IIHUVIG2ofIuiuHW2vZkeJBYbtpT0e8gaBUgs\nfyMDARIXZJGBAAkhAwESQgYCpH3162GaHn6/PpoOu/4XI8ooAqQ99TxPb3pYjiBN7P4o4kju\nqXn68bwsT/P06/hvQAojjuSO+j19f/v9NM1vEL2enP5N316fev+FRhUg7ajv05/jg7/LCaTl\ncXpaXhn7ec/MkFaAtKMuRnIfc6S/bzOmE2NoTAHSjloF6cDQ4QQ1sX43tgBpR62D9Pcwc3qa\nftwrKWQiQNpRn+O3P+fL39+m5+NECY0rQNpRH6t2f+Yf5yA9TY8zx2FwcQD31Ok60t+LC7Lf\njpdo0cACpD31/O14Z8Pj8gHS2xrD0zT9vnNmSClA2ldPP+bze+3eF+um6fmuWSG1AMmB/nBb\nw/ACJAd6YM1ueAHS3TWx1BBAgHR3ze+L4mhkARJCBgIkhAwESAgZCJAQMhAgIWQgQELIQICE\nkIEACSED/T+Kiz5IL4iHbAAAAABJRU5ErkJggg==", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "ggplot(Data2, aes(x=(City),fill=Company, y=Profit)) +\n", + " geom_bar(stat=\"identity\")+\n", + " theme(axis.text.x = element_text(angle = 90, hjust = 1))\n", + "\n", + "ggplot(Data2, aes(x=City,fill=Company, y=(Users))) +\n", + " geom_bar(stat=\"identity\")+\n", + " theme(axis.text.x = element_text(angle = 90, hjust = 1))" + ] + }, + { + "cell_type": "markdown", + "id": "ba2bd436", + "metadata": {}, + "source": [ + "As we can see, there is a relationship between users of the company and the profit generated.\n", + "The yellow company clearly has a greater presence in all cities than the pink company, and generates a greater benefit in general, since it has a greater number of users.\n", + "The cities with the highest number of users and therefore economic benefit are New York, Chicago, Los Angeles and Washington.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "id": "5a32a94e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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qkdCUlg6o3att50Se1ISAJTb9S29aZLakdCEph6o7atN11SOxKSwNQbtW296ZLa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\n2mubaiekLQmp9tqm2glpS0KqvbapdkLakpBqr22qnZC2JKTaa5t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ZUghXwH9I+73v19iN3Y3V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\nrkJggg==", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "profityear<-data.frame(Data2$`Date of Travel`,(Data2$`Price Charged`-Data2$`Cost of Trip`),Data2$Users,Data2$Company)\n", + "profityear<-profityear[order(profityear$Data2..Date.of.Travel.), ]\n", + "colnames(profityear)<-c(\"Date\",\"Profit\",\"Users\",\"Company\")\n", + "profityear$Date<-ceiling_date(profityear$Date, unit=c( \"year\"))\n", + "\n", + "ggplot(profityear, aes(x=Date, fill= Company,y=(Users))) +\n", + " geom_bar(stat=\"identity\")+\n", + " theme(axis.text.x = element_text(angle = 90, hjust = 1))\n", + "\n", + "ggplot(profityear, aes(x=Date, fill= Company,y=(Profit))) +\n", + " geom_bar(stat=\"identity\")+\n", + " theme(axis.text.x = element_text(angle = 90, hjust = 1))\n" + ] + }, + { + "cell_type": "markdown", + "id": "c786710f", + "metadata": {}, + "source": [ + "In this case, we analyze the number of users and profit generated by each of the companies over the three years. As we can see, the yellow company has the largest number of users over the three years, with the largest number of users in 2017.\n", + "In the case of the benefits generated, the yellow company is superior to the pink one, with a higher profit in 2017.\n" + ] + }, + { + "cell_type": "markdown", + "id": "552b49c9", + "metadata": {}, + "source": [ + "\n", + "# 3.Time series analysis for profits\n", + "\n", + "## 3.1 Descriptive\n", + "\n", + "In this case we are going to study the profit that each of the companies has over time. The idea is to understand which of the two companies will generate a greater profit in the future, and which will make it more interesting for the investor.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "id": "c4b758c2", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message in parse_period_unit(unit):\n", + "\"Unit argument longer than 1. Taking first element.\"\n", + "Warning message in parse_period_unit(unit):\n", + "\"Unit argument longer than 1. Taking first element.\"\n" + ] + } + ], + "source": [ + "yellow<-filter(Data2, Company == \"Yellow Cab\")\n", + "yellowT<-data.frame(yellow$`Date of Travel`,(yellow$`Price Charged`-yellow$`Cost of Trip`))\n", + "yellowT<-yellowT[order(yellowT$yellow..Date.of.Travel.), ]\n", + "colnames(yellowT)<-c(\"Date\",\"Profit\")\n", + "yellowT$Date<-ceiling_date(yellowT$Date, unit=c(\"month\", \"year\"))\n", + "yellowT<-aggregate(Profit ~ Date, yellowT, sum)\n", + "\n", + "\n", + "\n", + "Pink<-filter(Data2,Company == \"Pink Cab\")\n", + "PinkT<-data.frame(Pink$`Date of Travel`,(Pink$`Price Charged`-Pink$`Cost of Trip`))\n", + "PinkT<-PinkT[order(PinkT$Pink..Date.of.Travel.), ]\n", + "colnames(PinkT)<-c(\"Date\",\"Profit\")\n", + "PinkT$Date<-ceiling_date(PinkT$Date, unit=c(\"month\", \"year\"))\n", + "PinkT<-aggregate(Profit ~ Date, PinkT, sum)" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "id": "8efa6e7c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ETS(M,N,M) \n", + "\n", + "Call:\n", + " ets(y = yellow, model = \"ZZZ\") \n", + "\n", + " Smoothing parameters:\n", + " alpha = 0.3264 \n", + " gamma = 7e-04 \n", + "\n", + " Initial states:\n", + " l = 1859190.7701 \n", + " s = 1.4849 1.2863 1.2555 1.1717 0.9193 0.8367\n", + " 0.8779 0.9149 0.8154 0.8361 0.7195 0.8819\n", + "\n", + " sigma: 0.107\n", + "\n", + " AIC AICc BIC \n", + "1017.462 1041.462 1041.215 \n", + "\n", + "Training set error measures:\n", + " ME RMSE MAE MPE MAPE MASE ACF1\n", + "Training set 1192.22 139129.9 99012.4 -0.9383157 6.277702 0.3706552 0.04015797\n", + "ETS(M,N,M) \n", + "\n", + "Call:\n", + " ets(y = Pink, model = \"ZZZ\") \n", + "\n", + " Smoothing parameters:\n", + " alpha = 0.3446 \n", + " gamma = 1e-04 \n", + "\n", + " Initial states:\n", + " l = 157561.5286 \n", + " s = 2.2939 1.8595 1.5909 1.0832 0.8865 0.6772\n", + " 0.5316 0.3807 0.5469 0.6706 0.5793 0.8997\n", + "\n", + " sigma: 0.167\n", + "\n", + " AIC AICc BIC \n", + "859.2666 883.2666 883.0194 \n", + "\n", + "Training set error measures:\n", + " ME RMSE MAE MPE MAPE MASE ACF1\n", + "Training set -1749.18 17148.66 12307.2 -3.913707 11.27729 0.3605177 -0.09557741\n" + ] + }, + { + "data": { + "image/png": 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Vk2DZZkh8Uz4Umv9aKdDWyRBBA8kDmKo8WEX0El7qS2s0W6ZiFSAERSjwmolPhm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\nPrcfIkGlcqgmaSlsiDS+bk2k2fQL//ectF5kOVSTtBgmRNLd2SA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bh7rHv7fEK7WikCpAsgRUgKpND5nYQpHPTa9Pb6HJ9+ShYgfcjy3iUgHUzB3W/b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\nSAgJCJAQEtD/Ay13bFXaYZy6AAAAAElFTkSuQmCC", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "yellow <- ts(yellowT$Profit, start = c(2016,1) , frequency =12)\n", + "yellowmulti<-(decompose(yellow))\n", + "yellowets<-ets(yellow,model = \"ZZZ\")\n", + "summary(yellowets)\n", + "autoplot(yellowets,main=\"ETS for Yellow cab\")\n", + "\n", + "Pink <- ts(PinkT$Profit, start = c(2016,1) , frequency = 12)\n", + "pinkmulti<-(decompose(Pink))\n", + "pinkets<-ets(Pink,model = \"ZZZ\")\n", + "summary(pinkets)\n", + "autoplot(pinkets,main=\"ETS for Pink cab\")" + ] + }, + { + "cell_type": "markdown", + "id": "eb3b625a", + "metadata": {}, + "source": [ + "Observing the series we see that a clearly upward trend for both companies. It seems that there is a seasonality for the months of December.\n", + "According to what the MAPE value indicates, the prediction error for the yellow company is 6.27% and for the pink company it is 11.27%.\n", + "With an error less than or close to 10% we will continue with this model for prediction." + ] + }, + { + "cell_type": "markdown", + "id": "85c239f9", + "metadata": {}, + "source": [ + "## 3.2.Seasonal component\n" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "id": "4bb88432", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "TT <- nrow(yellowets$states)\n", + "componenteEstacional <- yellowets$states[TT, 13:2]\n", + "\n", + "ggplot() +\n", + "geom_line(aes(x = 1:12, y = componenteEstacional)) +\n", + "geom_hline(yintercept = 1, colour = \"blue\", lty = 2) +\n", + "ggtitle(\"Seasonal component for Yellow cab\") +\n", + "xlab(\"\") +\n", + "ylab(\"Efecto estacional\") +\n", + "scale_x_continuous(breaks= 1:12,\n", + "labels = c(\"Ene\", \"Feb\", \"Mar\", \"Abr\", \"May\", \"Jun\",\n", + "\"Jul\", \"Ago\", \"Sep\", \"Oct\", \"Nov\", \"Dic\"))\n", + "\n", + "TT <- nrow(pinkets$states)\n", + "componenteEstacional <- pinkets$states[TT, 13:2]\n", + "\n", + "ggplot() +\n", + "geom_line(aes(x = 1:12, y = componenteEstacional)) +\n", + "geom_hline(yintercept = 1, colour = \"blue\", lty = 2) +\n", + "ggtitle(\"Sesonal component for Pin cab\") +\n", + "xlab(\"\") +\n", + "ylab(\"Efecto estacional\") +\n", + "scale_x_continuous(breaks= 1:12,\n", + "labels = c(\"Ene\", \"Feb\", \"Mar\", \"Abr\", \"May\", \"Jun\",\n", + "\"Jul\", \"Ago\", \"Sep\", \"Oct\", \"Nov\", \"Dic\"))\n" + ] + }, + { + "cell_type": "markdown", + "id": "f924e2b0", + "metadata": {}, + "source": [ + "We can observe a clear seasonality in both cases. The fourth quarter of the year seems to have a clear increase in the profit obtained by each company. While from January to July, the profits of both companies are average, we see an increase from August, having a very high peak in December. This may be due to holiday periods, since we understand that in December due to Christmas there is an increase in the use of cab companys.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "511d69ae", + "metadata": {}, + "source": [ + "## 3.3 Forecast and outliers\n" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "id": "c405ae4f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " Point Forecast Lo 95 Hi 95\n", + "Jan 2019 1603809 1267353 1940264\n", + "Feb 2019 1308462 1019557 1597367\n", + "Mar 2019 1520560 1168857 1872263\n", + "Apr 2019 1482863 1124967 1840759\n", + "May 2019 1664004 1246306 2081702\n", + "Jun 2019 1596642 1180975 2012308\n", + "Jul 2019 1521762 1111884 1931641\n", + "Aug 2019 1672033 1207091 2136975\n", + "Sep 2019 2131130 1520476 2741784\n", + "Oct 2019 2283516 1610389 2956644\n", + "Nov 2019 2339586 1631162 3048010\n", + "Dec 2019 2700728 1861818 3539638" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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4v0Jrt2iLQGRLJ494MNiLQGRLJ498PfiLSG8dCVVcce//UecULWYnmnEalEJEQa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\nRAIQAJEABPgflVwAJ1Yz6vgAAAAASUVORK5CYII=", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "yellowpred <- forecast(yellowets, h = 12, level = 95)\n", + "autoplot(yellowpred,\n", + "xlab = \"\",\n", + "ylab = \"Volumen de exportaciones (Millones de Euros)\",\n", + "main = \"Figura 4. Volumen de exportaciones entre UE y Marruecos (2002-2020) y predicción (2020-2023)\",\n", + "PI = FALSE)\n", + "yellowpred" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "id": "8f2ad265", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Scale for 'x' is already present. Adding another scale for 'x', which will\n", + "replace the existing scale.\n", + "\n" + ] + }, + { + "data": { + "image/png": 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Awhrc3ZhTIcGk+s+V7Oeo+0uzVCSL07u1ASUpTd3Vuky/XE5afhi302IX9RWAth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\nAqjA/wGV1p5sm3dgLwAAAABJRU5ErkJggg==", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "erroryellow <- residuals(yellowets, type = \"response\")\n", + "sderroryellow <- sd(erroryellow)\n", + "autoplot(erroryellow,\n", + "xlab = \"\",\n", + "ylab = \"Error\",\n", + "main = \"Figura 5. Error + Intervención\",\n", + "colour = \"black\") +\n", + "geom_hline(yintercept = c(-3, -2, 2 ,3)*sderroryellow,\n", + "colour = c(\"red\", \"blue\", \"blue\", \"red\"), lty = 2) +\n", + "scale_x_continuous(breaks= seq(2016, 2020, 1))" + ] + }, + { + "cell_type": "markdown", + "id": "38cd59db", + "metadata": {}, + "source": [ + "\n", + "We found an outilier corresponding to the first data taken in 2016. This makes us think about the first day that the company begins to operate, therefore it is normal that that day the benefits generated are lower than expected\n" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "id": "5323da4d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " Point Forecast Lo 95 Hi 95\n", + "Jan 2019 112951.48 75989.44 149913.52\n", + "Feb 2019 72727.08 47517.07 97937.09\n", + "Mar 2019 84195.86 53458.59 114933.12\n", + "Apr 2019 68665.31 42389.25 94941.37\n", + "May 2019 47794.14 28698.06 66890.22\n", + "Jun 2019 66747.97 38994.42 94501.52\n", + "Jul 2019 85025.21 48338.25 121712.16\n", + "Aug 2019 111300.72 61586.14 161015.29\n", + "Sep 2019 135996.48 73247.31 198745.66\n", + "Oct 2019 199739.24 104717.83 294760.65\n", + "Nov 2019 233457.63 119138.53 347776.73\n", + "Dec 2019 287999.40 143053.13 432945.68" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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df8xj5Ecg4iCdjVKxsYtWt3FsljBSBSABaRnLGI5BwfrE0H8inXwiMO7SZBJOcg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\n3ALtfAAAAABJRU5ErkJggg==", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "pinkpred <- forecast(pinkets, h = 12, level = 95)\n", + "autoplot(pinkpred,\n", + "xlab = \"\",\n", + "ylab = \"Volumen de exportaciones (Millones de Euros)\",\n", + "main = \"Figura 4. Volumen de exportaciones entre UE y Marruecos (2002-2020) y predicción (2020-2023)\",\n", + "PI = FALSE)\n", + "pinkpred" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "id": "3045c0c4", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Scale for 'x' is already present. Adding another scale for 'x', which will\n", + "replace the existing scale.\n", + "\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "errorpink <- residuals(pinkets, type = \"response\")\n", + "sderrorpink <- sd(errorpink)\n", + "autoplot(errorpink,\n", + "xlab = \"\",\n", + "ylab = \"Error\",\n", + "main = \"Figura 5. Error + Intervención\",\n", + "colour = \"black\") +\n", + "geom_hline(yintercept = c(-3, -2, 2 ,3)*sderrorpink,\n", + "colour = c(\"red\", \"blue\", \"blue\", \"red\"), lty = 2) +\n", + "scale_x_continuous(breaks= seq(2016, 2020, 1))" + ] + }, + { + "cell_type": "markdown", + "id": "0fbc65ad", + "metadata": {}, + "source": [ + "\n", + "We found an outilier corresponding to the first data taken in 2016. This makes us think about the first day that the company begins to operate, therefore it is normal that that day the benefits generated are lower than expected" + ] + }, + { + "cell_type": "markdown", + "id": "beb384e1", + "metadata": {}, + "source": [ + "# 4. Conclusion\n", + "\n", + "\n", + "After the analysis carried out, we can affirm that the company in which it should be invested, and therefore the one that will generate the most profit and the one that will have the most users is the yellow company for the following reasons:\n", + "\n", + "The yellow company has a greater number of users per city than the pink company.\n", + "\n", + "The yellow company generates greater profit per city than the pink company\n", + "\n", + "The yellow company has a greater number of users per year than the pink company\n", + "\n", + "The yellow company generates more profit per year than the pink company\n", + "\n", + "The yellow company will generate a future profit greater than the pink company\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ec63f8c6", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "4.0.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}