Evidencia 1

Importar librerias

library(readr)

## Warning: package 'readr' was built under R version 4.3.1

library(dplyr)

## Warning: package 'dplyr' was built under R version 4.3.1

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(cluster)

Importar Dataset

df <- read.csv("C:\\Users\\naila\\OneDrive\\Documentos\\1 TEC\\5 QUINTO SEMESTRE\\Minería de datos\\Datos bienes y raices CDMXF 1.csv")
#print(df)

Data preprocessing

# Convert categorical variables to numeric
df <- df %>%
  mutate_at(vars(Cocina_equip, Gimnasio, Amueblado, Alberca, Terraza, Elevador), ~ifelse(. == "Si", 1, 0))

# Convert to numeric
df$Estacionamiento <- as.numeric(df$Estacionamiento)

## Warning: NAs introducidos por coerción

df$Alcaldia_numeric <- as.numeric(factor(df$Alcaldia))
df$Colonia_numeric <- as.numeric(factor(df$Colonia))

# Check for missing values and handle if necessary
print(colSums(is.na(df)))

##         Alcaldia          Colonia               X1               X2 
##                0                0                0                0 
##               X3               X4               X5               X6 
##                0                0                0                0 
##               X7               X8               X9              X10 
##                0                0                0                0 
##     Cocina_equip         Gimnasio        Amueblado          Alberca 
##                0                0                0                0 
##          Terraza         Elevador    m2_construido            Banos 
##                0                0                0                0 
##        Recamaras  Estacionamiento           Precio Alcaldia_numeric 
##                0                1                0                0 
##  Colonia_numeric 
##                0

# Handle missing values if needed
df <- na.omit(df)

# Check again for NA's
print(colSums(is.na(df)))

##         Alcaldia          Colonia               X1               X2 
##                0                0                0                0 
##               X3               X4               X5               X6 
##                0                0                0                0 
##               X7               X8               X9              X10 
##                0                0                0                0 
##     Cocina_equip         Gimnasio        Amueblado          Alberca 
##                0                0                0                0 
##          Terraza         Elevador    m2_construido            Banos 
##                0                0                0                0 
##        Recamaras  Estacionamiento           Precio Alcaldia_numeric 
##                0                0                0                0 
##  Colonia_numeric 
##                0

# Example: Create a new variable representing the total number of amenities
df$Total_Amenities <- rowSums(df[, c("Cocina_equip", "Gimnasio", "Amueblado", "Alberca", "Terraza", "Elevador")])

# Standardize numeric variables
numeric_columns <- df %>% select_if(is.numeric)
df_scaled <- scale(numeric_columns)

Clustering

# Perform k-means clustering
set.seed(123)
k <- 4  # choose the number of clusters based on your analysis
kmeans_result <- kmeans(df_scaled, centers = k)
df$Cluster <- as.factor(kmeans_result$cluster)
numeric_columns$Cluster <- as.numeric(kmeans_result$cluster)

# Analyze the characteristics of each cluster
cluster_summary_numeric <- numeric_columns %>% group_by(Cluster) %>% summarise_all(mean)
print(cluster_summary_numeric) # cluster with only numeric variables

## # A tibble: 4 × 25
##   Cluster    X1    X2    X3    X4    X5    X6    X7     X8    X9   X10
##     <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl>
## 1       1 1.53   5.20  44.8  19.6  28.2  3.94 0.577 0.0998  9.34  47.5
## 2       2 0.765  5.81  36.4  11.6  22.4  1.50 0.152 0.0315  5.54  26.3
## 3       3 1.30   5.61  42.1  17.1  24.0  3.17 0.480 0.0434  6.70  35.7
## 4       4 1.99   4.70  45.7  20.3  30.5  5.61 5.48  0.151   8.69  48.3
## # ℹ 14 more variables: Cocina_equip <dbl>, Gimnasio <dbl>, Amueblado <dbl>,
## #   Alberca <dbl>, Terraza <dbl>, Elevador <dbl>, m2_construido <dbl>,
## #   Banos <dbl>, Recamaras <dbl>, Estacionamiento <dbl>, Precio <dbl>,
## #   Alcaldia_numeric <dbl>, Colonia_numeric <dbl>, Total_Amenities <dbl>

cluster_summary_numeric$Alcaldia_numeric <-  round(cluster_summary_numeric$Alcaldia_numeric)
cluster_summary_numeric$Colonia_numeric <- round(cluster_summary_numeric$Colonia_numeric)
cluster_summary_numeric$Total_Amenities <- round(cluster_summary_numeric$Total_Amenities)
print(cluster_summary_numeric)

## # A tibble: 4 × 25
##   Cluster    X1    X2    X3    X4    X5    X6    X7     X8    X9   X10
##     <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl>
## 1       1 1.53   5.20  44.8  19.6  28.2  3.94 0.577 0.0998  9.34  47.5
## 2       2 0.765  5.81  36.4  11.6  22.4  1.50 0.152 0.0315  5.54  26.3
## 3       3 1.30   5.61  42.1  17.1  24.0  3.17 0.480 0.0434  6.70  35.7
## 4       4 1.99   4.70  45.7  20.3  30.5  5.61 5.48  0.151   8.69  48.3
## # ℹ 14 more variables: Cocina_equip <dbl>, Gimnasio <dbl>, Amueblado <dbl>,
## #   Alberca <dbl>, Terraza <dbl>, Elevador <dbl>, m2_construido <dbl>,
## #   Banos <dbl>, Recamaras <dbl>, Estacionamiento <dbl>, Precio <dbl>,
## #   Alcaldia_numeric <dbl>, Colonia_numeric <dbl>, Total_Amenities <dbl>

cluster_summary <- df %>% group_by(Cluster) %>% summarise_all(mean)

## Warning: There were 8 warnings in `summarise()`.
## The first warning was:
## ℹ In argument: `Alcaldia = (function (x, ...) ...`.
## ℹ In group 1: `Cluster = 1`.
## Caused by warning in `mean.default()`:
## ! argument is not numeric or logical: returning NA
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 7 remaining warnings.

print(cluster_summary) # cluster with categorical variables

## # A tibble: 4 × 27
##   Cluster Alcaldia Colonia    X1    X2    X3    X4    X5    X6    X7     X8
##   <fct>      <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>
## 1 1             NA      NA 1.53   5.20  44.8  19.6  28.2  3.94 0.577 0.0998
## 2 2             NA      NA 0.765  5.81  36.4  11.6  22.4  1.50 0.152 0.0315
## 3 3             NA      NA 1.30   5.61  42.1  17.1  24.0  3.17 0.480 0.0434
## 4 4             NA      NA 1.99   4.70  45.7  20.3  30.5  5.61 5.48  0.151 
## # ℹ 16 more variables: X9 <dbl>, X10 <dbl>, Cocina_equip <dbl>, Gimnasio <dbl>,
## #   Amueblado <dbl>, Alberca <dbl>, Terraza <dbl>, Elevador <dbl>,
## #   m2_construido <dbl>, Banos <dbl>, Recamaras <dbl>, Estacionamiento <dbl>,
## #   Precio <dbl>, Alcaldia_numeric <dbl>, Colonia_numeric <dbl>,
## #   Total_Amenities <dbl>

cluster_summary$Alcaldia_numeric <-  round(cluster_summary$Alcaldia_numeric)
cluster_summary$Colonia_numeric <- round(cluster_summary$Colonia_numeric)
cluster_summary$Total_Amenities <- round(cluster_summary$Total_Amenities)
print(cluster_summary)

## # A tibble: 4 × 27
##   Cluster Alcaldia Colonia    X1    X2    X3    X4    X5    X6    X7     X8
##   <fct>      <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>
## 1 1             NA      NA 1.53   5.20  44.8  19.6  28.2  3.94 0.577 0.0998
## 2 2             NA      NA 0.765  5.81  36.4  11.6  22.4  1.50 0.152 0.0315
## 3 3             NA      NA 1.30   5.61  42.1  17.1  24.0  3.17 0.480 0.0434
## 4 4             NA      NA 1.99   4.70  45.7  20.3  30.5  5.61 5.48  0.151 
## # ℹ 16 more variables: X9 <dbl>, X10 <dbl>, Cocina_equip <dbl>, Gimnasio <dbl>,
## #   Amueblado <dbl>, Alberca <dbl>, Terraza <dbl>, Elevador <dbl>,
## #   m2_construido <dbl>, Banos <dbl>, Recamaras <dbl>, Estacionamiento <dbl>,
## #   Precio <dbl>, Alcaldia_numeric <dbl>, Colonia_numeric <dbl>,
## #   Total_Amenities <dbl>

Sets de Entrenamiento y de Evaluacion

# Split data into training and testing sets
set.seed(123)
train_indices <- sample(1:nrow(numeric_columns), 0.8 * nrow(numeric_columns))
train_data <- numeric_columns[train_indices, ]
test_data <- numeric_columns[-train_indices, ]

# Fit a linear regression model
model <- lm(Precio ~ ., data = train_data)

Predicciones

# Make predictions on the test set
predictions <- predict(model, numeric_columns = test_data)

# Evaluate the model
rmse <- sqrt(mean((predictions - test_data$Precio)^2))

## Warning in predictions - test_data$Precio: longitud de objeto mayor no es
## múltiplo de la longitud de uno menor

print(paste("Root Mean Squared Error:", rmse))

## [1] "Root Mean Squared Error: 6322.4724217328"

# Example: Predict property values for a new data point
new_property <- data.frame(
  Alcaldia_numeric = 2,
  Colonia_numeric = 5,
  Total_Amenities = 2,
  Cocina_equip = 1,
  Gimnasio = 0,
  Amueblado = 1,
  Alberca = 1,
  Terraza = 0,
  Elevador = 1,
  m2_construido = 200,
  Banos = 2,
  Recamaras = 3,
  Estacionamiento = 2,
  Cluster = 3  # Assign the cluster based on your analysis
)

predicted_value <- predict(model, numeric_columns = new_property)
print(paste("Predicted Property Value:", predicted_value))

##   [1] "Predicted Property Value: 915.726864103557" 
##   [2] "Predicted Property Value: 8237.83012776578" 
##   [3] "Predicted Property Value: 11726.1259916677" 
##   [4] "Predicted Property Value: 8545.18119537925" 
##   [5] "Predicted Property Value: 1830.47065863579" 
##   [6] "Predicted Property Value: 6889.00586422357" 
##   [7] "Predicted Property Value: 1653.76194505858" 
##   [8] "Predicted Property Value: 1905.5719573471"  
##   [9] "Predicted Property Value: 714.166315376613" 
##  [10] "Predicted Property Value: 2534.30832561676" 
##  [11] "Predicted Property Value: 3442.0000255412"  
##  [12] "Predicted Property Value: 10084.3634949554" 
##  [13] "Predicted Property Value: 7789.79205448643" 
##  [14] "Predicted Property Value: 7527.26088766479" 
##  [15] "Predicted Property Value: 1894.15300091525" 
##  [16] "Predicted Property Value: 679.331346562553" 
##  [17] "Predicted Property Value: 2850.89470806453" 
##  [18] "Predicted Property Value: 10282.3520994381" 
##  [19] "Predicted Property Value: 1053.18613782468" 
##  [20] "Predicted Property Value: 20830.2215140373" 
##  [21] "Predicted Property Value: 1653.76194505858" 
##  [22] "Predicted Property Value: 5152.43441621755" 
##  [23] "Predicted Property Value: 2690.35450823296" 
##  [24] "Predicted Property Value: 7902.38359095179" 
##  [25] "Predicted Property Value: 3750.7132932445"  
##  [26] "Predicted Property Value: 2192.68441905403" 
##  [27] "Predicted Property Value: 2132.858191833"   
##  [28] "Predicted Property Value: 9737.6218444336"  
##  [29] "Predicted Property Value: 1516.90888877372" 
##  [30] "Predicted Property Value: 2078.71788256145" 
##  [31] "Predicted Property Value: 12957.8387010561" 
##  [32] "Predicted Property Value: 2357.15205495679" 
##  [33] "Predicted Property Value: 2981.2277735245"  
##  [34] "Predicted Property Value: 500.659148699201" 
##  [35] "Predicted Property Value: 484.44202501118"  
##  [36] "Predicted Property Value: 12308.6899368178" 
##  [37] "Predicted Property Value: 3340.3563799142"  
##  [38] "Predicted Property Value: 1935.72581046192" 
##  [39] "Predicted Property Value: 1583.71861236848" 
##  [40] "Predicted Property Value: 955.590141966899" 
##  [41] "Predicted Property Value: 750.796733727854" 
##  [42] "Predicted Property Value: 4052.38555194867" 
##  [43] "Predicted Property Value: 3002.2291635068"  
##  [44] "Predicted Property Value: 874.659910004837" 
##  [45] "Predicted Property Value: 14794.4075376791" 
##  [46] "Predicted Property Value: 11598.9550312776" 
##  [47] "Predicted Property Value: 10609.3608402734" 
##  [48] "Predicted Property Value: 1436.78533725623" 
##  [49] "Predicted Property Value: 374.002952363267" 
##  [50] "Predicted Property Value: 2478.12366601734" 
##  [51] "Predicted Property Value: 355.299751463155" 
##  [52] "Predicted Property Value: 1029.86983699961" 
##  [53] "Predicted Property Value: 8841.04866365159" 
##  [54] "Predicted Property Value: -863.715818239138"
##  [55] "Predicted Property Value: 1114.01923392254" 
##  [56] "Predicted Property Value: 936.278968854873" 
##  [57] "Predicted Property Value: 4943.60245372797" 
##  [58] "Predicted Property Value: 3633.5722688589"  
##  [59] "Predicted Property Value: 12964.821916393"  
##  [60] "Predicted Property Value: 1029.59700606434" 
##  [61] "Predicted Property Value: 1372.93815873053" 
##  [62] "Predicted Property Value: 4687.05788962999" 
##  [63] "Predicted Property Value: 1455.33839778191" 
##  [64] "Predicted Property Value: 724.938900118585" 
##  [65] "Predicted Property Value: 6507.31482479903" 
##  [66] "Predicted Property Value: 1165.06856340784" 
##  [67] "Predicted Property Value: 1406.19488773836" 
##  [68] "Predicted Property Value: 4625.0501757077"  
##  [69] "Predicted Property Value: 883.898525849403" 
##  [70] "Predicted Property Value: 3148.07734359977" 
##  [71] "Predicted Property Value: 368.811718689188" 
##  [72] "Predicted Property Value: 1939.54841201025" 
##  [73] "Predicted Property Value: 10229.4649223634" 
##  [74] "Predicted Property Value: 1235.28872787416" 
##  [75] "Predicted Property Value: 472.366078653475" 
##  [76] "Predicted Property Value: 4450.05111045247" 
##  [77] "Predicted Property Value: 3285.04651903094" 
##  [78] "Predicted Property Value: -79.6335975190596"
##  [79] "Predicted Property Value: 3525.27744357339" 
##  [80] "Predicted Property Value: -1066.52152195679"
##  [81] "Predicted Property Value: 135.293476418139" 
##  [82] "Predicted Property Value: 8493.99737313873" 
##  [83] "Predicted Property Value: 708.805410511028" 
##  [84] "Predicted Property Value: 768.432225688792" 
##  [85] "Predicted Property Value: 11848.4278537137" 
##  [86] "Predicted Property Value: 14673.7004742708" 
##  [87] "Predicted Property Value: 688.320957612744" 
##  [88] "Predicted Property Value: 6746.76474397881" 
##  [89] "Predicted Property Value: 503.19418508776"  
##  [90] "Predicted Property Value: 572.968344573466" 
##  [91] "Predicted Property Value: 3881.6792872375"  
##  [92] "Predicted Property Value: 5019.48560540896" 
##  [93] "Predicted Property Value: 816.820277255607" 
##  [94] "Predicted Property Value: 1220.58576939927" 
##  [95] "Predicted Property Value: 9105.8153558039"  
##  [96] "Predicted Property Value: 594.171633387632" 
##  [97] "Predicted Property Value: 13273.2465953509" 
##  [98] "Predicted Property Value: 503.19418508776"  
##  [99] "Predicted Property Value: 1425.80660143284" 
## [100] "Predicted Property Value: 1725.02972664969" 
## [101] "Predicted Property Value: 1114.84026049807" 
## [102] "Predicted Property Value: 2201.61184188324" 
## [103] "Predicted Property Value: 9123.75604832622" 
## [104] "Predicted Property Value: 423.967987518871" 
## [105] "Predicted Property Value: 1022.54133975631" 
## [106] "Predicted Property Value: 6734.59857832145" 
## [107] "Predicted Property Value: 1544.49301613665" 
## [108] "Predicted Property Value: 1810.98799226509" 
## [109] "Predicted Property Value: 1761.79874613559" 
## [110] "Predicted Property Value: 13688.2199088136" 
## [111] "Predicted Property Value: 1366.61663628939" 
## [112] "Predicted Property Value: -831.069435094075"
## [113] "Predicted Property Value: 2950.72924905987" 
## [114] "Predicted Property Value: 1423.54287493772" 
## [115] "Predicted Property Value: 5911.52145514856" 
## [116] "Predicted Property Value: 2824.95680026333" 
## [117] "Predicted Property Value: 16532.834317885"  
## [118] "Predicted Property Value: 895.560748379968" 
## [119] "Predicted Property Value: 2620.15424215311" 
## [120] "Predicted Property Value: 1735.05264530005" 
## [121] "Predicted Property Value: 13175.7889353586" 
## [122] "Predicted Property Value: 9812.66237284463" 
## [123] "Predicted Property Value: 2340.78318784818" 
## [124] "Predicted Property Value: 719.534113228171" 
## [125] "Predicted Property Value: 825.974762882067" 
## [126] "Predicted Property Value: 4146.31170129259" 
## [127] "Predicted Property Value: 18113.5152486682" 
## [128] "Predicted Property Value: 1944.1253802675"  
## [129] "Predicted Property Value: 651.684847142795" 
## [130] "Predicted Property Value: -119.606290318404"
## [131] "Predicted Property Value: 1440.40762955443" 
## [132] "Predicted Property Value: 544.96790968964"  
## [133] "Predicted Property Value: 3085.10690819049" 
## [134] "Predicted Property Value: 8841.04866365159" 
## [135] "Predicted Property Value: 1094.85347501172" 
## [136] "Predicted Property Value: 2314.54278408855" 
## [137] "Predicted Property Value: 930.886519708754" 
## [138] "Predicted Property Value: 7535.76978953665" 
## [139] "Predicted Property Value: 6021.32724276701" 
## [140] "Predicted Property Value: 9645.11397924182" 
## [141] "Predicted Property Value: 2879.31077342077" 
## [142] "Predicted Property Value: 847.487523028884" 
## [143] "Predicted Property Value: 4421.67871117763" 
## [144] "Predicted Property Value: 13374.5081013028" 
## [145] "Predicted Property Value: 529.884984527346" 
## [146] "Predicted Property Value: 7600.89863906812" 
## [147] "Predicted Property Value: 216.212688342286" 
## [148] "Predicted Property Value: 10155.2592427512" 
## [149] "Predicted Property Value: 1210.90975754749" 
## [150] "Predicted Property Value: 17947.4718728926" 
## [151] "Predicted Property Value: 237.785290460699" 
## [152] "Predicted Property Value: 3041.38106776296" 
## [153] "Predicted Property Value: 10534.9771565102" 
## [154] "Predicted Property Value: 48.8718301608133" 
## [155] "Predicted Property Value: 16009.3043489561" 
## [156] "Predicted Property Value: 5383.58671166268" 
## [157] "Predicted Property Value: 3451.0816790833"  
## [158] "Predicted Property Value: 18129.6814423286" 
## [159] "Predicted Property Value: 2484.72222905102" 
## [160] "Predicted Property Value: 3421.96275636959" 
## [161] "Predicted Property Value: 8909.46237561297" 
## [162] "Predicted Property Value: 1496.76904972503" 
## [163] "Predicted Property Value: 12894.3743425097" 
## [164] "Predicted Property Value: 590.427010721783" 
## [165] "Predicted Property Value: 3510.32397221059" 
## [166] "Predicted Property Value: 915.322007010093" 
## [167] "Predicted Property Value: 2374.75487827508" 
## [168] "Predicted Property Value: 1422.84772632749" 
## [169] "Predicted Property Value: 5022.53977391908" 
## [170] "Predicted Property Value: 3221.37057070199" 
## [171] "Predicted Property Value: 4530.35587962796" 
## [172] "Predicted Property Value: 5188.54505249211" 
## [173] "Predicted Property Value: 686.519233850265" 
## [174] "Predicted Property Value: 2850.63327952199" 
## [175] "Predicted Property Value: 262.744895034151" 
## [176] "Predicted Property Value: 179.966911703881" 
## [177] "Predicted Property Value: 2076.25023320643" 
## [178] "Predicted Property Value: 5251.09032660692" 
## [179] "Predicted Property Value: 806.533760384191" 
## [180] "Predicted Property Value: 1348.87890763455" 
## [181] "Predicted Property Value: 11462.4831152374" 
## [182] "Predicted Property Value: 13021.4288995434" 
## [183] "Predicted Property Value: 13018.4347759717" 
## [184] "Predicted Property Value: 93.6509693476783" 
## [185] "Predicted Property Value: 9387.70130200566" 
## [186] "Predicted Property Value: 16007.1480698663" 
## [187] "Predicted Property Value: 1433.14360051478" 
## [188] "Predicted Property Value: 16274.8926195602" 
## [189] "Predicted Property Value: 83.4248190569052" 
## [190] "Predicted Property Value: 1496.76904972503" 
## [191] "Predicted Property Value: 2556.91904056026" 
## [192] "Predicted Property Value: 473.176787287061" 
## [193] "Predicted Property Value: 1429.11592226434" 
## [194] "Predicted Property Value: 2249.90528031033" 
## [195] "Predicted Property Value: 798.179810938421" 
## [196] "Predicted Property Value: 1933.20367466269" 
## [197] "Predicted Property Value: 1303.92023463224" 
## [198] "Predicted Property Value: 10432.7029521775" 
## [199] "Predicted Property Value: 1033.55107452171" 
## [200] "Predicted Property Value: 409.240454565787" 
## [201] "Predicted Property Value: 210.727910141928" 
## [202] "Predicted Property Value: 268.234407878679" 
## [203] "Predicted Property Value: 4668.69173234466" 
## [204] "Predicted Property Value: 3287.68687910859" 
## [205] "Predicted Property Value: -128.76334770211" 
## [206] "Predicted Property Value: 5729.91446024631" 
## [207] "Predicted Property Value: 1540.98230665668" 
## [208] "Predicted Property Value: 10668.8587967056" 
## [209] "Predicted Property Value: 146.584637735333" 
## [210] "Predicted Property Value: 4038.46093617153" 
## [211] "Predicted Property Value: 260.845411736681" 
## [212] "Predicted Property Value: 3412.06778145044" 
## [213] "Predicted Property Value: 10123.3357808307" 
## [214] "Predicted Property Value: 1735.40499101286" 
## [215] "Predicted Property Value: 11294.8502819121" 
## [216] "Predicted Property Value: 900.720669384167" 
## [217] "Predicted Property Value: 898.875063596861" 
## [218] "Predicted Property Value: 2309.03042435149" 
## [219] "Predicted Property Value: 1964.6689114281"  
## [220] "Predicted Property Value: -95.7863928124767"
## [221] "Predicted Property Value: 1521.18224015519" 
## [222] "Predicted Property Value: 2897.10281002873" 
## [223] "Predicted Property Value: 2778.99682242794" 
## [224] "Predicted Property Value: 6131.77163561528" 
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