├── Documentos teóricos ├── Evaluación del modelo de ML.pdf ├── Metricas de clasificacion.png ├── Métricas de Evaluación para modelos de regresión.pdf ├── Preguntas ├── README.md ├── Sesgo y Varianza.pdf └── resumen ML.pdf ├── Ejemplo con KMeans - dataset Penguins.ipynb ├── Feature scaling.ipynb ├── Feature selection.-.ipynb ├── Funcion_Aplicar_varios_algoritmos.ipynb ├── Principal Component Analysis-.ipynb ├── Proyecto final Data Science - Coderhouse ├── Predicción de Diabetes │ ├── Deteccion_diabetes_ProyectoFinal_Camandone_Belén .ipynb │ └── README.md └── README.md ├── Proyecto-Dataset-Iris ├── Ejercicio Iris - ML clasificación (2).ipynb └── README.md ├── Proyecto_calorias ├── ML_calorías quemadas por minutos corriendo.ipynb ├── README.md ├── calories_time.csv └── calories_time_weight_speed (1).csv ├── Proyecto_salarios ├── ML_regresión_lineal_salarios (1).ipynb ├── README.md └── data_rrhh.csv ├── README.md ├── Recommender System Final Project ├── Final task - Recommender System - Camandone Belén Part 2.ipynb ├── Final_task_Recommender_System_Camandone Belén Part 1.ipynb ├── ml-capstone-Camandone Belén.pptx.pdf └── readme.md ├── SMOTE.ipynb ├── Scikit-plot..ipynb ├── Trampa dummy-.ipynb ├── Transformaciones de Variables Categóricas.ipynb ├── Técnicas_de_selección_de_datos_.ipynb ├── Unsupervised Machine Learning Proyecto Final ├── Proyecto Final Aprendizaje No Supervisado Camandone Belén - 2024.ipynb └── readme.md └── _Librería Lazypredict.ipynb /Documentos teóricos/Evaluación del modelo de ML.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bcamandone/Machine-Learning/a185c926938671814f5722df26f82e6238897902/Documentos teóricos/Evaluación del modelo de ML.pdf -------------------------------------------------------------------------------- /Documentos teóricos/Metricas de clasificacion.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bcamandone/Machine-Learning/a185c926938671814f5722df26f82e6238897902/Documentos teóricos/Metricas de clasificacion.png -------------------------------------------------------------------------------- /Documentos teóricos/Métricas de Evaluación para modelos de regresión.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bcamandone/Machine-Learning/a185c926938671814f5722df26f82e6238897902/Documentos teóricos/Métricas de Evaluación para modelos de regresión.pdf -------------------------------------------------------------------------------- /Documentos teóricos/Preguntas: -------------------------------------------------------------------------------- 1 | 2 | 1) ¿Cuál es la diferencia entre aprendizaje supervisado y no supervisado en machine learning? 3 | 4 | El aprendizaje supervisado necesita para el entrenamiento de datos etiquetados. Ejemplo, para realizar una clasificación primero se deben etiquetar los datos a utilizar. 5 | En cambio, el no supervisado, no necesita etiquetar los datos de entrada de forma explícita. 6 | 7 | 2) Explique el trade-off sesgo-varianza. 8 | 9 | Se refiere al equilibrio entre la capacidad de un modelo para ajustarse bien a los datos de entrenamiento (bajo sesgo) y su capacidad para generalizar a datos nuevos (baja varianza). 10 | Si el modelo tiene un sesgo alto es lo mismo que decir que el modelo tiene una complejidad baja(es muy simple). A medida que aumenta la complejidad el sesgo disminuye. 11 | Alto sesgo: cuando un modelo tiene un alto sesgo, tiende a no ajustarse a los datos, lo que significa que no captura los patrones subyacentes. 12 | Alta varianza: cuando un modelo tiene una alta varianza, es demasiado complejo y se ajusta al ruido de los datos, lo que lleva a una generalización deficiente. 13 | Encontrar el equilibrio adecuado entre sesgo y varianza es crucial para crear modelos que funcionen bien tanto con datos de entrenamiento como de prueba. 14 | 15 | 3) ¿Qué es el sobreajuste y cómo se puede prevenir? 16 | 17 | El sobreajuste, también conocido como overfitting en inglés, es un fenómeno donde un modelo se ajusta demasiado bien a los datos de entrenamiento, pero tiene un rendimiento deficiente cuando se enfrenta a datos nuevos, es decir, 18 | datos que no ha visto durante el entrenamiento. 19 | 20 | Acciones para prevenir: 21 | 22 | - Simplificar el modelo (por ejemplo, reducir su complejidad). 23 | - Utilice técnicas de regularización 24 | - Emplear validación cruzada(Cross Validation) para evaluar el rendimiento del modelo. 25 | - Selección de características(Feature selection): Seleccionar cuidadosamente las características más relevantes para el problema en cuestión 26 | y eliminar aquellas que puedan introducir ruido en el modelo puede ayudar a prevenir el sobreajuste. 27 | 28 | 4) ¿Qué es la validación cruzada (Cross Validation) y por qué es importante? 29 | 30 | La validación cruzada es una técnica utilizada para evaluar el desempeño de un modelo. Implica dividir los datos en múltiples subconjuntos, llamados "folds" o "pliegues", y luego utilizar estos subconjuntos de manera rotativa para entrenar y evaluar el modelo. 31 | Esta técnica es importante porque proporciona una estimación más sólida del rendimiento de un modelo, lo que ayuda a detectar problemas como el sobreajuste. 32 | 33 | 34 | 5) Explicar cómo funciona el área bajo la curva(AUC) 35 | 36 | El área bajo la curva(AUC) es una medida numérica del rendimiento de un modelo de clasificación binaria que podemos obtener al graficar la Curva ROC. 37 | Varía entre 0 y 1, donde un valor de 1 indica un clasificador perfecto y un valor de 0.5 indica un rendimiento similar al azar. Cuanto mayor sea el AUC, mejor será el rendimiento del clasificador. 38 | 39 | -------------------------------------------------------------------------------- /Documentos teóricos/README.md: -------------------------------------------------------------------------------- 1 | # Mis resúmenes 📑 2 | 3 | 4 | ## Qué vas a encontrar en esta carpeta? 5 | 6 | - Resumen ML 7 | 8 | - Evaluación del modelo de ML 9 | 10 | - Métricas de Evaluación para modelos de clasificacion 11 | 12 | - Métricas de Evaluación para modelos de regresión 13 | 14 | - Preguntas teóricas 15 | 16 | - Sesgo y Varianza 17 | -------------------------------------------------------------------------------- /Documentos teóricos/Sesgo y Varianza.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bcamandone/Machine-Learning/a185c926938671814f5722df26f82e6238897902/Documentos teóricos/Sesgo y Varianza.pdf -------------------------------------------------------------------------------- /Documentos teóricos/resumen ML.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bcamandone/Machine-Learning/a185c926938671814f5722df26f82e6238897902/Documentos teóricos/resumen ML.pdf -------------------------------------------------------------------------------- /Feature scaling.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "id": "f9ea4a1d", 6 | "metadata": {}, 7 | "source": [ 8 | "# Feature scaling" 9 | ] 10 | }, 11 | { 12 | "cell_type": "markdown", 13 | "id": "cda048f1", 14 | "metadata": {}, 15 | "source": [ 16 | "***¿Por qué es bueno escalar?*** Existen modelos sensibles a las escalas como la regresión logística, máquinas de soporte vectorial (SVM), redes neuronales y algoritmos basados en la distancia (como KNN).\n", 17 | "\n", 18 | "Si las características tienen diferentes rangos, los modelos pueden dar más importancia a las características con valores más grandes." 19 | ] 20 | }, 21 | { 22 | "cell_type": "code", 23 | "execution_count": 6, 24 | "id": "b220a289", 25 | "metadata": {}, 26 | "outputs": [], 27 | "source": [ 28 | "from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler\n", 29 | "import numpy as np\n", 30 | "import pandas as pd\n", 31 | "from sklearn.datasets import load_iris\n", 32 | "from sklearn.model_selection import train_test_split" 33 | ] 34 | }, 35 | { 36 | "cell_type": "code", 37 | "execution_count": 7, 38 | "id": "852d1e24", 39 | "metadata": {}, 40 | "outputs": [], 41 | "source": [ 42 | "# Cargar el dataset iris\n", 43 | "iris = load_iris()\n", 44 | "X = pd.DataFrame(iris.data, columns=iris.feature_names)\n", 45 | "y = iris.target\n", 46 | "\n", 47 | "# Dividir el dataset en entrenamiento y prueba\n", 48 | "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n" 49 | ] 50 | }, 51 | { 52 | "cell_type": "markdown", 53 | "id": "8f934e4f", 54 | "metadata": {}, 55 | "source": [ 56 | "## StandardScaler" 57 | ] 58 | }, 59 | { 60 | "cell_type": "markdown", 61 | "id": "ddb7a526", 62 | "metadata": {}, 63 | "source": [ 64 | "### $\\frac{x_i - \\mu}{\\sigma}$ $\\mu$: valor medio, $\\sigma$: desviación estándar" 65 | ] 66 | }, 67 | { 68 | "cell_type": "markdown", 69 | "id": "baa1668b", 70 | "metadata": {}, 71 | "source": [ 72 | "Cada valor de la característica X se le resta la media μ y el resultante se divide por la desviación estándar σ. \n", 73 | "\n", 74 | "Es útil cuando los datos tienen una distribución que se asemeja a una distribución normal.\n" 75 | ] 76 | }, 77 | { 78 | "cell_type": "code", 79 | "execution_count": 8, 80 | "id": "d3f80fe2", 81 | "metadata": {}, 82 | "outputs": [ 83 | { 84 | "name": "stdout", 85 | "output_type": "stream", 86 | "text": [ 87 | "Primeras filas de X_train estandarizado:\n" 88 | ] 89 | }, 90 | { 91 | "data": { 92 | "text/html": [ 93 | "
\n", 94 | "\n", 107 | "\n", 108 | " \n", 109 | " \n", 110 | " \n", 111 | " \n", 112 | " \n", 113 | " \n", 114 | " \n", 115 | " \n", 116 | " \n", 117 | " \n", 118 | " \n", 119 | " \n", 120 | " \n", 121 | " \n", 122 | " \n", 123 | " \n", 124 | " \n", 125 | " \n", 126 | " \n", 127 | " \n", 128 | " \n", 129 | " \n", 130 | " \n", 131 | " \n", 132 | " \n", 133 | " \n", 134 | " \n", 135 | " \n", 136 | " \n", 137 | " \n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | " \n", 142 | " \n", 143 | " \n", 144 | " \n", 145 | " \n", 146 | " \n", 147 | " \n", 148 | " \n", 149 | " \n", 150 | " \n", 151 | " \n", 152 | " \n", 153 | " \n", 154 | "
sepal length (cm)sepal width (cm)petal length (cm)petal width (cm)
0-0.413416-1.462003-0.099511-0.323398
10.551222-0.5025630.7177030.353032
20.6718020.2170160.9511920.758890
30.912961-0.0228440.3090960.217746
41.6364401.4163151.3014271.705891
\n", 155 | "
" 156 | ], 157 | "text/plain": [ 158 | " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n", 159 | "0 -0.413416 -1.462003 -0.099511 -0.323398\n", 160 | "1 0.551222 -0.502563 0.717703 0.353032\n", 161 | "2 0.671802 0.217016 0.951192 0.758890\n", 162 | "3 0.912961 -0.022844 0.309096 0.217746\n", 163 | "4 1.636440 1.416315 1.301427 1.705891" 164 | ] 165 | }, 166 | "execution_count": 8, 167 | "metadata": {}, 168 | "output_type": "execute_result" 169 | } 170 | ], 171 | "source": [ 172 | "# Estandarización (media 0, desviación estándar 1)\n", 173 | "scaler_standard = StandardScaler()\n", 174 | "X_train_standard = scaler_standard.fit_transform(X_train)\n", 175 | "X_test_standard = scaler_standard.transform(X_test)\n", 176 | "\n", 177 | "print(\"Primeras filas de X_train estandarizado:\")\n", 178 | "pd.DataFrame(X_train_standard, columns=X.columns).head()\n" 179 | ] 180 | }, 181 | { 182 | "cell_type": "markdown", 183 | "id": "22aec1a6", 184 | "metadata": {}, 185 | "source": [ 186 | "## MinMaxScaler $\\frac{x_i - min(x)}{max(x) - min(x)}$" 187 | ] 188 | }, 189 | { 190 | "cell_type": "markdown", 191 | "id": "273fc8f4", 192 | "metadata": {}, 193 | "source": [ 194 | "Con el escalador min-max, podemos transformar y escalar nuestros valores de características de tal manera que cada valor está dentro del rango de [0, 1]. \n", 195 | "\n", 196 | "Sin embargo, la clase MinMaxScaler en scikit-learn también le permite especificar su propio límite superior e inferior en el rango de valores escalados utilizando la variable feature_range. \n" 197 | ] 198 | }, 199 | { 200 | "cell_type": "code", 201 | "execution_count": 9, 202 | "id": "eef1ae7a", 203 | "metadata": {}, 204 | "outputs": [ 205 | { 206 | "name": "stdout", 207 | "output_type": "stream", 208 | "text": [ 209 | "\n", 210 | "Primeras filas de X_train escalado con Min-Max:\n" 211 | ] 212 | }, 213 | { 214 | "data": { 215 | "text/html": [ 216 | "
\n", 217 | "\n", 230 | "\n", 231 | " \n", 232 | " \n", 233 | " \n", 234 | " \n", 235 | " \n", 236 | " \n", 237 | " \n", 238 | " \n", 239 | " \n", 240 | " \n", 241 | " \n", 242 | " \n", 243 | " \n", 244 | " \n", 245 | " \n", 246 | " \n", 247 | " \n", 248 | " \n", 249 | " \n", 250 | " \n", 251 | " \n", 252 | " \n", 253 | " \n", 254 | " \n", 255 | " \n", 256 | " \n", 257 | " \n", 258 | " \n", 259 | " \n", 260 | " \n", 261 | " \n", 262 | " \n", 263 | " \n", 264 | " \n", 265 | " \n", 266 | " \n", 267 | " \n", 268 | " \n", 269 | " \n", 270 | " \n", 271 | " \n", 272 | " \n", 273 | " \n", 274 | " \n", 275 | " \n", 276 | " \n", 277 | "
sepal length (cm)sepal width (cm)petal length (cm)petal width (cm)
00.3529410.1818180.4642860.375000
10.5882350.3636360.7142860.583333
20.6176470.5000000.7857140.708333
30.6764710.4545450.5892860.541667
40.8529410.7272730.8928571.000000
\n", 278 | "
" 279 | ], 280 | "text/plain": [ 281 | " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n", 282 | "0 0.352941 0.181818 0.464286 0.375000\n", 283 | "1 0.588235 0.363636 0.714286 0.583333\n", 284 | "2 0.617647 0.500000 0.785714 0.708333\n", 285 | "3 0.676471 0.454545 0.589286 0.541667\n", 286 | "4 0.852941 0.727273 0.892857 1.000000" 287 | ] 288 | }, 289 | "execution_count": 9, 290 | "metadata": {}, 291 | "output_type": "execute_result" 292 | } 293 | ], 294 | "source": [ 295 | "# Escalado Min-Max (reescala características al rango [0, 1])\n", 296 | "scaler_minmax = MinMaxScaler()\n", 297 | "X_train_minmax = scaler_minmax.fit_transform(X_train)\n", 298 | "X_test_minmax = scaler_minmax.transform(X_test)\n", 299 | "\n", 300 | "print(\"\\nPrimeras filas de X_train escalado con Min-Max:\")\n", 301 | "pd.DataFrame(X_train_minmax, columns=X.columns).head()\n" 302 | ] 303 | }, 304 | { 305 | "cell_type": "markdown", 306 | "id": "152864fc", 307 | "metadata": {}, 308 | "source": [ 309 | "## RobustScaler $\\frac{x_i - mediana(x)}{IQR_{(1,3)}(x)}$" 310 | ] 311 | }, 312 | { 313 | "cell_type": "markdown", 314 | "id": "ac12c200", 315 | "metadata": {}, 316 | "source": [ 317 | "Utiliza la mediana y el rango intercuartílico (IQR) para escalar las características, lo que lo hace más resistente a los outliers." 318 | ] 319 | }, 320 | { 321 | "cell_type": "code", 322 | "execution_count": 10, 323 | "id": "a30623a4", 324 | "metadata": {}, 325 | "outputs": [ 326 | { 327 | "name": "stdout", 328 | "output_type": "stream", 329 | "text": [ 330 | "\n", 331 | "Primeras filas de X_train escalado con RobustScaler:\n" 332 | ] 333 | }, 334 | { 335 | "data": { 336 | "text/html": [ 337 | "
\n", 338 | "\n", 351 | "\n", 352 | " \n", 353 | " \n", 354 | " \n", 355 | " \n", 356 | " \n", 357 | " \n", 358 | " \n", 359 | " \n", 360 | " \n", 361 | " \n", 362 | " \n", 363 | " \n", 364 | " \n", 365 | " \n", 366 | " \n", 367 | " \n", 368 | " \n", 369 | " \n", 370 | " \n", 371 | " \n", 372 | " \n", 373 | " \n", 374 | " \n", 375 | " \n", 376 | " \n", 377 | " \n", 378 | " \n", 379 | " \n", 380 | " \n", 381 | " \n", 382 | " \n", 383 | " \n", 384 | " \n", 385 | " \n", 386 | " \n", 387 | " \n", 388 | " \n", 389 | " \n", 390 | " \n", 391 | " \n", 392 | " \n", 393 | " \n", 394 | " \n", 395 | " \n", 396 | " \n", 397 | " \n", 398 | "
sepal length (cm)sepal width (cm)petal length (cm)petal width (cm)
0-0.230769-1.2-0.176471-0.214286
10.384615-0.40.2352940.142857
20.4615380.20.3529410.357143
30.6153850.00.0294120.071429
41.0769231.20.5294120.857143
\n", 399 | "
" 400 | ], 401 | "text/plain": [ 402 | " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n", 403 | "0 -0.230769 -1.2 -0.176471 -0.214286\n", 404 | "1 0.384615 -0.4 0.235294 0.142857\n", 405 | "2 0.461538 0.2 0.352941 0.357143\n", 406 | "3 0.615385 0.0 0.029412 0.071429\n", 407 | "4 1.076923 1.2 0.529412 0.857143" 408 | ] 409 | }, 410 | "execution_count": 10, 411 | "metadata": {}, 412 | "output_type": "execute_result" 413 | } 414 | ], 415 | "source": [ 416 | "# Escalado robusto \n", 417 | "scaler_robust = RobustScaler()\n", 418 | "X_train_robust = scaler_robust.fit_transform(X_train)\n", 419 | "X_test_robust = scaler_robust.transform(X_test)\n", 420 | "\n", 421 | "print(\"\\nPrimeras filas de X_train escalado con RobustScaler:\")\n", 422 | "pd.DataFrame(X_train_robust, columns=X.columns).head()\n" 423 | ] 424 | } 425 | ], 426 | "metadata": { 427 | "kernelspec": { 428 | "display_name": "Python 3 (ipykernel)", 429 | "language": "python", 430 | "name": "python3" 431 | }, 432 | "language_info": { 433 | "codemirror_mode": { 434 | "name": "ipython", 435 | "version": 3 436 | }, 437 | "file_extension": ".py", 438 | "mimetype": "text/x-python", 439 | "name": "python", 440 | "nbconvert_exporter": "python", 441 | "pygments_lexer": "ipython3", 442 | "version": "3.11.4" 443 | } 444 | }, 445 | "nbformat": 4, 446 | "nbformat_minor": 5 447 | } 448 | -------------------------------------------------------------------------------- /Funcion_Aplicar_varios_algoritmos.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 20, 6 | "id": "24f0f807", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "import pandas as pd\n", 11 | "from sklearn.linear_model import LogisticRegression\n", 12 | "from sklearn.neighbors import KNeighborsClassifier\n", 13 | "from sklearn.ensemble import RandomForestClassifier \n", 14 | "from sklearn.metrics import f1_score\n", 15 | "from sklearn.metrics import roc_auc_score\n", 16 | "from sklearn.metrics import precision_score\n", 17 | "from sklearn.metrics import recall_score\n", 18 | "from sklearn.metrics import accuracy_score\n", 19 | "import warnings\n", 20 | "warnings.filterwarnings(\"ignore\")" 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": 11, 26 | "id": "1d4352c6", 27 | "metadata": {}, 28 | "outputs": [], 29 | "source": [ 30 | "X_train = pd.read_csv(\"X_train.csv\") " 31 | ] 32 | }, 33 | { 34 | "cell_type": "code", 35 | "execution_count": 12, 36 | "id": "63770ee0", 37 | "metadata": {}, 38 | "outputs": [], 39 | "source": [ 40 | "y_train = pd.read_csv(\"y_train.csv\") " 41 | ] 42 | }, 43 | { 44 | "cell_type": "code", 45 | "execution_count": 13, 46 | "id": "90209f4b", 47 | "metadata": {}, 48 | "outputs": [], 49 | "source": [ 50 | "X_test = pd.read_csv(\"X_test.csv\") " 51 | ] 52 | }, 53 | { 54 | "cell_type": "code", 55 | "execution_count": 14, 56 | "id": "515093b1", 57 | "metadata": {}, 58 | "outputs": [], 59 | "source": [ 60 | "y_test = pd.read_csv(\"y_test.csv\") " 61 | ] 62 | }, 63 | { 64 | "cell_type": "code", 65 | "execution_count": 15, 66 | "id": "54e8fce8", 67 | "metadata": {}, 68 | "outputs": [], 69 | "source": [ 70 | "def evaluate_model(model, X_train, y_train, X_test, y_test):\n", 71 | " model.fit(X_train, y_train)\n", 72 | " y_train_pred = model.predict(X_train)\n", 73 | " y_test_pred = model.predict(X_test)\n", 74 | " df = pd.DataFrame({'Accuracy_train': [accuracy_score(y_train, y_train_pred)],\n", 75 | " 'Accuracy_test': [accuracy_score(y_test, y_test_pred)],\n", 76 | " 'Precision_train': [precision_score(y_train, y_train_pred)],\n", 77 | " 'Precision_test': [precision_score(y_test, y_test_pred)],\n", 78 | " 'Recall_train': [recall_score(y_train, y_train_pred)],\n", 79 | " 'Recall_test': [recall_score(y_test, y_test_pred)],\n", 80 | " 'f1_score_train': [f1_score(y_train, y_train_pred)],\n", 81 | " 'f1_score_test': [f1_score(y_test, y_test_pred)],\n", 82 | " 'Roc_auc_train': [roc_auc_score(y_train, y_train_pred)],\n", 83 | " 'Roc_auc_test': [roc_auc_score(y_test, y_test_pred)],\n", 84 | " \n", 85 | " })\n", 86 | " return df" 87 | ] 88 | }, 89 | { 90 | "cell_type": "code", 91 | "execution_count": 16, 92 | "id": "a717df77", 93 | "metadata": {}, 94 | "outputs": [], 95 | "source": [ 96 | "models = {\n", 97 | " 'Logistic Regression': LogisticRegression(random_state=42),\n", 98 | " 'KNN':KNeighborsClassifier(),\n", 99 | " 'Random Forest': RandomForestClassifier(random_state = 42),\n", 100 | "}" 101 | ] 102 | }, 103 | { 104 | "cell_type": "code", 105 | "execution_count": 21, 106 | "id": "a41b2b5f", 107 | "metadata": {}, 108 | "outputs": [], 109 | "source": [ 110 | "# iterar a través del diccionario y evaluar cada modelo\n", 111 | "results = []\n", 112 | "for name, model in models.items():\n", 113 | " model_results = evaluate_model(model, X_train, y_train, X_test, y_test)\n", 114 | " model_results['model'] = name\n", 115 | " results.append(model_results)" 116 | ] 117 | }, 118 | { 119 | "cell_type": "code", 120 | "execution_count": 22, 121 | "id": "92822939", 122 | "metadata": {}, 123 | "outputs": [], 124 | "source": [ 125 | "resultados = pd.concat(results, axis=0).reset_index(drop=True)" 126 | ] 127 | }, 128 | { 129 | "cell_type": "code", 130 | "execution_count": 23, 131 | "id": "4dc5a126", 132 | "metadata": {}, 133 | "outputs": [ 134 | { 135 | "data": { 136 | "text/html": [ 137 | "
\n", 138 | "\n", 151 | "\n", 152 | " \n", 153 | " \n", 154 | " \n", 155 | " \n", 156 | " \n", 157 | " \n", 158 | " \n", 159 | " \n", 160 | " \n", 161 | " \n", 162 | " \n", 163 | " \n", 164 | " \n", 165 | " \n", 166 | " \n", 167 | " \n", 168 | " \n", 169 | " \n", 170 | " \n", 171 | " \n", 172 | " \n", 173 | " \n", 174 | " \n", 175 | " \n", 176 | " \n", 177 | " \n", 178 | " \n", 179 | " \n", 180 | " \n", 181 | " \n", 182 | " \n", 183 | " \n", 184 | " \n", 185 | " \n", 186 | " \n", 187 | " \n", 188 | " \n", 189 | " \n", 190 | " \n", 191 | " \n", 192 | " \n", 193 | " \n", 194 | " \n", 195 | " \n", 196 | " \n", 197 | " \n", 198 | " \n", 199 | " \n", 200 | " \n", 201 | " \n", 202 | " \n", 203 | " \n", 204 | " \n", 205 | " \n", 206 | " \n", 207 | " \n", 208 | " \n", 209 | " \n", 210 | " \n", 211 | " \n", 212 | "
Accuracy_trainAccuracy_testPrecision_trainPrecision_testRecall_trainRecall_testf1_score_trainf1_score_testRoc_auc_trainRoc_auc_testmodel
00.8102140.8058650.8536080.8548950.7480540.7400320.7973520.7933280.8101050.806328Logistic Regression
10.8527080.7954820.9054350.8522910.7871080.7182850.8421350.7795710.8525940.796025KNN
20.9322230.7851610.9656890.8072340.8960440.7531560.9295640.7792580.9321600.785387Random Forest
\n", 213 | "
" 214 | ], 215 | "text/plain": [ 216 | " Accuracy_train Accuracy_test Precision_train Precision_test \\\n", 217 | "0 0.810214 0.805865 0.853608 0.854895 \n", 218 | "1 0.852708 0.795482 0.905435 0.852291 \n", 219 | "2 0.932223 0.785161 0.965689 0.807234 \n", 220 | "\n", 221 | " Recall_train Recall_test f1_score_train f1_score_test Roc_auc_train \\\n", 222 | "0 0.748054 0.740032 0.797352 0.793328 0.810105 \n", 223 | "1 0.787108 0.718285 0.842135 0.779571 0.852594 \n", 224 | "2 0.896044 0.753156 0.929564 0.779258 0.932160 \n", 225 | "\n", 226 | " Roc_auc_test model \n", 227 | "0 0.806328 Logistic Regression \n", 228 | "1 0.796025 KNN \n", 229 | "2 0.785387 Random Forest " 230 | ] 231 | }, 232 | "execution_count": 23, 233 | "metadata": {}, 234 | "output_type": "execute_result" 235 | } 236 | ], 237 | "source": [ 238 | "resultados.sort_values(by='Accuracy_test', ascending=False)" 239 | ] 240 | } 241 | ], 242 | "metadata": { 243 | "kernelspec": { 244 | "display_name": "Python 3 (ipykernel)", 245 | "language": "python", 246 | "name": "python3" 247 | }, 248 | "language_info": { 249 | "codemirror_mode": { 250 | "name": "ipython", 251 | "version": 3 252 | }, 253 | "file_extension": ".py", 254 | "mimetype": "text/x-python", 255 | "name": "python", 256 | "nbconvert_exporter": "python", 257 | "pygments_lexer": "ipython3", 258 | "version": "3.9.13" 259 | } 260 | }, 261 | "nbformat": 4, 262 | "nbformat_minor": 5 263 | } 264 | -------------------------------------------------------------------------------- /Principal Component Analysis-.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 24, 6 | "id": "988ac54d", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "#Librerias basicas\n", 11 | "import pandas as pd\n", 12 | "import matplotlib.pyplot as plt\n", 13 | "import seaborn as sns\n", 14 | "\n", 15 | "import warnings\n", 16 | "warnings.filterwarnings(\"ignore\")" 17 | ] 18 | }, 19 | { 20 | "cell_type": "markdown", 21 | "id": "937ea1c8", 22 | "metadata": {}, 23 | "source": [ 24 | "## ¿Qué es Principal Component Analysis?\n", 25 | "\n", 26 | "Básicamente, PCA es una técnica de reducción de dimensionalidad lineal (algoritmo) que transforma un conjunto de variables correlacionadas en un número más pequeño de variables no correlacionadas llamadas componentes principales, mientras retiene tanta variación de los datos originales como sea posible." 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "execution_count": 4, 32 | "id": "e0033ca9", 33 | "metadata": {}, 34 | "outputs": [], 35 | "source": [ 36 | "#Importar el conjunto de datos\n", 37 | "dataset = pd.read_csv(\"iris.csv\")" 38 | ] 39 | }, 40 | { 41 | "cell_type": "code", 42 | "execution_count": 5, 43 | "id": "11045ba1", 44 | "metadata": {}, 45 | "outputs": [ 46 | { 47 | "data": { 48 | "text/html": [ 49 | "
\n", 50 | "\n", 63 | "\n", 64 | " \n", 65 | " \n", 66 | " \n", 67 | " \n", 68 | " \n", 69 | " \n", 70 | " \n", 71 | " \n", 72 | " \n", 73 | " \n", 74 | " \n", 75 | " \n", 76 | " \n", 77 | " \n", 78 | " \n", 79 | " \n", 80 | " \n", 81 | " \n", 82 | " \n", 83 | " \n", 84 | " \n", 85 | " \n", 86 | " \n", 87 | " \n", 88 | " \n", 89 | " \n", 90 | " \n", 91 | " \n", 92 | " \n", 93 | " \n", 94 | " \n", 95 | " \n", 96 | " \n", 97 | " \n", 98 | " \n", 99 | " \n", 100 | " \n", 101 | " \n", 102 | " \n", 103 | " \n", 104 | " \n", 105 | " \n", 106 | " \n", 107 | " \n", 108 | " \n", 109 | " \n", 110 | " \n", 111 | " \n", 112 | " \n", 113 | " \n", 114 | " \n", 115 | " \n", 116 | "
sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
05.13.51.40.2setosa
14.93.01.40.2setosa
24.73.21.30.2setosa
34.63.11.50.2setosa
45.03.61.40.2setosa
\n", 117 | "
" 118 | ], 119 | "text/plain": [ 120 | " sepal_length sepal_width petal_length petal_width species\n", 121 | "0 5.1 3.5 1.4 0.2 setosa\n", 122 | "1 4.9 3.0 1.4 0.2 setosa\n", 123 | "2 4.7 3.2 1.3 0.2 setosa\n", 124 | "3 4.6 3.1 1.5 0.2 setosa\n", 125 | "4 5.0 3.6 1.4 0.2 setosa" 126 | ] 127 | }, 128 | "execution_count": 5, 129 | "metadata": {}, 130 | "output_type": "execute_result" 131 | } 132 | ], 133 | "source": [ 134 | "#Veamos el dataset\n", 135 | "dataset.head()" 136 | ] 137 | }, 138 | { 139 | "cell_type": "code", 140 | "execution_count": 9, 141 | "id": "59a394ee", 142 | "metadata": {}, 143 | "outputs": [], 144 | "source": [ 145 | "#Preprocesamiento\n", 146 | "X = dataset.drop('species', 1)\n", 147 | "y = dataset['species']" 148 | ] 149 | }, 150 | { 151 | "cell_type": "code", 152 | "execution_count": 10, 153 | "id": "5455602f", 154 | "metadata": {}, 155 | "outputs": [], 156 | "source": [ 157 | "#Separamos en train y test\n", 158 | "from sklearn.model_selection import train_test_split\n", 159 | "\n", 160 | "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)" 161 | ] 162 | }, 163 | { 164 | "cell_type": "code", 165 | "execution_count": 11, 166 | "id": "ebb4ec14", 167 | "metadata": {}, 168 | "outputs": [], 169 | "source": [ 170 | "#Normalizamos los datos para que PCA funcione mejor\n", 171 | "from sklearn.preprocessing import StandardScaler\n", 172 | "\n", 173 | "sc = StandardScaler()\n", 174 | "X_train = sc.fit_transform(X_train)\n", 175 | "X_test = sc.transform(X_test)" 176 | ] 177 | }, 178 | { 179 | "cell_type": "code", 180 | "execution_count": 12, 181 | "id": "63b92fdd", 182 | "metadata": {}, 183 | "outputs": [], 184 | "source": [ 185 | "#Aplicamos PCA\n", 186 | "from sklearn.decomposition import PCA\n", 187 | "\n", 188 | "pca = PCA()\n", 189 | "X_train = pca.fit_transform(X_train)\n", 190 | "X_test = pca.transform(X_test)" 191 | ] 192 | }, 193 | { 194 | "cell_type": "code", 195 | "execution_count": 13, 196 | "id": "e683a085", 197 | "metadata": {}, 198 | "outputs": [ 199 | { 200 | "data": { 201 | "text/plain": [ 202 | "array([0.72551423, 0.23000922, 0.03960774, 0.00486882])" 203 | ] 204 | }, 205 | "execution_count": 13, 206 | "metadata": {}, 207 | "output_type": "execute_result" 208 | } 209 | ], 210 | "source": [ 211 | "#Análisis de la varianza explicada para cada componente\n", 212 | "explained_variance = pca.explained_variance_ratio_\n", 213 | "explained_variance" 214 | ] 215 | }, 216 | { 217 | "cell_type": "markdown", 218 | "id": "e1965326", 219 | "metadata": {}, 220 | "source": [ 221 | "El primer componente principal es responsable de la varianza del 72,55%. De manera similar, el segundo componente principal causa una variación del 23% en el conjunto de datos. En conjunto, podemos decir que (72,55 + 23) el 95,55% por ciento de la información de clasificación contenida en el conjunto de características es capturada por los dos primeros componentes principales." 222 | ] 223 | }, 224 | { 225 | "cell_type": "code", 226 | "execution_count": 15, 227 | "id": "ec6a5f27", 228 | "metadata": {}, 229 | "outputs": [], 230 | "source": [ 231 | "components_ = dataset.columns[0:4]\n", 232 | "\n", 233 | "comp_df_b = pd.DataFrame(list(zip(components_,pca.explained_variance_ratio_*100)),columns=[\"Componentes\",\"Contribucion\"])" 234 | ] 235 | }, 236 | { 237 | "cell_type": "code", 238 | "execution_count": 23, 239 | "id": "47dda0ba", 240 | "metadata": {}, 241 | "outputs": [ 242 | { 243 | "data": { 244 | "image/png": "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\n", 245 | "text/plain": [ 246 | "
" 247 | ] 248 | }, 249 | "metadata": {}, 250 | "output_type": "display_data" 251 | } 252 | ], 253 | "source": [ 254 | "plt.figure(figsize=(6, 4))\n", 255 | "\n", 256 | "sns.barplot(\n", 257 | " data=comp_df_b,\n", 258 | " x=\"Componentes\", \n", 259 | " y=\"Contribucion\");\n", 260 | "\n", 261 | "plt.title(\"Porcentaje de contribución a la varianza\")\n", 262 | "plt.show()" 263 | ] 264 | } 265 | ], 266 | "metadata": { 267 | "kernelspec": { 268 | "display_name": "Python 3 (ipykernel)", 269 | "language": "python", 270 | "name": "python3" 271 | }, 272 | "language_info": { 273 | "codemirror_mode": { 274 | "name": "ipython", 275 | "version": 3 276 | }, 277 | "file_extension": ".py", 278 | "mimetype": "text/x-python", 279 | "name": "python", 280 | "nbconvert_exporter": "python", 281 | "pygments_lexer": "ipython3", 282 | "version": "3.9.13" 283 | } 284 | }, 285 | "nbformat": 4, 286 | "nbformat_minor": 5 287 | } 288 | -------------------------------------------------------------------------------- /Proyecto final Data Science - Coderhouse/Predicción de Diabetes/README.md: -------------------------------------------------------------------------------- 1 | ## Proyecto final Data Science - 04/2023 2 | 3 | Objetivo: 4 | Aplicar distintos algoritmos de Machine learning que puedan predecir si una persona tiene o no la enfermedad de diabetes, en funcion a los atributos utilizados como input del modelo. 5 | 6 | Contexto: 7 | Contamos con un informe donde se estudió un grupo de personas y se guardaron datos sobre determinados indicadores de salud. Los siguientes datos estan etiquetados, contamos con una variable que nos dice si los encuestados tienen diabetes, no tienen diabetes o tienen pre diabetes. 8 | 9 | Si bien no existe una cura para la diabetes, estrategias como perder peso, comer sano, mantenerse activo y recibir tratamientos médicos pueden mitigar los daños de esta enfermedad en muchos pacientes. El diagnóstico temprano puede conducir a cambios en el estilo de vida y a un tratamiento más eficaz, lo que convierte a los modelos predictivos del riesgo de diabetes en herramientas importantes para el público y los funcionarios de salud pública. 10 | -------------------------------------------------------------------------------- /Proyecto final Data Science - Coderhouse/README.md: -------------------------------------------------------------------------------- 1 | ## Entrenamiento y optimización de modelos de Machine Learning 2 | 3 | Requisitos base 4 | 5 | Un Notebook (Colab o Jupyter) que debe contener: 6 | 7 | - Abstracto con motivación y audiencia: Descripción de alto nivel de lo que motiva a analizar los datos elegidos y audiencia que se podría beneficiar de este análisis. 8 | - Preguntas/Problema que buscamos resolver: Si bien puede haber más de una problemática a resolver, la problemática principal debe encuadrarse como un problema de clasificación o regresión. 9 | - Breve Análisis Exploratorio de Datos (EDA): Análisis descriptivo de los datos mediante visualizaciones y herramientas estadísticas, análisis de valores faltantes. 10 | - Ingeniería de atributos: Creación de nuevas variables, transformación de variables existentes (i.e normalización de variables, encoding, etc.) 11 | - Entrenamiento y Testeo: Entrenamiento y testeo de al menos 2 modelos distintos de Machine Learning utilizando algún método de validación cruzada. 12 | - Optimización: Utilizar alguna técnica de optimización de hiperparámetros (e.g gridsearch, randomizedsearch, etc.) 13 | - Selección de modelos: utilizar las métricas apropiadas para la selección del mejor modelo (e.g AUC, MSE, etc.) 14 | -------------------------------------------------------------------------------- /Proyecto-Dataset-Iris/README.md: -------------------------------------------------------------------------------- 1 | # Iris 2 | 3 | ![pr2_DD8391110A5C5F6F787B](https://user-images.githubusercontent.com/86261762/197586185-d474b236-f31b-4d98-b5b0-3fed800f4508.png) 4 | 5 | Sin duda alguna el famoso dataset de Iris es el primer conjunto de datos con el que nos enfrentamos al aprender sobre proyectos de machine learning. 6 | En este proyecto se contruye mediante un algoritmo de clasificación, un clasificador para las flores del tipo Iris. Este conjunto de datos contiene 50 muestras de cada una de tres especies de Iris: Iris setosa, Iris virginica e Iris versicolor, para cada una de estas especies se midieron cuatro rasgos de cada muestra: la longitud y el ancho del sépalos y pétalos. 7 | 8 | Librerías 9 | 10 | - pandas 11 | - numpy 12 | - seaborn 13 | - matplotlib 14 | - sklearn 15 | 16 | -------------------------------------------------------------------------------- /Proyecto_calorias/README.md: -------------------------------------------------------------------------------- 1 | ## Machine Learning - Regresión lineal 2 | 3 | 4 | ![2](https://user-images.githubusercontent.com/86261762/207149442-3089993c-a56e-45d4-af6d-ce6099812ed0.png) 5 | 6 | 7 | En este proyecto de ML vamos a predecir cuantas calorías se queman en función a: 8 | 9 | 1) Los minutos que corre una persona, para el caso de regresión lineal simple. 10 | 2) Los minutos que corre una persona, la velocidad y su peso corporal, para el caso de regresión lineal múltiple. 11 | 12 | El objetivo es encontrar los parámetros de la recta que mejor se ajustan a esos datos. 13 | 14 | 15 | Librerías utilizadas: 16 | 17 | - numpy 18 | - pandas 19 | - matplotlib. 20 | - sklearn 21 | 22 | Para resolver este problema vamos a realizar los siguientes pasos: 23 | 24 | - Carga de datos 25 | - Cambio de estructura de datos 26 | - Visualización de los datos 27 | - Creacción del modelo (hipótesis) y ajuste 28 | - Obtención del modelo (hipótesis) 29 | - Visualización del modelo 30 | - Medición de la calidad de los resultados 31 | -------------------------------------------------------------------------------- /Proyecto_salarios/README.md: -------------------------------------------------------------------------------- 1 | # Rvisión salarial utilizando Regresión lineal 2 | 3 | Contexto empresarial: Eres un científico de datos en una organización. Su empresa está pasando por una revisión interna de sus prácticas de contratación y compensación 4 | a los empleados. En los últimos años, su empresa ha tenido poco éxito en la conversión de candidatas mujeres de alta calidad que deseaba contratar. 5 | La gerencia plantea la hipótesis de que esto se debe a una posible discriminación salarial y quiere averiguar qué la está causando. 6 | 7 | Problema empresarial: Como parte de la revisión interna, el departamento de recursos humanos se ha acercado a usted para investigar específicamente la siguiente pregunta: 8 | "En general, ¿se les paga más a los hombres que a las mujeres en su organización? Si es así, ¿qué esta conduciendo esta brecha?" 9 | 10 | Contexto analítico: El departamento de recursos humanos le ha proporcionado una base de datos de empleados que contiene información sobre varios atributos 11 | como rendimiento, educación, ingresos, antigüedad, etc. 12 | Deberá usar técnicas de regresión lineal en este conjunto de datos para resolver el problema comercial descrito anteriormente. 13 | La regresión lineal cuantifica la correlación entre la variable dependiente (salario) y las variables independientes (por ejemplo, educación, ingresos, antigüedad, etc.) 14 | -------------------------------------------------------------------------------- /Proyecto_salarios/data_rrhh.csv: -------------------------------------------------------------------------------- 1 | jobtitle,gender,age,performance,education,department,seniority,income,bonus 2 | Graphic Designer,Female,18,5,College,Operations,2,42363,9938 3 | Software Engineer,Male,21,5,College,Management,5,108476,11128 4 | Warehouse Associate,Female,19,4,PhD,Administration,5,90208,9268 5 | Software Engineer,Male,20,5,Masters,Sales,4,108080,10154 6 | Graphic Designer,Male,26,5,Masters,Engineering,5,99464,9319 7 | IT,Female,20,5,PhD,Operations,4,70890,10126 8 | Graphic Designer,Female,20,5,College,Sales,4,67585,10541 9 | Software Engineer,Male,18,4,PhD,Engineering,5,97523,10240 10 | Graphic Designer,Female,33,5,High School,Engineering,5,112976,9836 11 | Sales Associate,Female,35,5,College,Engineering,5,106524,9941 12 | Graphic Designer,Male,24,5,PhD,Engineering,5,102261,10212 13 | Driver,Female,18,5,College,Management,3,62759,10124 14 | Financial Analyst,Female,19,5,College,Sales,3,84007,8990 15 | Warehouse Associate,Female,30,5,Masters,Administration,5,86220,9583 16 | Warehouse Associate,Female,35,5,PhD,Operations,4,95584,9745 17 | Marketing Associate,Female,27,5,PhD,Management,3,73357,10334 18 | Financial Analyst,Female,23,5,PhD,Administration,5,88422,10768 19 | Warehouse Associate,Female,24,5,College,Administration,5,99545,9949 20 | Sales Associate,Male,21,5,High School,Engineering,5,90386,9461 21 | Data Scientist,Female,30,5,College,Engineering,5,92067,9838 22 | Warehouse Associate,Male,36,5,PhD,Engineering,5,108446,9210 23 | Financial Analyst,Male,24,5,PhD,Sales,3,83323,9329 24 | Warehouse Associate,Male,24,5,PhD,Sales,3,85205,9792 25 | Driver,Female,35,5,College,Management,1,72038,9031 26 | Software Engineer,Male,21,4,PhD,Engineering,5,132823,9625 27 | Graphic Designer,Female,26,5,College,Engineering,2,71182,10015 28 | Software Engineer,Male,19,5,Masters,Administration,4,100305,9618 29 | Graphic Designer,Male,35,5,PhD,Management,3,88566,9469 30 | Warehouse Associate,Male,34,5,PhD,Engineering,4,104271,10177 31 | IT,Female,38,5,College,Administration,5,112392,10504 32 | Software Engineer,Male,20,5,Masters,Management,2,66359,10137 33 | Sales Associate,Male,35,5,Masters,Management,5,103007,10512 34 | Marketing Associate,Female,26,5,PhD,Operations,4,80306,9233 35 | Data Scientist,Female,22,5,High School,Engineering,3,74523,9972 36 | Data Scientist,Female,45,5,PhD,Management,5,113252,10139 37 | Software Engineer,Male,27,5,PhD,Management,4,96040,10050 38 | Software Engineer,Male,21,5,High School,Management,4,91674,9780 39 | Software Engineer,Male,31,5,High School,Operations,3,92928,9094 40 | IT,Female,33,5,College,Management,4,84638,9409 41 | IT,Male,19,5,College,Administration,4,78986,9023 42 | Data Scientist,Male,29,5,PhD,Administration,5,96355,9784 43 | Data Scientist,Male,32,5,PhD,Management,3,87121,8703 44 | Software Engineer,Male,31,5,PhD,Sales,2,88724,8949 45 | Warehouse Associate,Male,22,5,Masters,Engineering,2,58219,9868 46 | Software Engineer,Male,25,5,Masters,Sales,5,119024,11293 47 | Software Engineer,Male,22,5,Masters,Management,3,81503,9729 48 | Data Scientist,Female,21,5,PhD,Operations,3,70643,10033 49 | Graphic Designer,Female,26,5,PhD,Sales,3,74167,10346 50 | Marketing Associate,Male,21,5,College,Management,5,86886,9424 51 | IT,Female,22,5,High School,Sales,4,102120,10215 52 | Sales Associate,Male,18,4,Masters,Operations,5,90780,9353 53 | Software Engineer,Male,18,4,Masters,Operations,4,89474,9104 54 | Marketing Associate,Female,35,5,PhD,Engineering,5,97376,9564 55 | Data Scientist,Female,33,5,Masters,Sales,2,89415,9654 56 | Software Engineer,Female,22,4,Masters,Operations,3,88037,8949 57 | Data Scientist,Female,24,4,College,Administration,4,71105,8623 58 | Driver,Female,31,5,High School,Engineering,2,62692,8886 59 | Software Engineer,Male,40,5,PhD,Management,4,112466,9493 60 | Data Scientist,Female,49,5,College,Operations,3,71193,8331 61 | Marketing Associate,Female,20,5,Masters,Administration,4,76286,9479 62 | Financial Analyst,Male,18,5,PhD,Engineering,4,97328,9874 63 | Graphic Designer,Male,34,5,Masters,Management,3,86766,9841 64 | Warehouse Associate,Female,41,5,College,Operations,2,82453,9564 65 | Software Engineer,Male,19,5,PhD,Administration,3,88814,10153 66 | Driver,Male,31,5,College,Management,4,82832,9958 67 | Sales Associate,Female,33,5,High School,Engineering,3,83034,8841 68 | Warehouse Associate,Female,53,5,College,Engineering,5,127085,9687 69 | Sales Associate,Male,24,5,High School,Administration,1,66168,9242 70 | Data Scientist,Male,18,5,PhD,Administration,4,59212,10209 71 | Data Scientist,Male,18,5,High School,Operations,2,55189,9405 72 | Manager,Male,25,5,College,Engineering,5,131311,11067 73 | Sales Associate,Female,31,5,College,Management,1,52633,9516 74 | Warehouse Associate,Female,31,4,PhD,Operations,5,101154,10324 75 | Financial Analyst,Female,42,5,College,Engineering,5,113640,8762 76 | Data Scientist,Male,22,4,High School,Management,2,50401,8590 77 | Marketing Associate,Female,23,5,PhD,Administration,3,46263,9432 78 | Warehouse Associate,Female,36,4,College,Management,3,74938,8234 79 | Marketing Associate,Female,39,5,College,Sales,5,103384,9168 80 | Warehouse Associate,Female,39,5,College,Engineering,2,77584,9798 81 | Graphic Designer,Male,23,5,Masters,Management,3,83031,10240 82 | Financial Analyst,Male,29,5,Masters,Engineering,5,111019,10042 83 | Warehouse Associate,Female,25,3,Masters,Engineering,4,80192,8747 84 | Warehouse Associate,Male,21,5,PhD,Management,1,48755,9931 85 | Warehouse Associate,Male,28,5,High School,Engineering,2,59044,9368 86 | Manager,Male,29,4,PhD,Engineering,5,146615,10173 87 | Data Scientist,Female,32,5,PhD,Engineering,1,46693,8457 88 | IT,Female,48,5,PhD,Operations,3,88152,8857 89 | Data Scientist,Female,38,5,Masters,Administration,5,127156,9979 90 | Graphic Designer,Male,31,5,PhD,Engineering,1,69462,8872 91 | Manager,Male,19,4,PhD,Sales,5,119162,9395 92 | Software Engineer,Male,26,5,PhD,Operations,2,68466,8384 93 | Financial Analyst,Female,36,5,Masters,Administration,3,92622,8509 94 | Marketing Associate,Female,34,5,Masters,Engineering,1,51331,9130 95 | Warehouse Associate,Male,49,5,High School,Sales,4,109419,8375 96 | Software Engineer,Male,18,5,College,Operations,3,80355,9945 97 | Driver,Female,21,4,Masters,Sales,4,77032,8725 98 | Financial Analyst,Female,47,5,High School,Sales,5,146190,8961 99 | Graphic Designer,Female,22,4,Masters,Administration,4,96045,9129 100 | Software Engineer,Male,29,5,High School,Management,1,82534,8914 101 | IT,Male,55,5,College,Management,5,127769,8802 102 | Marketing Associate,Female,34,5,High School,Sales,5,96061,9093 103 | Warehouse Associate,Female,48,5,College,Management,5,111342,10122 104 | Financial Analyst,Female,43,5,PhD,Management,3,117554,8369 105 | Financial Analyst,Female,30,5,High School,Engineering,5,109369,10052 106 | Warehouse Associate,Female,25,4,Masters,Operations,4,92358,9611 107 | Financial Analyst,Female,21,4,Masters,Operations,4,91978,8647 108 | Sales Associate,Male,28,5,PhD,Administration,1,52290,9330 109 | IT,Male,18,5,High School,Engineering,1,47036,9130 110 | Warehouse Associate,Male,46,5,PhD,Sales,1,76517,8502 111 | Marketing Associate,Female,29,4,Masters,Administration,5,89822,8818 112 | Driver,Male,32,4,PhD,Engineering,4,90368,8215 113 | Financial Analyst,Male,26,5,College,Engineering,4,106194,9421 114 | Warehouse Associate,Female,51,5,High School,Management,3,87684,8645 115 | Marketing Associate,Female,40,5,Masters,Administration,3,62040,8056 116 | Sales Associate,Female,52,5,Masters,Engineering,2,84132,7204 117 | Financial Analyst,Female,29,4,Masters,Administration,4,88047,8459 118 | Driver,Female,37,5,Masters,Administration,2,65012,8357 119 | Marketing Associate,Female,23,4,Masters,Engineering,4,59251,8596 120 | Graphic Designer,Female,18,4,PhD,Operations,3,78462,8743 121 | Driver,Male,40,5,College,Management,2,97414,8833 122 | Driver,Female,18,4,Masters,Administration,2,42722,8515 123 | Marketing Associate,Female,26,4,College,Management,3,63695,8897 124 | Data Scientist,Male,51,5,PhD,Operations,4,110731,8643 125 | Software Engineer,Male,54,5,PhD,Administration,4,125572,8245 126 | Driver,Male,49,5,Masters,Operations,2,72754,8014 127 | IT,Female,26,5,College,Administration,2,53956,9454 128 | Sales Associate,Male,58,5,High School,Engineering,5,135013,8972 129 | IT,Female,49,5,College,Management,3,108495,8668 130 | Financial Analyst,Female,27,4,High School,Administration,4,87608,7511 131 | Data Scientist,Male,38,5,College,Administration,5,101173,9255 132 | IT,Male,53,5,PhD,Management,5,126828,9310 133 | Financial Analyst,Male,49,5,High School,Engineering,4,102716,8290 134 | Marketing Associate,Male,26,4,High School,Engineering,5,77793,8700 135 | Marketing Associate,Female,44,5,High School,Sales,1,57469,7539 136 | Graphic Designer,Male,33,5,Masters,Management,3,83758,8498 137 | Graphic Designer,Male,22,5,PhD,Administration,1,40187,8549 138 | Financial Analyst,Male,41,5,Masters,Engineering,3,98531,6855 139 | Financial Analyst,Male,26,4,College,Operations,4,104435,7802 140 | Data Scientist,Male,29,5,High School,Administration,1,67617,9075 141 | Driver,Female,24,4,High School,Administration,5,91049,10375 142 | Manager,Female,32,5,PhD,Operations,1,98519,8943 143 | Marketing Associate,Female,36,4,High School,Operations,5,91713,7627 144 | Warehouse Associate,Female,41,4,Masters,Engineering,3,88994,8873 145 | Financial Analyst,Male,44,5,Masters,Administration,4,119381,9345 146 | Financial Analyst,Female,43,5,High School,Sales,5,113180,9089 147 | Warehouse Associate,Male,48,5,PhD,Management,2,68639,9553 148 | Software Engineer,Female,28,3,PhD,Engineering,4,103763,8383 149 | Software Engineer,Male,40,5,College,Management,1,68295,7902 150 | Manager,Male,21,4,High School,Engineering,3,103107,9036 151 | Warehouse Associate,Female,19,4,College,Sales,2,64844,9042 152 | Driver,Female,61,5,PhD,Sales,4,129132,8472 153 | Warehouse Associate,Female,57,5,High School,Engineering,3,93470,8578 154 | Data Scientist,Male,28,4,Masters,Administration,5,97625,7985 155 | Graphic Designer,Female,52,5,College,Administration,1,66529,7067 156 | Data Scientist,Female,48,5,Masters,Management,1,100115,7679 157 | Warehouse Associate,Female,57,5,PhD,Administration,2,86423,8058 158 | Sales Associate,Male,30,4,Masters,Sales,5,115165,8841 159 | Data Scientist,Female,53,5,Masters,Engineering,5,133495,8552 160 | Marketing Associate,Male,47,5,Masters,Operations,3,86516,8049 161 | IT,Male,18,4,Masters,Administration,4,69855,8347 162 | Sales Associate,Female,35,4,PhD,Sales,1,84844,7205 163 | Graphic Designer,Female,51,5,College,Operations,3,108241,8602 164 | Warehouse Associate,Female,51,5,High School,Sales,3,106478,8744 165 | Financial Analyst,Male,38,4,College,Management,5,114421,7444 166 | Software Engineer,Male,24,4,Masters,Management,1,79208,8602 167 | Software Engineer,Male,49,5,Masters,Administration,5,142105,9653 168 | Sales Associate,Male,22,5,High School,Administration,1,57783,9306 169 | Marketing Associate,Female,33,5,High School,Operations,2,46903,8130 170 | Financial Analyst,Male,36,5,Masters,Operations,1,71858,7475 171 | Marketing Associate,Female,27,5,High School,Management,1,44325,8867 172 | Driver,Male,39,5,High School,Engineering,4,100183,8832 173 | Graphic Designer,Male,19,4,Masters,Sales,1,61000,8872 174 | Software Engineer,Male,59,5,Masters,Engineering,1,109623,6897 175 | Marketing Associate,Female,20,3,PhD,Sales,5,60070,8273 176 | IT,Female,29,4,Masters,Management,3,72542,8109 177 | Driver,Male,54,5,PhD,Administration,4,110049,8928 178 | IT,Female,30,4,Masters,Engineering,2,80981,8201 179 | IT,Female,42,5,College,Operations,2,83499,8950 180 | Driver,Female,42,4,Masters,Management,4,106788,8974 181 | Warehouse Associate,Female,50,4,Masters,Engineering,5,108922,8477 182 | IT,Female,43,5,PhD,Administration,5,127062,9619 183 | Software Engineer,Male,64,5,College,Sales,4,120593,7573 184 | Manager,Male,23,4,PhD,Administration,1,90685,8816 185 | Marketing Associate,Female,33,5,High School,Engineering,1,49830,8543 186 | Software Engineer,Male,39,4,High School,Sales,4,106896,7918 187 | Sales Associate,Male,58,5,PhD,Sales,2,129425,7524 188 | Marketing Associate,Female,24,3,Masters,Operations,5,82338,7414 189 | Marketing Associate,Female,37,4,PhD,Administration,3,86036,7567 190 | Graphic Designer,Male,53,5,High School,Operations,2,93073,8188 191 | Software Engineer,Male,33,5,PhD,Administration,2,87352,8435 192 | Software Engineer,Male,56,5,PhD,Administration,5,139760,9229 193 | Financial Analyst,Female,52,5,High School,Engineering,2,100070,7169 194 | Warehouse Associate,Male,40,5,Masters,Sales,4,102815,9090 195 | Software Engineer,Male,43,4,PhD,Management,5,101262,7794 196 | Data Scientist,Male,19,5,High School,Operations,2,58054,9585 197 | Sales Associate,Female,53,4,Masters,Operations,5,129609,7392 198 | Sales Associate,Male,44,5,Masters,Administration,2,100091,9185 199 | Manager,Male,23,4,Masters,Sales,3,103479,7850 200 | Driver,Female,51,5,College,Operations,1,72827,7285 201 | IT,Male,20,4,PhD,Engineering,2,74073,8509 202 | Financial Analyst,Male,47,5,College,Administration,2,64852,8023 203 | Graphic Designer,Male,49,5,College,Management,4,106887,8347 204 | Software Engineer,Male,26,4,College,Administration,2,77309,7957 205 | Graphic Designer,Female,44,4,High School,Administration,4,100416,7513 206 | Software Engineer,Male,18,3,Masters,Engineering,2,88482,7385 207 | Data Scientist,Female,32,4,College,Management,4,78623,9012 208 | Sales Associate,Male,18,4,High School,Management,4,80135,9007 209 | Warehouse Associate,Female,63,5,High School,Sales,2,106158,7654 210 | Graphic Designer,Female,37,4,College,Engineering,4,89411,9522 211 | Graphic Designer,Male,22,4,Masters,Sales,2,57398,7827 212 | Marketing Associate,Female,64,5,PhD,Sales,2,95687,7279 213 | Graphic Designer,Female,39,4,PhD,Operations,5,120294,8090 214 | Manager,Male,20,4,College,Sales,2,107140,8199 215 | Financial Analyst,Female,39,5,College,Operations,1,76773,7278 216 | Data Scientist,Female,37,4,Masters,Engineering,2,98010,8210 217 | Data Scientist,Female,39,4,PhD,Management,3,91099,8306 218 | Financial Analyst,Male,29,4,High School,Management,4,89559,8338 219 | Software Engineer,Male,58,5,High School,Management,3,120156,7200 220 | Driver,Male,38,5,PhD,Engineering,4,102966,8978 221 | Marketing Associate,Female,64,5,PhD,Sales,5,128970,7346 222 | Warehouse Associate,Female,51,4,High School,Management,5,121376,7832 223 | Financial Analyst,Male,53,5,Masters,Administration,3,112941,7404 224 | Graphic Designer,Male,46,5,High School,Engineering,4,102736,9736 225 | Driver,Female,28,4,High School,Management,1,64008,7185 226 | IT,Male,52,5,Masters,Engineering,1,90024,7350 227 | Financial Analyst,Female,48,4,Masters,Management,5,129406,7280 228 | IT,Female,34,5,College,Operations,1,54245,8245 229 | Marketing Associate,Female,27,4,High School,Administration,5,67530,9311 230 | Graphic Designer,Male,18,4,High School,Sales,4,75506,9289 231 | Graphic Designer,Female,31,4,College,Engineering,2,81826,8375 232 | Software Engineer,Female,27,4,High School,Operations,1,65711,7789 233 | Sales Associate,Male,29,3,Masters,Management,2,61164,6346 234 | Driver,Female,41,5,Masters,Administration,1,73801,8317 235 | Manager,Male,62,5,College,Administration,5,157410,7840 236 | Graphic Designer,Male,23,4,High School,Operations,1,50601,7351 237 | Data Scientist,Male,29,4,High School,Engineering,3,65907,7668 238 | Data Scientist,Male,38,5,High School,Sales,2,80937,8335 239 | Driver,Male,34,4,College,Administration,5,90330,7022 240 | Sales Associate,Female,62,5,College,Operations,2,93268,7657 241 | IT,Male,40,4,College,Administration,5,109345,7791 242 | Software Engineer,Male,27,4,Masters,Engineering,1,66084,7769 243 | Financial Analyst,Male,35,4,PhD,Operations,5,98900,7960 244 | IT,Male,19,4,College,Engineering,3,78113,9335 245 | Software Engineer,Male,36,4,Masters,Engineering,5,130826,9694 246 | Sales Associate,Male,23,3,High School,Engineering,3,85928,7229 247 | IT,Male,43,5,College,Operations,4,98515,9090 248 | Graphic Designer,Female,48,4,College,Administration,4,103133,7793 249 | Marketing Associate,Female,20,4,College,Sales,4,69254,8609 250 | Graphic Designer,Male,27,4,College,Sales,5,114680,8472 251 | Driver,Male,25,3,PhD,Engineering,5,92687,7634 252 | Graphic Designer,Male,21,3,Masters,Engineering,4,72301,8391 253 | Financial Analyst,Male,24,4,Masters,Administration,1,67749,8517 254 | Data Scientist,Female,61,5,Masters,Management,2,97000,7757 255 | Manager,Male,52,5,High School,Operations,2,104526,7253 256 | Data Scientist,Male,26,4,High School,Management,3,80494,8449 257 | Financial Analyst,Female,30,4,High School,Operations,2,40341,7320 258 | Software Engineer,Male,29,4,High School,Engineering,1,74316,8761 259 | IT,Female,46,5,PhD,Sales,3,102247,8307 260 | Driver,Male,49,4,College,Sales,5,104657,6839 261 | Warehouse Associate,Female,37,3,High School,Engineering,5,92722,8010 262 | Sales Associate,Female,51,5,Masters,Management,1,72120,9084 263 | Warehouse Associate,Female,28,4,Masters,Operations,1,62260,7740 264 | Manager,Male,45,5,High School,Engineering,3,123422,8894 265 | Warehouse Associate,Male,48,5,College,Sales,1,62097,7075 266 | Data Scientist,Male,53,5,High School,Management,2,102910,8335 267 | Data Scientist,Female,29,4,High School,Engineering,3,84021,9111 268 | Warehouse Associate,Female,19,4,High School,Operations,1,37026,8636 269 | Driver,Male,18,3,PhD,Operations,3,56318,8407 270 | IT,Male,28,4,High School,Operations,3,64958,7910 271 | Data Scientist,Female,31,4,High School,Operations,2,55954,7999 272 | Graphic Designer,Female,58,4,PhD,Sales,3,108820,7776 273 | IT,Female,41,5,High School,Operations,3,100014,8621 274 | Driver,Male,36,4,PhD,Operations,3,88213,8581 275 | Sales Associate,Female,34,3,High School,Sales,4,108288,7491 276 | Driver,Male,56,5,Masters,Sales,5,100859,8388 277 | Financial Analyst,Female,30,4,Masters,Administration,5,114007,9248 278 | Warehouse Associate,Male,37,4,High School,Management,3,83025,8105 279 | IT,Male,28,4,High School,Administration,5,95964,9147 280 | Warehouse Associate,Female,47,4,College,Administration,2,94973,7885 281 | Graphic Designer,Male,44,4,High School,Sales,4,102108,8339 282 | Data Scientist,Male,24,4,Masters,Sales,2,60736,7873 283 | IT,Male,47,5,PhD,Operations,1,82126,6971 284 | Financial Analyst,Male,33,5,Masters,Operations,1,69878,8389 285 | IT,Female,22,4,Masters,Sales,1,37780,8443 286 | Graphic Designer,Male,57,4,Masters,Management,4,132873,7266 287 | Financial Analyst,Male,20,4,High School,Operations,2,65164,7827 288 | IT,Female,65,5,Masters,Administration,4,121210,7949 289 | Graphic Designer,Female,49,4,PhD,Engineering,2,95169,7415 290 | Sales Associate,Male,19,3,PhD,Management,4,94492,8209 291 | Driver,Female,39,4,PhD,Operations,2,75901,7402 292 | Sales Associate,Male,33,3,Masters,Operations,5,100463,6332 293 | Sales Associate,Female,40,4,Masters,Operations,5,92276,9551 294 | Driver,Female,21,2,College,Engineering,5,92458,6966 295 | Graphic Designer,Male,46,4,High School,Administration,4,88571,7464 296 | Financial Analyst,Male,39,4,PhD,Management,3,87409,7168 297 | Warehouse Associate,Male,58,5,College,Sales,4,133927,8278 298 | Manager,Male,33,4,Masters,Sales,1,90482,7094 299 | Marketing Associate,Female,27,4,High School,Sales,4,87435,9230 300 | Graphic Designer,Female,65,5,High School,Operations,4,130251,7750 301 | Manager,Female,34,3,Masters,Management,2,106560,7257 302 | Software Engineer,Male,56,5,College,Operations,3,107366,7774 303 | Warehouse Associate,Female,34,4,High School,Sales,1,56172,7739 304 | Marketing Associate,Female,35,4,Masters,Sales,2,58807,7316 305 | Financial Analyst,Female,49,4,Masters,Sales,4,126626,7144 306 | Manager,Female,24,3,PhD,Sales,3,104202,8456 307 | Warehouse Associate,Male,39,4,PhD,Operations,3,77793,8093 308 | Warehouse Associate,Male,45,4,High School,Operations,2,84930,6914 309 | Driver,Male,32,3,Masters,Engineering,2,69367,6378 310 | Graphic Designer,Male,65,5,Masters,Operations,5,116751,8505 311 | Software Engineer,Male,52,4,Masters,Administration,5,139841,7641 312 | Driver,Female,52,4,College,Administration,2,78750,6666 313 | Warehouse Associate,Female,38,3,College,Administration,5,93306,8265 314 | Financial Analyst,Male,19,4,Masters,Operations,2,60892,8462 315 | Graphic Designer,Male,20,2,Masters,Engineering,5,99543,6111 316 | Sales Associate,Female,29,3,PhD,Operations,2,56745,6495 317 | Data Scientist,Female,29,4,College,Operations,1,59458,7939 318 | Driver,Female,18,2,Masters,Sales,4,71824,6397 319 | Software Engineer,Male,64,5,High School,Operations,2,111903,6824 320 | IT,Male,55,5,College,Sales,3,104361,7813 321 | Warehouse Associate,Female,22,2,High School,Sales,5,91568,7139 322 | Software Engineer,Male,59,4,Masters,Engineering,4,155834,7101 323 | Financial Analyst,Male,30,5,Masters,Sales,1,83620,7864 324 | Marketing Associate,Female,50,5,PhD,Administration,1,81995,7840 325 | Graphic Designer,Female,27,4,PhD,Sales,1,66125,8465 326 | Driver,Male,27,3,College,Engineering,5,89678,7778 327 | Sales Associate,Female,57,5,Masters,Operations,3,98321,8980 328 | Manager,Male,48,4,College,Management,1,109346,7730 329 | Manager,Female,62,4,Masters,Sales,4,155203,7808 330 | IT,Male,64,5,PhD,Management,2,98100,7460 331 | Data Scientist,Female,29,3,High School,Management,4,73306,7765 332 | Driver,Male,40,5,College,Engineering,2,68797,7817 333 | Financial Analyst,Male,58,4,College,Management,4,120154,6751 334 | Warehouse Associate,Female,22,3,College,Sales,4,76865,8145 335 | Graphic Designer,Female,59,4,PhD,Management,4,125770,8013 336 | Financial Analyst,Female,36,4,High School,Administration,5,119931,8400 337 | Marketing Associate,Female,55,4,Masters,Operations,1,71794,6685 338 | Driver,Female,54,4,College,Management,2,87998,7028 339 | Software Engineer,Male,63,4,High School,Administration,5,140445,7905 340 | Warehouse Associate,Male,18,2,College,Administration,5,85306,7735 341 | Sales Associate,Female,31,3,High School,Operations,3,77275,7089 342 | Manager,Male,48,4,PhD,Sales,4,143887,7622 343 | Driver,Female,58,4,High School,Sales,5,130720,7246 344 | Software Engineer,Female,41,4,PhD,Sales,1,86675,7947 345 | Graphic Designer,Female,22,3,College,Operations,2,56954,7550 346 | IT,Male,63,5,Masters,Operations,5,133472,8688 347 | Data Scientist,Male,23,3,Masters,Administration,1,62753,7242 348 | Sales Associate,Male,63,5,PhD,Operations,4,128173,8360 349 | Software Engineer,Female,28,3,College,Engineering,5,112491,8727 350 | Marketing Associate,Female,22,4,PhD,Operations,1,36548,8090 351 | Driver,Female,62,4,PhD,Engineering,1,88781,6413 352 | Software Engineer,Male,65,4,High School,Management,5,157852,6669 353 | Financial Analyst,Male,51,4,PhD,Operations,4,113730,6578 354 | Driver,Male,57,5,College,Management,1,68403,6893 355 | Manager,Male,59,4,PhD,Sales,5,176789,6773 356 | Data Scientist,Female,45,3,Masters,Sales,5,129226,6912 357 | Graphic Designer,Male,24,3,College,Engineering,2,59399,6692 358 | Sales Associate,Male,61,5,PhD,Management,1,102473,8468 359 | Manager,Female,60,4,PhD,Management,3,160614,8354 360 | Sales Associate,Female,28,3,PhD,Sales,3,89570,7411 361 | Marketing Associate,Female,41,4,College,Sales,3,73989,8780 362 | Financial Analyst,Female,35,4,Masters,Management,2,67762,7449 363 | Financial Analyst,Male,35,4,College,Administration,4,90307,7663 364 | Driver,Female,38,3,Masters,Management,2,77751,6554 365 | Data Scientist,Female,57,4,High School,Management,1,87150,6814 366 | Driver,Female,19,2,College,Sales,2,79291,5000 367 | Graphic Designer,Male,55,5,Masters,Engineering,1,79144,8328 368 | Manager,Male,62,5,PhD,Engineering,3,150467,8100 369 | Driver,Female,31,3,Masters,Engineering,2,57070,6615 370 | Marketing Associate,Female,49,4,College,Sales,4,102201,7640 371 | Driver,Male,37,4,College,Engineering,3,70088,8244 372 | Marketing Associate,Female,24,3,PhD,Operations,4,57782,7144 373 | Manager,Male,32,3,High School,Operations,4,117629,6291 374 | Sales Associate,Male,28,3,Masters,Administration,5,118653,7036 375 | Marketing Associate,Female,38,2,High School,Management,5,71772,5764 376 | Software Engineer,Male,65,5,Masters,Sales,1,134758,7500 377 | Marketing Associate,Female,53,5,College,Administration,1,66438,6935 378 | Warehouse Associate,Male,46,3,High School,Operations,4,87134,6184 379 | Marketing Associate,Female,57,4,College,Operations,3,79674,6993 380 | Marketing Associate,Female,45,3,College,Engineering,4,85161,6682 381 | Warehouse Associate,Female,34,2,PhD,Management,5,114479,6700 382 | Marketing Associate,Male,25,3,College,Operations,4,65547,6530 383 | Data Scientist,Female,33,3,High School,Administration,3,71946,6386 384 | Financial Analyst,Male,64,5,High School,Operations,3,96777,7736 385 | Marketing Associate,Female,23,3,PhD,Administration,3,62054,6955 386 | Software Engineer,Male,26,3,High School,Sales,2,61527,6345 387 | Warehouse Associate,Female,24,3,High School,Management,1,64624,8253 388 | Driver,Male,49,4,Masters,Administration,5,118629,6740 389 | Data Scientist,Male,19,2,PhD,Management,5,80171,6454 390 | Sales Associate,Female,47,3,College,Administration,4,105410,6363 391 | Software Engineer,Male,36,4,PhD,Management,3,112134,8587 392 | Warehouse Associate,Female,42,3,Masters,Engineering,1,90005,6631 393 | Marketing Associate,Female,50,5,High School,Operations,1,70374,7466 394 | Manager,Male,21,2,College,Administration,5,121682,7189 395 | Marketing Associate,Female,49,4,PhD,Operations,5,104355,7555 396 | IT,Female,49,4,College,Engineering,1,75811,5520 397 | Sales Associate,Male,48,3,Masters,Engineering,5,113544,6839 398 | Graphic Designer,Female,22,2,Masters,Operations,4,107025,6577 399 | Financial Analyst,Male,20,3,PhD,Operations,3,78564,7875 400 | Financial Analyst,Male,47,5,College,Operations,3,91905,7614 401 | Financial Analyst,Male,60,5,High School,Engineering,4,121728,7932 402 | Marketing Associate,Male,27,4,College,Management,1,54817,6519 403 | Sales Associate,Male,20,3,College,Operations,1,37898,6448 404 | IT,Male,59,5,PhD,Operations,1,79544,6760 405 | Data Scientist,Female,20,3,High School,Engineering,1,40056,7632 406 | IT,Female,45,4,College,Operations,2,70846,7404 407 | Marketing Associate,Female,34,3,High School,Sales,5,79707,7196 408 | Software Engineer,Male,59,4,High School,Operations,4,124817,7268 409 | Manager,Male,53,4,College,Management,1,123114,6946 410 | Financial Analyst,Female,20,3,High School,Operations,3,62377,6999 411 | Data Scientist,Male,28,3,Masters,Sales,3,82850,5794 412 | IT,Female,38,4,High School,Sales,2,75932,7003 413 | Manager,Male,43,3,PhD,Management,2,121949,6739 414 | Marketing Associate,Female,36,3,PhD,Sales,3,82755,7325 415 | Data Scientist,Male,45,3,PhD,Administration,5,122037,8147 416 | Warehouse Associate,Female,19,2,Masters,Administration,2,78021,6970 417 | IT,Male,18,4,PhD,Sales,1,56309,7559 418 | Financial Analyst,Male,59,5,PhD,Management,1,90002,8123 419 | IT,Male,34,3,College,Sales,4,91066,6257 420 | Manager,Male,32,3,Masters,Management,5,118088,7721 421 | Software Engineer,Male,29,3,Masters,Operations,2,92099,7246 422 | Financial Analyst,Female,46,4,College,Administration,4,116758,7744 423 | Marketing Associate,Female,49,5,High School,Sales,2,96500,8092 424 | Manager,Male,48,3,College,Management,5,138851,6929 425 | Marketing Associate,Female,34,4,College,Administration,1,49632,7169 426 | Driver,Female,45,3,PhD,Management,3,76700,7242 427 | IT,Male,58,4,College,Management,5,131275,6997 428 | Manager,Male,21,2,PhD,Operations,3,89648,6573 429 | Graphic Designer,Female,26,2,Masters,Administration,5,100516,6737 430 | Manager,Male,20,2,PhD,Operations,4,112854,6564 431 | Software Engineer,Male,60,4,Masters,Sales,5,155676,6586 432 | Driver,Male,57,4,College,Sales,4,135781,6042 433 | Sales Associate,Female,47,3,College,Engineering,2,84935,6122 434 | Warehouse Associate,Male,31,4,High School,Engineering,2,66407,7522 435 | Driver,Female,56,4,PhD,Sales,3,101249,7329 436 | Manager,Male,57,4,Masters,Administration,4,143189,6321 437 | Graphic Designer,Male,63,5,PhD,Engineering,1,105800,7822 438 | Software Engineer,Male,63,5,College,Operations,2,124871,7327 439 | Data Scientist,Male,23,3,High School,Sales,3,83894,6922 440 | Manager,Female,45,2,High School,Management,5,144146,6340 441 | Marketing Associate,Female,46,4,PhD,Sales,2,69268,7106 442 | Sales Associate,Male,63,5,High School,Sales,1,85948,7646 443 | Software Engineer,Male,20,2,PhD,Engineering,4,92289,6643 444 | Financial Analyst,Male,45,4,High School,Operations,2,75397,6490 445 | Software Engineer,Male,65,4,High School,Engineering,1,102597,5937 446 | Sales Associate,Female,63,4,PhD,Engineering,3,127561,7552 447 | Warehouse Associate,Male,26,3,High School,Sales,3,72945,7279 448 | Software Engineer,Male,60,3,PhD,Management,5,145632,6439 449 | Marketing Associate,Female,36,3,Masters,Sales,3,77223,5272 450 | Manager,Male,63,5,Masters,Operations,1,130093,6416 451 | Marketing Associate,Male,33,3,PhD,Administration,2,53220,5153 452 | Warehouse Associate,Female,41,3,College,Engineering,3,92080,6897 453 | Graphic Designer,Female,55,3,College,Sales,5,118762,6545 454 | Sales Associate,Female,21,2,College,Operations,5,85398,7113 455 | Data Scientist,Male,31,3,College,Operations,1,75227,6027 456 | Data Scientist,Male,41,3,Masters,Administration,4,104314,6611 457 | Manager,Male,27,3,High School,Operations,1,83262,6884 458 | Data Scientist,Female,55,3,Masters,Administration,4,123172,5797 459 | Data Scientist,Male,41,4,PhD,Administration,1,67040,7412 460 | Warehouse Associate,Male,33,3,College,Administration,1,77655,6581 461 | Data Scientist,Female,46,3,College,Administration,3,107476,6182 462 | Software Engineer,Male,45,4,Masters,Operations,2,89811,6471 463 | Graphic Designer,Male,47,3,College,Administration,5,106893,7958 464 | Software Engineer,Male,58,4,Masters,Engineering,5,154039,7304 465 | Warehouse Associate,Male,64,4,High School,Operations,3,116774,6160 466 | Software Engineer,Male,40,2,High School,Management,2,98281,5646 467 | Marketing Associate,Female,18,3,High School,Engineering,2,34208,6620 468 | Software Engineer,Male,42,3,Masters,Engineering,5,121821,6499 469 | Software Engineer,Male,30,2,Masters,Engineering,5,113122,6060 470 | Software Engineer,Male,39,4,PhD,Management,1,92154,7642 471 | Manager,Male,31,3,PhD,Engineering,2,110643,7291 472 | Data Scientist,Female,34,3,PhD,Administration,1,82345,7211 473 | Data Scientist,Male,32,3,PhD,Sales,4,103908,8109 474 | Marketing Associate,Female,24,3,High School,Engineering,3,49303,6615 475 | IT,Female,46,4,High School,Operations,3,98456,7177 476 | Warehouse Associate,Male,49,3,PhD,Operations,4,94927,6274 477 | Software Engineer,Male,30,2,College,Administration,5,76654,6696 478 | Manager,Male,46,4,Masters,Sales,1,107859,6989 479 | Manager,Female,53,4,High School,Operations,3,129885,7314 480 | Graphic Designer,Male,62,5,Masters,Sales,1,85702,6556 481 | Financial Analyst,Female,63,4,Masters,Operations,1,97797,5239 482 | Manager,Male,18,1,Masters,Management,5,115567,4775 483 | Sales Associate,Female,30,3,High School,Administration,4,91566,6996 484 | Sales Associate,Female,61,3,PhD,Management,4,131608,6537 485 | Sales Associate,Male,58,4,PhD,Administration,3,118888,7438 486 | Graphic Designer,Male,55,4,College,Sales,2,75833,6942 487 | Financial Analyst,Female,36,3,College,Engineering,2,65750,6177 488 | Graphic Designer,Male,65,4,College,Management,4,123371,6931 489 | Marketing Associate,Female,49,4,Masters,Sales,3,76806,6662 490 | Warehouse Associate,Male,38,3,College,Management,3,83774,7127 491 | Warehouse Associate,Female,22,1,PhD,Operations,5,114733,6290 492 | Financial Analyst,Male,22,3,PhD,Administration,2,69320,6765 493 | Sales Associate,Male,51,3,PhD,Operations,4,110746,5947 494 | Graphic Designer,Male,61,4,Masters,Engineering,2,105632,6394 495 | Manager,Male,62,4,PhD,Engineering,1,117648,4765 496 | Driver,Female,21,1,PhD,Management,4,89170,6722 497 | Sales Associate,Male,28,3,College,Management,3,85986,8160 498 | Financial Analyst,Male,32,4,Masters,Sales,3,78132,8035 499 | Manager,Male,57,4,Masters,Operations,3,135073,6898 500 | Driver,Female,52,4,High School,Operations,1,78751,6258 501 | Software Engineer,Male,64,5,High School,Sales,2,118231,7440 502 | Data Scientist,Male,58,5,High School,Operations,1,86651,7250 503 | IT,Female,36,3,PhD,Management,3,75518,6711 504 | Data Scientist,Male,44,3,Masters,Management,3,114693,6832 505 | Financial Analyst,Male,39,3,Masters,Engineering,4,116875,5479 506 | IT,Male,23,4,PhD,Administration,1,64468,8113 507 | Marketing Associate,Male,40,2,PhD,Sales,5,116834,5516 508 | Marketing Associate,Female,18,2,Masters,Management,5,75294,6496 509 | Graphic Designer,Female,63,3,College,Management,5,127608,6558 510 | Financial Analyst,Female,43,3,College,Sales,5,116069,6432 511 | Driver,Female,50,3,High School,Engineering,2,86122,6295 512 | Warehouse Associate,Male,23,2,College,Management,4,81988,7160 513 | Warehouse Associate,Female,22,2,Masters,Sales,2,68300,5953 514 | Manager,Male,60,4,College,Sales,2,148178,6777 515 | Financial Analyst,Female,50,3,PhD,Operations,2,94590,5327 516 | Sales Associate,Female,33,2,College,Engineering,4,87418,6430 517 | Warehouse Associate,Male,25,3,College,Administration,1,68384,7443 518 | Manager,Male,48,3,PhD,Operations,1,106056,4161 519 | Software Engineer,Male,35,2,Masters,Management,3,110886,6228 520 | Graphic Designer,Female,37,2,Masters,Management,3,104082,5477 521 | IT,Female,43,3,Masters,Management,4,94354,7052 522 | Warehouse Associate,Male,53,3,PhD,Engineering,5,128669,6866 523 | Financial Analyst,Male,40,3,PhD,Sales,3,88725,6243 524 | Manager,Male,56,3,Masters,Engineering,4,149771,5495 525 | Financial Analyst,Male,45,4,Masters,Management,2,98535,8001 526 | Data Scientist,Female,57,4,PhD,Sales,1,90713,6552 527 | IT,Male,33,3,College,Management,3,88749,7433 528 | Graphic Designer,Male,49,3,Masters,Operations,5,126100,6336 529 | Manager,Male,23,3,College,Sales,3,110594,6753 530 | Manager,Male,60,4,College,Management,3,157644,7213 531 | Manager,Female,41,3,PhD,Management,1,113009,6812 532 | Financial Analyst,Male,20,2,PhD,Engineering,4,105601,5943 533 | Financial Analyst,Male,56,4,High School,Engineering,3,100769,6453 534 | Warehouse Associate,Male,38,3,High School,Administration,2,82244,6020 535 | Marketing Associate,Female,55,4,High School,Administration,2,72030,5569 536 | Driver,Female,20,1,College,Management,3,66203,5627 537 | Driver,Female,34,2,High School,Management,3,67210,5662 538 | Manager,Female,32,2,PhD,Management,2,115383,5878 539 | Graphic Designer,Female,49,3,PhD,Engineering,4,120697,7205 540 | Graphic Designer,Male,29,3,PhD,Operations,2,70311,6235 541 | Data Scientist,Female,49,3,College,Engineering,4,125770,7301 542 | Warehouse Associate,Male,33,1,College,Engineering,5,100465,5520 543 | Software Engineer,Male,30,2,College,Administration,4,83264,6251 544 | Warehouse Associate,Male,61,3,PhD,Engineering,4,126375,6630 545 | Driver,Male,25,3,College,Engineering,1,43076,5829 546 | Data Scientist,Female,37,2,College,Sales,5,97846,7198 547 | Sales Associate,Female,24,2,Masters,Management,3,76522,6900 548 | Driver,Male,42,3,PhD,Operations,4,111726,5989 549 | Sales Associate,Male,55,4,College,Administration,3,112188,6689 550 | Data Scientist,Male,35,2,College,Operations,5,98959,6572 551 | Sales Associate,Female,35,2,College,Management,4,87131,6356 552 | IT,Male,40,3,College,Administration,3,77881,5569 553 | IT,Male,36,3,Masters,Engineering,2,65225,6462 554 | Driver,Male,30,4,Masters,Operations,1,78472,8439 555 | Financial Analyst,Male,58,4,College,Operations,4,126269,6490 556 | Warehouse Associate,Male,61,3,High School,Management,3,106233,5792 557 | IT,Female,37,3,High School,Sales,2,56656,5914 558 | IT,Female,42,3,High School,Engineering,2,70157,6016 559 | Data Scientist,Female,41,2,PhD,Engineering,2,98366,6398 560 | Manager,Male,59,4,College,Engineering,2,127013,6124 561 | Software Engineer,Male,53,3,High School,Management,3,106146,6618 562 | Manager,Male,22,2,Masters,Operations,4,90736,6219 563 | Graphic Designer,Male,44,3,PhD,Sales,1,66611,5924 564 | Manager,Male,57,3,PhD,Operations,5,150914,5982 565 | Graphic Designer,Male,30,2,Masters,Management,4,70559,6497 566 | Data Scientist,Female,33,3,College,Sales,1,73798,6224 567 | Graphic Designer,Male,22,2,High School,Sales,3,69734,5383 568 | Marketing Associate,Female,52,3,High School,Sales,3,94763,5668 569 | Financial Analyst,Female,23,2,College,Administration,5,101534,6861 570 | Graphic Designer,Male,23,1,PhD,Management,5,91504,5136 571 | Sales Associate,Female,19,1,College,Management,4,90426,5020 572 | Manager,Male,20,2,Masters,Operations,3,100183,6363 573 | IT,Female,62,4,College,Operations,2,99369,6746 574 | Graphic Designer,Female,40,3,College,Operations,1,55845,6616 575 | Financial Analyst,Female,26,3,College,Administration,2,48570,6604 576 | Data Scientist,Male,56,3,Masters,Engineering,3,98165,5470 577 | Data Scientist,Female,57,3,Masters,Operations,3,113274,6186 578 | Software Engineer,Male,23,2,Masters,Engineering,3,95754,6596 579 | Data Scientist,Male,27,2,College,Engineering,1,57915,4584 580 | Manager,Male,53,4,College,Sales,2,121506,6122 581 | Manager,Male,37,3,Masters,Management,1,121151,5934 582 | Marketing Associate,Female,46,3,College,Management,3,86241,5814 583 | Data Scientist,Male,40,2,Masters,Management,2,87682,4152 584 | Software Engineer,Male,42,2,College,Administration,3,114029,6842 585 | IT,Male,43,3,College,Management,1,67323,4680 586 | Data Scientist,Male,28,3,High School,Management,2,68049,6660 587 | IT,Male,39,3,High School,Engineering,4,93335,5243 588 | Driver,Female,55,3,PhD,Operations,2,100433,5090 589 | Marketing Associate,Female,62,3,PhD,Operations,5,123242,5453 590 | Sales Associate,Female,32,2,PhD,Administration,2,87585,6476 591 | IT,Female,18,2,Masters,Sales,1,45747,4890 592 | Marketing Associate,Female,41,3,High School,Administration,2,36972,7058 593 | Sales Associate,Female,29,3,High School,Management,3,64754,7377 594 | IT,Female,44,2,PhD,Operations,5,132267,5679 595 | Driver,Male,53,3,PhD,Administration,3,111043,5838 596 | Warehouse Associate,Male,25,2,Masters,Engineering,1,58130,6327 597 | IT,Male,27,3,PhD,Operations,3,90542,7554 598 | Manager,Male,40,2,College,Administration,2,124891,5436 599 | Marketing Associate,Female,54,3,PhD,Engineering,5,112507,7460 600 | Marketing Associate,Female,64,4,College,Sales,5,100316,6010 601 | Software Engineer,Male,63,3,PhD,Engineering,2,134757,5944 602 | Manager,Male,42,3,College,Sales,4,125339,6019 603 | IT,Female,62,4,College,Management,3,111120,6536 604 | Data Scientist,Female,36,3,High School,Operations,3,76523,7122 605 | Driver,Female,50,3,College,Operations,2,91160,5863 606 | Manager,Male,28,3,College,Sales,2,105795,6179 607 | Financial Analyst,Female,18,2,High School,Operations,2,66935,6508 608 | Sales Associate,Male,19,1,PhD,Management,3,75238,5308 609 | Data Scientist,Female,55,2,Masters,Sales,5,138365,5359 610 | Software Engineer,Male,42,2,College,Management,1,103978,5146 611 | IT,Male,65,4,Masters,Sales,5,165229,6506 612 | Warehouse Associate,Male,54,2,Masters,Engineering,4,118322,5224 613 | Graphic Designer,Male,39,2,Masters,Operations,5,106883,5831 614 | Marketing Associate,Female,45,2,High School,Management,2,59006,5876 615 | Manager,Male,30,1,PhD,Engineering,4,125164,5888 616 | Software Engineer,Male,53,4,High School,Operations,1,89225,5751 617 | Driver,Male,63,4,High School,Sales,2,101716,5881 618 | Marketing Associate,Female,45,3,Masters,Sales,3,74305,7126 619 | Marketing Associate,Female,30,3,Masters,Administration,1,38451,5562 620 | Graphic Designer,Female,48,1,High School,Operations,5,113465,3846 621 | Sales Associate,Female,27,2,College,Operations,2,66377,5803 622 | Financial Analyst,Male,31,3,PhD,Engineering,1,79142,5445 623 | Graphic Designer,Female,20,1,Masters,Operations,4,55569,5236 624 | Graphic Designer,Male,60,4,Masters,Engineering,1,97240,5412 625 | Manager,Female,25,1,Masters,Management,5,119033,6381 626 | Marketing Associate,Female,65,3,Masters,Administration,5,106315,5618 627 | Driver,Male,24,2,College,Engineering,3,81301,5713 628 | Software Engineer,Male,23,1,PhD,Management,2,79462,4673 629 | IT,Female,28,1,PhD,Sales,5,105598,5380 630 | Data Scientist,Male,30,2,College,Operations,3,72497,6452 631 | Financial Analyst,Female,23,1,High School,Management,4,78084,5598 632 | Graphic Designer,Male,50,3,Masters,Management,1,84803,5406 633 | Graphic Designer,Female,18,2,College,Administration,1,41603,6092 634 | Driver,Female,47,2,Masters,Operations,2,78002,5175 635 | Warehouse Associate,Male,56,3,Masters,Administration,2,79274,4618 636 | IT,Male,20,2,PhD,Management,1,49622,5452 637 | Sales Associate,Male,62,3,College,Management,3,129620,5608 638 | Software Engineer,Male,36,2,High School,Administration,4,87848,5166 639 | Driver,Male,24,2,College,Management,3,51906,6274 640 | Sales Associate,Female,44,2,High School,Engineering,3,81866,5183 641 | Financial Analyst,Male,24,2,PhD,Engineering,3,71590,5845 642 | Warehouse Associate,Male,19,2,High School,Sales,2,63928,4998 643 | Driver,Male,44,4,High School,Administration,4,83333,7113 644 | Marketing Associate,Male,49,3,High School,Administration,4,92797,5405 645 | Financial Analyst,Male,36,3,PhD,Operations,1,93067,5103 646 | Graphic Designer,Male,22,2,High School,Sales,3,72422,5697 647 | Driver,Male,59,3,College,Operations,3,120159,4265 648 | Data Scientist,Female,39,3,Masters,Operations,3,91711,6775 649 | Marketing Associate,Female,50,3,Masters,Sales,5,93805,6044 650 | Marketing Associate,Female,58,3,Masters,Operations,5,109931,5878 651 | Marketing Associate,Female,42,2,College,Administration,5,88365,4668 652 | Financial Analyst,Male,58,4,Masters,Operations,3,104744,6425 653 | Warehouse Associate,Female,44,3,High School,Administration,1,79334,6505 654 | Driver,Male,41,2,Masters,Administration,4,95795,4898 655 | Financial Analyst,Female,58,3,PhD,Engineering,1,98512,4548 656 | Data Scientist,Female,33,2,High School,Operations,4,78910,5927 657 | Manager,Male,22,1,College,Management,2,103156,6277 658 | Driver,Male,35,2,High School,Administration,5,92760,4826 659 | Warehouse Associate,Female,42,1,PhD,Operations,5,125574,5688 660 | Data Scientist,Male,62,4,College,Operations,3,93742,6580 661 | Sales Associate,Male,50,2,Masters,Engineering,5,124470,5454 662 | Software Engineer,Male,51,2,Masters,Engineering,3,132323,4872 663 | Sales Associate,Male,22,1,High School,Sales,4,70595,5234 664 | Software Engineer,Male,32,2,College,Operations,1,71278,4701 665 | Data Scientist,Male,33,2,High School,Engineering,2,53855,4980 666 | IT,Male,35,1,College,Management,4,98423,3879 667 | Software Engineer,Male,47,2,Masters,Operations,4,126239,5396 668 | Sales Associate,Male,53,2,Masters,Management,5,101133,4700 669 | Warehouse Associate,Male,23,1,High School,Administration,4,92516,5866 670 | Data Scientist,Male,49,3,College,Administration,3,93320,7236 671 | Warehouse Associate,Female,45,1,High School,Administration,5,106963,5084 672 | IT,Female,26,1,PhD,Sales,5,111770,5345 673 | IT,Male,33,2,Masters,Engineering,5,106584,5426 674 | Marketing Associate,Female,26,2,High School,Operations,3,57672,6476 675 | Warehouse Associate,Male,50,3,Masters,Administration,1,90612,5913 676 | Graphic Designer,Female,35,2,High School,Engineering,5,97768,7233 677 | Financial Analyst,Male,31,2,PhD,Administration,3,82790,5826 678 | Graphic Designer,Female,50,3,Masters,Administration,1,76103,5173 679 | Financial Analyst,Male,38,3,Masters,Administration,1,84628,5680 680 | Warehouse Associate,Female,59,2,College,Management,2,93441,5499 681 | Marketing Associate,Male,42,2,Masters,Management,5,110636,5239 682 | Data Scientist,Female,55,3,PhD,Administration,1,108366,5108 683 | Manager,Male,38,2,Masters,Management,3,135106,5066 684 | Warehouse Associate,Female,55,2,PhD,Administration,3,85453,5135 685 | Graphic Designer,Male,54,3,High School,Management,3,100160,5562 686 | Data Scientist,Female,48,2,College,Administration,3,79765,5329 687 | Manager,Male,48,2,PhD,Engineering,4,157169,5798 688 | Financial Analyst,Female,63,3,PhD,Engineering,3,129683,5754 689 | Financial Analyst,Female,20,1,College,Management,2,76908,4756 690 | Financial Analyst,Female,53,3,High School,Management,1,93696,5029 691 | Driver,Male,40,3,High School,Sales,2,88230,5512 692 | Sales Associate,Female,46,2,Masters,Management,2,83095,5284 693 | IT,Female,26,2,College,Sales,3,81325,5647 694 | Warehouse Associate,Female,62,2,PhD,Engineering,2,102106,5195 695 | Graphic Designer,Female,43,1,PhD,Engineering,1,80635,4322 696 | Marketing Associate,Female,32,2,College,Sales,3,73404,5038 697 | Data Scientist,Male,44,3,High School,Sales,1,83674,4854 698 | IT,Male,62,3,Masters,Engineering,4,118314,4892 699 | Graphic Designer,Female,57,2,Masters,Sales,5,133741,4925 700 | Software Engineer,Male,50,3,PhD,Sales,1,119522,4650 701 | Sales Associate,Male,40,2,High School,Administration,3,84592,4635 702 | Sales Associate,Male,64,3,PhD,Sales,2,121589,5133 703 | Manager,Female,28,1,High School,Engineering,4,104329,4962 704 | Warehouse Associate,Male,24,1,College,Engineering,3,71371,5220 705 | Data Scientist,Male,59,4,Masters,Sales,1,96582,6350 706 | Data Scientist,Male,62,3,PhD,Sales,3,108778,4357 707 | Driver,Male,63,3,PhD,Management,4,128520,6151 708 | Sales Associate,Female,34,1,High School,Management,5,102745,6303 709 | Manager,Female,62,4,Masters,Administration,3,146008,6235 710 | Marketing Associate,Female,44,2,PhD,Management,2,68364,4076 711 | Driver,Male,31,2,High School,Administration,4,62753,5735 712 | Data Scientist,Female,27,1,High School,Administration,5,96584,5293 713 | Software Engineer,Male,32,1,College,Engineering,5,128680,4677 714 | Manager,Male,55,2,College,Management,5,152710,6107 715 | Software Engineer,Male,36,1,High School,Operations,5,109136,4572 716 | Data Scientist,Female,53,2,PhD,Operations,2,102265,4760 717 | Marketing Associate,Female,33,1,High School,Management,5,81249,4845 718 | IT,Female,56,3,Masters,Management,2,101791,6083 719 | Marketing Associate,Female,62,4,College,Sales,1,77584,4524 720 | Driver,Female,36,1,Masters,Administration,3,88175,5004 721 | Data Scientist,Female,21,1,PhD,Engineering,2,65816,6817 722 | IT,Male,33,2,Masters,Sales,1,84878,5009 723 | Marketing Associate,Female,28,1,Masters,Engineering,5,73685,4439 724 | IT,Female,27,1,Masters,Administration,2,55560,4449 725 | Software Engineer,Male,36,2,Masters,Administration,2,94745,5930 726 | Data Scientist,Female,43,2,PhD,Sales,3,106452,5346 727 | Sales Associate,Male,46,2,College,Management,2,95505,4549 728 | Software Engineer,Male,25,1,High School,Engineering,4,103181,4566 729 | Software Engineer,Male,40,2,Masters,Sales,1,120096,4690 730 | Warehouse Associate,Female,47,2,College,Administration,2,87575,4422 731 | Sales Associate,Male,51,3,High School,Management,2,98499,6274 732 | Driver,Female,60,2,PhD,Sales,3,113140,4079 733 | Manager,Male,56,2,PhD,Administration,4,152081,4621 734 | Data Scientist,Female,40,1,Masters,Management,5,100505,4626 735 | IT,Female,60,3,Masters,Management,2,104589,4536 736 | Manager,Male,36,2,Masters,Sales,3,134450,5214 737 | IT,Female,48,3,High School,Management,1,61081,6337 738 | Manager,Male,37,2,PhD,Operations,1,100819,5390 739 | Software Engineer,Male,64,2,PhD,Sales,5,157277,4844 740 | Marketing Associate,Female,51,2,High School,Management,2,79237,4453 741 | Driver,Male,20,2,High School,Sales,2,68175,5698 742 | Driver,Female,61,2,College,Management,4,108475,4489 743 | Software Engineer,Male,65,3,High School,Sales,2,122624,4165 744 | Graphic Designer,Female,34,1,College,Management,4,99942,5352 745 | Data Scientist,Female,58,2,PhD,Engineering,2,108296,4708 746 | Graphic Designer,Male,45,2,Masters,Management,2,101273,3817 747 | Sales Associate,Male,18,1,Masters,Administration,3,59334,5729 748 | Manager,Male,43,1,PhD,Management,4,152625,4702 749 | IT,Female,62,3,College,Sales,4,126370,6032 750 | Financial Analyst,Female,45,2,Masters,Administration,5,122142,5435 751 | Driver,Female,19,1,High School,Operations,4,75919,5613 752 | Data Scientist,Male,26,1,Masters,Operations,3,90526,4191 753 | IT,Female,47,2,PhD,Administration,3,103416,5536 754 | Manager,Male,59,3,College,Engineering,3,133910,5795 755 | Driver,Female,63,3,High School,Sales,1,104468,4466 756 | Sales Associate,Female,57,2,High School,Management,4,117202,5068 757 | Data Scientist,Female,51,1,PhD,Sales,5,139141,4969 758 | Warehouse Associate,Female,58,1,Masters,Management,4,134109,4184 759 | Sales Associate,Female,24,1,High School,Administration,2,66887,5014 760 | Data Scientist,Male,41,1,College,Operations,5,77839,4458 761 | Driver,Male,21,1,High School,Sales,2,61018,5513 762 | IT,Female,58,2,High School,Sales,4,124847,3100 763 | Financial Analyst,Female,33,2,PhD,Administration,1,50758,5386 764 | Graphic Designer,Female,34,1,PhD,Operations,3,75316,5861 765 | Driver,Male,54,3,PhD,Sales,2,118093,4115 766 | Software Engineer,Male,31,1,College,Operations,4,106503,5225 767 | Graphic Designer,Female,55,2,College,Administration,3,111502,4413 768 | Sales Associate,Female,47,1,PhD,Engineering,4,116642,4621 769 | Financial Analyst,Male,55,3,College,Operations,1,84683,4476 770 | IT,Female,45,2,PhD,Sales,4,113283,5404 771 | Marketing Associate,Female,48,2,High School,Administration,4,86538,4212 772 | Marketing Associate,Female,62,3,Masters,Engineering,2,77742,5430 773 | Software Engineer,Male,22,1,College,Sales,1,67913,4045 774 | Financial Analyst,Female,54,2,College,Sales,2,114171,3768 775 | Manager,Male,47,1,Masters,Engineering,4,155826,4964 776 | Graphic Designer,Female,63,2,College,Operations,4,106204,4534 777 | Graphic Designer,Female,38,1,High School,Administration,4,111041,3727 778 | Software Engineer,Male,62,2,PhD,Engineering,3,138184,4664 779 | Financial Analyst,Female,63,3,College,Sales,2,95795,5723 780 | Sales Associate,Male,60,3,High School,Administration,3,106798,5201 781 | Marketing Associate,Female,65,3,High School,Engineering,3,98575,5410 782 | Warehouse Associate,Male,37,2,Masters,Administration,1,82343,5232 783 | Software Engineer,Female,57,1,College,Engineering,3,117131,2324 784 | Sales Associate,Male,26,2,High School,Sales,2,78625,4073 785 | Financial Analyst,Male,27,2,PhD,Administration,4,99134,6255 786 | Marketing Associate,Female,20,2,College,Administration,2,38613,6079 787 | Marketing Associate,Female,26,2,College,Operations,1,62866,5114 788 | Graphic Designer,Male,44,1,Masters,Engineering,5,107818,5281 789 | Graphic Designer,Female,47,1,High School,Operations,5,108731,4793 790 | Manager,Male,21,1,College,Management,2,114621,3927 791 | Marketing Associate,Female,23,1,High School,Sales,2,47739,4829 792 | Data Scientist,Male,45,1,Masters,Operations,3,104290,3474 793 | Financial Analyst,Male,65,4,Masters,Sales,1,111403,5078 794 | IT,Male,52,2,College,Administration,5,93802,5404 795 | Manager,Male,30,1,High School,Sales,5,138163,3415 796 | Financial Analyst,Male,50,2,Masters,Operations,3,115613,3881 797 | Sales Associate,Female,18,1,Masters,Engineering,1,64920,4967 798 | Software Engineer,Female,36,1,PhD,Management,1,79177,3661 799 | Warehouse Associate,Male,25,1,College,Operations,1,64994,4310 800 | Driver,Male,53,3,PhD,Sales,2,100941,5992 801 | Manager,Male,55,1,High School,Management,5,163208,4095 802 | Sales Associate,Male,24,1,PhD,Operations,2,51837,4472 803 | Graphic Designer,Male,56,2,Masters,Management,4,114048,5097 804 | Manager,Female,59,2,High School,Management,2,136215,3603 805 | Marketing Associate,Female,55,3,High School,Administration,1,63327,4066 806 | Software Engineer,Male,64,2,PhD,Engineering,4,139068,4126 807 | Sales Associate,Male,63,2,Masters,Engineering,3,136209,3910 808 | Software Engineer,Male,55,2,PhD,Management,3,121910,4494 809 | Graphic Designer,Male,43,2,PhD,Management,2,82057,5268 810 | Sales Associate,Male,51,2,High School,Engineering,1,96023,4148 811 | Marketing Associate,Female,31,1,High School,Sales,3,52578,5538 812 | Driver,Female,61,2,College,Administration,4,114436,4249 813 | Marketing Associate,Female,56,2,Masters,Sales,4,99543,4442 814 | Sales Associate,Female,41,2,College,Sales,1,60983,5022 815 | Software Engineer,Male,49,3,Masters,Administration,1,82277,5460 816 | Marketing Associate,Female,23,1,College,Sales,2,55885,3795 817 | Graphic Designer,Male,29,1,Masters,Operations,3,73142,4712 818 | Graphic Designer,Male,53,2,High School,Operations,4,107065,3652 819 | Driver,Female,61,2,PhD,Engineering,2,107064,4202 820 | Marketing Associate,Female,56,2,High School,Operations,5,107370,4916 821 | Marketing Associate,Female,25,1,High School,Sales,1,39741,4981 822 | Sales Associate,Male,64,2,High School,Management,3,120579,4687 823 | Driver,Female,55,2,High School,Engineering,3,103242,4495 824 | Driver,Female,42,1,Masters,Sales,5,120112,5070 825 | Sales Associate,Female,61,2,High School,Sales,2,83308,3653 826 | Marketing Associate,Female,26,1,High School,Management,1,36585,4373 827 | Graphic Designer,Female,59,2,College,Engineering,4,115344,4827 828 | Sales Associate,Male,36,2,High School,Management,1,49553,5784 829 | IT,Female,54,2,Masters,Administration,3,96982,4750 830 | Financial Analyst,Female,24,1,High School,Management,4,80030,5481 831 | Driver,Male,58,2,College,Sales,4,110157,4275 832 | Financial Analyst,Female,32,1,High School,Administration,4,84047,4683 833 | Marketing Associate,Male,47,2,PhD,Sales,3,93054,4243 834 | Data Scientist,Female,53,2,Masters,Engineering,2,84764,4409 835 | Warehouse Associate,Female,61,1,Masters,Management,3,120277,4617 836 | Marketing Associate,Female,56,2,High School,Sales,3,81661,4965 837 | Manager,Female,45,1,High School,Engineering,5,149893,5120 838 | Software Engineer,Male,45,2,High School,Management,1,81515,5001 839 | Driver,Male,55,2,Masters,Operations,3,95898,4309 840 | Warehouse Associate,Male,42,2,PhD,Operations,3,90000,4774 841 | Manager,Male,60,3,High School,Management,2,111896,5597 842 | Driver,Male,53,1,Masters,Engineering,5,130082,4734 843 | Sales Associate,Female,60,2,PhD,Engineering,5,130417,4694 844 | Marketing Associate,Female,34,1,College,Engineering,2,38855,4503 845 | Software Engineer,Male,25,1,High School,Administration,4,81057,5259 846 | Data Scientist,Male,41,1,High School,Management,5,117215,4322 847 | Graphic Designer,Female,49,1,High School,Administration,4,96232,3697 848 | Financial Analyst,Male,64,1,PhD,Operations,5,141186,3415 849 | Manager,Male,52,2,High School,Operations,2,114943,3438 850 | Data Scientist,Male,43,1,PhD,Administration,4,107640,3896 851 | Manager,Male,31,2,College,Operations,1,80259,4791 852 | Marketing Associate,Female,48,1,College,Operations,5,86563,4415 853 | Data Scientist,Male,20,1,High School,Operations,2,61589,3990 854 | Warehouse Associate,Male,35,1,High School,Administration,2,74829,4263 855 | Graphic Designer,Female,44,1,Masters,Administration,2,72248,3634 856 | Sales Associate,Male,49,2,High School,Operations,3,91342,4516 857 | Sales Associate,Male,49,2,Masters,Operations,2,101140,4851 858 | Marketing Associate,Male,51,2,PhD,Sales,1,62600,4300 859 | Software Engineer,Male,21,1,High School,Engineering,2,65313,4685 860 | Manager,Male,41,1,College,Administration,5,123153,4111 861 | IT,Male,27,1,High School,Management,3,57500,3665 862 | Manager,Male,37,1,PhD,Administration,3,115604,4098 863 | Software Engineer,Male,18,1,High School,Sales,1,55555,4390 864 | Data Scientist,Male,58,1,PhD,Engineering,5,124166,3875 865 | IT,Male,50,2,Masters,Management,3,101050,3879 866 | Data Scientist,Female,56,1,College,Engineering,5,125828,3961 867 | Graphic Designer,Female,45,1,High School,Management,3,75017,5576 868 | Marketing Associate,Female,40,1,Masters,Engineering,4,100415,3871 869 | Manager,Female,57,1,Masters,Operations,3,145095,3889 870 | Data Scientist,Female,63,2,High School,Administration,5,115641,4339 871 | Software Engineer,Male,59,1,College,Sales,5,160460,2930 872 | Financial Analyst,Male,45,2,High School,Administration,2,85947,3909 873 | Financial Analyst,Female,48,1,College,Engineering,5,119058,4037 874 | Warehouse Associate,Female,56,1,College,Administration,4,106722,4044 875 | Financial Analyst,Female,41,2,PhD,Operations,3,91129,4825 876 | Graphic Designer,Male,59,1,PhD,Sales,4,135512,3955 877 | Financial Analyst,Female,49,2,College,Operations,1,92891,3578 878 | Financial Analyst,Male,64,3,High School,Administration,3,124151,3392 879 | Data Scientist,Female,58,1,High School,Sales,5,141005,3384 880 | Manager,Male,36,1,High School,Operations,2,115742,3661 881 | IT,Male,31,1,Masters,Sales,2,93358,3223 882 | IT,Female,44,1,High School,Sales,5,131411,3084 883 | Sales Associate,Female,61,2,College,Sales,1,96900,4157 884 | IT,Male,59,3,Masters,Sales,2,98995,4596 885 | IT,Male,57,3,High School,Sales,3,109645,5472 886 | Financial Analyst,Male,39,2,College,Sales,1,61609,3738 887 | Marketing Associate,Female,56,2,High School,Engineering,3,88820,4289 888 | Data Scientist,Male,61,1,Masters,Administration,5,122352,3590 889 | Marketing Associate,Female,53,1,Masters,Management,4,112489,4237 890 | Software Engineer,Male,29,1,High School,Sales,1,86650,3518 891 | Data Scientist,Male,62,3,High School,Operations,2,101284,4993 892 | Manager,Male,41,1,Masters,Sales,2,119794,3712 893 | Graphic Designer,Male,58,2,Masters,Operations,1,96609,3044 894 | Marketing Associate,Female,38,1,PhD,Engineering,1,53839,3270 895 | Financial Analyst,Male,29,1,Masters,Operations,2,84725,3274 896 | IT,Female,57,2,High School,Administration,1,64807,3887 897 | IT,Female,28,1,High School,Management,1,41151,4296 898 | Financial Analyst,Female,48,1,College,Administration,4,114031,4510 899 | Driver,Male,33,1,High School,Management,1,59493,3955 900 | Manager,Female,42,1,High School,Management,2,119893,3721 901 | Manager,Male,60,2,College,Sales,1,131980,3841 902 | Sales Associate,Female,52,1,High School,Sales,3,94154,3964 903 | Marketing Associate,Female,60,1,College,Management,4,114906,4766 904 | Driver,Female,63,1,Masters,Management,4,115981,2661 905 | Data Scientist,Male,37,1,PhD,Administration,2,78836,3588 906 | Driver,Male,64,2,Masters,Operations,2,112719,3382 907 | Marketing Associate,Female,59,1,Masters,Sales,3,93018,2730 908 | Marketing Associate,Female,55,2,College,Sales,2,69928,4811 909 | Software Engineer,Female,57,2,High School,Engineering,1,104623,3791 910 | Driver,Male,55,2,College,Administration,2,103328,4543 911 | Financial Analyst,Male,47,2,High School,Management,1,68643,4674 912 | Graphic Designer,Female,33,1,High School,Sales,1,63914,3521 913 | Sales Associate,Male,45,2,High School,Engineering,1,67296,4780 914 | Graphic Designer,Female,44,1,College,Sales,3,90474,3530 915 | Driver,Female,22,1,High School,Administration,1,60026,4605 916 | Marketing Associate,Female,41,1,PhD,Management,5,108587,4275 917 | Financial Analyst,Female,61,1,Masters,Engineering,4,139042,2982 918 | Driver,Male,65,1,High School,Operations,5,116803,3431 919 | Sales Associate,Female,57,1,Masters,Operations,3,101423,3641 920 | Software Engineer,Male,36,1,Masters,Operations,1,86375,3368 921 | IT,Male,31,1,College,Operations,1,49449,4149 922 | Marketing Associate,Female,56,2,High School,Sales,3,95995,4045 923 | Software Engineer,Male,51,1,College,Management,2,118282,3599 924 | IT,Female,47,1,PhD,Operations,3,99472,3943 925 | Manager,Female,55,1,High School,Administration,3,135638,2824 926 | IT,Male,50,1,College,Operations,3,85648,2728 927 | Data Scientist,Female,58,1,PhD,Management,4,121457,4723 928 | Data Scientist,Female,57,1,PhD,Operations,2,114141,3746 929 | Manager,Male,58,1,PhD,Management,4,179726,4284 930 | Marketing Associate,Female,64,1,College,Administration,5,107572,3107 931 | Sales Associate,Female,65,2,PhD,Sales,5,142505,4214 932 | Financial Analyst,Male,43,1,PhD,Sales,4,108719,4143 933 | IT,Male,36,1,High School,Sales,4,109832,3528 934 | Software Engineer,Male,60,1,PhD,Administration,4,127250,4263 935 | Sales Associate,Female,50,1,Masters,Sales,3,106279,3892 936 | Software Engineer,Male,37,1,High School,Sales,1,81547,3510 937 | Data Scientist,Male,56,1,Masters,Administration,2,95798,2530 938 | Manager,Female,47,1,High School,Management,3,106916,3959 939 | Manager,Male,59,2,PhD,Administration,1,124782,4618 940 | Warehouse Associate,Male,53,1,College,Operations,4,126256,3463 941 | IT,Female,59,1,PhD,Engineering,3,92257,3908 942 | Software Engineer,Male,62,1,High School,Sales,3,124660,2200 943 | IT,Female,32,2,High School,Engineering,1,59377,5236 944 | Marketing Associate,Female,38,1,PhD,Engineering,2,76726,4204 945 | IT,Female,54,1,High School,Engineering,5,112502,3988 946 | Data Scientist,Male,65,2,Masters,Sales,2,128730,3247 947 | Manager,Male,61,2,College,Sales,1,136836,4070 948 | IT,Male,51,1,High School,Engineering,3,89272,2600 949 | Financial Analyst,Female,62,1,High School,Operations,3,101515,2251 950 | Software Engineer,Male,36,1,Masters,Operations,1,93855,4099 951 | Graphic Designer,Male,30,1,College,Operations,2,36642,4388 952 | Sales Associate,Male,60,1,College,Operations,3,112604,3366 953 | Financial Analyst,Female,49,1,PhD,Engineering,1,67089,3078 954 | Driver,Female,51,1,PhD,Management,2,97789,3536 955 | Data Scientist,Male,62,1,PhD,Administration,3,123333,3353 956 | IT,Male,62,1,PhD,Management,2,105771,2978 957 | Marketing Associate,Female,46,1,College,Administration,1,54945,2991 958 | Data Scientist,Female,65,1,PhD,Operations,2,121239,3078 959 | Data Scientist,Female,64,2,High School,Sales,1,87314,3688 960 | Driver,Female,65,1,PhD,Operations,3,124530,3647 961 | Driver,Female,45,1,College,Sales,2,67150,3255 962 | Financial Analyst,Male,39,1,College,Engineering,4,99643,3789 963 | Sales Associate,Male,54,1,College,Administration,3,92468,3242 964 | Financial Analyst,Female,47,1,College,Operations,2,63300,3655 965 | IT,Male,55,2,Masters,Operations,1,91447,3196 966 | Marketing Associate,Female,58,2,High School,Operations,1,60271,3927 967 | Warehouse Associate,Male,34,1,Masters,Operations,1,45915,4765 968 | Marketing Associate,Female,53,1,Masters,Operations,1,66028,3745 969 | Sales Associate,Male,57,1,PhD,Administration,5,113031,3409 970 | Software Engineer,Male,62,1,High School,Sales,3,132815,3367 971 | Manager,Male,52,1,High School,Management,1,110078,1703 972 | Data Scientist,Male,57,1,Masters,Sales,3,106918,3180 973 | Software Engineer,Male,60,1,High School,Administration,3,106045,3079 974 | Warehouse Associate,Male,65,1,High School,Operations,2,107479,2051 975 | Software Engineer,Male,64,1,Masters,Sales,1,109606,2193 976 | Marketing Associate,Female,44,1,High School,Engineering,3,91300,3680 977 | Sales Associate,Male,56,1,High School,Management,1,88195,2375 978 | Financial Analyst,Female,54,1,Masters,Operations,2,90999,2963 979 | Sales Associate,Female,58,1,College,Operations,3,86795,3453 980 | IT,Female,59,1,College,Sales,3,110627,2041 981 | Marketing Associate,Female,52,1,PhD,Sales,3,97696,3888 982 | IT,Male,64,1,Masters,Sales,1,92950,2125 983 | Sales Associate,Male,56,1,High School,Operations,1,82164,2611 984 | Software Engineer,Male,62,1,High School,Management,3,119069,2788 985 | Marketing Associate,Female,59,1,College,Engineering,1,62738,2656 986 | IT,Female,53,1,High School,Administration,2,112169,2763 987 | Manager,Male,55,1,High School,Administration,1,120574,2683 988 | Data Scientist,Male,54,1,College,Sales,2,97311,3083 989 | Marketing Associate,Female,59,1,High School,Operations,3,98796,3042 990 | Sales Associate,Male,62,1,Masters,Administration,2,102593,1823 991 | Financial Analyst,Female,65,1,High School,Administration,2,96665,2645 992 | Graphic Designer,Female,61,1,Masters,Engineering,1,91030,3318 993 | IT,Female,65,1,Masters,Administration,1,106945,2041 994 | Graphic Designer,Female,63,1,College,Administration,2,81545,3418 995 | Marketing Associate,Female,65,1,Masters,Administration,1,80789,1884 996 | Marketing Associate,Female,64,1,PhD,Administration,2,85253,2777 997 | Marketing Associate,Female,61,1,High School,Administration,1,62644,3270 998 | Data Scientist,Male,57,1,Masters,Sales,2,108977,3567 999 | Financial Analyst,Male,48,1,High School,Operations,1,92347,2724 1000 | Financial Analyst,Male,65,2,High School,Administration,1,97376,2225 1001 | Financial Analyst,Male,60,1,PhD,Sales,2,123108,2244 -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Machine Learning 2 | 3 | ![Machine-learning](https://user-images.githubusercontent.com/86261762/236062595-1f6b68a2-f71e-425d-927d-e42322a73d40.jpg) 4 | 5 | 6 | El ‘machine learning’ –aprendizaje automático– es una rama de la inteligencia artificial que permite que las máquinas aprendan sin ser expresamente programadas para ello. Una habilidad indispensable para hacer sistemas capaces de identificar patrones entre los datos para hacer predicciones. Esta tecnología está presente en un sinfín de aplicaciones como las recomendaciones de Netflix o Spotify, las respuestas inteligentes de Gmail o el habla de Siri y Alexa. 7 | En definitiva, el ‘machine learning’ es un maestro del reconocimiento de patrones, y es capaz de convertir una muestra de datos en un programa informático capaz de extraer inferencias de nuevos conjuntos de datos para los que no ha sido entrenado previamente. Esta capacidad de aprendizaje se emplea para la mejora de motores de búsqueda, la robótica, el diagnóstico médico o incluso la detección del fraude en el uso de tarjetas de crédito. 8 | 9 | ## Qué vas a encontrar en esta carpeta? 10 | 11 | - [Documentos teóricos](https://github.com/bcamandone/Machine-Learning/tree/main/Documentos%20te%C3%B3ricos) 12 | - [Proyecto final Data Science - Coderhouse](https://github.com/bcamandone/Machine-Learning/tree/main/Proyecto%20final%20Data%20Science%20-%20Coderhouse) 13 | - [Proyecto Dataset-Iris](https://github.com/bcamandone/Machine-Learning/tree/main/Proyecto-Dataset-Iris) 14 | - [Proyecto calorías](https://github.com/bcamandone/Machine-Learning/tree/main/Proyecto_calorias) 15 | - [Proyecto salarios](https://github.com/bcamandone/Machine-Learning/tree/main/Proyecto_salarios) 16 | - [KMeans - Ejemplo con dataset Penguins](https://github.com/bcamandone/Machine-Learning/blob/main/Ejemplo%20con%20KMeans%20-%20dataset%20Penguins.ipynb) 17 | - [Feature selection](https://github.com/bcamandone/Machine-Learning/blob/main/Feature%20selection.-.ipynb) 18 | - [Función para aplicar varios algoritmos](https://github.com/bcamandone/Machine-Learning/blob/main/Funcion_Aplicar_varios_algoritmos.ipynb) 19 | - [Principal Component Analysis(PCA)](https://github.com/bcamandone/Machine-Learning/blob/main/Principal%20Component%20Analysis-.ipynb) 20 | - [SMOTE](https://github.com/bcamandone/Machine-Learning/blob/main/SMOTE.ipynb) 21 | - [Trampa dummy](https://github.com/bcamandone/Machine-Learning/blob/main/Scikit-plot..ipynb) 22 | - [Scikit-plot](https://github.com/bcamandone/Machine-Learning/blob/main/Trampa%20dummy-.ipynb) 23 | - [Librería Lazypredict](https://github.com/bcamandone/Machine-Learning/blob/main/_Librer%C3%ADa%20Lazypredict.ipynb) 24 | -------------------------------------------------------------------------------- /Recommender System Final Project/ml-capstone-Camandone Belén.pptx.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bcamandone/Machine-Learning/a185c926938671814f5722df26f82e6238897902/Recommender System Final Project/ml-capstone-Camandone Belén.pptx.pdf -------------------------------------------------------------------------------- /Recommender System Final Project/readme.md: -------------------------------------------------------------------------------- 1 | ## Capstone Project 2 | The final task of this capstone project is to create a presentation based on the outcomes of all tasks in previous modules and labs. 3 | Your presentation will develop into a story of all your machine learning journey in this project, and it should be compelling and easy to understand. 4 | 5 | -Uploaded your completed presentation in PDF format 6 | 7 | -Completed the required Introduction slide 8 | 9 | -Completed the required Exploratory Data Analysis slides 10 | 11 | -Completed the required content-based recommender system using user profile and course genres slides 12 | 13 | -Completed the required content-based recommender system using course similarity slides 14 | 15 | -Completed the required content-based recommender system using user profile clustering slides 16 | 17 | -Completed the required KNN-based collaborative filtering slide 18 | 19 | -Completed the required NMF-based collaborative filtering slide 20 | 21 | -Completed the required neural network embedding based collaborative filtering slide 22 | 23 | -Completed the required collaborative filtering algorithms evaluation slides 24 | 25 | -Completed the required Conclusion slide 26 | 27 | -Applied your creativity to improve the presentation beyond the template 28 | 29 | -Displayed any innovative insights 30 | -------------------------------------------------------------------------------- /SMOTE.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "id": "ab7ea8fe", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "import pandas as pd" 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": 2, 16 | "id": "90dd833b", 17 | "metadata": {}, 18 | "outputs": [], 19 | "source": [ 20 | "data = pd.read_csv('creditcard.csv')" 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": 3, 26 | "id": "3deb0b07", 27 | "metadata": {}, 28 | "outputs": [ 29 | { 30 | "data": { 31 | "text/plain": [ 32 | "Index(['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10',\n", 33 | " 'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20',\n", 34 | " 'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount',\n", 35 | " 'Class'],\n", 36 | " dtype='object')" 37 | ] 38 | }, 39 | "execution_count": 3, 40 | "metadata": {}, 41 | "output_type": "execute_result" 42 | } 43 | ], 44 | "source": [ 45 | "data.columns" 46 | ] 47 | }, 48 | { 49 | "cell_type": "code", 50 | "execution_count": 4, 51 | "id": "e87e5d7d", 52 | "metadata": {}, 53 | "outputs": [ 54 | { 55 | "data": { 56 | "text/html": [ 57 | "
\n", 58 | "\n", 71 | "\n", 72 | " \n", 73 | " \n", 74 | " \n", 75 | " \n", 76 | " \n", 77 | " \n", 78 | " \n", 79 | " \n", 80 | " \n", 81 | " \n", 82 | " \n", 83 | " \n", 84 | " \n", 85 | " \n", 86 | " \n", 87 | " \n", 88 | " \n", 89 | " \n", 90 | " \n", 91 | " \n", 92 | " \n", 93 | " \n", 94 | " \n", 95 | " \n", 96 | " \n", 97 | " \n", 98 | " \n", 99 | " \n", 100 | " \n", 101 | " \n", 102 | " \n", 103 | " \n", 104 | " \n", 105 | " \n", 106 | " \n", 107 | " \n", 108 | " \n", 109 | " \n", 110 | " \n", 111 | " \n", 112 | " \n", 113 | " \n", 114 | " \n", 115 | " \n", 116 | " \n", 117 | " \n", 118 | " \n", 119 | " \n", 120 | " \n", 121 | " \n", 122 | " \n", 123 | " \n", 124 | " \n", 125 | " \n", 126 | " \n", 127 | " \n", 128 | " \n", 129 | " \n", 130 | " \n", 131 | " \n", 132 | " \n", 133 | " \n", 134 | " \n", 135 | " \n", 136 | " \n", 137 | " \n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | " \n", 142 | " \n", 143 | " \n", 144 | " \n", 145 | " \n", 146 | " \n", 147 | " \n", 148 | " \n", 149 | " \n", 150 | " \n", 151 | " \n", 152 | " \n", 153 | " \n", 154 | " \n", 155 | " \n", 156 | " \n", 157 | " \n", 158 | " \n", 159 | " \n", 160 | " \n", 161 | " \n", 162 | " \n", 163 | " \n", 164 | " \n", 165 | " \n", 166 | " \n", 167 | " \n", 168 | " \n", 169 | " \n", 170 | " \n", 171 | " \n", 172 | " \n", 173 | " \n", 174 | " \n", 175 | " \n", 176 | " \n", 177 | " \n", 178 | " \n", 179 | " \n", 180 | " \n", 181 | " \n", 182 | " \n", 183 | " \n", 184 | " \n", 185 | " \n", 186 | " \n", 187 | " \n", 188 | " \n", 189 | " \n", 190 | " \n", 191 | " \n", 192 | " \n", 193 | " \n", 194 | " \n", 195 | " \n", 196 | " \n", 197 | " \n", 198 | " \n", 199 | " \n", 200 | " \n", 201 | " \n", 202 | " \n", 203 | " \n", 204 | " \n", 205 | " \n", 206 | " \n", 207 | " \n", 208 | " \n", 209 | " \n", 210 | " \n", 211 | " \n", 212 | " \n", 213 | " \n", 214 | " \n", 215 | " \n", 216 | " \n", 217 | " \n", 218 | " \n", 219 | " \n", 220 | "
TimeV1V2V3V4V5V6V7V8V9...V21V22V23V24V25V26V27V28AmountClass
00.0-1.359807-0.0727812.5363471.378155-0.3383210.4623880.2395990.0986980.363787...-0.0183070.277838-0.1104740.0669280.128539-0.1891150.133558-0.021053149.620
10.01.1918570.2661510.1664800.4481540.060018-0.082361-0.0788030.085102-0.255425...-0.225775-0.6386720.101288-0.3398460.1671700.125895-0.0089830.0147242.690
21.0-1.358354-1.3401631.7732090.379780-0.5031981.8004990.7914610.247676-1.514654...0.2479980.7716790.909412-0.689281-0.327642-0.139097-0.055353-0.059752378.660
31.0-0.966272-0.1852261.792993-0.863291-0.0103091.2472030.2376090.377436-1.387024...-0.1083000.005274-0.190321-1.1755750.647376-0.2219290.0627230.061458123.500
42.0-1.1582330.8777371.5487180.403034-0.4071930.0959210.592941-0.2705330.817739...-0.0094310.798278-0.1374580.141267-0.2060100.5022920.2194220.21515369.990
\n", 221 | "

5 rows × 31 columns

\n", 222 | "
" 223 | ], 224 | "text/plain": [ 225 | " Time V1 V2 V3 V4 V5 V6 V7 \\\n", 226 | "0 0.0 -1.359807 -0.072781 2.536347 1.378155 -0.338321 0.462388 0.239599 \n", 227 | "1 0.0 1.191857 0.266151 0.166480 0.448154 0.060018 -0.082361 -0.078803 \n", 228 | "2 1.0 -1.358354 -1.340163 1.773209 0.379780 -0.503198 1.800499 0.791461 \n", 229 | "3 1.0 -0.966272 -0.185226 1.792993 -0.863291 -0.010309 1.247203 0.237609 \n", 230 | "4 2.0 -1.158233 0.877737 1.548718 0.403034 -0.407193 0.095921 0.592941 \n", 231 | "\n", 232 | " V8 V9 ... V21 V22 V23 V24 V25 \\\n", 233 | "0 0.098698 0.363787 ... -0.018307 0.277838 -0.110474 0.066928 0.128539 \n", 234 | "1 0.085102 -0.255425 ... -0.225775 -0.638672 0.101288 -0.339846 0.167170 \n", 235 | "2 0.247676 -1.514654 ... 0.247998 0.771679 0.909412 -0.689281 -0.327642 \n", 236 | "3 0.377436 -1.387024 ... -0.108300 0.005274 -0.190321 -1.175575 0.647376 \n", 237 | "4 -0.270533 0.817739 ... -0.009431 0.798278 -0.137458 0.141267 -0.206010 \n", 238 | "\n", 239 | " V26 V27 V28 Amount Class \n", 240 | "0 -0.189115 0.133558 -0.021053 149.62 0 \n", 241 | "1 0.125895 -0.008983 0.014724 2.69 0 \n", 242 | "2 -0.139097 -0.055353 -0.059752 378.66 0 \n", 243 | "3 -0.221929 0.062723 0.061458 123.50 0 \n", 244 | "4 0.502292 0.219422 0.215153 69.99 0 \n", 245 | "\n", 246 | "[5 rows x 31 columns]" 247 | ] 248 | }, 249 | "execution_count": 4, 250 | "metadata": {}, 251 | "output_type": "execute_result" 252 | } 253 | ], 254 | "source": [ 255 | "data.head()" 256 | ] 257 | }, 258 | { 259 | "cell_type": "code", 260 | "execution_count": 5, 261 | "id": "8b841634", 262 | "metadata": {}, 263 | "outputs": [ 264 | { 265 | "data": { 266 | "text/plain": [ 267 | "Time 0\n", 268 | "V1 0\n", 269 | "V2 0\n", 270 | "V3 0\n", 271 | "V4 0\n", 272 | "V5 0\n", 273 | "V6 0\n", 274 | "V7 0\n", 275 | "V8 0\n", 276 | "V9 0\n", 277 | "V10 0\n", 278 | "V11 0\n", 279 | "V12 0\n", 280 | "V13 0\n", 281 | "V14 0\n", 282 | "V15 0\n", 283 | "V16 0\n", 284 | "V17 0\n", 285 | "V18 0\n", 286 | "V19 0\n", 287 | "V20 0\n", 288 | "V21 0\n", 289 | "V22 0\n", 290 | "V23 0\n", 291 | "V24 0\n", 292 | "V25 0\n", 293 | "V26 0\n", 294 | "V27 0\n", 295 | "V28 0\n", 296 | "Amount 0\n", 297 | "Class 0\n", 298 | "dtype: int64" 299 | ] 300 | }, 301 | "execution_count": 5, 302 | "metadata": {}, 303 | "output_type": "execute_result" 304 | } 305 | ], 306 | "source": [ 307 | "data.isnull().sum()" 308 | ] 309 | }, 310 | { 311 | "cell_type": "code", 312 | "execution_count": 6, 313 | "id": "736ce555", 314 | "metadata": {}, 315 | "outputs": [ 316 | { 317 | "name": "stdout", 318 | "output_type": "stream", 319 | "text": [ 320 | "No Fraude 99.83 % del dataset\n", 321 | "Fraude 0.17 % del dataset\n" 322 | ] 323 | } 324 | ], 325 | "source": [ 326 | "print('No Fraude', round(data['Class'].value_counts()[0]/len(data) * 100,2), '% del dataset')\n", 327 | "print('Fraude', round(data['Class'].value_counts()[1]/len(data) * 100,2), '% del dataset')" 328 | ] 329 | }, 330 | { 331 | "cell_type": "code", 332 | "execution_count": 7, 333 | "id": "3c795e21", 334 | "metadata": {}, 335 | "outputs": [], 336 | "source": [ 337 | "X = data.drop('Class', axis=1)\n", 338 | "y = data['Class']" 339 | ] 340 | }, 341 | { 342 | "cell_type": "code", 343 | "execution_count": 8, 344 | "id": "661ba9e3", 345 | "metadata": {}, 346 | "outputs": [ 347 | { 348 | "data": { 349 | "text/plain": [ 350 | "0 284315\n", 351 | "1 492\n", 352 | "Name: Class, dtype: int64" 353 | ] 354 | }, 355 | "execution_count": 8, 356 | "metadata": {}, 357 | "output_type": "execute_result" 358 | } 359 | ], 360 | "source": [ 361 | "y.value_counts()" 362 | ] 363 | }, 364 | { 365 | "cell_type": "code", 366 | "execution_count": 9, 367 | "id": "f97563b4", 368 | "metadata": {}, 369 | "outputs": [], 370 | "source": [ 371 | "from imblearn.over_sampling import SMOTE\n", 372 | "oversample = SMOTE()\n", 373 | "X_s, y_s = oversample.fit_resample(X, y)" 374 | ] 375 | }, 376 | { 377 | "cell_type": "code", 378 | "execution_count": 10, 379 | "id": "9beac7a3", 380 | "metadata": {}, 381 | "outputs": [ 382 | { 383 | "data": { 384 | "text/plain": [ 385 | "0 284315\n", 386 | "1 284315\n", 387 | "Name: Class, dtype: int64" 388 | ] 389 | }, 390 | "execution_count": 10, 391 | "metadata": {}, 392 | "output_type": "execute_result" 393 | } 394 | ], 395 | "source": [ 396 | "y_s.value_counts()" 397 | ] 398 | } 399 | ], 400 | "metadata": { 401 | "kernelspec": { 402 | "display_name": "Python 3 (ipykernel)", 403 | "language": "python", 404 | "name": "python3" 405 | }, 406 | "language_info": { 407 | "codemirror_mode": { 408 | "name": "ipython", 409 | "version": 3 410 | }, 411 | "file_extension": ".py", 412 | "mimetype": "text/x-python", 413 | "name": "python", 414 | "nbconvert_exporter": "python", 415 | "pygments_lexer": "ipython3", 416 | "version": "3.11.4" 417 | } 418 | }, 419 | "nbformat": 4, 420 | "nbformat_minor": 5 421 | } 422 | -------------------------------------------------------------------------------- /Trampa dummy-.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 21, 6 | "id": "8f26ffd0", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "import pandas as pd\n" 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": 22, 16 | "id": "84322f3b", 17 | "metadata": {}, 18 | "outputs": [], 19 | "source": [ 20 | "df = pd.read_csv('https://datasets-humai.s3.amazonaws.com/datasets/titanic_clase4.csv')" 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": 23, 26 | "id": "705c6e45", 27 | "metadata": {}, 28 | "outputs": [ 29 | { 30 | "data": { 31 | "text/html": [ 32 | "
\n", 33 | "\n", 46 | "\n", 47 | " \n", 48 | " \n", 49 | " \n", 50 | " \n", 51 | " \n", 52 | " \n", 53 | " \n", 54 | " \n", 55 | " \n", 56 | " \n", 57 | " \n", 58 | " \n", 59 | " \n", 60 | " \n", 61 | " \n", 62 | " \n", 63 | " \n", 64 | " \n", 65 | " \n", 66 | " \n", 67 | " \n", 68 | " \n", 69 | " \n", 70 | " \n", 71 | " \n", 72 | " \n", 73 | " \n", 74 | " \n", 75 | " \n", 76 | " \n", 77 | " \n", 78 | " \n", 79 | " \n", 80 | " \n", 81 | " \n", 82 | " \n", 83 | " \n", 84 | " \n", 85 | " \n", 86 | " \n", 87 | " \n", 88 | " \n", 89 | " \n", 90 | " \n", 91 | " \n", 92 | " \n", 93 | " \n", 94 | " \n", 95 | " \n", 96 | " \n", 97 | " \n", 98 | " \n", 99 | " \n", 100 | " \n", 101 | " \n", 102 | " \n", 103 | " \n", 104 | " \n", 105 | " \n", 106 | " \n", 107 | " \n", 108 | " \n", 109 | " \n", 110 | " \n", 111 | " \n", 112 | " \n", 113 | " \n", 114 | " \n", 115 | " \n", 116 | " \n", 117 | " \n", 118 | " \n", 119 | " \n", 120 | " \n", 121 | " \n", 122 | " \n", 123 | " \n", 124 | " \n", 125 | " \n", 126 | " \n", 127 | " \n", 128 | " \n", 129 | " \n", 130 | " \n", 131 | " \n", 132 | " \n", 133 | " \n", 134 | " \n", 135 | " \n", 136 | " \n", 137 | " \n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | "
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
\n", 142 | "
" 143 | ], 144 | "text/plain": [ 145 | " PassengerId Survived Pclass \\\n", 146 | "0 1 0 3 \n", 147 | "1 2 1 1 \n", 148 | "2 3 1 3 \n", 149 | "3 4 1 1 \n", 150 | "4 5 0 3 \n", 151 | "\n", 152 | " Name Sex Age SibSp \\\n", 153 | "0 Braund, Mr. Owen Harris male 22.0 1 \n", 154 | "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", 155 | "2 Heikkinen, Miss. Laina female 26.0 0 \n", 156 | "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", 157 | "4 Allen, Mr. William Henry male 35.0 0 \n", 158 | "\n", 159 | " Parch Ticket Fare Cabin Embarked \n", 160 | "0 0 A/5 21171 7.2500 NaN S \n", 161 | "1 0 PC 17599 71.2833 C85 C \n", 162 | "2 0 STON/O2. 3101282 7.9250 NaN S \n", 163 | "3 0 113803 53.1000 C123 S \n", 164 | "4 0 373450 8.0500 NaN S " 165 | ] 166 | }, 167 | "execution_count": 23, 168 | "metadata": {}, 169 | "output_type": "execute_result" 170 | } 171 | ], 172 | "source": [ 173 | "df.head()" 174 | ] 175 | }, 176 | { 177 | "cell_type": "code", 178 | "execution_count": 24, 179 | "id": "a97dcd37", 180 | "metadata": {}, 181 | "outputs": [], 182 | "source": [ 183 | "columns = ['Survived', 'Pclass', 'Sex', 'Age', 'SibSp', 'Fare', 'Embarked']\n", 184 | "df = titanic[columns]" 185 | ] 186 | }, 187 | { 188 | "cell_type": "code", 189 | "execution_count": 25, 190 | "id": "d75b9de6", 191 | "metadata": {}, 192 | "outputs": [], 193 | "source": [ 194 | "cat_vars = ['Sex', 'Pclass', 'Embarked']\n", 195 | "\n", 196 | "for var in cat_vars:\n", 197 | " cat_list='var'+'_'+var\n", 198 | " cat_list = pd.get_dummies(df[var], prefix=var, drop_first=True) #el parametro drop_first elimina la posibilidad de caer en una trampa dummy\n", 199 | " df_1 = df.join(cat_list)\n", 200 | " df = df_1" 201 | ] 202 | }, 203 | { 204 | "cell_type": "code", 205 | "execution_count": 26, 206 | "id": "5fda435d", 207 | "metadata": {}, 208 | "outputs": [ 209 | { 210 | "data": { 211 | "text/html": [ 212 | "
\n", 213 | "\n", 226 | "\n", 227 | " \n", 228 | " \n", 229 | " \n", 230 | " \n", 231 | " \n", 232 | " \n", 233 | " \n", 234 | " \n", 235 | " \n", 236 | " \n", 237 | " \n", 238 | " \n", 239 | " \n", 240 | " \n", 241 | " \n", 242 | " \n", 243 | " \n", 244 | " \n", 245 | " \n", 246 | " \n", 247 | " \n", 248 | " \n", 249 | " \n", 250 | " \n", 251 | " \n", 252 | " \n", 253 | " \n", 254 | " \n", 255 | " \n", 256 | " \n", 257 | " \n", 258 | " \n", 259 | " \n", 260 | " \n", 261 | " \n", 262 | " \n", 263 | " \n", 264 | " \n", 265 | " \n", 266 | " \n", 267 | " \n", 268 | " \n", 269 | " \n", 270 | " \n", 271 | " \n", 272 | " \n", 273 | " \n", 274 | " \n", 275 | " \n", 276 | " \n", 277 | " \n", 278 | " \n", 279 | " \n", 280 | " \n", 281 | " \n", 282 | " \n", 283 | " \n", 284 | " \n", 285 | " \n", 286 | " \n", 287 | " \n", 288 | " \n", 289 | " \n", 290 | " \n", 291 | "
SurvivedPclassSexAgeSibSpFareEmbarkedSex_malePclass_2Pclass_3Embarked_QEmbarked_S
003male22.017.2500S10101
111female38.0171.2833C00000
213female26.007.9250S00101
\n", 292 | "
" 293 | ], 294 | "text/plain": [ 295 | " Survived Pclass Sex Age SibSp Fare Embarked Sex_male \\\n", 296 | "0 0 3 male 22.0 1 7.2500 S 1 \n", 297 | "1 1 1 female 38.0 1 71.2833 C 0 \n", 298 | "2 1 3 female 26.0 0 7.9250 S 0 \n", 299 | "\n", 300 | " Pclass_2 Pclass_3 Embarked_Q Embarked_S \n", 301 | "0 0 1 0 1 \n", 302 | "1 0 0 0 0 \n", 303 | "2 0 1 0 1 " 304 | ] 305 | }, 306 | "execution_count": 26, 307 | "metadata": {}, 308 | "output_type": "execute_result" 309 | } 310 | ], 311 | "source": [ 312 | "df.head(3)" 313 | ] 314 | }, 315 | { 316 | "cell_type": "code", 317 | "execution_count": 27, 318 | "id": "dd607f80", 319 | "metadata": {}, 320 | "outputs": [], 321 | "source": [ 322 | "data_vars = df.columns.values.tolist()\n", 323 | "to_keep = [i for i in data_vars if i not in cat_vars]\n", 324 | "df_final=df[to_keep]" 325 | ] 326 | }, 327 | { 328 | "cell_type": "code", 329 | "execution_count": 28, 330 | "id": "61dd68b6", 331 | "metadata": {}, 332 | "outputs": [ 333 | { 334 | "data": { 335 | "text/html": [ 336 | "
\n", 337 | "\n", 350 | "\n", 351 | " \n", 352 | " \n", 353 | " \n", 354 | " \n", 355 | " \n", 356 | " \n", 357 | " \n", 358 | " \n", 359 | " \n", 360 | " \n", 361 | " \n", 362 | " \n", 363 | " \n", 364 | " \n", 365 | " \n", 366 | " \n", 367 | " \n", 368 | " \n", 369 | " \n", 370 | " \n", 371 | " \n", 372 | " \n", 373 | " \n", 374 | " \n", 375 | " \n", 376 | " \n", 377 | " \n", 378 | " \n", 379 | " \n", 380 | " \n", 381 | " \n", 382 | " \n", 383 | " \n", 384 | " \n", 385 | " \n", 386 | " \n", 387 | " \n", 388 | " \n", 389 | " \n", 390 | " \n", 391 | " \n", 392 | " \n", 393 | " \n", 394 | " \n", 395 | " \n", 396 | " \n", 397 | " \n", 398 | " \n", 399 | " \n", 400 | " \n", 401 | " \n", 402 | " \n", 403 | " \n", 404 | " \n", 405 | " \n", 406 | " \n", 407 | " \n", 408 | " \n", 409 | " \n", 410 | " \n", 411 | " \n", 412 | " \n", 413 | " \n", 414 | " \n", 415 | " \n", 416 | " \n", 417 | " \n", 418 | " \n", 419 | " \n", 420 | " \n", 421 | " \n", 422 | " \n", 423 | " \n", 424 | " \n", 425 | " \n", 426 | " \n", 427 | "
SurvivedAgeSibSpFareSex_malePclass_2Pclass_3Embarked_QEmbarked_S
0022.017.250010101
1138.0171.283300000
2126.007.925000101
3135.0153.100000001
4035.008.050010101
\n", 428 | "
" 429 | ], 430 | "text/plain": [ 431 | " Survived Age SibSp Fare Sex_male Pclass_2 Pclass_3 Embarked_Q \\\n", 432 | "0 0 22.0 1 7.2500 1 0 1 0 \n", 433 | "1 1 38.0 1 71.2833 0 0 0 0 \n", 434 | "2 1 26.0 0 7.9250 0 0 1 0 \n", 435 | "3 1 35.0 1 53.1000 0 0 0 0 \n", 436 | "4 0 35.0 0 8.0500 1 0 1 0 \n", 437 | "\n", 438 | " Embarked_S \n", 439 | "0 1 \n", 440 | "1 0 \n", 441 | "2 1 \n", 442 | "3 1 \n", 443 | "4 1 " 444 | ] 445 | }, 446 | "execution_count": 28, 447 | "metadata": {}, 448 | "output_type": "execute_result" 449 | } 450 | ], 451 | "source": [ 452 | "df_final.head()" 453 | ] 454 | } 455 | ], 456 | "metadata": { 457 | "kernelspec": { 458 | "display_name": "Python 3 (ipykernel)", 459 | "language": "python", 460 | "name": "python3" 461 | }, 462 | "language_info": { 463 | "codemirror_mode": { 464 | "name": "ipython", 465 | "version": 3 466 | }, 467 | "file_extension": ".py", 468 | "mimetype": "text/x-python", 469 | "name": "python", 470 | "nbconvert_exporter": "python", 471 | "pygments_lexer": "ipython3", 472 | "version": "3.9.13" 473 | } 474 | }, 475 | "nbformat": 4, 476 | "nbformat_minor": 5 477 | } 478 | -------------------------------------------------------------------------------- /Transformaciones de Variables Categóricas.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "### Transformaciones de Variables Categóricas" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 53, 13 | "metadata": { 14 | "id": "MMHzykyQfQQN", 15 | "outputId": "709fd4c9-4e33-4618-c445-a392d0c824bf" 16 | }, 17 | "outputs": [ 18 | { 19 | "data": { 20 | "text/html": [ 21 | "
\n", 22 | "\n", 35 | "\n", 36 | " \n", 37 | " \n", 38 | " \n", 39 | " \n", 40 | " \n", 41 | " \n", 42 | " \n", 43 | " \n", 44 | " \n", 45 | " \n", 46 | " \n", 47 | " \n", 48 | " \n", 49 | " \n", 50 | " \n", 51 | " \n", 52 | " \n", 53 | " \n", 54 | " \n", 55 | " \n", 56 | " \n", 57 | " \n", 58 | " \n", 59 | " \n", 60 | " \n", 61 | " \n", 62 | " \n", 63 | " \n", 64 | " \n", 65 | " \n", 66 | " \n", 67 | " \n", 68 | " \n", 69 | " \n", 70 | "
edadgeneroclase
020MasculinoPrimera
130MasculinoSegunda
240FemeninoSegunda
350FemeninoTercera
\n", 71 | "
" 72 | ], 73 | "text/plain": [ 74 | " edad genero clase\n", 75 | "0 20 Masculino Primera\n", 76 | "1 30 Masculino Segunda\n", 77 | "2 40 Femenino Segunda\n", 78 | "3 50 Femenino Tercera" 79 | ] 80 | }, 81 | "execution_count": 53, 82 | "metadata": {}, 83 | "output_type": "execute_result" 84 | } 85 | ], 86 | "source": [ 87 | "import pandas as pd\n", 88 | "\n", 89 | "df = pd.DataFrame(data={\n", 90 | " 'edad': [20, 30, 40, 50],\n", 91 | " 'genero': ['Masculino', 'Masculino', 'Femenino', 'Femenino'], \n", 92 | " 'clase': ['Primera', 'Segunda', 'Segunda','Tercera',]\n", 93 | "})\n", 94 | "\n", 95 | "df" 96 | ] 97 | }, 98 | { 99 | "cell_type": "markdown", 100 | "metadata": { 101 | "id": "CVKT04n2fQQO" 102 | }, 103 | "source": [ 104 | "
\n", 105 | "\n", 106 | "\n", 107 | "# 1. Codificación Binaria\n", 108 | "\n", 109 | "\n", 110 | "* Vamos a transformar la variable genero a una variable binaria.\n", 111 | "\n", 112 | "\n", 113 | "* Esto lo podemos hacer con la clase **\"LabelBinarizer()\"** de scikit-learn de la siguiente manera:" 114 | ] 115 | }, 116 | { 117 | "cell_type": "code", 118 | "execution_count": 54, 119 | "metadata": { 120 | "id": "EuOo4Us6fQQO", 121 | "outputId": "fac92611-04c6-40ce-9434-314ce62a761b" 122 | }, 123 | "outputs": [ 124 | { 125 | "data": { 126 | "text/html": [ 127 | "
\n", 128 | "\n", 141 | "\n", 142 | " \n", 143 | " \n", 144 | " \n", 145 | " \n", 146 | " \n", 147 | " \n", 148 | " \n", 149 | " \n", 150 | " \n", 151 | " \n", 152 | " \n", 153 | " \n", 154 | " \n", 155 | " \n", 156 | " \n", 157 | " \n", 158 | " \n", 159 | " \n", 160 | " \n", 161 | " \n", 162 | " \n", 163 | " \n", 164 | " \n", 165 | " \n", 166 | " \n", 167 | " \n", 168 | " \n", 169 | " \n", 170 | " \n", 171 | "
generogenero_binario
0Masculino1
1Masculino1
2Femenino0
3Femenino0
\n", 172 | "
" 173 | ], 174 | "text/plain": [ 175 | " genero genero_binario\n", 176 | "0 Masculino 1\n", 177 | "1 Masculino 1\n", 178 | "2 Femenino 0\n", 179 | "3 Femenino 0" 180 | ] 181 | }, 182 | "execution_count": 54, 183 | "metadata": {}, 184 | "output_type": "execute_result" 185 | } 186 | ], 187 | "source": [ 188 | "from sklearn.preprocessing import LabelBinarizer\n", 189 | "\n", 190 | "lb = LabelBinarizer()\n", 191 | "df['genero_binario'] = lb.fit_transform(df['genero'])\n", 192 | "df[['genero', 'genero_binario']]" 193 | ] 194 | }, 195 | { 196 | "cell_type": "markdown", 197 | "metadata": { 198 | "id": "FnW2yQtvfQQO" 199 | }, 200 | "source": [ 201 | "
\n", 202 | "\n", 203 | "\n", 204 | "# 2. Codificación One Hot Encode\n", 205 | "\n" 206 | ] 207 | }, 208 | { 209 | "cell_type": "code", 210 | "execution_count": 55, 211 | "metadata": { 212 | "id": "xDu0ecPYfQQO", 213 | "outputId": "a93c60f1-6aa2-4768-b075-3ece2fa05956" 214 | }, 215 | "outputs": [], 216 | "source": [ 217 | "from sklearn.preprocessing import OneHotEncoder" 218 | ] 219 | }, 220 | { 221 | "cell_type": "code", 222 | "execution_count": 56, 223 | "metadata": {}, 224 | "outputs": [ 225 | { 226 | "data": { 227 | "text/plain": [ 228 | "array([[0., 0.],\n", 229 | " [1., 0.],\n", 230 | " [1., 0.],\n", 231 | " [0., 1.]])" 232 | ] 233 | }, 234 | "execution_count": 56, 235 | "metadata": {}, 236 | "output_type": "execute_result" 237 | } 238 | ], 239 | "source": [ 240 | "ohe = OneHotEncoder(drop='first')\n", 241 | "x = df[['clase']].values\n", 242 | "x_one_hot = ohe.fit_transform(x).toarray()\n", 243 | "x_one_hot" 244 | ] 245 | }, 246 | { 247 | "cell_type": "code", 248 | "execution_count": 57, 249 | "metadata": {}, 250 | "outputs": [ 251 | { 252 | "data": { 253 | "text/html": [ 254 | "
\n", 255 | "\n", 268 | "\n", 269 | " \n", 270 | " \n", 271 | " \n", 272 | " \n", 273 | " \n", 274 | " \n", 275 | " \n", 276 | " \n", 277 | " \n", 278 | " \n", 279 | " \n", 280 | " \n", 281 | " \n", 282 | " \n", 283 | " \n", 284 | " \n", 285 | " \n", 286 | " \n", 287 | " \n", 288 | " \n", 289 | " \n", 290 | " \n", 291 | " \n", 292 | " \n", 293 | " \n", 294 | " \n", 295 | " \n", 296 | " \n", 297 | " \n", 298 | " \n", 299 | " \n", 300 | " \n", 301 | " \n", 302 | " \n", 303 | " \n", 304 | " \n", 305 | " \n", 306 | " \n", 307 | " \n", 308 | " \n", 309 | " \n", 310 | " \n", 311 | " \n", 312 | " \n", 313 | " \n", 314 | " \n", 315 | " \n", 316 | " \n", 317 | " \n", 318 | "
edadgeneroclasegenero_binarioclase_0clase_1
020MasculinoPrimera10.00.0
130MasculinoSegunda11.00.0
240FemeninoSegunda01.00.0
350FemeninoTercera00.01.0
\n", 319 | "
" 320 | ], 321 | "text/plain": [ 322 | " edad genero clase genero_binario clase_0 clase_1\n", 323 | "0 20 Masculino Primera 1 0.0 0.0\n", 324 | "1 30 Masculino Segunda 1 1.0 0.0\n", 325 | "2 40 Femenino Segunda 0 1.0 0.0\n", 326 | "3 50 Femenino Tercera 0 0.0 1.0" 327 | ] 328 | }, 329 | "execution_count": 57, 330 | "metadata": {}, 331 | "output_type": "execute_result" 332 | } 333 | ], 334 | "source": [ 335 | "df_one_hot = pd.DataFrame(x_one_hot, columns = [\"clase_\"+str(int(i)) for i in range(x_one_hot.shape[1])])\n", 336 | "df_all = pd.concat([df, df_one_hot], axis=1)\n", 337 | "df_all" 338 | ] 339 | }, 340 | { 341 | "cell_type": "markdown", 342 | "metadata": {}, 343 | "source": [ 344 | "La \"trampa de las variables dummy\" es un problema que puede ocurrir cuando utilizamos variables categóricas en un modelo de regresión lineal (u otros modelos) y las convertimos en variables dummy o indicadoras. Las variables dummy se utilizan para transformar una variable categórica en varias columnas binarias (0 o 1), permitiendo que los modelos de regresión trabajen con variables que no son numéricas.\n", 345 | "\n", 346 | "¿Qué es una variable dummy?\n", 347 | "Cuando tienes una variable categórica (como \"color\" con valores \"rojo\", \"verde\", y \"azul\"), necesitas transformarla en varias columnas para que el modelo pueda procesarlas. Esto se hace con variables dummy. Si \"color\" tiene tres valores, las variables dummy se verían así:\n", 348 | "\n", 349 | "\"rojo\" → (1, 0, 0)\n", 350 | "\"verde\" → (0, 1, 0)\n", 351 | "\"azul\" → (0, 0, 1)" 352 | ] 353 | }, 354 | { 355 | "cell_type": "markdown", 356 | "metadata": {}, 357 | "source": [ 358 | "#### ¿Por qué eliminar una categoría?\n", 359 | "\n", 360 | "Esto evita que haya redundancia entre las variables. Por ejemplo, si ya sabes que una fila no pertenece a las clases \"Segunda\" ni \"Tercera\", automáticamente sabes que pertenece a \"Primera\". Esto soluciona el problema de la multicolinealidad, mejorando la estabilidad del modelo." 361 | ] 362 | }, 363 | { 364 | "cell_type": "markdown", 365 | "metadata": { 366 | "id": "WXvFp08QfQQP" 367 | }, 368 | "source": [ 369 | "### Codificación One Hot Encode en Pandas\n", 370 | "\n", 371 | "\n", 372 | "* Esta transformación también nos la permite hacer la librería de Pandas de la siguiente manera:" 373 | ] 374 | }, 375 | { 376 | "cell_type": "code", 377 | "execution_count": 58, 378 | "metadata": {}, 379 | "outputs": [ 380 | { 381 | "data": { 382 | "text/html": [ 383 | "
\n", 384 | "\n", 397 | "\n", 398 | " \n", 399 | " \n", 400 | " \n", 401 | " \n", 402 | " \n", 403 | " \n", 404 | " \n", 405 | " \n", 406 | " \n", 407 | " \n", 408 | " \n", 409 | " \n", 410 | " \n", 411 | " \n", 412 | " \n", 413 | " \n", 414 | " \n", 415 | " \n", 416 | " \n", 417 | " \n", 418 | " \n", 419 | " \n", 420 | " \n", 421 | " \n", 422 | " \n", 423 | " \n", 424 | " \n", 425 | " \n", 426 | " \n", 427 | " \n", 428 | " \n", 429 | " \n", 430 | " \n", 431 | " \n", 432 | " \n", 433 | " \n", 434 | " \n", 435 | " \n", 436 | " \n", 437 | " \n", 438 | " \n", 439 | " \n", 440 | " \n", 441 | " \n", 442 | "
edadgenerogenero_binarioclase_Segundaclase_Tercera
020Masculino100
130Masculino110
240Femenino010
350Femenino001
\n", 443 | "
" 444 | ], 445 | "text/plain": [ 446 | " edad genero genero_binario clase_Segunda clase_Tercera\n", 447 | "0 20 Masculino 1 0 0\n", 448 | "1 30 Masculino 1 1 0\n", 449 | "2 40 Femenino 0 1 0\n", 450 | "3 50 Femenino 0 0 1" 451 | ] 452 | }, 453 | "execution_count": 58, 454 | "metadata": {}, 455 | "output_type": "execute_result" 456 | } 457 | ], 458 | "source": [ 459 | "import pandas as pd\n", 460 | "\n", 461 | "\n", 462 | "df_encoded = pd.get_dummies(df, columns=['clase'], drop_first=True)\n", 463 | "\n", 464 | "# Mostrar el DataFrame con las nuevas columnas\n", 465 | "df_encoded\n" 466 | ] 467 | }, 468 | { 469 | "cell_type": "markdown", 470 | "metadata": { 471 | "id": "fo1i2yWGfQQQ" 472 | }, 473 | "source": [ 474 | "
\n", 475 | "\n", 476 | "\n", 477 | "\n", 478 | "# 3. Variables con un orden natural\n", 479 | "\n", 480 | "\n" 481 | ] 482 | }, 483 | { 484 | "cell_type": "code", 485 | "execution_count": 59, 486 | "metadata": { 487 | "id": "EiLYj-HDfQQQ", 488 | "outputId": "8b4f5074-dcf0-4669-d04f-25e8dd63a7a2" 489 | }, 490 | "outputs": [], 491 | "source": [ 492 | "df = pd.DataFrame({\n", 493 | " 'nombre': ['Juan', 'María', 'Luis', 'Ana'],\n", 494 | " 'nivel_educativo': ['Primaria', 'Posgrado', 'Secundaria', 'Pregrado']\n", 495 | "})\n", 496 | "\n" 497 | ] 498 | }, 499 | { 500 | "cell_type": "code", 501 | "execution_count": 60, 502 | "metadata": {}, 503 | "outputs": [ 504 | { 505 | "data": { 506 | "text/html": [ 507 | "
\n", 508 | "\n", 521 | "\n", 522 | " \n", 523 | " \n", 524 | " \n", 525 | " \n", 526 | " \n", 527 | " \n", 528 | " \n", 529 | " \n", 530 | " \n", 531 | " \n", 532 | " \n", 533 | " \n", 534 | " \n", 535 | " \n", 536 | " \n", 537 | " \n", 538 | " \n", 539 | " \n", 540 | " \n", 541 | " \n", 542 | " \n", 543 | " \n", 544 | " \n", 545 | " \n", 546 | " \n", 547 | " \n", 548 | " \n", 549 | " \n", 550 | " \n", 551 | " \n", 552 | " \n", 553 | " \n", 554 | " \n", 555 | " \n", 556 | "
nombrenivel_educativonivel_educativo_encoded
0JuanPrimaria0
1MaríaPosgrado3
2LuisSecundaria1
3AnaPregrado2
\n", 557 | "
" 558 | ], 559 | "text/plain": [ 560 | " nombre nivel_educativo nivel_educativo_encoded\n", 561 | "0 Juan Primaria 0\n", 562 | "1 María Posgrado 3\n", 563 | "2 Luis Secundaria 1\n", 564 | "3 Ana Pregrado 2" 565 | ] 566 | }, 567 | "execution_count": 60, 568 | "metadata": {}, 569 | "output_type": "execute_result" 570 | } 571 | ], 572 | "source": [ 573 | "# Definir el orden correcto de las categorías\n", 574 | "orden_nivel_educativo = ['Primaria', 'Secundaria', 'Pregrado', 'Posgrado']\n", 575 | "\n", 576 | "# Convertir la columna 'nivel_educativo' en una variable categórica con el orden definido\n", 577 | "df['nivel_educativo'] = pd.Categorical(df['nivel_educativo'], categories=orden_nivel_educativo, ordered=True)\n", 578 | "\n", 579 | "# Mapear las categorías al número correspondiente según su orden\n", 580 | "df['nivel_educativo_encoded'] = df['nivel_educativo'].cat.codes\n", 581 | "\n", 582 | "# Mostrar el DataFrame con la columna codificada\n", 583 | "df" 584 | ] 585 | } 586 | ], 587 | "metadata": { 588 | "colab": { 589 | "provenance": [] 590 | }, 591 | "kernelspec": { 592 | "display_name": "Python 3 (ipykernel)", 593 | "language": "python", 594 | "name": "python3" 595 | }, 596 | "language_info": { 597 | "codemirror_mode": { 598 | "name": "ipython", 599 | "version": 3 600 | }, 601 | "file_extension": ".py", 602 | "mimetype": "text/x-python", 603 | "name": "python", 604 | "nbconvert_exporter": "python", 605 | "pygments_lexer": "ipython3", 606 | "version": "3.11.4" 607 | } 608 | }, 609 | "nbformat": 4, 610 | "nbformat_minor": 1 611 | } 612 | -------------------------------------------------------------------------------- /Unsupervised Machine Learning Proyecto Final/readme.md: -------------------------------------------------------------------------------- 1 | 2 | ## IBM Machine Learning Professional Certificate - Unsupervised Machine Learning 3 | 4 | Required in your Project: 5 | 6 | - Main objective of the analysis that also specifies whether your model will be focused on clustering or dimensionality reduction and the benefits that your analysis brings to the business or stakeholders of this data. 7 | 8 | - Brief description of the data set you chose, a summary of its attributes, and an outline of what you are trying to accomplish with this analysis. 9 | 10 | - Brief summary of data exploration and actions taken for data cleaning orfeature engineering. 11 | 12 | - Summary of training at least three variations of the unsupervised model you selected. For example, you can use different clustering techniques or different hyperparameters. 13 | 14 | - A paragraph explaining which of your Unsupervised Learning models you recommend as a final model that best fits your needs in terms. 15 | 16 | - Summary Key Findings and Insights, which walks your reader through the main findings of your modeling exercise. 17 | 18 | - Suggestions for next steps in analyzing this data 19 | 20 | -------------------------------------------------------------------------------- /_Librería Lazypredict.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 50, 6 | "id": "2c92e51b", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "import pandas as pd \n", 11 | "from sklearn.model_selection import train_test_split\n", 12 | "import lazypredict \n", 13 | "from lazypredict.Supervised import LazyRegressor" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": 51, 19 | "id": "0da3b1bf", 20 | "metadata": {}, 21 | "outputs": [], 22 | "source": [ 23 | "# Definimos la semilla\n", 24 | "SEMILLA = 42" 25 | ] 26 | }, 27 | { 28 | "cell_type": "code", 29 | "execution_count": 52, 30 | "id": "e1f1c656", 31 | "metadata": {}, 32 | "outputs": [], 33 | "source": [ 34 | "# Lectura del dataset\n", 35 | "data = pd.read_csv('properati_caba_2021.csv')" 36 | ] 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": 53, 41 | "id": "1dbc66f4", 42 | "metadata": {}, 43 | "outputs": [ 44 | { 45 | "data": { 46 | "text/html": [ 47 | "
\n", 48 | "\n", 61 | "\n", 62 | " \n", 63 | " \n", 64 | " \n", 65 | " \n", 66 | " \n", 67 | " \n", 68 | " \n", 69 | " \n", 70 | " \n", 71 | " \n", 72 | " \n", 73 | " \n", 74 | " \n", 75 | " \n", 76 | " \n", 77 | " \n", 78 | " \n", 79 | " \n", 80 | " \n", 81 | " \n", 82 | " \n", 83 | " \n", 84 | " \n", 85 | " \n", 86 | " \n", 87 | " \n", 88 | " \n", 89 | " \n", 90 | " \n", 91 | " \n", 92 | " \n", 93 | " \n", 94 | " \n", 95 | " \n", 96 | " \n", 97 | " \n", 98 | " \n", 99 | " \n", 100 | " \n", 101 | " \n", 102 | " \n", 103 | " \n", 104 | " \n", 105 | " \n", 106 | " \n", 107 | " \n", 108 | " \n", 109 | " \n", 110 | " \n", 111 | " \n", 112 | " \n", 113 | " \n", 114 | " \n", 115 | " \n", 116 | " \n", 117 | " \n", 118 | " \n", 119 | " \n", 120 | " \n", 121 | " \n", 122 | " \n", 123 | " \n", 124 | " \n", 125 | " \n", 126 | " \n", 127 | " \n", 128 | " \n", 129 | " \n", 130 | " \n", 131 | " \n", 132 | " \n", 133 | " \n", 134 | " \n", 135 | " \n", 136 | " \n", 137 | " \n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | " \n", 142 | " \n", 143 | " \n", 144 | "
idlatlonl3roomsbathroomssurface_totalsurface_coveredpriceproperty_type
05dmWZ4uqAU4kpJw0AEc/Hw==-34.62-58.40San Cristobal7.006.00534.00384.00470000.00Casa
1kFa6ndbLuJ2k2zfzI1fY3A==-34.56-58.46Belgrano1.001.0025.0025.0060000.00Departamento
2S0fct9jgpfmuqmOaPntC/Q==-34.62-58.38Monserrat1.001.0040.0040.0082500.00Departamento
3sduUfHxdOh9PuRwJruEcyA==-34.62-58.38San Telmo1.001.0040.0040.0082500.00Departamento
4Tl1ebIQJyPOXV2XJMEImQg==-34.62-58.38Constitución1.001.0040.0040.0082500.00Departamento
\n", 145 | "
" 146 | ], 147 | "text/plain": [ 148 | " id lat lon l3 rooms bathrooms \\\n", 149 | "0 5dmWZ4uqAU4kpJw0AEc/Hw== -34.62 -58.40 San Cristobal 7.00 6.00 \n", 150 | "1 kFa6ndbLuJ2k2zfzI1fY3A== -34.56 -58.46 Belgrano 1.00 1.00 \n", 151 | "2 S0fct9jgpfmuqmOaPntC/Q== -34.62 -58.38 Monserrat 1.00 1.00 \n", 152 | "3 sduUfHxdOh9PuRwJruEcyA== -34.62 -58.38 San Telmo 1.00 1.00 \n", 153 | "4 Tl1ebIQJyPOXV2XJMEImQg== -34.62 -58.38 Constitución 1.00 1.00 \n", 154 | "\n", 155 | " surface_total surface_covered price property_type \n", 156 | "0 534.00 384.00 470000.00 Casa \n", 157 | "1 25.00 25.00 60000.00 Departamento \n", 158 | "2 40.00 40.00 82500.00 Departamento \n", 159 | "3 40.00 40.00 82500.00 Departamento \n", 160 | "4 40.00 40.00 82500.00 Departamento " 161 | ] 162 | }, 163 | "execution_count": 53, 164 | "metadata": {}, 165 | "output_type": "execute_result" 166 | } 167 | ], 168 | "source": [ 169 | "# Observamos los primeros registros del dataframe\n", 170 | "data.head()" 171 | ] 172 | }, 173 | { 174 | "cell_type": "code", 175 | "execution_count": 54, 176 | "id": "90ad4a14", 177 | "metadata": {}, 178 | "outputs": [ 179 | { 180 | "data": { 181 | "text/html": [ 182 | "
\n", 183 | "\n", 196 | "\n", 197 | " \n", 198 | " \n", 199 | " \n", 200 | " \n", 201 | " \n", 202 | " \n", 203 | " \n", 204 | " \n", 205 | " \n", 206 | " \n", 207 | " \n", 208 | " \n", 209 | " \n", 210 | " \n", 211 | " \n", 212 | " \n", 213 | " \n", 214 | " \n", 215 | " \n", 216 | " \n", 217 | " \n", 218 | " \n", 219 | " \n", 220 | " \n", 221 | " \n", 222 | " \n", 223 | " \n", 224 | " \n", 225 | " \n", 226 | " \n", 227 | " \n", 228 | " \n", 229 | " \n", 230 | " \n", 231 | " \n", 232 | " \n", 233 | " \n", 234 | " \n", 235 | " \n", 236 | " \n", 237 | " \n", 238 | " \n", 239 | " \n", 240 | " \n", 241 | " \n", 242 | " \n", 243 | " \n", 244 | " \n", 245 | " \n", 246 | " \n", 247 | " \n", 248 | " \n", 249 | " \n", 250 | " \n", 251 | " \n", 252 | " \n", 253 | " \n", 254 | " \n", 255 | " \n", 256 | " \n", 257 | " \n", 258 | " \n", 259 | " \n", 260 | " \n", 261 | " \n", 262 | " \n", 263 | " \n", 264 | " \n", 265 | " \n", 266 | " \n", 267 | " \n", 268 | " \n", 269 | " \n", 270 | " \n", 271 | " \n", 272 | " \n", 273 | " \n", 274 | " \n", 275 | " \n", 276 | " \n", 277 | " \n", 278 | " \n", 279 | " \n", 280 | " \n", 281 | "
latlonroomsbathroomssurface_totalsurface_coveredprice
lat1.00-0.14-0.010.100.020.040.15
lon-0.141.000.050.080.050.090.18
rooms-0.010.051.000.620.740.750.49
bathrooms0.100.080.621.000.740.750.67
surface_total0.020.050.740.741.000.970.72
surface_covered0.040.090.750.750.971.000.76
price0.150.180.490.670.720.761.00
\n", 282 | "
" 283 | ], 284 | "text/plain": [ 285 | " lat lon rooms bathrooms surface_total surface_covered \\\n", 286 | "lat 1.00 -0.14 -0.01 0.10 0.02 0.04 \n", 287 | "lon -0.14 1.00 0.05 0.08 0.05 0.09 \n", 288 | "rooms -0.01 0.05 1.00 0.62 0.74 0.75 \n", 289 | "bathrooms 0.10 0.08 0.62 1.00 0.74 0.75 \n", 290 | "surface_total 0.02 0.05 0.74 0.74 1.00 0.97 \n", 291 | "surface_covered 0.04 0.09 0.75 0.75 0.97 1.00 \n", 292 | "price 0.15 0.18 0.49 0.67 0.72 0.76 \n", 293 | "\n", 294 | " price \n", 295 | "lat 0.15 \n", 296 | "lon 0.18 \n", 297 | "rooms 0.49 \n", 298 | "bathrooms 0.67 \n", 299 | "surface_total 0.72 \n", 300 | "surface_covered 0.76 \n", 301 | "price 1.00 " 302 | ] 303 | }, 304 | "execution_count": 54, 305 | "metadata": {}, 306 | "output_type": "execute_result" 307 | } 308 | ], 309 | "source": [ 310 | "# Observamos la matriz de correlación entre las variables numéricas\n", 311 | "data.corr()" 312 | ] 313 | }, 314 | { 315 | "cell_type": "code", 316 | "execution_count": 55, 317 | "id": "85b88a5f", 318 | "metadata": {}, 319 | "outputs": [], 320 | "source": [ 321 | "# Separamos al dataset en X (variables predictoras) e y (variable a predecir)\n", 322 | "X = df[[ 'rooms', 'bathrooms', 'surface_total', 'surface_covered']]\n", 323 | "y = df['price']" 324 | ] 325 | }, 326 | { 327 | "cell_type": "code", 328 | "execution_count": 56, 329 | "id": "76759d00", 330 | "metadata": {}, 331 | "outputs": [], 332 | "source": [ 333 | "from sklearn.model_selection import train_test_split\n", 334 | "# Realizamos el split de X e y en los sets de entrenamiento (train) y test\n", 335 | "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=SEMILLA)" 336 | ] 337 | }, 338 | { 339 | "cell_type": "code", 340 | "execution_count": 57, 341 | "id": "f9a8ea4b", 342 | "metadata": {}, 343 | "outputs": [ 344 | { 345 | "name": "stderr", 346 | "output_type": "stream", 347 | "text": [ 348 | "100%|██████████| 42/42 [06:42<00:00, 9.58s/it]" 349 | ] 350 | }, 351 | { 352 | "name": "stdout", 353 | "output_type": "stream", 354 | "text": [ 355 | "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000606 seconds.\n", 356 | "You can set `force_row_wise=true` to remove the overhead.\n", 357 | "And if memory is not enough, you can set `force_col_wise=true`.\n", 358 | "[LightGBM] [Info] Total Bins 541\n", 359 | "[LightGBM] [Info] Number of data points in the train set: 32124, number of used features: 4\n", 360 | "[LightGBM] [Info] Start training from score 245499.655491\n" 361 | ] 362 | }, 363 | { 364 | "name": "stderr", 365 | "output_type": "stream", 366 | "text": [ 367 | "\n" 368 | ] 369 | } 370 | ], 371 | "source": [ 372 | "# Para tareas de regresión\n", 373 | "reg = LazyRegressor(verbose=0, ignore_warnings=True, predictions=True, random_state = SEMILLA)\n", 374 | "models, predictions = reg.fit(X_train, X_test, y_train, y_test)" 375 | ] 376 | }, 377 | { 378 | "cell_type": "code", 379 | "execution_count": 58, 380 | "id": "57f95645", 381 | "metadata": {}, 382 | "outputs": [ 383 | { 384 | "data": { 385 | "text/html": [ 386 | "
\n", 387 | "\n", 400 | "\n", 401 | " \n", 402 | " \n", 403 | " \n", 404 | " \n", 405 | " \n", 406 | " \n", 407 | " \n", 408 | " \n", 409 | " \n", 410 | " \n", 411 | " \n", 412 | " \n", 413 | " \n", 414 | " \n", 415 | " \n", 416 | " \n", 417 | " \n", 418 | " \n", 419 | " \n", 420 | " \n", 421 | " \n", 422 | " \n", 423 | " \n", 424 | " \n", 425 | " \n", 426 | " \n", 427 | " \n", 428 | " \n", 429 | " \n", 430 | " \n", 431 | " \n", 432 | " \n", 433 | " \n", 434 | " \n", 435 | " \n", 436 | " \n", 437 | " \n", 438 | " \n", 439 | " \n", 440 | " \n", 441 | " \n", 442 | " \n", 443 | " \n", 444 | " \n", 445 | " \n", 446 | " \n", 447 | " \n", 448 | " \n", 449 | " \n", 450 | " \n", 451 | " \n", 452 | " \n", 453 | " \n", 454 | " \n", 455 | " \n", 456 | " \n", 457 | " \n", 458 | " \n", 459 | " \n", 460 | " \n", 461 | " \n", 462 | " \n", 463 | " \n", 464 | " \n", 465 | " \n", 466 | " \n", 467 | " \n", 468 | " \n", 469 | " \n", 470 | " \n", 471 | " \n", 472 | " \n", 473 | " \n", 474 | " \n", 475 | " \n", 476 | " \n", 477 | " \n", 478 | " \n", 479 | " \n", 480 | " \n", 481 | " \n", 482 | " \n", 483 | " \n", 484 | " \n", 485 | " \n", 486 | " \n", 487 | " \n", 488 | " \n", 489 | " \n", 490 | " \n", 491 | " \n", 492 | " \n", 493 | " \n", 494 | " \n", 495 | " \n", 496 | " \n", 497 | " \n", 498 | " \n", 499 | " \n", 500 | " \n", 501 | " \n", 502 | " \n", 503 | " \n", 504 | " \n", 505 | " \n", 506 | " \n", 507 | " \n", 508 | " \n", 509 | " \n", 510 | " \n", 511 | " \n", 512 | " \n", 513 | " \n", 514 | " \n", 515 | " \n", 516 | " \n", 517 | " \n", 518 | " \n", 519 | " \n", 520 | " \n", 521 | " \n", 522 | " \n", 523 | " \n", 524 | "
Adjusted R-SquaredR-SquaredRMSETime Taken
Model
RandomForestRegressor0.650.65197526.484.60
LassoLarsCV0.640.64199850.170.05
LarsCV0.640.64199850.170.11
OrthogonalMatchingPursuitCV0.640.64199850.170.03
LassoLarsIC0.640.64199850.170.06
Lars0.640.64199850.170.23
TransformedTargetRegressor0.640.64199850.170.03
LinearRegression0.640.64199850.170.04
LassoLars0.640.64199850.940.03
Lasso0.640.64199850.950.03
Ridge0.640.64199854.960.02
BayesianRidge0.640.64199859.180.05
GradientBoostingRegressor0.640.64199883.021.30
RidgeCV0.640.64199898.040.03
LassoCV0.640.64200042.250.31
\n", 525 | "
" 526 | ], 527 | "text/plain": [ 528 | " Adjusted R-Squared R-Squared RMSE \\\n", 529 | "Model \n", 530 | "RandomForestRegressor 0.65 0.65 197526.48 \n", 531 | "LassoLarsCV 0.64 0.64 199850.17 \n", 532 | "LarsCV 0.64 0.64 199850.17 \n", 533 | "OrthogonalMatchingPursuitCV 0.64 0.64 199850.17 \n", 534 | "LassoLarsIC 0.64 0.64 199850.17 \n", 535 | "Lars 0.64 0.64 199850.17 \n", 536 | "TransformedTargetRegressor 0.64 0.64 199850.17 \n", 537 | "LinearRegression 0.64 0.64 199850.17 \n", 538 | "LassoLars 0.64 0.64 199850.94 \n", 539 | "Lasso 0.64 0.64 199850.95 \n", 540 | "Ridge 0.64 0.64 199854.96 \n", 541 | "BayesianRidge 0.64 0.64 199859.18 \n", 542 | "GradientBoostingRegressor 0.64 0.64 199883.02 \n", 543 | "RidgeCV 0.64 0.64 199898.04 \n", 544 | "LassoCV 0.64 0.64 200042.25 \n", 545 | "\n", 546 | " Time Taken \n", 547 | "Model \n", 548 | "RandomForestRegressor 4.60 \n", 549 | "LassoLarsCV 0.05 \n", 550 | "LarsCV 0.11 \n", 551 | "OrthogonalMatchingPursuitCV 0.03 \n", 552 | "LassoLarsIC 0.06 \n", 553 | "Lars 0.23 \n", 554 | "TransformedTargetRegressor 0.03 \n", 555 | "LinearRegression 0.04 \n", 556 | "LassoLars 0.03 \n", 557 | "Lasso 0.03 \n", 558 | "Ridge 0.02 \n", 559 | "BayesianRidge 0.05 \n", 560 | "GradientBoostingRegressor 1.30 \n", 561 | "RidgeCV 0.03 \n", 562 | "LassoCV 0.31 " 563 | ] 564 | }, 565 | "execution_count": 58, 566 | "metadata": {}, 567 | "output_type": "execute_result" 568 | } 569 | ], 570 | "source": [ 571 | "models.head(15)" 572 | ] 573 | }, 574 | { 575 | "cell_type": "code", 576 | "execution_count": 59, 577 | "id": "67becea8", 578 | "metadata": {}, 579 | "outputs": [ 580 | { 581 | "data": { 582 | "text/html": [ 583 | "
\n", 584 | "\n", 597 | "\n", 598 | " \n", 599 | " \n", 600 | " \n", 601 | " \n", 602 | " \n", 603 | " \n", 604 | " \n", 605 | " \n", 606 | " \n", 607 | " \n", 608 | " \n", 609 | " \n", 610 | " \n", 611 | " \n", 612 | " \n", 613 | " \n", 614 | " \n", 615 | " \n", 616 | " \n", 617 | " \n", 618 | " \n", 619 | " \n", 620 | " \n", 621 | " \n", 622 | " \n", 623 | " \n", 624 | " \n", 625 | " \n", 626 | " \n", 627 | " \n", 628 | " \n", 629 | " \n", 630 | " \n", 631 | " \n", 632 | " \n", 633 | " \n", 634 | " \n", 635 | " \n", 636 | " \n", 637 | " \n", 638 | " \n", 639 | " \n", 640 | " \n", 641 | " \n", 642 | " \n", 643 | " \n", 644 | " \n", 645 | " \n", 646 | " \n", 647 | " \n", 648 | " \n", 649 | " \n", 650 | " \n", 651 | " \n", 652 | " \n", 653 | " \n", 654 | " \n", 655 | " \n", 656 | " \n", 657 | " \n", 658 | " \n", 659 | " \n", 660 | " \n", 661 | " \n", 662 | " \n", 663 | " \n", 664 | " \n", 665 | " \n", 666 | " \n", 667 | " \n", 668 | " \n", 669 | " \n", 670 | " \n", 671 | " \n", 672 | " \n", 673 | " \n", 674 | " \n", 675 | " \n", 676 | " \n", 677 | " \n", 678 | " \n", 679 | " \n", 680 | " \n", 681 | " \n", 682 | " \n", 683 | " \n", 684 | " \n", 685 | " \n", 686 | " \n", 687 | " \n", 688 | " \n", 689 | " \n", 690 | " \n", 691 | " \n", 692 | " \n", 693 | " \n", 694 | " \n", 695 | " \n", 696 | " \n", 697 | " \n", 698 | " \n", 699 | " \n", 700 | " \n", 701 | " \n", 702 | " \n", 703 | " \n", 704 | " \n", 705 | " \n", 706 | " \n", 707 | " \n", 708 | " \n", 709 | " \n", 710 | " \n", 711 | " \n", 712 | " \n", 713 | " \n", 714 | " \n", 715 | " \n", 716 | " \n", 717 | " \n", 718 | " \n", 719 | " \n", 720 | " \n", 721 | " \n", 722 | " \n", 723 | " \n", 724 | " \n", 725 | " \n", 726 | " \n", 727 | " \n", 728 | " \n", 729 | " \n", 730 | " \n", 731 | " \n", 732 | " \n", 733 | " \n", 734 | " \n", 735 | " \n", 736 | " \n", 737 | " \n", 738 | " \n", 739 | " \n", 740 | " \n", 741 | " \n", 742 | " \n", 743 | " \n", 744 | " \n", 745 | " \n", 746 | "
AdaBoostRegressorBaggingRegressorBayesianRidgeDecisionTreeRegressorDummyRegressorElasticNetElasticNetCVExtraTreeRegressorExtraTreesRegressorGammaRegressor...RANSACRegressorRandomForestRegressorRidgeRidgeCVSGDRegressorSVRTransformedTargetRegressorTweedieRegressorXGBRegressorLGBMRegressor
0228968.84214928.57180022.93207747.48245499.66191236.47244862.47207747.48207747.48174113.15...180061.19209371.87180038.49179881.35180233.31154946.08180056.17202048.48188592.42188580.82
1294374.96273537.08320488.60220725.00245499.66329842.74247122.24299000.00265603.50250171.98...250576.81252777.32320494.04320439.26306135.61161310.78320500.23325697.63318698.81298953.89
2112564.8194430.07100085.4494290.30245499.66114400.22243411.6994290.3094290.30139537.87...109747.8694360.00100091.05100034.46106628.80150499.26100097.42129835.0097081.09100720.71
3744567.56242700.00515875.50130000.00245499.66548703.57250178.81630000.00289404.00394957.25...448760.09367419.26515773.57516804.31496984.95160433.09515657.77510097.69372152.19428587.70
4112564.81137013.53120031.06139345.56245499.66132591.67243663.21139345.56139345.56144895.42...125485.36138948.17120031.38120028.17126168.38150863.50120031.75145090.58134329.66128959.05
\n", 747 | "

5 rows × 39 columns

\n", 748 | "
" 749 | ], 750 | "text/plain": [ 751 | " AdaBoostRegressor BaggingRegressor BayesianRidge DecisionTreeRegressor \\\n", 752 | "0 228968.84 214928.57 180022.93 207747.48 \n", 753 | "1 294374.96 273537.08 320488.60 220725.00 \n", 754 | "2 112564.81 94430.07 100085.44 94290.30 \n", 755 | "3 744567.56 242700.00 515875.50 130000.00 \n", 756 | "4 112564.81 137013.53 120031.06 139345.56 \n", 757 | "\n", 758 | " DummyRegressor ElasticNet ElasticNetCV ExtraTreeRegressor \\\n", 759 | "0 245499.66 191236.47 244862.47 207747.48 \n", 760 | "1 245499.66 329842.74 247122.24 299000.00 \n", 761 | "2 245499.66 114400.22 243411.69 94290.30 \n", 762 | "3 245499.66 548703.57 250178.81 630000.00 \n", 763 | "4 245499.66 132591.67 243663.21 139345.56 \n", 764 | "\n", 765 | " ExtraTreesRegressor GammaRegressor ... RANSACRegressor \\\n", 766 | "0 207747.48 174113.15 ... 180061.19 \n", 767 | "1 265603.50 250171.98 ... 250576.81 \n", 768 | "2 94290.30 139537.87 ... 109747.86 \n", 769 | "3 289404.00 394957.25 ... 448760.09 \n", 770 | "4 139345.56 144895.42 ... 125485.36 \n", 771 | "\n", 772 | " RandomForestRegressor Ridge RidgeCV SGDRegressor SVR \\\n", 773 | "0 209371.87 180038.49 179881.35 180233.31 154946.08 \n", 774 | "1 252777.32 320494.04 320439.26 306135.61 161310.78 \n", 775 | "2 94360.00 100091.05 100034.46 106628.80 150499.26 \n", 776 | "3 367419.26 515773.57 516804.31 496984.95 160433.09 \n", 777 | "4 138948.17 120031.38 120028.17 126168.38 150863.50 \n", 778 | "\n", 779 | " TransformedTargetRegressor TweedieRegressor XGBRegressor LGBMRegressor \n", 780 | "0 180056.17 202048.48 188592.42 188580.82 \n", 781 | "1 320500.23 325697.63 318698.81 298953.89 \n", 782 | "2 100097.42 129835.00 97081.09 100720.71 \n", 783 | "3 515657.77 510097.69 372152.19 428587.70 \n", 784 | "4 120031.75 145090.58 134329.66 128959.05 \n", 785 | "\n", 786 | "[5 rows x 39 columns]" 787 | ] 788 | }, 789 | "execution_count": 59, 790 | "metadata": {}, 791 | "output_type": "execute_result" 792 | } 793 | ], 794 | "source": [ 795 | "predictions.head()" 796 | ] 797 | } 798 | ], 799 | "metadata": { 800 | "kernelspec": { 801 | "display_name": "Python 3 (ipykernel)", 802 | "language": "python", 803 | "name": "python3" 804 | }, 805 | "language_info": { 806 | "codemirror_mode": { 807 | "name": "ipython", 808 | "version": 3 809 | }, 810 | "file_extension": ".py", 811 | "mimetype": "text/x-python", 812 | "name": "python", 813 | "nbconvert_exporter": "python", 814 | "pygments_lexer": "ipython3", 815 | "version": "3.11.4" 816 | } 817 | }, 818 | "nbformat": 4, 819 | "nbformat_minor": 5 820 | } 821 | --------------------------------------------------------------------------------