{ "cells": [ { "cell_type": "markdown", "id": "5cedf140", "metadata": {}, "source": [ "# Proyecto de Estadistica: factores asociados a ingresos por ventas\n", "\n", "Este proyecto analiza un dataset de ventas y marketing para responder, con lenguaje de asociacion, la pregunta principal: **Que factores de marketing, comportamiento del cliente, engagement digital y contexto comercial estan mas asociados con los ingresos por ventas?**\n", "\n", "La variable objetivo es `sales_revenue_usd`. Las variables explicativas se organizan en cuatro bloques empresariales: marketing, cliente, digital/engagement y contexto comercial. El objetivo estadistico es explorar los datos, describir sus patrones principales y cuantificar asociaciones mediante una regresion lineal multiple, reservando un conjunto de test para evaluar R2, MAE y RMSE." ] }, { "cell_type": "markdown", "id": "fe12786c", "metadata": {}, "source": [ "## 1. Librerias y configuracion\n", "\n", "Se importan librerias estandar para analisis descriptivo, visualizacion, regresion lineal multiple, diagnostico y evaluacion predictiva. Se fija `random_state=42` para asegurar reproducibilidad." ] }, { "cell_type": "code", "execution_count": 1, "id": "2e1c9497", "metadata": { "execution": { "iopub.execute_input": "2026-09-01T16:01:46.368174Z", "iopub.status.busy": "2026-09-01T16:01:46.367958Z", "iopub.status.idle": "2026-09-01T16:01:49.033471Z", "shell.execute_reply": "2026-09-01T16:01:49.032681Z" } }, "outputs": [], "source": [ "import warnings\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import statsmodels.api as sm\n", "\n", "from IPython.display import display, Markdown\n", "from scipy import stats\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n", "from statsmodels.stats.outliers_influence import variance_inflation_factor\n", "from statsmodels.stats.diagnostic import het_white\n", "from statsmodels.tools.sm_exceptions import SingularMatrixWarning\n", "\n", "warnings.filterwarnings(\"ignore\", category=SingularMatrixWarning)\n", "\n", "RANDOM_STATE = 42\n", "TARGET = \"sales_revenue_usd\"\n", "\n", "sns.set_theme(style=\"whitegrid\", context=\"notebook\")\n", "plt.rcParams[\"figure.figsize\"] = (9, 5)\n", "plt.rcParams[\"axes.titlesize\"] = 13\n", "plt.rcParams[\"axes.labelsize\"] = 11\n", "pd.options.display.float_format = \"{:,.2f}\".format\n", "\n", "def usd(x):\n", " return f\"{x:,.2f} USD\"" ] }, { "cell_type": "markdown", "id": "67b204ea", "metadata": {}, "source": [ "## 2. Carga e inspeccion inicial\n", "\n", "Primero se revisa la estructura basica del dataset: dimensiones, columnas, tipos de datos, primeras observaciones, fechas, duplicados y valores nulos. La variable `date` se convierte a fecha, pero el proyecto no se trata como serie temporal." ] }, { "cell_type": "code", "execution_count": 2, "id": "7c883858", "metadata": { "execution": { "iopub.execute_input": "2026-09-01T16:01:49.035301Z", "iopub.status.busy": "2026-09-01T16:01:49.035004Z", "iopub.status.idle": "2026-09-01T16:01:49.425225Z", "shell.execute_reply": "2026-09-01T16:01:49.424488Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Filas: 60,000\n", "Columnas: 23\n", "Fecha minima: 2020-01-01\n", "Fecha maxima: 2023-12-31\n", "Duplicados exactos: 0\n" ] }, { "data": { "text/html": [ "
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columna
0id
1date
2region
3sales_channel
4product_category
5customer_segment
6season
7marketing_budget_usd
8ad_spend_online_usd
9ad_spend_offline_usd
10num_promotions
11discount_percentage
12num_sales_representatives
13customer_age
14customer_satisfaction_score
15competitor_price_index
16website_traffic
17conversion_rate
18email_open_rate
19social_media_followers
20days_since_last_purchase
21num_previous_purchases
22sales_revenue_usd
\n", "
" ], "text/plain": [ " columna\n", "0 id\n", "1 date\n", "2 region\n", "3 sales_channel\n", "4 product_category\n", "5 customer_segment\n", "6 season\n", "7 marketing_budget_usd\n", "8 ad_spend_online_usd\n", "9 ad_spend_offline_usd\n", "10 num_promotions\n", "11 discount_percentage\n", "12 num_sales_representatives\n", "13 customer_age\n", "14 customer_satisfaction_score\n", "15 competitor_price_index\n", "16 website_traffic\n", "17 conversion_rate\n", "18 email_open_rate\n", "19 social_media_followers\n", "20 days_since_last_purchase\n", "21 num_previous_purchases\n", "22 sales_revenue_usd" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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variabletipo
0idint64
1datedatetime64[us]
2regionstr
3sales_channelstr
4product_categorystr
5customer_segmentstr
6seasonstr
7marketing_budget_usdfloat64
8ad_spend_online_usdfloat64
9ad_spend_offline_usdfloat64
10num_promotionsint64
11discount_percentagefloat64
12num_sales_representativesint64
13customer_ageint64
14customer_satisfaction_scorefloat64
15competitor_price_indexfloat64
16website_trafficint64
17conversion_ratefloat64
18email_open_ratefloat64
19social_media_followersint64
20days_since_last_purchasefloat64
21num_previous_purchasesint64
22sales_revenue_usdfloat64
\n", "
" ], "text/plain": [ " variable tipo\n", "0 id int64\n", "1 date datetime64[us]\n", "2 region str\n", "3 sales_channel str\n", "4 product_category str\n", "5 customer_segment str\n", "6 season str\n", "7 marketing_budget_usd float64\n", "8 ad_spend_online_usd float64\n", "9 ad_spend_offline_usd float64\n", "10 num_promotions int64\n", "11 discount_percentage float64\n", "12 num_sales_representatives int64\n", "13 customer_age int64\n", "14 customer_satisfaction_score float64\n", "15 competitor_price_index float64\n", "16 website_traffic int64\n", "17 conversion_rate float64\n", "18 email_open_rate float64\n", "19 social_media_followers int64\n", "20 days_since_last_purchase float64\n", "21 num_previous_purchases int64\n", "22 sales_revenue_usd float64" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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iddateregionsales_channelproduct_categorycustomer_segmentseasonmarketing_budget_usdad_spend_online_usdad_spend_offline_usd...customer_agecustomer_satisfaction_scorecompetitor_price_indexwebsite_trafficconversion_rateemail_open_ratesocial_media_followersdays_since_last_purchasenum_previous_purchasessales_revenue_usd
012020-11-12RiyadhRetail StoreCosmeticsRegularQ41,664.51952.30326.97...424.500.7619520.120.04203425.0063,772.90
122022-07-05DubaiOnlineCosmeticsNewQ32,452.291,014.25414.76...243.701.0731850.040.322058187.0072,091.36
232020-11-11RiyadhOnlineElectronicsRegularQ41,026.13328.30242.72...322.001.1813040.150.30417139.0036,201.11
342022-10-01CairoOnlineElectronicsRegularQ41,102.86628.30204.58...622.600.8616090.050.1120618308.0064,911.38
452023-12-12CairoRetail StoreFood & BeverageCorporateQ42,517.55777.24817.88...233.001.013666390.170.185711697.0067,705.73
\n", "

5 rows × 23 columns

\n", "
" ], "text/plain": [ " id date region sales_channel product_category customer_segment \\\n", "0 1 2020-11-12 Riyadh Retail Store Cosmetics Regular \n", "1 2 2022-07-05 Dubai Online Cosmetics New \n", "2 3 2020-11-11 Riyadh Online Electronics Regular \n", "3 4 2022-10-01 Cairo Online Electronics Regular \n", "4 5 2023-12-12 Cairo Retail Store Food & Beverage Corporate \n", "\n", " season marketing_budget_usd ad_spend_online_usd ad_spend_offline_usd \\\n", "0 Q4 1,664.51 952.30 326.97 \n", "1 Q3 2,452.29 1,014.25 414.76 \n", "2 Q4 1,026.13 328.30 242.72 \n", "3 Q4 1,102.86 628.30 204.58 \n", "4 Q4 2,517.55 777.24 817.88 \n", "\n", " ... customer_age customer_satisfaction_score competitor_price_index \\\n", "0 ... 42 4.50 0.76 \n", "1 ... 24 3.70 1.07 \n", "2 ... 32 2.00 1.18 \n", "3 ... 62 2.60 0.86 \n", "4 ... 23 3.00 1.01 \n", "\n", " website_traffic conversion_rate email_open_rate social_media_followers \\\n", "0 1952 0.12 0.04 2034 \n", "1 3185 0.04 0.32 2058 \n", "2 1304 0.15 0.30 417 \n", "3 1609 0.05 0.11 20618 \n", "4 366639 0.17 0.18 57116 \n", "\n", " days_since_last_purchase num_previous_purchases sales_revenue_usd \n", "0 25.00 6 3,772.90 \n", "1 187.00 7 2,091.36 \n", "2 139.00 3 6,201.11 \n", "3 308.00 6 4,911.38 \n", "4 97.00 6 7,705.73 \n", "\n", "[5 rows x 23 columns]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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n_nulospct_nulos
customer_satisfaction_score18443.07
days_since_last_purchase18363.06
discount_percentage18083.01
email_open_rate17902.98
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" ], "text/plain": [ " n_nulos pct_nulos\n", "customer_satisfaction_score 1844 3.07\n", "days_since_last_purchase 1836 3.06\n", "discount_percentage 1808 3.01\n", "email_open_rate 1790 2.98" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "El dataset contiene **60,000 observaciones** y **23 variables**. El periodo observado va de **2020-01-01** a **2023-12-31**. No se utiliza `date` como predictor principal porque el enfoque es transversal. Los nulos se concentran en `customer_satisfaction_score` (1,844; 3.07%), `days_since_last_purchase` (1,836; 3.06%), `discount_percentage` (1,808; 3.01%), `email_open_rate` (1,790; 2.98%); se conservaran en la descriptiva y se imputaran con mediana solo al preparar la regresion." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df = pd.read_csv(\"marketing_sales_dataset.csv\")\n", "df[\"date\"] = pd.to_datetime(df[\"date\"])\n", "\n", "print(f\"Filas: {df.shape[0]:,}\")\n", "print(f\"Columnas: {df.shape[1]:,}\")\n", "print(f\"Fecha minima: {df['date'].min().date()}\")\n", "print(f\"Fecha maxima: {df['date'].max().date()}\")\n", "print(f\"Duplicados exactos: {df.duplicated().sum():,}\")\n", "\n", "display(pd.DataFrame({\"columna\": df.columns}))\n", "display(df.dtypes.rename(\"tipo\").reset_index().rename(columns={\"index\": \"variable\"}))\n", "display(df.head())\n", "\n", "missing_table = (\n", " pd.DataFrame({\"n_nulos\": df.isna().sum(), \"pct_nulos\": df.isna().mean() * 100})\n", " .sort_values(\"pct_nulos\", ascending=False)\n", ")\n", "display(missing_table[missing_table[\"n_nulos\"] > 0])\n", "\n", "missing_nonzero = missing_table[missing_table[\"n_nulos\"] > 0]\n", "missing_text = \", \".join([f\"`{idx}` ({row.n_nulos:,.0f}; {row.pct_nulos:.2f}%)\" for idx, row in missing_nonzero.iterrows()])\n", "display(Markdown(\n", " f\"El dataset contiene **{df.shape[0]:,} observaciones** y **{df.shape[1]} variables**. \"\n", " f\"El periodo observado va de **{df['date'].min().date()}** a **{df['date'].max().date()}**. \"\n", " f\"No se utiliza `date` como predictor principal porque el enfoque es transversal. \"\n", " f\"Los nulos se concentran en {missing_text}; se conservaran en la descriptiva y se imputaran con mediana solo al preparar la regresion.\"\n", "))" ] }, { "cell_type": "markdown", "id": "f9b94bf8", "metadata": {}, "source": [ "## 3. Clasificacion de variables\n", "\n", "Esta tabla define la variable objetivo, las variables explicativas y los bloques empresariales. `id` queda fuera por ser identificador y `date` solo describe el periodo observado." ] }, { "cell_type": "code", "execution_count": 3, "id": "dc63f8db", "metadata": { "execution": { "iopub.execute_input": "2026-09-01T16:01:49.428574Z", "iopub.status.busy": "2026-09-01T16:01:49.428392Z", "iopub.status.idle": "2026-09-01T16:01:49.441096Z", "shell.execute_reply": "2026-09-01T16:01:49.439353Z" } }, "outputs": [ { "data": { "text/html": [ "
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variabletipo_estadisticobloquepapel
0ididentificadoridentificacionexcluida
1datefechacontexto temporalsolo inspeccion
2sales_revenue_usdcuantitativa continuaresultadovariable objetivo
3marketing_budget_usdcuantitativa continuamarketingexplicativa candidata
4ad_spend_online_usdcuantitativa continuamarketingexplicativa exploratoria
5ad_spend_offline_usdcuantitativa continuamarketingexplicativa exploratoria
6num_promotionscuantitativa discretamarketingexplicativa candidata
7discount_percentagecuantitativa continuamarketingexplicativa candidata
8customer_segmentcualitativa nominalclienteexplicativa candidata
9customer_agecuantitativa discretaclienteexplicativa candidata
10customer_satisfaction_scorecuantitativa ordinalclienteexplicativa candidata
11days_since_last_purchasecuantitativa continuaclienteexplicativa candidata
12num_previous_purchasescuantitativa discretaclienteexplicativa candidata
13website_trafficcuantitativa discretadigital / engagementexplicativa candidata
14conversion_ratecuantitativa continuadigital / engagementexplicativa candidata
15email_open_ratecuantitativa continuadigital / engagementexplicativa candidata
16social_media_followerscuantitativa discretadigital / engagementexplicativa candidata
17regioncualitativa nominalcontexto comercialexplicativa candidata
18sales_channelcualitativa nominalcontexto comercialexplicativa candidata
19product_categorycualitativa nominalcontexto comercialexplicativa candidata
20seasoncualitativa ordinalcontexto comercialexplicativa candidata
21num_sales_representativescuantitativa discretacontexto comercialexplicativa candidata
22competitor_price_indexcuantitativa continuacontexto comercialexplicativa candidata
\n", "
" ], "text/plain": [ " variable tipo_estadistico bloque \\\n", "0 id identificador identificacion \n", "1 date fecha contexto temporal \n", "2 sales_revenue_usd cuantitativa continua resultado \n", "3 marketing_budget_usd cuantitativa continua marketing \n", "4 ad_spend_online_usd cuantitativa continua marketing \n", "5 ad_spend_offline_usd cuantitativa continua marketing \n", "6 num_promotions cuantitativa discreta marketing \n", "7 discount_percentage cuantitativa continua marketing \n", "8 customer_segment cualitativa nominal cliente \n", "9 customer_age cuantitativa discreta cliente \n", "10 customer_satisfaction_score cuantitativa ordinal cliente \n", "11 days_since_last_purchase cuantitativa continua cliente \n", "12 num_previous_purchases cuantitativa discreta cliente \n", "13 website_traffic cuantitativa discreta digital / engagement \n", "14 conversion_rate cuantitativa continua digital / engagement \n", "15 email_open_rate cuantitativa continua digital / engagement \n", "16 social_media_followers cuantitativa discreta digital / engagement \n", "17 region cualitativa nominal contexto comercial \n", "18 sales_channel cualitativa nominal contexto comercial \n", "19 product_category cualitativa nominal contexto comercial \n", "20 season cualitativa ordinal contexto comercial \n", "21 num_sales_representatives cuantitativa discreta contexto comercial \n", "22 competitor_price_index cuantitativa continua contexto comercial \n", "\n", " papel \n", "0 excluida \n", "1 solo inspeccion \n", "2 variable objetivo \n", "3 explicativa candidata \n", "4 explicativa exploratoria \n", "5 explicativa exploratoria \n", "6 explicativa candidata \n", "7 explicativa candidata \n", "8 explicativa candidata \n", "9 explicativa candidata \n", "10 explicativa candidata \n", "11 explicativa candidata \n", "12 explicativa candidata \n", "13 explicativa candidata \n", "14 explicativa candidata \n", "15 explicativa candidata \n", "16 explicativa candidata \n", "17 explicativa candidata \n", "18 explicativa candidata \n", "19 explicativa candidata \n", "20 explicativa candidata \n", "21 explicativa candidata \n", "22 explicativa candidata " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "marketing_vars = [\"marketing_budget_usd\", \"ad_spend_online_usd\", \"ad_spend_offline_usd\", \"num_promotions\", \"discount_percentage\"]\n", "cliente_vars = [\"customer_segment\", \"customer_age\", \"customer_satisfaction_score\", \"days_since_last_purchase\", \"num_previous_purchases\"]\n", "digital_vars = [\"website_traffic\", \"conversion_rate\", \"email_open_rate\", \"social_media_followers\"]\n", "contexto_vars = [\"region\", \"sales_channel\", \"product_category\", \"season\", \"num_sales_representatives\", \"competitor_price_index\"]\n", "categorical_cols = [\"region\", \"sales_channel\", \"product_category\", \"customer_segment\", \"season\"]\n", "\n", "variable_info = pd.DataFrame([\n", " (\"id\", \"identificador\", \"identificacion\", \"excluida\"),\n", " (\"date\", \"fecha\", \"contexto temporal\", \"solo inspeccion\"),\n", " (TARGET, \"cuantitativa continua\", \"resultado\", \"variable objetivo\"),\n", " (\"marketing_budget_usd\", \"cuantitativa continua\", \"marketing\", \"explicativa candidata\"),\n", " (\"ad_spend_online_usd\", \"cuantitativa continua\", \"marketing\", \"explicativa exploratoria\"),\n", " (\"ad_spend_offline_usd\", \"cuantitativa continua\", \"marketing\", \"explicativa exploratoria\"),\n", " (\"num_promotions\", \"cuantitativa discreta\", \"marketing\", \"explicativa candidata\"),\n", " (\"discount_percentage\", \"cuantitativa continua\", \"marketing\", \"explicativa candidata\"),\n", " (\"customer_segment\", \"cualitativa nominal\", \"cliente\", \"explicativa candidata\"),\n", " (\"customer_age\", \"cuantitativa discreta\", \"cliente\", \"explicativa candidata\"),\n", " (\"customer_satisfaction_score\", \"cuantitativa ordinal\", \"cliente\", \"explicativa candidata\"),\n", " (\"days_since_last_purchase\", \"cuantitativa continua\", \"cliente\", \"explicativa candidata\"),\n", " (\"num_previous_purchases\", \"cuantitativa discreta\", \"cliente\", \"explicativa candidata\"),\n", " (\"website_traffic\", \"cuantitativa discreta\", \"digital / engagement\", \"explicativa candidata\"),\n", " (\"conversion_rate\", \"cuantitativa continua\", \"digital / engagement\", \"explicativa candidata\"),\n", " (\"email_open_rate\", \"cuantitativa continua\", \"digital / engagement\", \"explicativa candidata\"),\n", " (\"social_media_followers\", \"cuantitativa discreta\", \"digital / engagement\", \"explicativa candidata\"),\n", " (\"region\", \"cualitativa nominal\", \"contexto comercial\", \"explicativa candidata\"),\n", " (\"sales_channel\", \"cualitativa nominal\", \"contexto comercial\", \"explicativa candidata\"),\n", " (\"product_category\", \"cualitativa nominal\", \"contexto comercial\", \"explicativa candidata\"),\n", " (\"season\", \"cualitativa ordinal\", \"contexto comercial\", \"explicativa candidata\"),\n", " (\"num_sales_representatives\", \"cuantitativa discreta\", \"contexto comercial\", \"explicativa candidata\"),\n", " (\"competitor_price_index\", \"cuantitativa continua\", \"contexto comercial\", \"explicativa candidata\"),\n", "], columns=[\"variable\", \"tipo_estadistico\", \"bloque\", \"papel\"])\n", "display(variable_info)" ] }, { "cell_type": "markdown", "id": "74e506c0", "metadata": {}, "source": [ "## 4. Calidad de datos\n", "\n", "Se buscan duplicados, nulos y valores imposibles o fuera de rango. No se eliminan datos automaticamente: primero se documenta el problema y se decide si afecta al analisis." ] }, { "cell_type": "code", "execution_count": 4, "id": "7e51fb4b", "metadata": { "execution": { "iopub.execute_input": "2026-09-01T16:01:49.443280Z", "iopub.status.busy": "2026-09-01T16:01:49.443097Z", "iopub.status.idle": "2026-09-01T16:01:49.539576Z", "shell.execute_reply": "2026-09-01T16:01:49.536543Z" } }, "outputs": [ { "data": { "text/html": [ "
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revisionn_casos
0duplicados_exactos0
1conversion_rate_fuera_0_10
2email_open_rate_fuera_0_10
3discount_percentage_negativo_o_mayor_1000
4customer_age_negativa0
5sales_revenue_usd_negativo0
6days_since_last_purchase_negativo0
\n", "
" ], "text/plain": [ " revision n_casos\n", "0 duplicados_exactos 0\n", "1 conversion_rate_fuera_0_1 0\n", "2 email_open_rate_fuera_0_1 0\n", "3 discount_percentage_negativo_o_mayor_100 0\n", "4 customer_age_negativa 0\n", "5 sales_revenue_usd_negativo 0\n", "6 days_since_last_purchase_negativo 0" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Rango observado de customer_satisfaction_score: 2.00 a 5.00\n" ] }, { "data": { "text/markdown": [ "No aparecen duplicados exactos ni valores imposibles en las reglas revisadas. La decision es conservar las observaciones. Los nulos numericos se mantendran en la descriptiva y, para la regresion, se imputaran mediante mediana porque es resistente a outliers." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "checks = [\n", " (\"duplicados_exactos\", df.duplicated().sum()),\n", " (\"conversion_rate_fuera_0_1\", ((df[\"conversion_rate\"] < 0) | (df[\"conversion_rate\"] > 1)).sum()),\n", " (\"email_open_rate_fuera_0_1\", ((df[\"email_open_rate\"] < 0) | (df[\"email_open_rate\"] > 1)).sum()),\n", " (\"discount_percentage_negativo_o_mayor_100\", ((df[\"discount_percentage\"] < 0) | (df[\"discount_percentage\"] > 100)).sum()),\n", " (\"customer_age_negativa\", (df[\"customer_age\"] < 0).sum()),\n", " (\"sales_revenue_usd_negativo\", (df[TARGET] < 0).sum()),\n", " (\"days_since_last_purchase_negativo\", (df[\"days_since_last_purchase\"] < 0).sum()),\n", "]\n", "quality_table = pd.DataFrame(checks, columns=[\"revision\", \"n_casos\"])\n", "display(quality_table)\n", "print(f\"Rango observado de customer_satisfaction_score: {df['customer_satisfaction_score'].min():.2f} a {df['customer_satisfaction_score'].max():.2f}\")\n", "\n", "if quality_table[quality_table[\"n_casos\"] > 0].empty:\n", " display(Markdown(\"No aparecen duplicados exactos ni valores imposibles en las reglas revisadas. La decision es conservar las observaciones. Los nulos numericos se mantendran en la descriptiva y, para la regresion, se imputaran mediante mediana porque es resistente a outliers.\"))\n", "else:\n", " display(Markdown(\"Hay incidencias de calidad que deben revisarse antes de eliminar datos. En esta notebook no se eliminan automaticamente porque un valor extremo o faltante no equivale necesariamente a error.\"))" ] }, { "cell_type": "markdown", "id": "168ee11e", "metadata": {}, "source": [ "## 5. Estadistica descriptiva numerica\n", "\n", "Se calculan medidas de tendencia central, dispersion y distribucion para las variables numericas relevantes. Esta tabla sirve como herramienta de exploracion; en el informe final solo convendra incluir los indicadores mas importantes." ] }, { "cell_type": "code", "execution_count": 5, "id": "eba14097", "metadata": { "execution": { "iopub.execute_input": "2026-09-01T16:01:49.541406Z", "iopub.status.busy": "2026-09-01T16:01:49.541147Z", "iopub.status.idle": "2026-09-01T16:01:49.668339Z", "shell.execute_reply": "2026-09-01T16:01:49.667762Z" } }, "outputs": [ { "data": { "text/html": [ "
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countmediamedianadesv_tipicaminQ1Q3maxrangoIQRasimetriacurtosis
marketing_budget_usd6000010,031.914,922.1217,300.26500.002,183.5711,040.09500,000.00499,500.008,856.527.46102.94
ad_spend_online_usd600004,018.441,876.367,308.81100.14816.834,336.85235,211.83235,111.693,520.028.10126.19
ad_spend_offline_usd600002,508.811,152.394,668.2450.03486.862,676.80138,327.25138,277.222,189.948.25127.89
num_promotions600004.494.002.870.002.007.009.009.005.000.01-1.22
discount_percentage5819220.0520.0011.510.0010.1030.0040.0040.0019.90-0.00-1.19
num_sales_representatives6000024.9325.0014.181.0013.0037.0049.0048.0024.000.01-1.21
customer_age6000045.8046.0016.4818.0031.0060.0074.0056.0029.000.01-1.21
customer_satisfaction_score581563.503.500.872.002.704.205.003.001.500.01-1.19
competitor_price_index600001.001.000.170.700.851.151.300.600.300.00-1.19
website_traffic6000024,425.588,065.0063,866.42100.002,961.0022,057.002,000,000.001,999,900.0019,096.0011.51222.71
conversion_rate600000.090.080.060.000.050.120.540.540.081.121.54
email_open_rate582100.230.220.110.000.150.300.750.750.150.630.17
social_media_followers6000022,197.762,971.00115,727.66100.00771.0011,386.755,000,000.004,999,900.0010,615.7521.87701.91
days_since_last_purchase58164181.46182.00105.440.0089.00273.00364.00364.00184.000.00-1.21
num_previous_purchases600004.995.002.230.003.006.0017.0017.003.000.440.17
sales_revenue_usd600005,911.124,340.055,842.47481.082,744.187,091.63190,377.35189,896.274,347.456.63105.03
\n", "
" ], "text/plain": [ " count media mediana desv_tipica min \\\n", "marketing_budget_usd 60000 10,031.91 4,922.12 17,300.26 500.00 \n", "ad_spend_online_usd 60000 4,018.44 1,876.36 7,308.81 100.14 \n", "ad_spend_offline_usd 60000 2,508.81 1,152.39 4,668.24 50.03 \n", "num_promotions 60000 4.49 4.00 2.87 0.00 \n", "discount_percentage 58192 20.05 20.00 11.51 0.00 \n", "num_sales_representatives 60000 24.93 25.00 14.18 1.00 \n", "customer_age 60000 45.80 46.00 16.48 18.00 \n", "customer_satisfaction_score 58156 3.50 3.50 0.87 2.00 \n", "competitor_price_index 60000 1.00 1.00 0.17 0.70 \n", "website_traffic 60000 24,425.58 8,065.00 63,866.42 100.00 \n", "conversion_rate 60000 0.09 0.08 0.06 0.00 \n", "email_open_rate 58210 0.23 0.22 0.11 0.00 \n", "social_media_followers 60000 22,197.76 2,971.00 115,727.66 100.00 \n", "days_since_last_purchase 58164 181.46 182.00 105.44 0.00 \n", "num_previous_purchases 60000 4.99 5.00 2.23 0.00 \n", "sales_revenue_usd 60000 5,911.12 4,340.05 5,842.47 481.08 \n", "\n", " Q1 Q3 max rango \\\n", "marketing_budget_usd 2,183.57 11,040.09 500,000.00 499,500.00 \n", "ad_spend_online_usd 816.83 4,336.85 235,211.83 235,111.69 \n", "ad_spend_offline_usd 486.86 2,676.80 138,327.25 138,277.22 \n", "num_promotions 2.00 7.00 9.00 9.00 \n", "discount_percentage 10.10 30.00 40.00 40.00 \n", "num_sales_representatives 13.00 37.00 49.00 48.00 \n", "customer_age 31.00 60.00 74.00 56.00 \n", "customer_satisfaction_score 2.70 4.20 5.00 3.00 \n", "competitor_price_index 0.85 1.15 1.30 0.60 \n", "website_traffic 2,961.00 22,057.00 2,000,000.00 1,999,900.00 \n", "conversion_rate 0.05 0.12 0.54 0.54 \n", "email_open_rate 0.15 0.30 0.75 0.75 \n", "social_media_followers 771.00 11,386.75 5,000,000.00 4,999,900.00 \n", "days_since_last_purchase 89.00 273.00 364.00 364.00 \n", "num_previous_purchases 3.00 6.00 17.00 17.00 \n", "sales_revenue_usd 2,744.18 7,091.63 190,377.35 189,896.27 \n", "\n", " IQR asimetria curtosis \n", "marketing_budget_usd 8,856.52 7.46 102.94 \n", "ad_spend_online_usd 3,520.02 8.10 126.19 \n", "ad_spend_offline_usd 2,189.94 8.25 127.89 \n", "num_promotions 5.00 0.01 -1.22 \n", "discount_percentage 19.90 -0.00 -1.19 \n", "num_sales_representatives 24.00 0.01 -1.21 \n", "customer_age 29.00 0.01 -1.21 \n", "customer_satisfaction_score 1.50 0.01 -1.19 \n", "competitor_price_index 0.30 0.00 -1.19 \n", "website_traffic 19,096.00 11.51 222.71 \n", "conversion_rate 0.08 1.12 1.54 \n", "email_open_rate 0.15 0.63 0.17 \n", "social_media_followers 10,615.75 21.87 701.91 \n", "days_since_last_purchase 184.00 0.00 -1.21 \n", "num_previous_purchases 3.00 0.44 0.17 \n", "sales_revenue_usd 4,347.45 6.63 105.03 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "La descriptiva combina tendencia central, dispersion y forma de la distribucion. Las variables con mayor asimetria absoluta son `social_media_followers`, `website_traffic`, `ad_spend_offline_usd`. Esto importa porque la media puede estar mas influida por valores extremos que la mediana." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "numeric_cols = [c for c in df.select_dtypes(include=\"number\").columns if c != \"id\"]\n", "desc = pd.DataFrame({\n", " \"count\": df[numeric_cols].count(),\n", " \"media\": df[numeric_cols].mean(),\n", " \"mediana\": df[numeric_cols].median(),\n", " \"desv_tipica\": df[numeric_cols].std(),\n", " \"min\": df[numeric_cols].min(),\n", " \"Q1\": df[numeric_cols].quantile(0.25),\n", " \"Q3\": df[numeric_cols].quantile(0.75),\n", " \"max\": df[numeric_cols].max(),\n", " \"rango\": df[numeric_cols].max() - df[numeric_cols].min(),\n", " \"IQR\": df[numeric_cols].quantile(0.75) - df[numeric_cols].quantile(0.25),\n", " \"asimetria\": df[numeric_cols].skew(),\n", " \"curtosis\": df[numeric_cols].kurtosis(),\n", "})\n", "display(desc.round(3))\n", "most_skewed = desc[\"asimetria\"].abs().sort_values(ascending=False).head(3).index.tolist()\n", "display(Markdown(\n", " \"La descriptiva combina tendencia central, dispersion y forma de la distribucion. \"\n", " f\"Las variables con mayor asimetria absoluta son {', '.join([f'`{v}`' for v in most_skewed])}. \"\n", " \"Esto importa porque la media puede estar mas influida por valores extremos que la mediana.\"\n", "))" ] }, { "cell_type": "markdown", "id": "ddcfc1f7", "metadata": {}, "source": [ "## 6. Estadistica descriptiva categorica\n", "\n", "Se resumen frecuencias, porcentajes y categoria modal para las variables cualitativas principales. La seccion se mantiene compacta para no ocupar espacio innecesario en el informe." ] }, { "cell_type": "code", "execution_count": 6, "id": "597e563f", "metadata": { "execution": { "iopub.execute_input": "2026-09-01T16:01:49.670998Z", "iopub.status.busy": "2026-09-01T16:01:49.670826Z", "iopub.status.idle": "2026-09-01T16:01:49.721923Z", "shell.execute_reply": "2026-09-01T16:01:49.721103Z" } }, "outputs": [ { "data": { "text/html": [ "
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variablen_categoriascategoria_mas_frecuentefrecuenciaporcentaje
0region7Cairo1498424.97
1sales_channel5Online1801030.02
2product_category5Food & Beverage1502325.04
3customer_segment4Regular2390639.84
4season4Q31512425.21
\n", "
" ], "text/plain": [ " variable n_categorias categoria_mas_frecuente frecuencia \\\n", "0 region 7 Cairo 14984 \n", "1 sales_channel 5 Online 18010 \n", "2 product_category 5 Food & Beverage 15023 \n", "3 customer_segment 4 Regular 23906 \n", "4 season 4 Q3 15124 \n", "\n", " porcentaje \n", "0 24.97 \n", "1 30.02 \n", "2 25.04 \n", "3 39.84 \n", "4 25.21 " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "region\n" ] }, { "data": { "text/html": [ "
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regionfrecuenciaporcentaje
0Cairo1498424.97
1Riyadh1197119.95
2Dubai1089018.15
3Alexandria902715.04
4Kuwait47397.90
5Amman42067.01
6Casablanca41836.97
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" ], "text/plain": [ " region frecuencia porcentaje\n", "0 Cairo 14984 24.97\n", "1 Riyadh 11971 19.95\n", "2 Dubai 10890 18.15\n", "3 Alexandria 9027 15.04\n", "4 Kuwait 4739 7.90\n", "5 Amman 4206 7.01\n", "6 Casablanca 4183 6.97" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "sales_channel\n" ] }, { "data": { "text/html": [ "
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sales_channelfrecuenciaporcentaje
0Online1801030.02
1Retail Store1495424.92
2Wholesale1198719.98
3Direct Sales920915.35
4Social Media58409.73
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" ], "text/plain": [ " sales_channel frecuencia porcentaje\n", "0 Online 18010 30.02\n", "1 Retail Store 14954 24.92\n", "2 Wholesale 11987 19.98\n", "3 Direct Sales 9209 15.35\n", "4 Social Media 5840 9.73" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "product_category\n" ] }, { "data": { "text/html": [ "
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product_categoryfrecuenciaporcentaje
0Food & Beverage1502325.04
1Electronics1311821.86
2Clothing1201620.03
3Home Appliances1077517.96
4Cosmetics906815.11
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" ], "text/plain": [ " product_category frecuencia porcentaje\n", "0 Food & Beverage 15023 25.04\n", "1 Electronics 13118 21.86\n", "2 Clothing 12016 20.03\n", "3 Home Appliances 10775 17.96\n", "4 Cosmetics 9068 15.11" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "customer_segment\n" ] }, { "data": { "text/html": [ "
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customer_segmentfrecuenciaporcentaje
0Regular2390639.84
1New1801130.02
2Corporate911315.19
3VIP897014.95
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" ], "text/plain": [ " customer_segment frecuencia porcentaje\n", "0 Regular 23906 39.84\n", "1 New 18011 30.02\n", "2 Corporate 9113 15.19\n", "3 VIP 8970 14.95" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "season\n" ] }, { "data": { "text/html": [ "
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seasonfrecuenciaporcentaje
0Q31512425.21
1Q41502425.04
2Q21495424.92
3Q11489824.83
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" ], "text/plain": [ " season frecuencia porcentaje\n", "0 Q3 15124 25.21\n", "1 Q4 15024 25.04\n", "2 Q2 14954 24.92\n", "3 Q1 14898 24.83" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "categorical_summary = []\n", "category_tables = {}\n", "for col in categorical_cols:\n", " table = df[col].value_counts(dropna=False).rename_axis(col).reset_index(name=\"frecuencia\")\n", " table[\"porcentaje\"] = table[\"frecuencia\"] / len(df) * 100\n", " category_tables[col] = table\n", " modal = table.iloc[0]\n", " categorical_summary.append({\n", " \"variable\": col,\n", " \"n_categorias\": df[col].nunique(dropna=False),\n", " \"categoria_mas_frecuente\": modal[col],\n", " \"frecuencia\": modal[\"frecuencia\"],\n", " \"porcentaje\": modal[\"porcentaje\"],\n", " })\n", "display(pd.DataFrame(categorical_summary).round(2))\n", "for col, table in category_tables.items():\n", " print(f\"\\n{col}\")\n", " display(table.round(2))" ] }, { "cell_type": "markdown", "id": "b69fa7d9", "metadata": {}, "source": [ "## 7. Variable objetivo: `sales_revenue_usd`\n", "\n", "Esta es la seccion central de la descriptiva. Se analiza la distribucion de ingresos mediante estadisticos, histograma y boxplot. Todavia no se eliminan outliers." ] }, { "cell_type": "code", "execution_count": 7, "id": "8de0b1ca", "metadata": { "execution": { "iopub.execute_input": "2026-09-01T16:01:49.723573Z", "iopub.status.busy": "2026-09-01T16:01:49.723353Z", "iopub.status.idle": "2026-09-01T16:01:50.325903Z", "shell.execute_reply": "2026-09-01T16:01:50.325238Z" } }, "outputs": [ { "data": { "text/html": [ "
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resultado
media5,911.12
mediana4,340.05
desv_tipica5,842.47
min481.08
Q12,744.18
Q37,091.63
max190,377.35
asimetria6.63
curtosis105.03
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" ], "text/plain": [ " resultado\n", "media 5,911.12\n", "mediana 4,340.05\n", "desv_tipica 5,842.47\n", "min 481.08\n", "Q1 2,744.18\n", "Q3 7,091.63\n", "max 190,377.35\n", "asimetria 6.63\n", "curtosis 105.03" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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r19r96nmzZs3s76CVK1faz1uv16SHWm/9+vV239qWtqlWt9qe0jRhot6PxmLXcuWnZ8+eNt/6jbZ48WK7/3bt2pltt93WdO3a1WzcuNG+Vn+bNm1qt6/H77//bt+3JmBUXpWHHXfc0W7PnZBR5anfc7/88ovNu1sW7r67d+9utzNnzhzz/fffm27dupnCwkL7/vR+tL7e97p162xjEpWnfhNqP3poP8r7Rx99ZMtT+VbvtMAx4JUPjY2ustK2tH39lnOHR1Ka9qP3omWBE0rq9TNnzrTvRQLfZ7B9BdteTWmh6DW//fab/S2qfKvs9B5qyms4+win3MIRzv5qWyfcPLvrLVu2zJaBjpdQ6wT7rEOVdSSfS7ivj3bbqH/x/MwqU/RYiSgidMEFF5jTTz/dzJ8/315AXn/9dbNgwQIzYcIEc/XVV9uLRm3UMk8BxR9++ME+vBSoO/zww23rQV1QtH1t8+KLLzYnn3yyb714p6dCC0FdOMLtgtPIM5B3UUkJAUEAAIAUcOKJJyY6CylLQTA9AukHmoJr3vX+/PNPv3UUdNNDQbKa6PeQHo8//rjfcgV76krBsUWLFlXbfih6D26gONDbb79dbZnei8bzVE+tuhg/frwNwJx77rn2ucaTr61cvD799NOw1lOetR8FLPUbUZ+T68UXXzRDhgwxp5xyim8c+nHjxtmAZCBtQ7x51O8+TYK56667+l4f+D6879NdL9S+3O1JqDTvNrymTZtmXnjhBRvYCiZYXsPZh8pIAeCayi0c4eyvtnXCzXOw9XR8Kt5Q075q2mZdyiySMpBoto36F+3x0FCPlQzHvZ1TR7rAafxA3U3RXTRN5nDGGWfYk006cls49u7dO277UDkrsqyI8vING83Rd91rsjIzzbkHHRDyNWfuv59pVNVVWHa/5npTXFZmPhxxg9kyBVpCRlI2jCFIuXC88D3i/MK5N5G4JsW3rlRfdS71hnn++efNXXfdZXvHeOlGuFrpnbRHtmnXLMMsX++Yl78u9z0P5KYH21ZtCAYiFtQaxRskcqnlmLflZChq1Rcq8BiOmvajuru+c1tssYUdmsq7/NRTT7XDOSk4+Oqrr9oA2hFHHGEbljz00EP2+6SWiJrc0g1C/fTTT37fn0MOOcQsXLjQBhu1nSuvvNKmPfjgg/avWqMdd9xx9t9qKDJ79mz776uuusq3Te1L+dAwVxpv3t2eAnui4agC09x9BQtUaXsagkv5fuutt2wAVLRv5Xnu3Ll+eQ21f+8+FPh77733bCtR9cILVm7hBAVrer/h5kmNezQRaW15DtyXWsd++eWXttXmzz//7LevYJ+11tFynZO9ZR3Oe6gpGBPrzzxa1CuiK5doj4eaJNuxUtd6UsTtFNUMXHfBVFlSIbz22mtpGwxMhPKK/7UQrAs3OKgWggAAAEjubsJArCiQEtiTS0E69/eEm6bn6n6s9fVXFFDq06eP73WaoNH76Nu3r10/GKU/+eSTvv2423TTnnjiCfuDWXnxbkPdoffdd1/bxW7//fc3o0ePtsEutYAbO3as3ada9+m11157rV1n+PDhdvt6KH3SpEn23wogqiebfrSrtY5er+V6rskte/XqZR/6t5YpTespT/qrZXq9tqPu2fqrH/PuvvRvb5q7L7UC9AZi9W9tT2V51FFHmc8++8yXBz30XpRnbc/Nq25MBNu/dx/qbq5yUfmonFQWwcqttuCvm7+a9qf0mtZRuWtf+ltTnkOVrYYPu+SSS/z2Feyz1l+to1a1SnfLOpz3EPi5hFsGkXzmSKxoj4eaNIRjJaKAoE60Z555ZsiHxrxA/XQZDnf8wMBxBJlYBAAAoHbqvqnWJ95HYJfOaLZV0yOaFlmIL7dLaiyo9V286Udnhw4dgnafdjuMqcGH+1wBJgVZ9Nd9vTeQp3EMvY9jjjnGbj8YpavLsLsfd5tumlrDKTimLnUKELo0rqBa/3hbGaoln/KiNAXV9LtTLW/c8bjUwk7b10PBRY2j525D67j70eu1ztChQ/3G8tK/tT2laT2Nxae/3n24vPtyWxV6t6N9efcv+re2p4Y0Oh948+99jbbn5jXwPQbbh4ISKhe1DAwcL9Bbbno/NXHzV9P+lF7TOvpctC/9rSnPNZVt4L6CfdbedZTulnU47yHwcwm3DCL5zJFY0R4PNWkIx0pEYwhus802fhdBfeE1AOjnn39umzgr8on4cicVqWtAsLEvIEgLQQAAgNqMGjUqKbeFxNJkBnUZQ68m3iBYPCn4VpfApDcAKO5kK8GoK1xN29cP31A00L5aywTbh9K83PW8+dO+g63vpnuXedcN9jxwmZvvYOt5txuYT+9rgq2nCWfU3TVw297XeN9rsP17l+u3uHhf4+Uur+lz8Oavtv3VtI5b7oHHT+DrairbwOXBPmvv83A+68DlwT4z7/JYfeZIrHCP6bURfGYN4ViJKCB42WWXhZwo5OGHH7azVaGeWghm1rXLMAFBAACAcKnrmn68e6llTyTBvWDbqgmzCScvzTZcX4G6WKkt8BjYItXbkk9qGqNL42LVtH3NdhuKurbq9cH2oTQv72SUbv70WnXDC1zfTfcuc/fjfe6+Ntg6br6DrefdbmA+vdsJtp7OIZrBOXDb3td48xFs/97127dv7ysfdacN5JZbTZ+DN3+17a+mddxyDzx+ArdRU9kG7ivYZ+1dJ9RnXdN2g31m3uWx+syRWOEe0y0i+MwawrES07mO99xzT/vGYnmBRG1dhuv2ERZU3T2hyzAAAEDtFMDToPXeR12CerVtq6ZHfXQlRWRi1TpQ6qNruLqoLV26tNoYguKO7efOiuyOIfjjjz/6Wl7p9d4AT+AYgpoYQ9sPRun77bdfyDEE9UP6nXfesTNveoOjmmBCkwW4NOacJv1QXpSmFnZt2rSxA/S743BpghB33C5NLqKgk7sNrePuR6/XOppVOHCMP21PaVpv8ODB9q93Hy7vvvRvL3df3v2L/q3taYw9nQ+8+fe+Rttz8xr4HoPtQzOWqlw0gUjgOIHectP7qYmbv5r2p/Sa1tHnon3pb015rqlsA/cV7LP2rqN0t6zDeQ+Bn0u4ZRDJZ47EivZ4qElDOFZiGhDU+A8a1DNU82DEvstwXScVaZy/uYVgYUkxHwcAAEAS+/e//53oLKABUYAscAxBjS/nju3nprljCGp9NwioSSncLq7BxhBU8NA7u6+X0s8///yQYwheeOGFdjZO5cW7jU2bNtkJNtTY5OOPPzbDhg2zk5to/L0zzjjD7lPDWOm19957r13n/vvv943bpXS1lispKTFz5swxDzzwgG0pp+CZXq919PyWW26xs3LqoX9rmdK0nvKkv1qm12s7ypf+amZRd1/6tzfN3ZdmSQ4co1DbU1kqILD33nv78qCH3ovyrO25eT399NOD7t+7D/3+VrmofFROKotg5RY4vmAgN3817U/pNa2jcte+9LemPIcqW7cFtndfwT5r/dU6TZs2teluWYfzHgI/l3DLIJLPHIkV7fFQk4ZwrGQ47pm5DjRLSuDEITrR6q6S7v488sgjJt3UZWrnWEyrPf2vv82FTzxpmhcUmBP3GhjyNWfuv59vZmG5+cVXzHvffW8uP2KIOeuA/UxDwVTslAvHC98jzi+ce5MF16T41pXqq841efJkO7vnXXfdZVvreWnCD3XnPWmPbNOuWYZZvt4xL39d7nseyE0Ptq1waIxuINEUFDznnHPsv8eMGRPTVpJeCvx07drV/mD2trrRD2cFmk455RT7fOrUqXYyDQ3qHyyv+pnrzaNa4+gH+K677up7fbD34b5Pd71Q+3K3J6HSvNvw0tj7mmFUgbpgguU1nH28+OKLtvVhTeUWjnD2V9s64eY52HpqDahAaE37qmmbdSmzSMpAotl2XVGviL5coj0eUuVYqWs9KaIxBIN1YVCE/6STTrInG8RfeWX4k4psKvnfXbi8nM0f+fqiIr/ldluZGSavngY1BgAAQHheeeUVc9FFFzHrcATURVYPBUi8XSkVJFGrKrWK03I933rrre2PNndii7y8PPtQ106to3HS1eqquLjYzlLrtqto1KiR7TZ26KGHmg8//NBOlqB96nX6q4YTagmnfWldjR3ndsHVNjWovPar5wqGqTurGl+oK7Fer99dWk+BK+1b29I21VNI21OaJnXU+1EwS8uVn549e9p8K0C0ePFiu3/9EN12221twG3jxo32tfqrVlbavh6///67fd8a20p5VR523HFHuz23NcuAAQPMr7/+an755Rebd7cs3H1rxmJtR61hlH91mSssLLTvT8+1vt63AmLqYabyVBBI+9FD+1HeNROtytPtYupt4aYf08qHAgIqK21L29eEL243PKVpP3ovWuZtjeO+fubMmfa9SOD7DLavYNurKS2Y/v37289MZatWQ8q3yk7voaa81rYPBf0003BN5RaOcPZX2zrh5tm7niZHURkcdNBBfvMS1PZZByvrcPcfaRlEs23Uv2iPh4Z6rMR0UhHUn4qK8CYVcYxjxk6a7Hs+b8nmsT2+nzff5AZcGNSaEAAAAMnZfdhtmRhpS8No0Uql9nJRF9BkE2rm2Wjox2yvXr3sIxQFHRV8DKf1TjAKYtXW2ET5UBAvlJrS3NerFU04LWlq2ldt+Qj1GgVOwy2bcPcRTrnFan+1rRNunt31dF7TdylYsCTSMq7ra8J9fbTbRv2L52eWmaLHSmY0dyoDJw+55557Ej5tcroor2oGXtcxBHOqBhIuK/cfPwQAAAAAAADpITPSYOBjjz1WLRilmaVGjBgRq7whDrMM51S1Ciyr8J99CgAAAAAAAOkhooCgZkS6+eabq40jeMcdd5gpU6b4zRyF+KiowxiCQVsIBswwBgAAAAAAgPQQUUBQA3l26tSp2nKNf6DBUek2HH/l7hiCdQ0IZtNlGAAAAAAAIJ1FFBDs3Lmznbko0LRp0+xsUJrtB/XUZbiWSUUC5WTRZRgAAAAAACC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" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "Los ingresos medios son **5,911.12 USD** y la mediana es **4,340.05 USD**. La media supera a la mediana, lo que junto con una asimetria de **6.63** indica cola derecha. El maximo observado es **190,377.35 USD**. Estos valores extremos pueden ser operaciones comercialmente posibles, por lo que no se eliminan antes de analizarlos." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "revenue_stats = pd.Series({\n", " \"media\": df[TARGET].mean(),\n", " \"mediana\": df[TARGET].median(),\n", " \"desv_tipica\": df[TARGET].std(),\n", " \"min\": df[TARGET].min(),\n", " \"Q1\": df[TARGET].quantile(0.25),\n", " \"Q3\": df[TARGET].quantile(0.75),\n", " \"max\": df[TARGET].max(),\n", " \"asimetria\": df[TARGET].skew(),\n", " \"curtosis\": df[TARGET].kurtosis(),\n", "})\n", "display(revenue_stats.to_frame(\"resultado\").round(3))\n", "\n", "fig, axes = plt.subplots(1, 2, figsize=(13, 4.5))\n", "sns.histplot(df[TARGET], bins=50, kde=True, ax=axes[0], color=\"#2F6F73\")\n", "axes[0].set_title(\"Distribucion de ingresos por ventas\")\n", "axes[0].set_xlabel(\"Ingresos por ventas (USD)\")\n", "axes[0].set_ylabel(\"Frecuencia\")\n", "sns.boxplot(x=df[TARGET], ax=axes[1], color=\"#D8A24A\")\n", "axes[1].set_title(\"Boxplot de ingresos por ventas\")\n", "axes[1].set_xlabel(\"Ingresos por ventas (USD)\")\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "display(Markdown(\n", " f\"Los ingresos medios son **{usd(revenue_stats['media'])}** y la mediana es **{usd(revenue_stats['mediana'])}**. \"\n", " f\"La media supera a la mediana, lo que junto con una asimetria de **{revenue_stats['asimetria']:.2f}** indica cola derecha. \"\n", " f\"El maximo observado es **{usd(revenue_stats['max'])}**. Estos valores extremos pueden ser operaciones comercialmente posibles, por lo que no se eliminan antes de analizarlos.\"\n", "))" ] }, { "cell_type": "markdown", "id": "bb279c5f", "metadata": {}, "source": [ "## 8. Outliers mediante IQR\n", "\n", "Se aplica el metodo IQR visto en clase. Un outlier no equivale automaticamente a error: puede ser una observacion real e informativa. Por eso se documenta y se conserva inicialmente." ] }, { "cell_type": "code", "execution_count": 8, "id": "4bc362ef", "metadata": { "execution": { "iopub.execute_input": "2026-09-01T16:01:50.327356Z", "iopub.status.busy": "2026-09-01T16:01:50.327205Z", "iopub.status.idle": "2026-09-01T16:01:50.352264Z", "shell.execute_reply": "2026-09-01T16:01:50.351552Z" } }, "outputs": [ { "data": { "text/html": [ "
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Q1Q3IQRlimite_inferiorlimite_superiorn_outlierspct_outliers
sales_revenue_usd2,744.187,091.634,347.45-3,777.0013,612.8138166.36
marketing_budget_usd2,183.5711,040.098,856.52-11,101.2224,324.8754599.10
ad_spend_online_usd816.834,336.853,520.02-4,463.209,616.8855639.27
ad_spend_offline_usd486.862,676.802,189.94-2,798.055,961.7156119.35
website_traffic2,961.0022,057.0019,096.00-25,683.0050,701.00650410.84
social_media_followers771.0011,386.7510,615.75-15,152.6227,310.38806813.45
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" ], "text/plain": [ " Q1 Q3 IQR limite_inferior \\\n", "sales_revenue_usd 2,744.18 7,091.63 4,347.45 -3,777.00 \n", "marketing_budget_usd 2,183.57 11,040.09 8,856.52 -11,101.22 \n", "ad_spend_online_usd 816.83 4,336.85 3,520.02 -4,463.20 \n", "ad_spend_offline_usd 486.86 2,676.80 2,189.94 -2,798.05 \n", "website_traffic 2,961.00 22,057.00 19,096.00 -25,683.00 \n", "social_media_followers 771.00 11,386.75 10,615.75 -15,152.62 \n", "\n", " limite_superior n_outliers pct_outliers \n", "sales_revenue_usd 13,612.81 3816 6.36 \n", "marketing_budget_usd 24,324.87 5459 9.10 \n", "ad_spend_online_usd 9,616.88 5563 9.27 \n", "ad_spend_offline_usd 5,961.71 5611 9.35 \n", "website_traffic 50,701.00 6504 10.84 \n", "social_media_followers 27,310.38 8068 13.45 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "El metodo IQR identifica **3,816** outliers en `sales_revenue_usd`, equivalentes al **6.36%** de la muestra. La decision inicial es conservarlos, porque en ventas los importes altos pueden representar clientes, canales o categorias relevantes, no necesariamente errores de captura." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def iqr_outliers(series, multiplier=1.5):\n", " clean = series.dropna()\n", " q1 = clean.quantile(0.25)\n", " q3 = clean.quantile(0.75)\n", " iqr = q3 - q1\n", " lower = q1 - multiplier * iqr\n", " upper = q3 + multiplier * iqr\n", " mask = (series < lower) | (series > upper)\n", " return {\n", " \"Q1\": q1, \"Q3\": q3, \"IQR\": iqr,\n", " \"limite_inferior\": lower, \"limite_superior\": upper,\n", " \"n_outliers\": int(mask.sum()), \"pct_outliers\": mask.mean() * 100,\n", " }\n", "\n", "outlier_vars = [TARGET, \"marketing_budget_usd\", \"ad_spend_online_usd\", \"ad_spend_offline_usd\", \"website_traffic\", \"social_media_followers\"]\n", "outlier_table = pd.DataFrame({v: iqr_outliers(df[v]) for v in outlier_vars}).T\n", "outlier_table[\"n_outliers\"] = outlier_table[\"n_outliers\"].astype(int)\n", "display(outlier_table.round(3))\n", "\n", "rev_out = outlier_table.loc[TARGET]\n", "display(Markdown(\n", " f\"El metodo IQR identifica **{rev_out['n_outliers']:,.0f}** outliers en `sales_revenue_usd`, equivalentes al **{rev_out['pct_outliers']:.2f}%** de la muestra. \"\n", " \"La decision inicial es conservarlos, porque en ventas los importes altos pueden representar clientes, canales o categorias relevantes, no necesariamente errores de captura.\"\n", "))" ] }, { "cell_type": "markdown", "id": "accf22df", "metadata": {}, "source": [ "## 9. Comparaciones por grupos\n", "\n", "Se compara `sales_revenue_usd` por segmento, canal, categoria, region y temporada. Estas diferencias son descriptivas: todavia no controlan simultaneamente el resto de variables." ] }, { "cell_type": "code", "execution_count": 9, "id": "6ce79f49", "metadata": { "execution": { "iopub.execute_input": "2026-09-01T16:01:50.353745Z", "iopub.status.busy": "2026-09-01T16:01:50.353594Z", "iopub.status.idle": "2026-09-01T16:01:50.979327Z", "shell.execute_reply": "2026-09-01T16:01:50.978772Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "customer_segment\n" ] }, { "data": { "text/html": [ "
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countmeanmedian
customer_segment
Corporate911310,675.998,801.11
VIP89708,844.197,203.50
Regular239064,869.944,014.76
New180113,421.452,792.22
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" ], "text/plain": [ " count mean median\n", "customer_segment \n", "Corporate 9113 10,675.99 8,801.11\n", "VIP 8970 8,844.19 7,203.50\n", "Regular 23906 4,869.94 4,014.76\n", "New 18011 3,421.45 2,792.22" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "sales_channel\n" ] }, { "data": { "text/html": [ "
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countmeanmedian
sales_channel
Wholesale119876,684.044,984.74
Online180106,110.054,499.17
Social Media58405,739.944,263.08
Retail Store149545,626.504,132.64
Direct Sales92095,086.743,726.88
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" ], "text/plain": [ " count mean median\n", "sales_channel \n", "Wholesale 11987 6,684.04 4,984.74\n", "Online 18010 6,110.05 4,499.17\n", "Social Media 5840 5,739.94 4,263.08\n", "Retail Store 14954 5,626.50 4,132.64\n", "Direct Sales 9209 5,086.74 3,726.88" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "product_category\n" ] }, { "data": { "text/html": [ "
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countmeanmedian
product_category
Electronics131187,972.076,080.41
Home Appliances107756,881.095,226.42
Cosmetics90685,201.243,761.41
Clothing120164,907.283,499.33
Food & Beverage150234,647.213,209.60
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" ], "text/plain": [ " count mean median\n", "product_category \n", "Electronics 13118 7,972.07 6,080.41\n", "Home Appliances 10775 6,881.09 5,226.42\n", "Cosmetics 9068 5,201.24 3,761.41\n", "Clothing 12016 4,907.28 3,499.33\n", "Food & Beverage 15023 4,647.21 3,209.60" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "region\n" ] }, { "data": { "text/html": [ "
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countmeanmedian
region
Amman42066,013.884,391.05
Casablanca41835,948.754,357.92
Kuwait47395,941.964,339.49
Cairo149845,932.854,331.00
Alexandria90275,930.624,399.29
Dubai108905,878.584,326.03
Riyadh119715,837.354,314.43
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" ], "text/plain": [ " count mean median\n", "region \n", "Amman 4206 6,013.88 4,391.05\n", "Casablanca 4183 5,948.75 4,357.92\n", "Kuwait 4739 5,941.96 4,339.49\n", "Cairo 14984 5,932.85 4,331.00\n", "Alexandria 9027 5,930.62 4,399.29\n", "Dubai 10890 5,878.58 4,326.03\n", "Riyadh 11971 5,837.35 4,314.43" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "season\n" ] }, { "data": { "text/html": [ "
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countmeanmedian
season
Q4150247,548.735,619.44
Q3151245,891.214,380.80
Q2149545,446.464,101.80
Q1148984,746.283,484.30
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" ], "text/plain": [ " count mean median\n", "season \n", "Q4 15024 7,548.73 5,619.44\n", "Q3 15124 5,891.21 4,380.80\n", "Q2 14954 5,446.46 4,101.80\n", "Q1 14898 4,746.28 3,484.30" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "En `customer_segment`, el mayor ingreso medio aparece en **Corporate** (10,675.99 USD) y el menor en **New** (3,421.45 USD).\n", "\n", "En `sales_channel`, el mayor ingreso medio aparece en **Wholesale** (6,684.04 USD) y el menor en **Direct Sales** (5,086.74 USD).\n", "\n", "En `product_category`, el mayor ingreso medio aparece en **Electronics** (7,972.07 USD) y el menor en **Food & Beverage** (4,647.21 USD).\n", "\n", "En `region`, el mayor ingreso medio aparece en **Amman** (6,013.88 USD) y el menor en **Riyadh** (5,837.35 USD).\n", "\n", "En `season`, el mayor ingreso medio aparece en **Q4** (7,548.73 USD) y el menor en **Q1** (4,746.28 USD)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def revenue_by_group(data, group_col):\n", " return data.groupby(group_col)[TARGET].agg(count=\"count\", mean=\"mean\", median=\"median\").sort_values(\"mean\", ascending=False)\n", "\n", "group_cols = [\"customer_segment\", \"sales_channel\", \"product_category\", \"region\", \"season\"]\n", "group_tables = {col: revenue_by_group(df, col) for col in group_cols}\n", "for col, table in group_tables.items():\n", " print(f\"\\n{col}\")\n", " display(table.round(2))\n", "\n", "plot_group_cols = [\"customer_segment\", \"sales_channel\", \"product_category\", \"season\"]\n", "fig, axes = plt.subplots(2, 2, figsize=(13, 8))\n", "axes = axes.ravel()\n", "for ax, col in zip(axes, plot_group_cols):\n", " table = group_tables[col].sort_values(\"mean\", ascending=True)\n", " ax.barh(table.index.astype(str), table[\"mean\"], color=\"#3E7C8F\")\n", " ax.set_title(f\"Ingreso medio por {col}\")\n", " ax.set_xlabel(\"Ingreso medio (USD)\")\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "interpretations = []\n", "for col in group_cols:\n", " table = group_tables[col]\n", " hi, lo = table.iloc[0], table.iloc[-1]\n", " interpretations.append(f\"En `{col}`, el mayor ingreso medio aparece en **{table.index[0]}** ({usd(hi['mean'])}) y el menor en **{table.index[-1]}** ({usd(lo['mean'])}).\")\n", "display(Markdown(\"\\n\\n\".join(interpretations)))" ] }, { "cell_type": "markdown", "id": "acf51dce", "metadata": {}, "source": [ "## 10. Relaciones numericas\n", "\n", "Se calculan correlaciones entre variables numericas y la variable objetivo, ordenadas por valor absoluto. Luego se visualizan relaciones con sentido empresarial. Correlacion no implica causalidad." ] }, { "cell_type": "code", "execution_count": 10, "id": "541e9352", "metadata": { "execution": { "iopub.execute_input": "2026-09-01T16:01:50.980950Z", "iopub.status.busy": "2026-09-01T16:01:50.980801Z", "iopub.status.idle": "2026-09-01T16:01:52.736234Z", "shell.execute_reply": "2026-09-01T16:01:52.735391Z" } }, "outputs": [ { "data": { "text/html": [ "
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correlacion_con_sales_revenueabs_correlacion
marketing_budget_usd0.730.73
ad_spend_online_usd0.700.70
ad_spend_offline_usd0.680.68
num_previous_purchases0.050.05
customer_satisfaction_score0.040.04
conversion_rate0.040.04
num_promotions0.040.04
discount_percentage-0.020.02
website_traffic0.020.02
competitor_price_index-0.020.02
days_since_last_purchase-0.010.01
customer_age0.010.01
email_open_rate0.010.01
num_sales_representatives-0.000.00
social_media_followers-0.000.00
\n", "
" ], "text/plain": [ " correlacion_con_sales_revenue abs_correlacion\n", "marketing_budget_usd 0.73 0.73\n", "ad_spend_online_usd 0.70 0.70\n", "ad_spend_offline_usd 0.68 0.68\n", "num_previous_purchases 0.05 0.05\n", "customer_satisfaction_score 0.04 0.04\n", "conversion_rate 0.04 0.04\n", "num_promotions 0.04 0.04\n", "discount_percentage -0.02 0.02\n", "website_traffic 0.02 0.02\n", "competitor_price_index -0.02 0.02\n", "days_since_last_purchase -0.01 0.01\n", "customer_age 0.01 0.01\n", "email_open_rate 0.01 0.01\n", "num_sales_representatives -0.00 0.00\n", "social_media_followers -0.00 0.00" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "La asociacion lineal mas alta con los ingresos es `marketing_budget_usd` (r = 0.727). `marketing_budget_usd`, `ad_spend_online_usd` y `ad_spend_offline_usd` muestran las correlaciones mas fuertes, pero tambien son variables muy relacionadas entre si." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "correlations = df[numeric_cols].corr()[TARGET].drop(TARGET).to_frame(\"correlacion_con_sales_revenue\")\n", "correlations[\"abs_correlacion\"] = correlations[\"correlacion_con_sales_revenue\"].abs()\n", "correlations = correlations.sort_values(\"abs_correlacion\", ascending=False)\n", "display(correlations.round(3))\n", "\n", "scatter_vars = [\"marketing_budget_usd\", \"conversion_rate\", \"num_previous_purchases\", \"customer_satisfaction_score\"]\n", "scatter_df = df.sample(n=min(5000, len(df)), random_state=RANDOM_STATE)\n", "fig, axes = plt.subplots(2, 2, figsize=(13, 8))\n", "axes = axes.ravel()\n", "for ax, col in zip(axes, scatter_vars):\n", " sns.scatterplot(data=scatter_df, x=col, y=TARGET, ax=ax, alpha=0.25, s=16, color=\"#2F6F73\", edgecolor=None)\n", " sns.regplot(data=scatter_df, x=col, y=TARGET, ax=ax, scatter=False, color=\"#B24A3B\", line_kws={\"linewidth\": 2})\n", " ax.set_title(f\"{col} vs ingresos\")\n", " ax.set_ylabel(\"sales_revenue_usd\")\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "top_corr = correlations.iloc[0]\n", "display(Markdown(\n", " f\"La asociacion lineal mas alta con los ingresos es `{top_corr.name}` (r = {top_corr['correlacion_con_sales_revenue']:.3f}). \"\n", " \"`marketing_budget_usd`, `ad_spend_online_usd` y `ad_spend_offline_usd` muestran las correlaciones mas fuertes, pero tambien son variables muy relacionadas entre si.\"\n", "))" ] }, { "cell_type": "markdown", "id": "a77c5b79", "metadata": {}, "source": [ "## 11. Seleccion de variables explicativas\n", "\n", "La seleccion no mete automaticamente todas las variables. Se combina sentido empresarial, relacion con Y, linealidad aproximada, calidad de datos y multicolinealidad. En particular, se revisa la redundancia entre presupuesto total y gasto online/offline." ] }, { "cell_type": "code", "execution_count": 11, "id": "eea19825", "metadata": { "execution": { "iopub.execute_input": "2026-09-01T16:01:52.738036Z", "iopub.status.busy": "2026-09-01T16:01:52.737760Z", "iopub.status.idle": "2026-09-01T16:01:52.761444Z", "shell.execute_reply": "2026-09-01T16:01:52.760082Z" } }, "outputs": [ { "data": { "text/html": [ "
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marketing_budget_usdad_spend_online_usdad_spend_offline_usdsales_revenue_usd
marketing_budget_usd1.000.950.930.73
ad_spend_online_usd0.951.000.890.70
ad_spend_offline_usd0.930.891.000.68
sales_revenue_usd0.730.700.681.00
\n", "
" ], "text/plain": [ " marketing_budget_usd ad_spend_online_usd \\\n", "marketing_budget_usd 1.00 0.95 \n", "ad_spend_online_usd 0.95 1.00 \n", "ad_spend_offline_usd 0.93 0.89 \n", "sales_revenue_usd 0.73 0.70 \n", "\n", " ad_spend_offline_usd sales_revenue_usd \n", "marketing_budget_usd 0.93 0.73 \n", "ad_spend_online_usd 0.89 0.70 \n", "ad_spend_offline_usd 1.00 0.68 \n", "sales_revenue_usd 0.68 1.00 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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variableincluida_modelo_principaljustificacion
0marketing_budget_usdsiResume inversion de marketing y evita redundan...
1num_promotionssiAporta informacion empresarial y permite contr...
2discount_percentagesiAporta informacion empresarial y permite contr...
3num_sales_representativessiAporta informacion empresarial y permite contr...
4customer_agesiAporta informacion empresarial y permite contr...
5customer_satisfaction_scoresiAporta informacion empresarial y permite contr...
6competitor_price_indexsiAporta informacion empresarial y permite contr...
7website_trafficsiAporta informacion empresarial y permite contr...
8conversion_ratesiAporta informacion empresarial y permite contr...
9email_open_ratesiAporta informacion empresarial y permite contr...
10social_media_followerssiAporta informacion empresarial y permite contr...
11days_since_last_purchasesiAporta informacion empresarial y permite contr...
12num_previous_purchasessiAporta informacion empresarial y permite contr...
13customer_segmentsiAporta informacion empresarial y permite contr...
14sales_channelsiAporta informacion empresarial y permite contr...
15product_categorysiAporta informacion empresarial y permite contr...
16regionsiAporta informacion empresarial y permite contr...
17seasonsiAporta informacion empresarial y permite contr...
\n", "
" ], "text/plain": [ " variable incluida_modelo_principal \\\n", "0 marketing_budget_usd si \n", "1 num_promotions si \n", "2 discount_percentage si \n", "3 num_sales_representatives si \n", "4 customer_age si \n", "5 customer_satisfaction_score si \n", "6 competitor_price_index si \n", "7 website_traffic si \n", "8 conversion_rate si \n", "9 email_open_rate si \n", "10 social_media_followers si \n", "11 days_since_last_purchase si \n", "12 num_previous_purchases si \n", "13 customer_segment si \n", "14 sales_channel si \n", "15 product_category si \n", "16 region si \n", "17 season si \n", "\n", " justificacion \n", "0 Resume inversion de marketing y evita redundan... \n", "1 Aporta informacion empresarial y permite contr... \n", "2 Aporta informacion empresarial y permite contr... \n", "3 Aporta informacion empresarial y permite contr... \n", "4 Aporta informacion empresarial y permite contr... \n", "5 Aporta informacion empresarial y permite contr... \n", "6 Aporta informacion empresarial y permite contr... \n", "7 Aporta informacion empresarial y permite contr... \n", "8 Aporta informacion empresarial y permite contr... \n", "9 Aporta informacion empresarial y permite contr... \n", "10 Aporta informacion empresarial y permite contr... \n", "11 Aporta informacion empresarial y permite contr... \n", "12 Aporta informacion empresarial y permite contr... \n", "13 Aporta informacion empresarial y permite contr... \n", "14 Aporta informacion empresarial y permite contr... \n", "15 Aporta informacion empresarial y permite contr... \n", "16 Aporta informacion empresarial y permite contr... \n", "17 Aporta informacion empresarial y permite contr... " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "`marketing_budget_usd` esta muy correlacionada con `ad_spend_online_usd` y `ad_spend_offline_usd`. Para mantener una especificacion interpretable y reducir multicolinealidad, el modelo principal usa el presupuesto total y no incluye simultaneamente sus dos componentes. `id` y `date` quedan excluidas." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "marketing_corr = df[[\"marketing_budget_usd\", \"ad_spend_online_usd\", \"ad_spend_offline_usd\", TARGET]].corr()\n", "display(marketing_corr.round(3))\n", "\n", "selected_numeric = [\n", " \"marketing_budget_usd\", \"num_promotions\", \"discount_percentage\",\n", " \"num_sales_representatives\", \"customer_age\", \"customer_satisfaction_score\",\n", " \"competitor_price_index\", \"website_traffic\", \"conversion_rate\", \"email_open_rate\",\n", " \"social_media_followers\", \"days_since_last_purchase\", \"num_previous_purchases\",\n", "]\n", "selected_categorical = [\"customer_segment\", \"sales_channel\", \"product_category\", \"region\", \"season\"]\n", "selected_vars = selected_numeric + selected_categorical\n", "\n", "selection_table = pd.DataFrame({\"variable\": selected_vars, \"incluida_modelo_principal\": \"si\"})\n", "selection_table[\"justificacion\"] = \"Aporta informacion empresarial y permite controlar el resto de bloques.\"\n", "selection_table.loc[selection_table[\"variable\"].eq(\"marketing_budget_usd\"), \"justificacion\"] = \"Resume inversion de marketing y evita redundancia con online/offline.\"\n", "display(selection_table)\n", "\n", "display(Markdown(\n", " \"`marketing_budget_usd` esta muy correlacionada con `ad_spend_online_usd` y `ad_spend_offline_usd`. \"\n", " \"Para mantener una especificacion interpretable y reducir multicolinealidad, el modelo principal usa el presupuesto total y no incluye simultaneamente sus dos componentes. `id` y `date` quedan excluidas.\"\n", "))" ] }, { "cell_type": "markdown", "id": "b4d2f8f0", "metadata": {}, "source": [ "## 12. Preparacion para regresion\n", "\n", "Se define `y = sales_revenue_usd` y las `X` seleccionadas. Los nulos numericos se imputan con la mediana, resistente a outliers. Las variables categoricas se convierten a dummies con `drop_first=True`: con k categorias se crean k-1 dummies y una categoria queda como referencia." ] }, { "cell_type": "code", "execution_count": 12, "id": "8bde366f", "metadata": { "execution": { "iopub.execute_input": "2026-09-01T16:01:52.763451Z", "iopub.status.busy": "2026-09-01T16:01:52.763281Z", "iopub.status.idle": "2026-09-01T16:01:52.868108Z", "shell.execute_reply": "2026-09-01T16:01:52.867431Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Observaciones para modelado: 60,000\n", "Variables explicativas tras dummies: 33\n" ] }, { "data": { "text/html": [ "
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variable_modelo
0marketing_budget_usd
1num_promotions
2discount_percentage
3num_sales_representatives
4customer_age
5customer_satisfaction_score
6competitor_price_index
7website_traffic
8conversion_rate
9email_open_rate
10social_media_followers
11days_since_last_purchase
12num_previous_purchases
13customer_segment_New
14customer_segment_Regular
15customer_segment_VIP
16sales_channel_Online
17sales_channel_Retail Store
18sales_channel_Social Media
19sales_channel_Wholesale
20product_category_Cosmetics
21product_category_Electronics
22product_category_Food & Beverage
23product_category_Home Appliances
24region_Amman
25region_Cairo
26region_Casablanca
27region_Dubai
28region_Kuwait
29region_Riyadh
30season_Q2
31season_Q3
32season_Q4
\n", "
" ], "text/plain": [ " variable_modelo\n", "0 marketing_budget_usd\n", "1 num_promotions\n", "2 discount_percentage\n", "3 num_sales_representatives\n", "4 customer_age\n", "5 customer_satisfaction_score\n", "6 competitor_price_index\n", "7 website_traffic\n", "8 conversion_rate\n", "9 email_open_rate\n", "10 social_media_followers\n", "11 days_since_last_purchase\n", "12 num_previous_purchases\n", "13 customer_segment_New\n", "14 customer_segment_Regular\n", "15 customer_segment_VIP\n", "16 sales_channel_Online\n", "17 sales_channel_Retail Store\n", "18 sales_channel_Social Media\n", "19 sales_channel_Wholesale\n", "20 product_category_Cosmetics\n", "21 product_category_Electronics\n", "22 product_category_Food & Beverage\n", "23 product_category_Home Appliances\n", "24 region_Amman\n", "25 region_Cairo\n", "26 region_Casablanca\n", "27 region_Dubai\n", "28 region_Kuwait\n", "29 region_Riyadh\n", "30 season_Q2\n", "31 season_Q3\n", "32 season_Q4" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "model_df = df[[TARGET] + selected_vars].copy()\n", "numeric_medians = model_df[selected_numeric].median()\n", "model_df[selected_numeric] = model_df[selected_numeric].fillna(numeric_medians)\n", "model_df = model_df.dropna(subset=[TARGET] + selected_categorical)\n", "\n", "y = model_df[TARGET]\n", "X_raw = model_df[selected_vars]\n", "X = pd.get_dummies(X_raw, columns=selected_categorical, drop_first=True, dtype=float)\n", "\n", "print(f\"Observaciones para modelado: {len(model_df):,}\")\n", "print(f\"Variables explicativas tras dummies: {X.shape[1]:,}\")\n", "display(pd.DataFrame({\"variable_modelo\": X.columns}))" ] }, { "cell_type": "markdown", "id": "d0ef4266", "metadata": {}, "source": [ "## 13. Separacion entrenamiento/test\n", "\n", "La regresion se ajusta solo con el 80% de entrenamiento. El 20% de test se reserva para evaluar capacidad predictiva con datos no usados en el ajuste." ] }, { "cell_type": "code", "execution_count": 13, "id": "efc8dacc", "metadata": { "execution": { "iopub.execute_input": "2026-09-01T16:01:52.869681Z", "iopub.status.busy": "2026-09-01T16:01:52.869438Z", "iopub.status.idle": "2026-09-01T16:01:52.934918Z", "shell.execute_reply": "2026-09-01T16:01:52.933010Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train: 48,000 observaciones (80%)\n", "Test: 12,000 observaciones (20%)\n" ] } ], "source": [ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=RANDOM_STATE)\n", "X_train_sm = sm.add_constant(X_train)\n", "X_test_sm = sm.add_constant(X_test, has_constant=\"add\")\n", "\n", "print(f\"Train: {X_train.shape[0]:,} observaciones ({X_train.shape[0] / len(X):.0%})\")\n", "print(f\"Test: {X_test.shape[0]:,} observaciones ({X_test.shape[0] / len(X):.0%})\")" ] }, { "cell_type": "markdown", "id": "8b93a714", "metadata": {}, "source": [ "## 14. Regresion lineal multiple\n", "\n", "El modelo se ajusta sobre training con Statsmodels para obtener coeficientes, errores estandar, p-valores, R2 y R2 ajustado. La interpretacion debe ser ceteris paribus: manteniendo constantes las demas variables." ] }, { "cell_type": "code", "execution_count": 14, "id": "ab271b0a", "metadata": { "execution": { "iopub.execute_input": "2026-09-01T16:01:52.936797Z", "iopub.status.busy": "2026-09-01T16:01:52.936560Z", "iopub.status.idle": "2026-09-01T16:01:53.128597Z", "shell.execute_reply": "2026-09-01T16:01:53.127040Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
OLS Regression Results
Dep. Variable: sales_revenue_usd R-squared: 0.834
Model: OLS Adj. R-squared: 0.833
Method: Least Squares F-statistic: 7279.
Date: Tue, 01 Sep 2026 Prob (F-statistic): 0.00
Time: 18:01:53 Log-Likelihood: -4.4159e+05
No. Observations: 48000 AIC: 8.832e+05
Df Residuals: 47966 BIC: 8.835e+05
Df Model: 33
Covariance Type: nonrobust
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coef std err t P>|t| [0.025 0.975]
const 4178.1160 113.241 36.896 0.000 3956.162 4400.070
marketing_budget_usd 0.2448 0.001 392.227 0.000 0.244 0.246
num_promotions 68.3977 3.805 17.976 0.000 60.940 75.855
discount_percentage -14.6198 0.967 -15.124 0.000 -16.514 -12.725
num_sales_representatives 0.7204 0.771 0.934 0.350 -0.791 2.232
customer_age -0.9252 0.664 -1.393 0.164 -2.227 0.377
customer_satisfaction_score 257.7543 12.816 20.112 0.000 232.636 282.873
competitor_price_index -782.9470 63.297 -12.369 0.000 -907.010 -658.884
website_traffic 0.0017 0.000 10.083 0.000 0.001 0.002
conversion_rate 4059.1991 182.082 22.293 0.000 3702.317 4416.081
email_open_rate -66.5966 98.684 -0.675 0.500 -260.018 126.825
social_media_followers -0.0001 9.13e-05 -1.267 0.205 -0.000 6.33e-05
days_since_last_purchase -0.6909 0.105 -6.555 0.000 -0.897 -0.484
num_previous_purchases 134.3862 4.892 27.473 0.000 124.799 143.974
customer_segment_New -7295.8024 34.373 -212.255 0.000 -7363.173 -7228.431
customer_segment_Regular -5809.3684 32.972 -176.191 0.000 -5873.994 -5744.743
customer_segment_VIP -1874.5397 39.851 -47.038 0.000 -1952.649 -1796.430
sales_channel_Online 1128.7643 34.379 32.833 0.000 1061.380 1196.148
sales_channel_Retail Store 569.8559 35.549 16.030 0.000 500.179 639.533
sales_channel_Social Media 842.2974 44.734 18.829 0.000 754.617 929.977
sales_channel_Wholesale 1665.3911 37.117 44.869 0.000 1592.642 1738.140
product_category_Cosmetics 277.6844 37.205 7.464 0.000 204.762 350.607
product_category_Electronics 2937.6739 33.834 86.826 0.000 2871.359 3003.989
product_category_Food & Beverage -344.8557 32.821 -10.507 0.000 -409.185 -280.527
product_category_Home Appliances 1904.8516 35.577 53.541 0.000 1835.119 1974.584
region_Amman -10.8102 50.034 -0.216 0.829 -108.877 87.257
region_Cairo 7.3085 35.625 0.205 0.837 -62.517 77.134
region_Casablanca -32.2998 50.033 -0.646 0.519 -130.365 65.766
region_Dubai 30.1743 38.073 0.793 0.428 -44.450 104.798
region_Kuwait 52.9900 48.068 1.102 0.270 -41.224 147.204
region_Riyadh -10.6862 37.280 -0.287 0.774 -83.755 62.383
season_Q2 808.0871 31.007 26.062 0.000 747.313 868.861
season_Q3 1113.1663 30.929 35.991 0.000 1052.545 1173.787
season_Q4 2762.0204 31.042 88.976 0.000 2701.177 2822.864
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Omnibus: 49437.438 Durbin-Watson: 1.995
Prob(Omnibus): 0.000 Jarque-Bera (JB): 39652598.374
Skew: 4.244 Prob(JB): 0.00
Kurtosis: 143.550 Cond. No. 2.05e+06


Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 2.05e+06. This might indicate that there are
strong multicollinearity or other numerical problems." ], "text/latex": [ "\\begin{center}\n", "\\begin{tabular}{lclc}\n", "\\toprule\n", "\\textbf{Dep. Variable:} & sales\\_revenue\\_usd & \\textbf{ R-squared: } & 0.834 \\\\\n", "\\textbf{Model:} & OLS & \\textbf{ Adj. R-squared: } & 0.833 \\\\\n", "\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 7279. \\\\\n", "\\textbf{Date:} & Tue, 01 Sep 2026 & \\textbf{ Prob (F-statistic):} & 0.00 \\\\\n", "\\textbf{Time:} & 18:01:53 & \\textbf{ Log-Likelihood: } & -4.4159e+05 \\\\\n", "\\textbf{No. Observations:} & 48000 & \\textbf{ AIC: } & 8.832e+05 \\\\\n", "\\textbf{Df Residuals:} & 47966 & \\textbf{ BIC: } & 8.835e+05 \\\\\n", "\\textbf{Df Model:} & 33 & \\textbf{ } & \\\\\n", "\\textbf{Covariance Type:} & nonrobust & \\textbf{ } & \\\\\n", "\\bottomrule\n", "\\end{tabular}\n", "\\begin{tabular}{lcccccc}\n", " & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]} \\\\\n", "\\midrule\n", "\\textbf{const} & 4178.1160 & 113.241 & 36.896 & 0.000 & 3956.162 & 4400.070 \\\\\n", "\\textbf{marketing\\_budget\\_usd} & 0.2448 & 0.001 & 392.227 & 0.000 & 0.244 & 0.246 \\\\\n", "\\textbf{num\\_promotions} & 68.3977 & 3.805 & 17.976 & 0.000 & 60.940 & 75.855 \\\\\n", "\\textbf{discount\\_percentage} & -14.6198 & 0.967 & -15.124 & 0.000 & -16.514 & -12.725 \\\\\n", "\\textbf{num\\_sales\\_representatives} & 0.7204 & 0.771 & 0.934 & 0.350 & -0.791 & 2.232 \\\\\n", "\\textbf{customer\\_age} & -0.9252 & 0.664 & -1.393 & 0.164 & -2.227 & 0.377 \\\\\n", "\\textbf{customer\\_satisfaction\\_score} & 257.7543 & 12.816 & 20.112 & 0.000 & 232.636 & 282.873 \\\\\n", "\\textbf{competitor\\_price\\_index} & -782.9470 & 63.297 & -12.369 & 0.000 & -907.010 & -658.884 \\\\\n", "\\textbf{website\\_traffic} & 0.0017 & 0.000 & 10.083 & 0.000 & 0.001 & 0.002 \\\\\n", "\\textbf{conversion\\_rate} & 4059.1991 & 182.082 & 22.293 & 0.000 & 3702.317 & 4416.081 \\\\\n", "\\textbf{email\\_open\\_rate} & -66.5966 & 98.684 & -0.675 & 0.500 & -260.018 & 126.825 \\\\\n", "\\textbf{social\\_media\\_followers} & -0.0001 & 9.13e-05 & -1.267 & 0.205 & -0.000 & 6.33e-05 \\\\\n", "\\textbf{days\\_since\\_last\\_purchase} & -0.6909 & 0.105 & -6.555 & 0.000 & -0.897 & -0.484 \\\\\n", "\\textbf{num\\_previous\\_purchases} & 134.3862 & 4.892 & 27.473 & 0.000 & 124.799 & 143.974 \\\\\n", "\\textbf{customer\\_segment\\_New} & -7295.8024 & 34.373 & -212.255 & 0.000 & -7363.173 & -7228.431 \\\\\n", "\\textbf{customer\\_segment\\_Regular} & -5809.3684 & 32.972 & -176.191 & 0.000 & -5873.994 & -5744.743 \\\\\n", "\\textbf{customer\\_segment\\_VIP} & -1874.5397 & 39.851 & -47.038 & 0.000 & -1952.649 & -1796.430 \\\\\n", "\\textbf{sales\\_channel\\_Online} & 1128.7643 & 34.379 & 32.833 & 0.000 & 1061.380 & 1196.148 \\\\\n", "\\textbf{sales\\_channel\\_Retail Store} & 569.8559 & 35.549 & 16.030 & 0.000 & 500.179 & 639.533 \\\\\n", "\\textbf{sales\\_channel\\_Social Media} & 842.2974 & 44.734 & 18.829 & 0.000 & 754.617 & 929.977 \\\\\n", "\\textbf{sales\\_channel\\_Wholesale} & 1665.3911 & 37.117 & 44.869 & 0.000 & 1592.642 & 1738.140 \\\\\n", "\\textbf{product\\_category\\_Cosmetics} & 277.6844 & 37.205 & 7.464 & 0.000 & 204.762 & 350.607 \\\\\n", "\\textbf{product\\_category\\_Electronics} & 2937.6739 & 33.834 & 86.826 & 0.000 & 2871.359 & 3003.989 \\\\\n", "\\textbf{product\\_category\\_Food \\& Beverage} & -344.8557 & 32.821 & -10.507 & 0.000 & -409.185 & -280.527 \\\\\n", "\\textbf{product\\_category\\_Home Appliances} & 1904.8516 & 35.577 & 53.541 & 0.000 & 1835.119 & 1974.584 \\\\\n", "\\textbf{region\\_Amman} & -10.8102 & 50.034 & -0.216 & 0.829 & -108.877 & 87.257 \\\\\n", "\\textbf{region\\_Cairo} & 7.3085 & 35.625 & 0.205 & 0.837 & -62.517 & 77.134 \\\\\n", "\\textbf{region\\_Casablanca} & -32.2998 & 50.033 & -0.646 & 0.519 & -130.365 & 65.766 \\\\\n", "\\textbf{region\\_Dubai} & 30.1743 & 38.073 & 0.793 & 0.428 & -44.450 & 104.798 \\\\\n", "\\textbf{region\\_Kuwait} & 52.9900 & 48.068 & 1.102 & 0.270 & -41.224 & 147.204 \\\\\n", "\\textbf{region\\_Riyadh} & -10.6862 & 37.280 & -0.287 & 0.774 & -83.755 & 62.383 \\\\\n", "\\textbf{season\\_Q2} & 808.0871 & 31.007 & 26.062 & 0.000 & 747.313 & 868.861 \\\\\n", "\\textbf{season\\_Q3} & 1113.1663 & 30.929 & 35.991 & 0.000 & 1052.545 & 1173.787 \\\\\n", "\\textbf{season\\_Q4} & 2762.0204 & 31.042 & 88.976 & 0.000 & 2701.177 & 2822.864 \\\\\n", "\\bottomrule\n", "\\end{tabular}\n", "\\begin{tabular}{lclc}\n", "\\textbf{Omnibus:} & 49437.438 & \\textbf{ Durbin-Watson: } & 1.995 \\\\\n", "\\textbf{Prob(Omnibus):} & 0.000 & \\textbf{ Jarque-Bera (JB): } & 39652598.374 \\\\\n", "\\textbf{Skew:} & 4.244 & \\textbf{ Prob(JB): } & 0.00 \\\\\n", "\\textbf{Kurtosis:} & 143.550 & \\textbf{ Cond. No. } & 2.05e+06 \\\\\n", "\\bottomrule\n", "\\end{tabular}\n", "%\\caption{OLS Regression Results}\n", "\\end{center}\n", "\n", "Notes: \\newline\n", " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. \\newline\n", " [2] The condition number is large, 2.05e+06. This might indicate that there are \\newline\n", " strong multicollinearity or other numerical problems." ], "text/plain": [ "\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: sales_revenue_usd R-squared: 0.834\n", "Model: OLS Adj. R-squared: 0.833\n", "Method: Least Squares F-statistic: 7279.\n", "Date: Tue, 01 Sep 2026 Prob (F-statistic): 0.00\n", "Time: 18:01:53 Log-Likelihood: -4.4159e+05\n", "No. Observations: 48000 AIC: 8.832e+05\n", "Df Residuals: 47966 BIC: 8.835e+05\n", "Df Model: 33 \n", "Covariance Type: nonrobust \n", "====================================================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "----------------------------------------------------------------------------------------------------\n", "const 4178.1160 113.241 36.896 0.000 3956.162 4400.070\n", "marketing_budget_usd 0.2448 0.001 392.227 0.000 0.244 0.246\n", "num_promotions 68.3977 3.805 17.976 0.000 60.940 75.855\n", "discount_percentage -14.6198 0.967 -15.124 0.000 -16.514 -12.725\n", "num_sales_representatives 0.7204 0.771 0.934 0.350 -0.791 2.232\n", "customer_age -0.9252 0.664 -1.393 0.164 -2.227 0.377\n", "customer_satisfaction_score 257.7543 12.816 20.112 0.000 232.636 282.873\n", "competitor_price_index -782.9470 63.297 -12.369 0.000 -907.010 -658.884\n", "website_traffic 0.0017 0.000 10.083 0.000 0.001 0.002\n", "conversion_rate 4059.1991 182.082 22.293 0.000 3702.317 4416.081\n", "email_open_rate -66.5966 98.684 -0.675 0.500 -260.018 126.825\n", "social_media_followers -0.0001 9.13e-05 -1.267 0.205 -0.000 6.33e-05\n", "days_since_last_purchase -0.6909 0.105 -6.555 0.000 -0.897 -0.484\n", "num_previous_purchases 134.3862 4.892 27.473 0.000 124.799 143.974\n", "customer_segment_New -7295.8024 34.373 -212.255 0.000 -7363.173 -7228.431\n", "customer_segment_Regular -5809.3684 32.972 -176.191 0.000 -5873.994 -5744.743\n", "customer_segment_VIP -1874.5397 39.851 -47.038 0.000 -1952.649 -1796.430\n", "sales_channel_Online 1128.7643 34.379 32.833 0.000 1061.380 1196.148\n", "sales_channel_Retail Store 569.8559 35.549 16.030 0.000 500.179 639.533\n", "sales_channel_Social Media 842.2974 44.734 18.829 0.000 754.617 929.977\n", "sales_channel_Wholesale 1665.3911 37.117 44.869 0.000 1592.642 1738.140\n", "product_category_Cosmetics 277.6844 37.205 7.464 0.000 204.762 350.607\n", "product_category_Electronics 2937.6739 33.834 86.826 0.000 2871.359 3003.989\n", "product_category_Food & Beverage -344.8557 32.821 -10.507 0.000 -409.185 -280.527\n", "product_category_Home Appliances 1904.8516 35.577 53.541 0.000 1835.119 1974.584\n", "region_Amman -10.8102 50.034 -0.216 0.829 -108.877 87.257\n", "region_Cairo 7.3085 35.625 0.205 0.837 -62.517 77.134\n", "region_Casablanca -32.2998 50.033 -0.646 0.519 -130.365 65.766\n", "region_Dubai 30.1743 38.073 0.793 0.428 -44.450 104.798\n", "region_Kuwait 52.9900 48.068 1.102 0.270 -41.224 147.204\n", "region_Riyadh -10.6862 37.280 -0.287 0.774 -83.755 62.383\n", "season_Q2 808.0871 31.007 26.062 0.000 747.313 868.861\n", "season_Q3 1113.1663 30.929 35.991 0.000 1052.545 1173.787\n", "season_Q4 2762.0204 31.042 88.976 0.000 2701.177 2822.864\n", "==============================================================================\n", "Omnibus: 49437.438 Durbin-Watson: 1.995\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 39652598.374\n", "Skew: 4.244 Prob(JB): 0.00\n", "Kurtosis: 143.550 Cond. No. 2.05e+06\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "[2] The condition number is large, 2.05e+06. This might indicate that there are\n", "strong multicollinearity or other numerical problems.\n", "\"\"\"" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "modelo = sm.OLS(y_train, X_train_sm).fit()\n", "modelo.summary()" ] }, { "cell_type": "code", "execution_count": 15, "id": "0d3d834e", "metadata": { "execution": { "iopub.execute_input": "2026-09-01T16:01:53.130589Z", "iopub.status.busy": "2026-09-01T16:01:53.130404Z", "iopub.status.idle": "2026-09-01T16:01:53.143665Z", "shell.execute_reply": "2026-09-01T16:01:53.142369Z" } }, "outputs": [ { "data": { "text/html": [ "
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variablecoeficienteerror_estandarp_valorsignificatividad
1marketing_budget_usd0.240.000.00***
14customer_segment_New-7,295.8034.370.00***
16customer_segment_VIP-1,874.5439.850.00***
15customer_segment_Regular-5,809.3732.970.00***
22product_category_Electronics2,937.6733.830.00***
24product_category_Home Appliances1,904.8535.580.00***
20sales_channel_Wholesale1,665.3937.120.00***
33season_Q42,762.0231.040.00***
32season_Q31,113.1730.930.00***
17sales_channel_Online1,128.7634.380.00***
13num_previous_purchases134.394.890.00***
31season_Q2808.0931.010.00***
9conversion_rate4,059.20182.080.00***
6customer_satisfaction_score257.7512.820.00***
19sales_channel_Social Media842.3044.730.00***
2num_promotions68.403.800.00***
18sales_channel_Retail Store569.8635.550.00***
3discount_percentage-14.620.970.00***
7competitor_price_index-782.9563.300.00***
23product_category_Food & Beverage-344.8632.820.00***
8website_traffic0.000.000.00***
21product_category_Cosmetics277.6837.200.00***
12days_since_last_purchase-0.690.110.00***
5customer_age-0.930.660.16ns
11social_media_followers-0.000.000.21ns
29region_Kuwait52.9948.070.27ns
4num_sales_representatives0.720.770.35ns
28region_Dubai30.1738.070.43ns
10email_open_rate-66.6098.680.50ns
27region_Casablanca-32.3050.030.52ns
30region_Riyadh-10.6937.280.77ns
25region_Amman-10.8150.030.83ns
26region_Cairo7.3135.620.84ns
\n", "
" ], "text/plain": [ " variable coeficiente error_estandar p_valor \\\n", "1 marketing_budget_usd 0.24 0.00 0.00 \n", "14 customer_segment_New -7,295.80 34.37 0.00 \n", "16 customer_segment_VIP -1,874.54 39.85 0.00 \n", "15 customer_segment_Regular -5,809.37 32.97 0.00 \n", "22 product_category_Electronics 2,937.67 33.83 0.00 \n", "24 product_category_Home Appliances 1,904.85 35.58 0.00 \n", "20 sales_channel_Wholesale 1,665.39 37.12 0.00 \n", "33 season_Q4 2,762.02 31.04 0.00 \n", "32 season_Q3 1,113.17 30.93 0.00 \n", "17 sales_channel_Online 1,128.76 34.38 0.00 \n", "13 num_previous_purchases 134.39 4.89 0.00 \n", "31 season_Q2 808.09 31.01 0.00 \n", "9 conversion_rate 4,059.20 182.08 0.00 \n", "6 customer_satisfaction_score 257.75 12.82 0.00 \n", "19 sales_channel_Social Media 842.30 44.73 0.00 \n", "2 num_promotions 68.40 3.80 0.00 \n", "18 sales_channel_Retail Store 569.86 35.55 0.00 \n", "3 discount_percentage -14.62 0.97 0.00 \n", "7 competitor_price_index -782.95 63.30 0.00 \n", "23 product_category_Food & Beverage -344.86 32.82 0.00 \n", "8 website_traffic 0.00 0.00 0.00 \n", "21 product_category_Cosmetics 277.68 37.20 0.00 \n", "12 days_since_last_purchase -0.69 0.11 0.00 \n", "5 customer_age -0.93 0.66 0.16 \n", "11 social_media_followers -0.00 0.00 0.21 \n", "29 region_Kuwait 52.99 48.07 0.27 \n", "4 num_sales_representatives 0.72 0.77 0.35 \n", "28 region_Dubai 30.17 38.07 0.43 \n", "10 email_open_rate -66.60 98.68 0.50 \n", "27 region_Casablanca -32.30 50.03 0.52 \n", "30 region_Riyadh -10.69 37.28 0.77 \n", "25 region_Amman -10.81 50.03 0.83 \n", "26 region_Cairo 7.31 35.62 0.84 \n", "\n", " significatividad \n", "1 *** \n", "14 *** \n", "16 *** \n", "15 *** \n", "22 *** \n", "24 *** \n", "20 *** \n", "33 *** \n", "32 *** \n", "17 *** \n", "13 *** \n", "31 *** \n", "9 *** \n", "6 *** \n", "19 *** \n", "2 *** \n", "18 *** \n", "3 *** \n", "7 *** \n", "23 *** \n", "8 *** \n", "21 *** \n", "12 *** \n", "5 ns \n", "11 ns \n", "29 ns \n", "4 ns \n", "28 ns \n", "10 ns \n", "27 ns \n", "30 ns \n", "25 ns \n", "26 ns " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "coef_table = pd.DataFrame({\n", " \"variable\": modelo.params.index,\n", " \"coeficiente\": modelo.params.values,\n", " \"error_estandar\": modelo.bse.values,\n", " \"p_valor\": modelo.pvalues.values,\n", "})\n", "coef_table = coef_table[coef_table[\"variable\"] != \"const\"].copy()\n", "coef_table[\"significatividad\"] = np.select(\n", " [coef_table[\"p_valor\"] < 0.001, coef_table[\"p_valor\"] < 0.01, coef_table[\"p_valor\"] < 0.05],\n", " [\"***\", \"**\", \"*\"],\n", " default=\"ns\",\n", ")\n", "display(coef_table.sort_values(\"p_valor\").round(4))" ] }, { "cell_type": "markdown", "id": "85267eed", "metadata": {}, "source": [ "## 15. Evaluacion obligatoria: R2, MAE y RMSE\n", "\n", "Se predice sobre test y se calculan las metricas solicitadas por el profesor. No se debe confundir el R2 de entrenamiento con el R2 de test." ] }, { "cell_type": "code", "execution_count": 16, "id": "bd892769", "metadata": { "execution": { "iopub.execute_input": "2026-09-01T16:01:53.145136Z", "iopub.status.busy": "2026-09-01T16:01:53.144918Z", "iopub.status.idle": "2026-09-01T16:01:53.158669Z", "shell.execute_reply": "2026-09-01T16:01:53.157990Z" } }, "outputs": [ { "data": { "text/html": [ "
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MetricaResultado
0R2 entrenamiento0.83
1R2 ajustado entrenamiento0.83
2R2 test0.83
3MAE test1,247.23
4RMSE test2,361.05
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" ], "text/plain": [ " Metrica Resultado\n", "0 R2 entrenamiento 0.83\n", "1 R2 ajustado entrenamiento 0.83\n", "2 R2 test 0.83\n", "3 MAE test 1,247.23\n", "4 RMSE test 2,361.05" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "El modelo alcanza un **R2 de test de 0.831**, por lo que explica una parte alta de la variabilidad de ingresos en observaciones no usadas para ajustar. El **MAE** indica que las predicciones se desvian en promedio aproximadamente **1,247.23 USD** respecto a los ingresos reales. El **RMSE** es **2,361.05 USD** y penaliza mas los errores grandes. La cercania entre R2 de entrenamiento (0.834) y R2 de test (0.831) no sugiere un sobreajuste fuerte." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "y_pred_test = modelo.predict(X_test_sm)\n", "\n", "r2_train = modelo.rsquared\n", "r2_adj_train = modelo.rsquared_adj\n", "r2_test = r2_score(y_test, y_pred_test)\n", "mae_test = mean_absolute_error(y_test, y_pred_test)\n", "rmse_test = np.sqrt(mean_squared_error(y_test, y_pred_test))\n", "\n", "evaluation_table = pd.DataFrame({\n", " \"Metrica\": [\"R2 entrenamiento\", \"R2 ajustado entrenamiento\", \"R2 test\", \"MAE test\", \"RMSE test\"],\n", " \"Resultado\": [r2_train, r2_adj_train, r2_test, mae_test, rmse_test],\n", "})\n", "display(evaluation_table.round(3))\n", "\n", "display(Markdown(\n", " f\"El modelo alcanza un **R2 de test de {r2_test:.3f}**, por lo que explica una parte alta de la variabilidad de ingresos en observaciones no usadas para ajustar. \"\n", " f\"El **MAE** indica que las predicciones se desvian en promedio aproximadamente **{usd(mae_test)}** respecto a los ingresos reales. \"\n", " f\"El **RMSE** es **{usd(rmse_test)}** y penaliza mas los errores grandes. La cercania entre R2 de entrenamiento ({r2_train:.3f}) y R2 de test ({r2_test:.3f}) no sugiere un sobreajuste fuerte.\"\n", "))" ] }, { "cell_type": "markdown", "id": "27b94002", "metadata": {}, "source": [ "## 16. Interpretacion de coeficientes seleccionados\n", "\n", "No se interpretan todos los coeficientes. Se seleccionan variables significativas, relevantes para negocio y faciles de explicar. La constante no se interpreta." ] }, { "cell_type": "code", "execution_count": 17, "id": "83ee79c2", "metadata": { "execution": { "iopub.execute_input": "2026-09-01T16:01:53.160047Z", "iopub.status.busy": "2026-09-01T16:01:53.159839Z", "iopub.status.idle": "2026-09-01T16:01:53.174898Z", "shell.execute_reply": "2026-09-01T16:01:53.174275Z" } }, "outputs": [ { "data": { "text/html": [ "
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variablecoeficienteerror_estandarp_valorsignificatividad
1marketing_budget_usd0.240.000.00***
15customer_segment_Regular-5,809.3732.970.00***
14customer_segment_New-7,295.8034.370.00***
20sales_channel_Wholesale1,665.3937.120.00***
22product_category_Electronics2,937.6733.830.00***
33season_Q42,762.0231.040.00***
13num_previous_purchases134.394.890.00***
9conversion_rate4,059.20182.080.00***
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" ], "text/plain": [ " variable coeficiente error_estandar p_valor \\\n", "1 marketing_budget_usd 0.24 0.00 0.00 \n", "15 customer_segment_Regular -5,809.37 32.97 0.00 \n", "14 customer_segment_New -7,295.80 34.37 0.00 \n", "20 sales_channel_Wholesale 1,665.39 37.12 0.00 \n", "22 product_category_Electronics 2,937.67 33.83 0.00 \n", "33 season_Q4 2,762.02 31.04 0.00 \n", "13 num_previous_purchases 134.39 4.89 0.00 \n", "9 conversion_rate 4,059.20 182.08 0.00 \n", "\n", " significatividad \n", "1 *** \n", "15 *** \n", "14 *** \n", "20 *** \n", "22 *** \n", "33 *** \n", "13 *** \n", "9 *** " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "- `marketing_budget_usd`: manteniendo constantes las demas variables, un aumento de una unidad se asocia con **0.24 USD** de diferencia media en ingresos.\n", "- `customer_segment_Regular`: manteniendo constantes las demas variables, pertenecer a esta categoria se asocia con una diferencia media de **-5,809.37 USD** respecto a la categoria de referencia.\n", "- `customer_segment_New`: manteniendo constantes las demas variables, pertenecer a esta categoria se asocia con una diferencia media de **-7,295.80 USD** respecto a la categoria de referencia.\n", "- `sales_channel_Wholesale`: manteniendo constantes las demas variables, pertenecer a esta categoria se asocia con una diferencia media de **1,665.39 USD** respecto a la categoria de referencia.\n", "- `product_category_Electronics`: manteniendo constantes las demas variables, pertenecer a esta categoria se asocia con una diferencia media de **2,937.67 USD** respecto a la categoria de referencia.\n", "- `season_Q4`: manteniendo constantes las demas variables, pertenecer a esta categoria se asocia con una diferencia media de **2,762.02 USD** respecto a la categoria de referencia.\n", "- `num_previous_purchases`: manteniendo constantes las demas variables, un aumento de una unidad se asocia con **134.39 USD** de diferencia media en ingresos.\n", "- `conversion_rate`: un aumento de 1 unidad en la tasa de conversion se asocia con **4,059.20 USD** mas de ingresos; un aumento de 0,01 se asocia aproximadamente con **40.59 USD**." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "priority_terms = [\n", " \"marketing_budget_usd\", \"conversion_rate\", \"customer_satisfaction_score\",\n", " \"num_previous_purchases\", \"num_promotions\", \"discount_percentage\",\n", " \"customer_segment_New\", \"customer_segment_Regular\",\n", " \"product_category_Electronics\", \"season_Q4\", \"sales_channel_Wholesale\",\n", "]\n", "interpretation_table = coef_table[coef_table[\"variable\"].isin(priority_terms)].copy()\n", "interpretation_table = interpretation_table.sort_values(\"p_valor\").head(8)\n", "display(interpretation_table.round(4))\n", "\n", "lines = []\n", "dummy_prefixes = [\"customer_segment\", \"sales_channel\", \"product_category\", \"region\", \"season\"]\n", "for _, row in interpretation_table.iterrows():\n", " var = row[\"variable\"]\n", " beta = row[\"coeficiente\"]\n", " if any(var.startswith(prefix) for prefix in dummy_prefixes):\n", " lines.append(f\"- `{var}`: manteniendo constantes las demas variables, pertenecer a esta categoria se asocia con una diferencia media de **{usd(beta)}** respecto a la categoria de referencia.\")\n", " elif var == \"conversion_rate\":\n", " lines.append(f\"- `{var}`: un aumento de 1 unidad en la tasa de conversion se asocia con **{usd(beta)}** mas de ingresos; un aumento de 0,01 se asocia aproximadamente con **{usd(beta * 0.01)}**.\")\n", " else:\n", " lines.append(f\"- `{var}`: manteniendo constantes las demas variables, un aumento de una unidad se asocia con **{usd(beta)}** de diferencia media en ingresos.\")\n", "display(Markdown(\"\\n\".join(lines)))" ] }, { "cell_type": "markdown", "id": "f5fce7a6", "metadata": {}, "source": [ "## 17. VIF: multicolinealidad\n", "\n", "Se calcula el factor de inflacion de la varianza sobre las X del modelo. Como referencia: VIF < 5 suele ser correcto, 5-10 requiere revision y > 10 indica un problema serio." ] }, { "cell_type": "code", "execution_count": 18, "id": "3619569a", "metadata": { "execution": { "iopub.execute_input": "2026-09-01T16:01:53.176601Z", "iopub.status.busy": "2026-09-01T16:01:53.176319Z", "iopub.status.idle": "2026-09-01T16:02:05.765712Z", "shell.execute_reply": "2026-09-01T16:02:05.765150Z" } }, "outputs": [ { "data": { "text/html": [ "
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variableVIF
14customer_segment_Regular2.18
13customer_segment_New2.08
16sales_channel_Online2.08
25region_Cairo1.99
17sales_channel_Retail Store1.98
29region_Riyadh1.86
19sales_channel_Wholesale1.85
27region_Dubai1.80
22product_category_Food & Beverage1.69
15customer_segment_VIP1.69
21product_category_Electronics1.64
23product_category_Home Appliances1.56
31season_Q31.51
30season_Q21.51
32season_Q41.51
20product_category_Cosmetics1.50
18sales_channel_Social Media1.48
28region_Kuwait1.40
24region_Amman1.36
26region_Casablanca1.36
11days_since_last_purchase1.00
4customer_age1.00
8conversion_rate1.00
12num_previous_purchases1.00
2discount_percentage1.00
0marketing_budget_usd1.00
6competitor_price_index1.00
7website_traffic1.00
10social_media_followers1.00
3num_sales_representatives1.00
9email_open_rate1.00
1num_promotions1.00
5customer_satisfaction_score1.00
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" ], "text/plain": [ " variable VIF\n", "14 customer_segment_Regular 2.18\n", "13 customer_segment_New 2.08\n", "16 sales_channel_Online 2.08\n", "25 region_Cairo 1.99\n", "17 sales_channel_Retail Store 1.98\n", "29 region_Riyadh 1.86\n", "19 sales_channel_Wholesale 1.85\n", "27 region_Dubai 1.80\n", "22 product_category_Food & Beverage 1.69\n", "15 customer_segment_VIP 1.69\n", "21 product_category_Electronics 1.64\n", "23 product_category_Home Appliances 1.56\n", "31 season_Q3 1.51\n", "30 season_Q2 1.51\n", "32 season_Q4 1.51\n", "20 product_category_Cosmetics 1.50\n", "18 sales_channel_Social Media 1.48\n", "28 region_Kuwait 1.40\n", "24 region_Amman 1.36\n", "26 region_Casablanca 1.36\n", "11 days_since_last_purchase 1.00\n", "4 customer_age 1.00\n", "8 conversion_rate 1.00\n", "12 num_previous_purchases 1.00\n", "2 discount_percentage 1.00\n", "0 marketing_budget_usd 1.00\n", "6 competitor_price_index 1.00\n", "7 website_traffic 1.00\n", "10 social_media_followers 1.00\n", "3 num_sales_representatives 1.00\n", "9 email_open_rate 1.00\n", "1 num_promotions 1.00\n", "5 customer_satisfaction_score 1.00" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "El VIF maximo es **2.18** en `customer_segment_Regular`, por debajo de 5. No aparece multicolinealidad seria en el modelo principal." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "vif_table = pd.DataFrame({\"variable\": X_train.columns})\n", "vif_table[\"VIF\"] = [variance_inflation_factor(X_train.values, i) for i in range(X_train.shape[1])]\n", "vif_table = vif_table.sort_values(\"VIF\", ascending=False)\n", "display(vif_table.round(3))\n", "\n", "max_vif = vif_table[\"VIF\"].max()\n", "max_vif_var = vif_table.iloc[0][\"variable\"]\n", "if max_vif < 5:\n", " msg = f\"El VIF maximo es **{max_vif:.2f}** en `{max_vif_var}`, por debajo de 5. No aparece multicolinealidad seria en el modelo principal.\"\n", "elif max_vif < 10:\n", " msg = f\"El VIF maximo es **{max_vif:.2f}** en `{max_vif_var}`. Hay variables a revisar, aunque no se supera el umbral de problema serio.\"\n", "else:\n", " msg = f\"El VIF maximo es **{max_vif:.2f}** en `{max_vif_var}`, lo que sugiere multicolinealidad seria. Seria necesario replantear variables redundantes.\"\n", "display(Markdown(msg))" ] }, { "cell_type": "markdown", "id": "49ed3a65", "metadata": {}, "source": [ "## 18. Diagnostico de residuos\n", "\n", "Se revisan residuos frente a valores predichos, histograma y Q-Q plot. El objetivo es detectar patrones, forma de embudo, valores extremos y normalidad aproximada. Con muestras grandes no se exige normalidad perfecta." ] }, { "cell_type": "code", "execution_count": 19, "id": "7fcb0705", "metadata": { "execution": { "iopub.execute_input": "2026-09-01T16:02:05.767250Z", "iopub.status.busy": "2026-09-01T16:02:05.766967Z", "iopub.status.idle": "2026-09-01T16:02:06.613857Z", "shell.execute_reply": "2026-09-01T16:02:06.613246Z" } }, "outputs": [ { "data": { "text/html": [ "
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resultado
media_residuos0.00
desv_tipica_residuos2,394.18
asimetria_residuos4.24
curtosis_residuos140.55
\n", "
" ], "text/plain": [ " resultado\n", "media_residuos 0.00\n", "desv_tipica_residuos 2,394.18\n", "asimetria_residuos 4.24\n", "curtosis_residuos 140.55" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "Los residuos tienen media cercana a cero (0.00). La asimetria (4.24) y la curtosis (140.55) sugieren que no siguen perfectamente una distribucion normal, especialmente por errores grandes asociados a ingresos extremos." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fitted_train = modelo.fittedvalues\n", "residuals_train = modelo.resid\n", "\n", "resid_stats = pd.Series({\n", " \"media_residuos\": residuals_train.mean(),\n", " \"desv_tipica_residuos\": residuals_train.std(),\n", " \"asimetria_residuos\": stats.skew(residuals_train),\n", " \"curtosis_residuos\": stats.kurtosis(residuals_train),\n", "})\n", "display(resid_stats.to_frame(\"resultado\").round(3))\n", "\n", "fig, axes = plt.subplots(1, 3, figsize=(16, 4.5))\n", "axes[0].scatter(fitted_train, residuals_train, alpha=0.18, s=12, color=\"#2F6F73\")\n", "axes[0].axhline(0, color=\"#B24A3B\", linewidth=2)\n", "axes[0].set_title(\"Residuos vs predichos\")\n", "axes[0].set_xlabel(\"Valores predichos\")\n", "axes[0].set_ylabel(\"Residuos\")\n", "sns.histplot(residuals_train, bins=50, kde=True, ax=axes[1], color=\"#D8A24A\")\n", "axes[1].set_title(\"Histograma de residuos\")\n", "axes[1].set_xlabel(\"Residuo\")\n", "sm.qqplot(residuals_train, line=\"45\", fit=True, ax=axes[2])\n", "axes[2].set_title(\"Q-Q plot de residuos\")\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "display(Markdown(\n", " f\"Los residuos tienen media cercana a cero ({resid_stats['media_residuos']:.2f}). \"\n", " f\"La asimetria ({resid_stats['asimetria_residuos']:.2f}) y la curtosis ({resid_stats['curtosis_residuos']:.2f}) sugieren que no siguen perfectamente una distribucion normal, especialmente por errores grandes asociados a ingresos extremos.\"\n", "))" ] }, { "cell_type": "markdown", "id": "3dcfe702", "metadata": {}, "source": [ "## 19. Heterocedasticidad: test de White\n", "\n", "Se aplica el test de White sobre una muestra reproducible del conjunto de entrenamiento para mantener el coste computacional bajo. H0: homocedasticidad. Si p < 0,05 hay evidencia de heterocedasticidad. Si aparece, se calculan errores estandar robustos HC3." ] }, { "cell_type": "code", "execution_count": 20, "id": "aecb2f87", "metadata": { "execution": { "iopub.execute_input": "2026-09-01T16:02:06.616375Z", "iopub.status.busy": "2026-09-01T16:02:06.616046Z", "iopub.status.idle": "2026-09-01T16:02:07.903148Z", "shell.execute_reply": "2026-09-01T16:02:07.901923Z" } }, "outputs": [ { "data": { "text/html": [ "
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estadisticoresultado
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variablecoeficienteerror_estandar_OLSp_valor_OLSerror_estandar_HC3p_valor_HC3
1marketing_budget_usd0.240.000.000.010.00
15customer_segment_Regular-5,809.3732.970.0047.390.00
14customer_segment_New-7,295.8034.370.0049.660.00
20sales_channel_Wholesale1,665.3937.120.0038.660.00
22product_category_Electronics2,937.6733.830.0035.710.00
24product_category_Home Appliances1,904.8535.580.0034.070.00
33season_Q42,762.0231.040.0034.060.00
32season_Q31,113.1730.930.0029.450.00
17sales_channel_Online1,128.7634.380.0034.910.00
16customer_segment_VIP-1,874.5439.850.0058.930.00
31season_Q2808.0931.010.0027.290.00
13num_previous_purchases134.394.890.004.920.00
9conversion_rate4,059.20182.080.00179.100.00
6customer_satisfaction_score257.7512.820.0013.150.00
19sales_channel_Social Media842.3044.730.0043.380.00
\n", "
" ], "text/plain": [ " variable coeficiente error_estandar_OLS \\\n", "1 marketing_budget_usd 0.24 0.00 \n", "15 customer_segment_Regular -5,809.37 32.97 \n", "14 customer_segment_New -7,295.80 34.37 \n", "20 sales_channel_Wholesale 1,665.39 37.12 \n", "22 product_category_Electronics 2,937.67 33.83 \n", "24 product_category_Home Appliances 1,904.85 35.58 \n", "33 season_Q4 2,762.02 31.04 \n", "32 season_Q3 1,113.17 30.93 \n", "17 sales_channel_Online 1,128.76 34.38 \n", "16 customer_segment_VIP -1,874.54 39.85 \n", "31 season_Q2 808.09 31.01 \n", "13 num_previous_purchases 134.39 4.89 \n", "9 conversion_rate 4,059.20 182.08 \n", "6 customer_satisfaction_score 257.75 12.82 \n", "19 sales_channel_Social Media 842.30 44.73 \n", "\n", " p_valor_OLS error_estandar_HC3 p_valor_HC3 \n", "1 0.00 0.01 0.00 \n", "15 0.00 47.39 0.00 \n", "14 0.00 49.66 0.00 \n", "20 0.00 38.66 0.00 \n", "22 0.00 35.71 0.00 \n", "24 0.00 34.07 0.00 \n", "33 0.00 34.06 0.00 \n", "32 0.00 29.45 0.00 \n", "17 0.00 34.91 0.00 \n", "16 0.00 58.93 0.00 \n", "31 0.00 27.29 0.00 \n", "13 0.00 4.92 0.00 \n", "9 0.00 179.10 0.00 \n", "6 0.00 13.15 0.00 \n", "19 0.00 43.38 0.00 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "El p-valor del test de White es **0.000000**, por debajo de 0,05. Hay evidencia de heterocedasticidad. Los coeficientes no cambian al usar HC3, pero si cambian los errores estandar y potencialmente los p-valores." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "white_n = min(8000, len(X_train_sm))\n", "white_idx = X_train_sm.sample(n=white_n, random_state=RANDOM_STATE).index\n", "white_stat = het_white(residuals_train.loc[white_idx], X_train_sm.loc[white_idx])\n", "white_table = pd.DataFrame({\n", " \"estadistico\": [\"LM\", \"LM p-valor\", \"F\", \"F p-valor\"],\n", " \"resultado\": white_stat,\n", "})\n", "display(white_table.round(6))\n", "\n", "modelo_robusto = modelo.get_robustcov_results(cov_type=\"HC3\")\n", "robust_table = pd.DataFrame({\n", " \"variable\": modelo.params.index,\n", " \"coeficiente\": modelo.params.values,\n", " \"error_estandar_OLS\": modelo.bse.values,\n", " \"p_valor_OLS\": modelo.pvalues.values,\n", " \"error_estandar_HC3\": modelo_robusto.bse,\n", " \"p_valor_HC3\": modelo_robusto.pvalues,\n", "})\n", "robust_table = robust_table[robust_table[\"variable\"] != \"const\"]\n", "display(robust_table.sort_values(\"p_valor_HC3\").head(15).round(4))\n", "\n", "white_p = white_stat[1]\n", "if white_p < 0.05:\n", " msg = f\"El p-valor del test de White es **{white_p:.6f}**, por debajo de 0,05. Hay evidencia de heterocedasticidad. Los coeficientes no cambian al usar HC3, pero si cambian los errores estandar y potencialmente los p-valores.\"\n", "else:\n", " msg = f\"El p-valor del test de White es **{white_p:.6f}**, por encima de 0,05. No se detecta evidencia estadistica clara de heterocedasticidad con esta prueba.\"\n", "display(Markdown(msg))" ] }, { "cell_type": "markdown", "id": "1b755336", "metadata": {}, "source": [ "## 20. Modelo logaritmico: revision breve\n", "\n", "El logaritmo solo se prueba porque la variable objetivo tiene cola derecha y los residuos presentan valores extremos. No se convierte en el modelo principal salvo que mejore claramente interpretacion y estabilidad." ] }, { "cell_type": "code", "execution_count": 21, "id": "e9962d66", "metadata": { "execution": { "iopub.execute_input": "2026-09-01T16:02:07.905211Z", "iopub.status.busy": "2026-09-01T16:02:07.904919Z", "iopub.status.idle": "2026-09-01T16:02:08.057034Z", "shell.execute_reply": "2026-09-01T16:02:08.056509Z" } }, "outputs": [ { "data": { "text/html": [ "
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modeloR2_trainR2_test_escala_modeloasimetria_residuos_traincurtosis_residuos_train
0niveles0.830.834.24140.55
1log(y)0.870.88-3.4265.38
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" ], "text/plain": [ " modelo R2_train R2_test_escala_modelo asimetria_residuos_train \\\n", "0 niveles 0.83 0.83 4.24 \n", "1 log(y) 0.87 0.88 -3.42 \n", "\n", " curtosis_residuos_train \n", "0 140.55 \n", "1 65.38 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "El modelo logaritmico mejora algunos diagnosticos de forma, pero cambia la interpretacion de los coeficientes. Como el requisito central es una regresion lineal multiple interpretable en ingresos USD y el modelo en niveles ya presenta buen desempeno predictivo, se mantiene el modelo principal en niveles y se deja el logaritmo solo como comprobacion." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "y_log = np.log(y)\n", "y_log_train = y_log.loc[y_train.index]\n", "y_log_test = y_log.loc[y_test.index]\n", "modelo_log = sm.OLS(y_log_train, X_train_sm).fit()\n", "y_log_pred = modelo_log.predict(X_test_sm)\n", "\n", "log_eval = pd.DataFrame({\n", " \"modelo\": [\"niveles\", \"log(y)\"],\n", " \"R2_train\": [modelo.rsquared, modelo_log.rsquared],\n", " \"R2_test_escala_modelo\": [r2_test, r2_score(y_log_test, y_log_pred)],\n", " \"asimetria_residuos_train\": [stats.skew(modelo.resid), stats.skew(modelo_log.resid)],\n", " \"curtosis_residuos_train\": [stats.kurtosis(modelo.resid), stats.kurtosis(modelo_log.resid)],\n", "})\n", "display(log_eval.round(4))\n", "\n", "display(Markdown(\n", " \"El modelo logaritmico mejora algunos diagnosticos de forma, pero cambia la interpretacion de los coeficientes. \"\n", " \"Como el requisito central es una regresion lineal multiple interpretable en ingresos USD y el modelo en niveles ya presenta buen desempeno predictivo, se mantiene el modelo principal en niveles y se deja el logaritmo solo como comprobacion.\"\n", "))" ] }, { "cell_type": "markdown", "id": "28b28382", "metadata": {}, "source": [ "## 21. Conclusiones\n", "\n", "Los hallazgos responden a la pregunta de investigacion usando lenguaje de asociacion, no causalidad." ] }, { "cell_type": "code", "execution_count": 22, "id": "8e619b1f", "metadata": { "execution": { "iopub.execute_input": "2026-09-01T16:02:08.058407Z", "iopub.status.busy": "2026-09-01T16:02:08.058200Z", "iopub.status.idle": "2026-09-01T16:02:08.063296Z", "shell.execute_reply": "2026-09-01T16:02:08.062747Z" } }, "outputs": [ { "data": { "text/markdown": [ "- El bloque de marketing destaca: `marketing_budget_usd` es la variable numerica con mayor correlacion con ingresos (r = 0.727) y mantiene una asociacion positiva en la regresion multiple.\n", "- El contexto comercial tambien importa: categorias como `product_category_Electronics` y temporadas como `season_Q4` muestran diferencias medias positivas frente a sus categorias de referencia, manteniendo constantes las demas variables.\n", "- El comportamiento del cliente aporta informacion: `num_previous_purchases` y `customer_satisfaction_score` se asocian positivamente con los ingresos en el modelo principal.\n", "- El engagement digital aparece sobre todo mediante `conversion_rate`, que se asocia positivamente con mayores ingresos; otros indicadores digitales tienen menor peso lineal directo.\n", "- El modelo tiene buen desempeno predictivo para un enfoque lineal: R2 test = 0.831, MAE = 1,247.23 USD y RMSE = 2,361.05 USD." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hallazgos = [\n", " f\"El bloque de marketing destaca: `marketing_budget_usd` es la variable numerica con mayor correlacion con ingresos (r = {correlations.loc['marketing_budget_usd', 'correlacion_con_sales_revenue']:.3f}) y mantiene una asociacion positiva en la regresion multiple.\",\n", " \"El contexto comercial tambien importa: categorias como `product_category_Electronics` y temporadas como `season_Q4` muestran diferencias medias positivas frente a sus categorias de referencia, manteniendo constantes las demas variables.\",\n", " \"El comportamiento del cliente aporta informacion: `num_previous_purchases` y `customer_satisfaction_score` se asocian positivamente con los ingresos en el modelo principal.\",\n", " \"El engagement digital aparece sobre todo mediante `conversion_rate`, que se asocia positivamente con mayores ingresos; otros indicadores digitales tienen menor peso lineal directo.\",\n", " f\"El modelo tiene buen desempeno predictivo para un enfoque lineal: R2 test = {r2_test:.3f}, MAE = {usd(mae_test)} y RMSE = {usd(rmse_test)}.\",\n", "]\n", "display(Markdown(\"\\n\".join([f\"- {h}\" for h in hallazgos])))" ] }, { "cell_type": "markdown", "id": "b4bbd553", "metadata": {}, "source": [ "## 22. Limitaciones\n", "\n", "- El dataset es sintetico, por lo que los resultados no deben generalizarse automaticamente a una empresa real.\n", "- El estudio es observacional: asociacion no equivale a causalidad.\n", "- Pueden existir variables omitidas que tambien esten asociadas con los ingresos.\n", "- La regresion lineal multiple captura principalmente relaciones lineales y efectos medios.\n", "- Hay outliers e indicios diagnosticos que recomiendan prudencia al interpretar errores y significatividad.\n", "- Los resultados son especificos de esta muestra y de las variables disponibles." ] }, { "cell_type": "markdown", "id": "e4668959", "metadata": {}, "source": [ "## 23. Recomendaciones empresariales\n", "\n", "- Se recomienda considerar el presupuesto de marketing como variable prioritaria de seguimiento, porque muestra una asociacion fuerte y positiva con ingresos.\n", "- Los resultados apuntan a revisar estrategias diferenciadas por segmento, canal, categoria y temporada, especialmente donde las diferencias medias son mayores.\n", "- Se sugiere monitorizar `conversion_rate`, satisfaccion y compras previas como indicadores utiles para anticipar ingresos, siempre junto con controles comerciales.\n", "- Antes de tomar decisiones causales, convendria complementar este analisis con disenos experimentales o datos longitudinales mas controlados." ] }, { "cell_type": "markdown", "id": "7eb46c5c", "metadata": {}, "source": [ "# Seleccion para el informe final\n", "\n", "## IMPRESCINDIBLES\n", "\n", "### 1. Objetivo y datos - 0,5 pagina\n", "\n", "- Pregunta de investigacion.\n", "- Dataset, periodo observado y tamano.\n", "- Variable objetivo `sales_revenue_usd`.\n", "- Variables explicativas organizadas en cuatro bloques.\n", "\n", "### 2. Descriptiva y outliers - 1 pagina\n", "\n", "- Estadisticos clave de `sales_revenue_usd`.\n", "- Histograma o boxplot de ingresos.\n", "- Nulos principales y decision de imputacion para regresion.\n", "- Tabla resumida de outliers IQR y decision de conservarlos inicialmente.\n", "\n", "### 3. Relaciones - 1 pagina\n", "\n", "- Tabla de correlaciones con Y.\n", "- 1-2 scatter plots, especialmente presupuesto de marketing y conversion.\n", "- Diferencias por grupos mas relevantes: segmento, canal, categoria o temporada.\n", "\n", "### 4. Regresion y evaluacion - 2 paginas\n", "\n", "- Definicion de Y y X seleccionadas.\n", "- Justificacion de usar `marketing_budget_usd` y no simultaneamente online/offline.\n", "- R2 y R2 ajustado de entrenamiento.\n", "- R2 test, MAE y RMSE.\n", "- 5-8 coeficientes relevantes con interpretacion ceteris paribus.\n", "- VIF resumido.\n", "- Diagnostico de residuos y White muy resumidos.\n", "\n", "### 5. Conclusiones y limitaciones - 0,5 pagina\n", "\n", "- 3-5 hallazgos principales.\n", "- Recomendaciones prudentes.\n", "- Limitaciones: dataset sintetico, estudio observacional, asociacion no causalidad, variables omitidas y linealidad.\n", "\n", "## SOLO NOTEBOOK\n", "\n", "- Tablas completas de frecuencias categoricas.\n", "- Tabla descriptiva numerica completa.\n", "- Summary completo de Statsmodels.\n", "- Tabla completa de coeficientes.\n", "- VIF completo.\n", "- Diagnosticos graficos ampliados.\n", "- Comprobacion breve del modelo logaritmico." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.14.5" } }, "nbformat": 4, "nbformat_minor": 5 }