geocoding

ML/geocoding/.env

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+API_KEY='AIzaSyAaqCt-qi-w7APxSjFXRgR3M5Z23Uj6NOE'
+BASE_URL='https://maps.googleapis.com/maps/api/geocode/json'

ML/geocoding/app.py

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+import os
+
+import folium
+import matplotlib.pyplot as plt
+import requests
+import streamlit as st
+from dotenv import load_dotenv
+from sklearn.cluster import KMeans
+from streamlit_folium import folium_static
+
+load_dotenv()
+
+st.set_page_config(layout='wide')  # Para usar todo el ancho de la página
+
+API_KEY = os.getenv('API_KEY')
+BASE_URL = os.getenv('BASE_URL')
+
+
+def geocode_address(address):
+    api_key = API_KEY
+    base_url = BASE_URL
+    params = {
+        'address': address,
+        'key': api_key
+        }
+
+    try:
+        response = requests.get(base_url, params=params)
+        data = response.json()
+
+        if data['status'] == 'OK' and len(data['results']) > 0:
+            location = data['results'][0]['geometry']['location']
+            latitude = location['lat']
+            longitude = location['lng']
+
+            return latitude, longitude
+        else:
+            st.error(
+                    'No se encontraron resultados para la dirección especificada.'
+                    )
+    except requests.exceptions.RequestException as e:
+        st.error('Error en la solicitud:', e)
+
+
+direcciones = [
+    'San Martín y Garibaldi',
+    'Avenida Emilio Civit s/n',
+    'Parque General San Martín',
+    'Calle Sarmiento, entre las calles Patricias Mendocinas y Garibaldi',
+    'Calle Belgrano y España',
+    'Calle Las Heras 50',
+    'Plaza Independencia',
+    'Avenida España y Costanera',
+    'Calle 9 de Julio 1228',
+    'Calle Chile 1754',
+    'Avenida Arístides Villanueva',
+    'Avenida Emilio Civit y España',
+    'Calle Chile y Avenida Colón',
+    'Calle Emilio Civit y Avenida San Martín',
+    'Acceso Este y Avenida San Francisco de Asís',
+    'Calle San Martín y Avellaneda',
+    'Plaza Pedro del Castillo',
+    'Calle San Martín y Avenida España',
+    'Calle Emilio Civit y Avenida San Martín',
+    'Avenida España',
+    'Calle Avellaneda y Patricias Mendocinas',
+    'Parque General San Martín',
+    'Ruta Nacional 7',
+    'Avenida Costanera y calle Peltier',
+    'Calle Montecaseros 2625',
+    'Calle Francisco Delgado 1220',
+    'Ruta Provincial 86, s/n',
+    'Ruta 15, km 23',
+    'Calle San Martín 2044',
+    'Ruta 7 y Acceso Sur'
+    ]
+
+coordenadas = []
+for direccion in direcciones:
+    resultado = geocode_address(direccion + ', Capital, Mendoza, Argentina')
+    if resultado:
+        coordenadas.append(resultado)
+    else:
+        coordenadas.append((None, None))
+
+max_k = len(coordenadas)
+inertias = []
+for k in range(2, max_k + 1):
+    modelo = KMeans(n_clusters=k, random_state=42)
+    modelo.fit(coordenadas)
+    inertias.append(modelo.inertia_)
+
+# Titulo
+
+# Ajustar el tamaño del gráfico del codo
+col1, col2, col3 = st.columns(3)
+
+with col2:
+    col2.title('Clientes por Zonas')
+
+    fig, ax = plt.subplots(figsize=(6, 4))
+    ax.plot(range(2, max_k + 1), inertias, marker='o')
+    ax.set_xlabel('Número de zonas (k)')
+    ax.set_ylabel('Inercia')
+    ax.set_title('Método del Codo')
+    st.pyplot(fig)
+
+k_optimo = st.number_input(
+        "Ingrese el valor óptimo de k según el gráfico:", min_value=2,
+        max_value=max_k, step=1
+        )
+
+modelo = KMeans(n_clusters=k_optimo, random_state=42)
+modelo.fit(coordenadas)
+
+etiquetas = modelo.labels_
+num_zonas = len(set(etiquetas))
+
+zonas = {}
+for i, etiqueta in enumerate(etiquetas):
+    if etiqueta not in zonas:
+        zonas[etiqueta] = []
+    zonas[etiqueta].append(direcciones[i])
+
+# Crear el mapa centrado en la primera coordenada
+primer_coordenada = coordenadas[0]
+mapa = folium.Map(location=primer_coordenada, zoom_start=15)
+
+colors = ['red', 'blue', 'green', 'purple', 'orange', 'darkred', 'lightred',
+          'beige', 'darkblue', 'darkgreen', 'cadetblue',
+          'darkpurple', 'white', 'pink', 'lightblue', 'lightgreen', 'gray',
+          'black', 'lightgray']  # Colores disponibles en Folium
+
+for i, coordenada in enumerate(coordenadas):
+    zona = etiquetas[i] + 1
+    color = colors[zona % len(colors)]
+    if coordenada != (None, None):
+        folium.Marker(
+                location=coordenada, popup=f'Zona: {zona}',
+                icon=folium.Icon(color=color)
+                ).add_to(mapa)
+
+# Mostrar el mapa y la leyenda en Streamlit en dos columnas
+col1, col2, col3 = st.columns(3)
+with col1:
+    folium_static(mapa, width=800, height=600)
+with col3:
+    # Zonas ordenadas de forma descendente
+    for zona, direcciones_zona in sorted(zonas.items(), reverse=False):
+        st.markdown(
+                f"<div style='background-color:gray;'><span style='color:black;font-weight:bold;font-size:18px'>**ZONA"
+                f" {zona + 1}:**</span></div>",
+                unsafe_allow_html=True
+                )
+        for direccion in direcciones_zona:
+            st.markdown(f"<div>- {direccion}</div>", unsafe_allow_html=True)
+        st.markdown("<hr>", unsafe_allow_html=True)

ML/geocoding/doctest.py

@@ -0,0 +1,5 @@
+import janitor
+import pandas as pd
+
+diabetes_df = pd.read_csv("https://nrvis.com/data/mldata/pima-indians-diabetes.csv", names=['pregnancies', 'glucose', 'blood_pressurte', 'skin_thinckness', 'insulin', 'bmi', 'diabetes_pedigree_function', 'age', 'outcome'])
+print(diabetes_df.info())

ML/geocoding/mapa.html

@@ -0,0 +1,393 @@
+<!DOCTYPE html>
+<html>
+<head>
+    
+    <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
+    
+        <script>
+            L_NO_TOUCH = false;
+            L_DISABLE_3D = false;
+        </script>
+    
+    <style>html, body {width: 100%;height: 100%;margin: 0;padding: 0;}</style>
+    <style>#map {position:absolute;top:0;bottom:0;right:0;left:0;}</style>
+    <script src="https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.js"></script>
+    <script src="https://code.jquery.com/jquery-1.12.4.min.js"></script>
+    <script src="https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/js/bootstrap.bundle.min.js"></script>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js"></script>
+    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.css"/>
+    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/css/bootstrap.min.css"/>
+    <link rel="stylesheet" href="https://netdna.bootstrapcdn.com/bootstrap/3.0.0/css/bootstrap.min.css"/>
+    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@fortawesome/fontawesome-free@6.2.0/css/all.min.css"/>
+    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css"/>
+    <link rel="stylesheet" href="https://cdn.jsdelivr.net/gh/python-visualization/folium/folium/templates/leaflet.awesome.rotate.min.css"/>
+    
+            <meta name="viewport" content="width=device-width,
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+            <style>
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+                    position: relative;
+                    width: 100.0%;
+                    height: 100.0%;
+                    left: 0.0%;
+                    top: 0.0%;
+                }
+                .leaflet-container { font-size: 1rem; }
+            </style>
+        
+</head>
+<body>
+    
+    
+            <div class="folium-map" id="map_a1f3ad7074dad87fc12508baa541fc65" ></div>
+        
+</body>
+<script>
+    
+    
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+                {
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+                    zoom: 15,
+                    zoomControl: true,
+                    preferCanvas: false,
+                }
+            );
+
+            
+
+        
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+            marker_b93dbbfe159140629cbf332f09d29fb3.setIcon(icon_f3eff6f0f41d10a6d2f8b923fdd1ef19);
+        
+    
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+                popup_a842edfd2578ea5cc15a45349fe70352.setContent(html_5952226ea8ba9fb25833269dddf881d7);
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+            marker_5fb01bff2859eb66072623e7ad9770bd.setIcon(icon_6c7815e67ee4acfcbdfe1f852f3834a2);
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+        
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+                popup_d7de584cc05fc008402fa0c6f109995d.setContent(html_29546a4b2f178012f1a69ad8339ef433);
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+        
+    
+            var icon_b01aa13d507a66bc9c2635134a99fa5f = L.AwesomeMarkers.icon(
+                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "green", "prefix": "glyphicon"}
+            );
+            marker_42ca96bf651128ed0c4cd9b5123810e0.setIcon(icon_b01aa13d507a66bc9c2635134a99fa5f);
+        
+    
+        var popup_e72e5359945fbb012812026e5c1ba4c2 = L.popup({"maxWidth": "100%"});
+
+        
+            
+                var html_da8d62fbe3e6f3578064566e4a1cff1b = $(`<div id="html_da8d62fbe3e6f3578064566e4a1cff1b" style="width: 100.0%; height: 100.0%;">Zona: 2</div>`)[0];
+                popup_e72e5359945fbb012812026e5c1ba4c2.setContent(html_da8d62fbe3e6f3578064566e4a1cff1b);
+            
+        
+
+        marker_42ca96bf651128ed0c4cd9b5123810e0.bindPopup(popup_e72e5359945fbb012812026e5c1ba4c2)
+        ;
+
+        
+    
+    
+            var marker_5dabd99b029782f8b9cd78ac26d62093 = L.marker(
+                [-31.234152, -64.4582696],
+                {}
+            ).addTo(map_a1f3ad7074dad87fc12508baa541fc65);
+        
+    
+            var icon_877898bc86e5c023c519334e6433aed4 = L.AwesomeMarkers.icon(
+                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "blue", "prefix": "glyphicon"}
+            );
+            marker_5dabd99b029782f8b9cd78ac26d62093.setIcon(icon_877898bc86e5c023c519334e6433aed4);
+        
+    
+        var popup_f4424169d52f5561070611bccf7ec318 = L.popup({"maxWidth": "100%"});
+
+        
+            
+                var html_9ceeb9fa4dd64455bee680a9253ce92e = $(`<div id="html_9ceeb9fa4dd64455bee680a9253ce92e" style="width: 100.0%; height: 100.0%;">Zona: 1</div>`)[0];
+                popup_f4424169d52f5561070611bccf7ec318.setContent(html_9ceeb9fa4dd64455bee680a9253ce92e);
+            
+        
+
+        marker_5dabd99b029782f8b9cd78ac26d62093.bindPopup(popup_f4424169d52f5561070611bccf7ec318)
+        ;
+
+        
+    
+    
+            var marker_c2f9f86bff3637c2e4c7c432201c3296 = L.marker(
+                [-31.234662, -64.4575523],
+                {}
+            ).addTo(map_a1f3ad7074dad87fc12508baa541fc65);
+        
+    
+            var icon_1b34080c3954afb91023ae98403ccbc2 = L.AwesomeMarkers.icon(
+                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "blue", "prefix": "glyphicon"}
+            );
+            marker_c2f9f86bff3637c2e4c7c432201c3296.setIcon(icon_1b34080c3954afb91023ae98403ccbc2);
+        
+    
+        var popup_273c92c917e5b50ca34588b1aaec4851 = L.popup({"maxWidth": "100%"});
+
+        
+            
+                var html_be5f558e220bb41bf1ae1aa7efd96f7f = $(`<div id="html_be5f558e220bb41bf1ae1aa7efd96f7f" style="width: 100.0%; height: 100.0%;">Zona: 1</div>`)[0];
+                popup_273c92c917e5b50ca34588b1aaec4851.setContent(html_be5f558e220bb41bf1ae1aa7efd96f7f);
+            
+        
+
+        marker_c2f9f86bff3637c2e4c7c432201c3296.bindPopup(popup_273c92c917e5b50ca34588b1aaec4851)
+        ;
+
+        
+    
+</script>
+</html>

ML/geocoding/modelo.py

@@ -0,0 +1,137 @@
+import random
+import os
+import requests
+from dotenv import load_dotenv
+import folium
+import streamlit as st
+import joblib
+from sklearn.cluster import KMeans
+import numpy as np
+import matplotlib.pyplot as plt
+
+load_dotenv()
+
+API_KEY = os.getenv('API_KEY')
+BASE_URL = os.getenv('BASE_URL')
+
+def geocode_address(address):
+    # Aquí debes insertar tu clave de API de Google Maps
+    api_key = API_KEY
+
+    # URL base de la API de Geocodificación de Google Maps
+    base_url = BASE_URL
+
+    # Parámetros de la solicitud
+    params = {
+        'address': address,
+        'key': api_key
+    }
+
+    try:
+        # Realizar la solicitud a la API de Geocodificación
+        response = requests.get(base_url, params=params)
+        data = response.json()
+
+        if data['status'] == 'OK' and len(data['results']) > 0:
+            # Obtener las coordenadas geográficas de la primera coincidencia
+            location = data['results'][0]['geometry']['location']
+            latitude = location['lat']
+            longitude = location['lng']
+
+            return latitude, longitude
+        else:
+            print('No se encontraron resultados para la dirección especificada.')
+    except requests.exceptions.RequestException as e:
+        print('Error en la solicitud:', e)
+
+
+# Direcciones ficticias en Cosquín, Córdoba, Argentina
+direcciones = [
+    'San Martín 123',
+    'Tucumán 456',
+    'Catamarca 789',
+    'Perón 1321',
+    'Corrientes 654',
+    'Laurencena 877',
+    'San Martín 234',
+    'Tucumán 567',
+    'Catamarca 890',
+    'San Martin 1432',
+    'Avenida parana 10',
+    'Japon 23'
+]
+
+# Obtener las coordenadas geográficas de cada dirección
+coordenadas = []
+for direccion in direcciones:
+    resultado = geocode_address(direccion + ', Cosquín, Córdoba, Argentina')
+    if resultado:
+        coordenadas.append(resultado)
+    else:
+        coordenadas.append((None, None))
+
+# Calcular el número de zonas (k) utilizando el método del codo
+max_k = len(coordenadas)
+inertias = []
+for k in range(2, max_k+1):
+    modelo = KMeans(n_clusters=k, random_state=42)
+    modelo.fit(coordenadas)
+    inertias.append(modelo.inertia_)
+
+# Graficar las inercias en función de k
+plt.plot(range(2, max_k+1), inertias, marker='o')
+plt.xlabel('Número de zonas (k)')
+plt.ylabel('Inercia')
+plt.title('Método del Codo')
+plt.show()
+
+# Elegir el valor de k óptimo
+k_optimo = int(input("Ingrese el valor óptimo de k según el gráfico: "))
+
+# Crear el modelo de K-Means con k_optimo zonas
+modelo = KMeans(n_clusters=k_optimo, random_state=42)
+modelo.fit(coordenadas)
+
+# Obtener las etiquetas de las zonas
+etiquetas = modelo.labels_
+
+# Contar el número de zonas
+num_zonas = len(set(etiquetas))
+
+# Crear un diccionario para almacenar las direcciones por zona
+zonas = {}
+for i, etiqueta in enumerate(etiquetas):
+    if etiqueta not in zonas:
+        zonas[etiqueta] = []
+    zonas[etiqueta].append(direcciones[i])
+
+# Mostrar las direcciones por zona
+for zona, direcciones_zona in zonas.items():
+    print(f"Zona {zona + 1}:")
+    for direccion in direcciones_zona:
+        print(direccion)
+    print()
+
+# Lista de colores predefinidos
+colores = ['red', 'blue', 'green', 'purple', 'orange', 'gray', 'pink',
+           'cyan', 'yellow', 'brown', 'black', 'white', 'violet']
+
+# Crear un mapa centrado en la primera coordenada
+primer_coordenada = coordenadas[0]
+mapa = folium.Map(location=primer_coordenada, zoom_start=15)
+
+# Agregar marcadores para cada coordenada con su zona y color correspondiente
+for i, coordenada in enumerate(coordenadas):
+    zona = etiquetas[i] + 1  # Sumar 1 para que las zonas se muestren como números a partir de 1
+    if coordenada != (None, None):
+        color = colores[zona % len(colores)]  # Obtener el color correspondiente a la zona
+        folium.Marker(location=coordenada, popup=f'Zona: {zona}', icon=folium.Icon(color=color)).add_to(mapa)
+    else:
+        folium.Marker(location=coordenada, popup='Ubicación desconocida').add_to(mapa)
+
+# Guardar el mapa como un archivo HTML
+mapa.save('mapa.html')
+
+# Abrir el archivo HTML en el navegador para visualizar el mapa
+import webbrowser
+webbrowser.open('mapa.html')

ML/geocoding/requirements.txt

@@ -0,0 +1,65 @@
+altair==4.2.2
+args==0.1.0
+attrs==23.1.0
+blinker==1.6.2
+branca==0.6.0
+cachetools==5.3.0
+certifi==2023.5.7
+charset-normalizer==3.1.0
+click==8.1.3
+clint==0.5.1
+contourpy==1.0.7
+coverage==7.2.5
+cycler==0.11.0
+decorator==5.1.1
+entrypoints==0.4
+folium==0.14.0
+fonttools==4.39.4
+gitdb==4.0.10
+GitPython==3.1.31
+idna==3.4
+importlib-metadata==6.6.0
+Jinja2==3.1.2
+joblib==1.2.0
+jsonschema==4.17.3
+kiwisolver==1.4.4
+mamba==0.11.2
+markdown-it-py==2.2.0
+MarkupSafe==2.1.2
+matplotlib==3.7.1
+mdurl==0.1.2
+numpy==1.24.3
+packaging==23.1
+pandas==2.0.1
+Pillow==9.5.0
+plotly==5.14.1
+protobuf==3.20.3
+pyarrow==12.0.0
+pydeck==0.8.1b0
+Pygments==2.15.1
+Pympler==1.0.1
+pyparsing==3.0.9
+pyrsistent==0.19.3
+python-dateutil==2.8.2
+python-dotenv==1.0.0
+pytz==2023.3
+requests==2.31.0
+rich==13.3.5
+scikit-learn==1.2.2
+scipy==1.10.1
+six==1.16.0
+smmap==5.0.0
+streamlit==1.22.0
+streamlit-folium==0.11.1
+tenacity==8.2.2
+threadpoolctl==3.1.0
+toml==0.10.2
+toolz==0.12.0
+tornado==6.3.2
+typing_extensions==4.6.0
+tzdata==2023.3
+tzlocal==5.0.1
+urllib3==2.0.2
+validators==0.20.0
+watchdog==3.0.0
+zipp==3.15.0