diff --git a/EDA Recomendation system.ipynb b/EDA Recomendation system.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Recommendation 1: Sydney - Score: 0\n",
+ "Recommendation 2: ibis Sydney Darling Harbour - Score: 0\n",
+ "Recommendation 3: Novotel Sydney on Darling Harbour - Score: 0\n",
+ "Recommendation 4: Oaks Goldsbrough Apartments - Score: 0\n",
+ "Recommendation 5: The Little Snail Restaurant - Score: 0\n"
+ ]
+ }
+ ],
+ "source": [
+ "import json\n",
+ "\n",
+ "# Load data from the JSON file\n",
+ "with open('sydney.json') as f:\n",
+ " data = json.load(f)\n",
+ "\n",
+ "# Extract the relevant information for analysis\n",
+ "results = data['results']\n",
+ "for result in results:\n",
+ " name = result['name']\n",
+ " location = result['geometry']['location']\n",
+ " latitude = location['lat']\n",
+ " longitude = location['lng']\n",
+ " # You can extract and process more information as needed for recommendation\n",
+ "\n",
+ "# Implement a scoring mechanism based on specific criteria\n",
+ "# Define weights for different parameters and calculate a score for each location\n",
+ "\n",
+ "# Example scoring function\n",
+ "def calculate_score(latitude, longitude):\n",
+ " # Define your scoring logic here\n",
+ " score = 0\n",
+ " # Add or subtract points based on specific parameters\n",
+ " # Consider factors such as population density, income levels, competitor proximity, etc.\n",
+ " return score\n",
+ "\n",
+ "# Iterate through the locations and calculate scores\n",
+ "scores = []\n",
+ "for result in results:\n",
+ " latitude = result['geometry']['location']['lat']\n",
+ " longitude = result['geometry']['location']['lng']\n",
+ " score = calculate_score(latitude, longitude)\n",
+ " scores.append((result, score))\n",
+ "\n",
+ "# Sort locations based on scores\n",
+ "sorted_scores = sorted(scores, key=lambda x: x[1], reverse=True)\n",
+ "\n",
+ "# Print the top recommendations\n",
+ "num_recommendations = 5 # Number of recommendations to display\n",
+ "for i in range(num_recommendations):\n",
+ " print(f\"Recommendation {i + 1}: {sorted_scores[i][0]['name']} - Score: {sorted_scores[i][1]}\")\n"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-10-29T10:11:35.245875400Z",
+ "start_time": "2023-10-29T10:11:35.173413600Z"
+ }
+ },
+ "id": "8e740d9ea8cd3a8b"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Collecting folium\n",
+ " Downloading folium-0.14.0-py2.py3-none-any.whl (102 kB)\n",
+ "Requirement already satisfied: jinja2>=2.9 in c:\\users\\vedant\\anaconda3\\lib\\site-packages (from folium) (3.1.2)\n",
+ "Requirement already satisfied: numpy in c:\\users\\vedant\\anaconda3\\lib\\site-packages (from folium) (1.23.5)\n",
+ "Collecting branca>=0.6.0\n",
+ " Downloading branca-0.6.0-py3-none-any.whl (24 kB)\n",
+ "Requirement already satisfied: requests in c:\\users\\vedant\\anaconda3\\lib\\site-packages (from folium) (2.27.1)\n",
+ "Requirement already satisfied: MarkupSafe>=2.0 in c:\\users\\vedant\\anaconda3\\lib\\site-packages (from jinja2>=2.9->folium) (2.0.1)\n",
+ "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\vedant\\anaconda3\\lib\\site-packages (from requests->folium) (3.3)\n",
+ "Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\users\\vedant\\anaconda3\\lib\\site-packages (from requests->folium) (2.0.4)\n",
+ "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\vedant\\anaconda3\\lib\\site-packages (from requests->folium) (1.26.17)\n",
+ "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\vedant\\anaconda3\\lib\\site-packages (from requests->folium) (2021.10.8)\n",
+ "Installing collected packages: branca, folium\n",
+ "Successfully installed branca-0.6.0 folium-0.14.0\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "WARNING: Ignoring invalid distribution -orch (c:\\users\\vedant\\anaconda3\\lib\\site-packages)\n",
+ "WARNING: Ignoring invalid distribution -orch (c:\\users\\vedant\\anaconda3\\lib\\site-packages)\n",
+ "WARNING: Ignoring invalid distribution -orch (c:\\users\\vedant\\anaconda3\\lib\\site-packages)\n",
+ "WARNING: Ignoring invalid distribution -orch (c:\\users\\vedant\\anaconda3\\lib\\site-packages)\n",
+ "WARNING: Ignoring invalid distribution -orch (c:\\users\\vedant\\anaconda3\\lib\\site-packages)\n",
+ "WARNING: Ignoring invalid distribution -orch (c:\\users\\vedant\\anaconda3\\lib\\site-packages)\n",
+ "WARNING: Ignoring invalid distribution -orch (c:\\users\\vedant\\anaconda3\\lib\\site-packages)\n",
+ "WARNING: Ignoring invalid distribution -orch (c:\\users\\vedant\\anaconda3\\lib\\site-packages)\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install folium"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-10-29T10:12:49.227890400Z",
+ "start_time": "2023-10-29T10:12:43.169190900Z"
+ }
+ },
+ "id": "235bc7c50c4b1409"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Top Recommendation: Adina Apartment Hotel Sydney Darling Harbour\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": "True"
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import json\n",
+ "import folium\n",
+ "from folium.plugins import HeatMap\n",
+ "\n",
+ "# Load data from the JSON file\n",
+ "with open('sydney.json') as f:\n",
+ " data = json.load(f)\n",
+ "\n",
+ "# Extract the relevant location data\n",
+ "locations = []\n",
+ "for result in data['results']:\n",
+ " location = result['geometry']['location']\n",
+ " name = result['name']\n",
+ " score = len(name) # Example scoring based on the length of the name\n",
+ " locations.append([location['lat'], location['lng'], score])\n",
+ "\n",
+ "# Create a base map\n",
+ "m = folium.Map(location=[-33.8688197, 151.2092955], zoom_start=12)\n",
+ "\n",
+ "# Create a heatmap layer\n",
+ "HeatMap(locations).add_to(m)\n",
+ "\n",
+ "# Save the map to an HTML file\n",
+ "m.save('heatmap.html')\n",
+ "\n",
+ "# Recommendation logic\n",
+ "# Sort locations based on the score\n",
+ "sorted_locations = sorted(locations, key=lambda x: x[2], reverse=True)\n",
+ "\n",
+ "# Display the top recommendation\n",
+ "top_recommendation = sorted_locations[0]\n",
+ "print(f\"Top Recommendation: {data['results'][locations.index(top_recommendation)]['name']}\")\n",
+ "\n",
+ "# Open the generated heatmap in a web browser\n",
+ "import webbrowser\n",
+ "webbrowser.open('heatmap.html')\n"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-10-29T10:17:58.221049600Z",
+ "start_time": "2023-10-29T10:17:57.905657300Z"
+ }
+ },
+ "id": "f5863d524b3f7bbd"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": [],
+ "metadata": {
+ "collapsed": false
+ },
+ "id": "980ad5fe495f8a23"
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/README.md b/README.md
index 877fb545..baf2cf83 100644
--- a/README.md
+++ b/README.md
@@ -1,32 +1,34 @@
-# Code-with-Google-Maps-2023 - Hack2skill
+#### Team Name - Spatial Sage
+#### Problem Statement - Geospatial Analytics and Business Intelligence
+#### Team Leader Email - vedantpangudwale@gmail.com
-Welcome to the official repository for the Code-with-Google-Maps-2023 organized by Hack2skill!
-
-## Getting Started
-
-To get started with the Code-with-Google-Maps-2023 repository, follow these steps:
-
-### Submission Instruction:
- 1. Fork this repository
- 2. Create a folder with your Team Name
- 3. Upload all the code and necessary files in the created folder
- 4. Upload a **README.md** file in your folder with the below mentioned informations.
- 5. Generate a Pull Request with your Team Name. (Example: submission-XYZ_team)
+### A Brief of the Prototype:
+ SpatialSage is our advanced tool that uses Google Maps to show where customers are most active. It helps businesses decide where to open stores and plan their marketing. By using real-time data from Google Maps, businesses get up-to-date insights to make informed decisions. SpatialSage is a user-friendly and intuitive tool that doesn't require advanced technical knowledge. Businesses of all sizes can easily integrate it into their decision-making processes. By leveraging real-time data from Google Maps, SpatialSage ensures that the information businesses rely on is always up-to-date and accurate. The tool provides actionable insights that help organizations optimize their operations and increase their chances of success. It offers a user-friendly platform for making informed decisions related to store openings, marketing strategies, and customer engagement. By visualizing customer activity and traffic patterns, Spatial Sage empowers businesses to thrive in an ever-changing marketplace. GOOGLE APIs: Maps JavaScript API: Embeds interactive Google Maps on the web tool, visualizing customer activity heatmaps and overlaying business data for user interaction and analysis. Places API: Retrieves surrounding places (e.g., cafes, competitors) near potential store locations, offering a comprehensive view of the local business landscape.
-### README.md must consist of the following information:
+BUSINESS LOGIC: Data Collection: Fetching real-time Google Maps data and integrate with business metrics for timely insights. Data Integration: Validate and merge datasets, converting addresses to coordinates with the Geocoding API. Analysis & Modeling: Use algorithms to identify high-activity areas, assess competitors, and predict business potential. Visualization & Interaction: Overlay insights on an interactive map using the Maps JavaScript API. Recommendation Generation: Rank potential locations based on activity, competition, and accessibility, providing actionable marketing strategies. Feedback & Refinement: Iteratively improve the tool based on user feedback and market changes. Deployment & Monitoring: Launch on a web platform, monitor performance, and optimize API usage.
-#### Team Name -
-#### Problem Statement -
-#### Team Leader Email -
+
-### A Brief of the Prototype:
- This section must include UML Diagrams and prototype description
### Tech Stack:
- List Down all technologies used to Build the prototype
+ HTML, CSS, Javascript, Python, Google Maps Places & Javascript API
### Step-by-Step Code Execution Instructions:
- This Section must contain a set of instructions required to clone and run the prototype so that it can be tested and deeply analyzed
-
+Clone the Repository:
+
+Open your terminal or command prompt.
+Run the following command to clone the repository to your local machine:
+git clone https://github.com/shubhambhardwaj7558/Spatial_Sage.git
+
+Navigate to the Project Directory:
+Change your current directory to the project's root folder:
+cd repository-name
+
+Install Dependencies:
+Check if your project has any dependencies, and if so, make sure to install them. You can typically find dependency information in a package.json file.
+
+Run the Prototype
+
### Future Scope:
- Write about the scalability and futuristic aspects of the prototype developed
+This prototype's future scope revolves around scalability and adaptability to handle growing data volumes, offering multi-platform support, integrating third-party services, and embracing advanced technologies like AI. It envisions enhancing user experiences, expanding globally, and promoting data-driven decision-making. Simultaneously, this prototype holds the potential to transform industries through innovative location-based services, personalized interactions, and the sustainability to meet evolving business and consumer needs. It promises to revolutionize how businesses utilize geographical data, positioning itself as a pivotal player in location-based technology's ever-evolving landscape. We will use Geocoding API that converts business addresses to map coordinates for accurate visualization, and vice-versa, ensuring seamless integration of business data on maps, Distance Matrix API which will help in accessibility of potential store locations by providing travel times/distances from key points, aiding in location evaluation and Geolocation API allpwing us to pinpoints users' current location, allowing field agents or businesses to access real-time insights about specific regions they're in.
+
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The best place to start a business is ........
+
+
+
+
WHERE DATA MEETS LOCATION
+
Navigate Your Business to Success
+
+
+
+
+
+
Coffee Shops
+
Enhance your coffee shop's success with our footfall heatmap data. We'll help you
+ discover the busiest times, optimize store layouts, and find the perfect locations for new
+ branches, ensuring that every cup of coffee is served in the heart of your customers' daily
+ routines.
+
+
+
+
+
+
CAFES & BARS
+
Elevate your cafe or bar's performance with our footfall heatmap insights. We
+ empower you to pinpoint peak hours, strategically seat patrons, and identify thriving
+ neighborhoods for expansion, ensuring every sip and conversation thrives in high-traffic
+ locales.
+
+
+
+
+
+
RESTAURANTS
+
Revamp your restaurant's success with our footfall heatmap analysis. We empower you
+ to discover busy dining times, optimize table arrangements, and pinpoint ideal locations for new
+ establishments, ensuring every meal is served where the crowds gather.
+
+
+
+
+
+
SHOPPING MALLS
+
Elevate your shopping mall's appeal with our footfall heatmap solutions. We provide
+ valuable insights into customer traffic patterns, helping you attract premium tenants, refine
+ rent strategies, and create an exceptional shopping experience in high-traffic areas.
+
+
+
+
+
+
REAL ESTATES
+
Transform real estate decisions with our footfall heatmap data. We enable
+ developers and agents to pinpoint prime locations for new properties, optimize infrastructure
+ planning, and make informed investment choices based on customer density and trends.