The basic idea of analyzing the Zomato dataset is to get a fair idea about the factors affecting the establishment of different types of restaurants at different places in Bengaluru. This Zomato data aims at analyzing demography of the location. Most importantly it will help new restaurants in deciding their theme, menus, cuisine, cost, etc for a particular location. It also aims at finding similarity between neighborhoods of Bengaluru on the basis of food.

Observations on the following are made:

1. Top restaurant chains in Bangaluru
2. How does the price differ between restaurants that accept online orders and those that don’t?
3. How many restaurants offer table reservations compared to those that do not?
4. Types of restaurants available
5. Top-rated restaurants
6. Restaurants located at various locations around Bangalore
7. Approximate cost for 2 people
8. How does the vote on restaurants accepting online orders compare to those refusing to accept them?
9. In what restaurant does the most costly rate for two people exist? What is the dish involved? The most popular dish to eat there?
10. Top ten most expensive and cheapest restaurants, based on an estimate for two people
11. Restaurants under 500 (budget hotels)
12. Budget-friendly restaurants with rating >4
13. Overall number of restaurants that have ratings >4 and are under budget (less than 500)
14. Hotels at various locations with affordable rates
15. Foodie’s hotspots
16. Heatmap of North Indian and South Indian restaurants
17. Chains with the most popularity for casual dining
18. Favorite dishes in various cuisines represented by a word cloud

The dataset contains 17 columns as shown below:

• url – url of the restaurant in the zomato website
• address – address of the restaurant in Bengaluru
• name – name of the restaurant
• online_order – whether online ordering is available in the restaurant or not
• book_table – table booking option available or not
• rate – overall rating of the restaurant out of 5
• votes – total number of rating for the restaurant as of the above mentioned date
• phone – phone number of the restaurant
• location – neighborhood in which the restaurant is located
• rest_type – restaurant type
• dish_liked – dishes people liked in the restaurant
• cuisines – food styles, separated by comma
• approx_cost(for two people) – approximate cost of meal for two people
• reviews_list – list of tuples containing reviews for the restaurant
• menu_item – list of menus available in the restaurant
• listed_in(type) – type of meal
• listed_in(city) – neighborhood in which the restaurant is listed

Work flow of process:

1. Data Collection
2. Data Cleaning
3. Performing EDA
4. Performing Geospatial Analysis
5. Performing Sentiment Analysis

• The Dataset “ZOMATO BANGALORE RESTAURANTS” is publicly available on Kaggle website with 51,717 records and 17 attributes as shown under the dataset section.

• This is an essential step to perform before creating a visualization.
• Clean, consistent data will be much easier to visualize.
• As a result, missing values are filled, data are filtered accordingly, and inappropriate data are removed.

• There are different types of charts Bar, Pie, Line, Scatter Plot, Column chart etc. which can visually present the data in a more understandable way.
• Below bar chart shows the most famous restaurant chains in Bangalore with number of outlets.

• The following pie chart shows the percentage of online orders accepted by restaurants.

• The below figure represents the bar chart for different types of restaurants.

• Bar graph of different varieties of cuisines in Bangalore.

• Below scatter plot with X axis denotes the ratings of the restaurants and Y axis denotes the approximate cost for 2 people.

• Box plot depicting the price difference between restaurants that accept online orders and those that do not

• Geospatial Analysis is useful for locating the geographical area in a particular region.

• For locating the restaurants in geographical map, we need latitudes, longitudes and count of restaurants.
• Extract the “Latitude” and “Longitude” w.r.t. different Locations using Python’s Geopy library.
• Generate a “BaseMap” of Bangalore using Python’s Folium library.

• Plot a HeatMap based on variety of use cases with the help of Python’s Folium “HeatMap” Plugins.
• The heatmap below depicts the clutter of restaurants in Bengaluru.

• Here are the Wordclouds developed using the built-in function in python called “WordCloud” for 9 different types of restaurants where customers left feedback.
• To generate the below pictured wordclouds using Python, feedbacks are preprocessed, null values are dropped and all characters and spaces are removed except alphabets.

• Jupyter Notebook is used as IDE.
• Among the Python libraries, Pandas and NumPy are used for handling data, preprocessing, and mathematical operations, respectively.
• Plotly, Seaborn, and Matplotlib are used for visualizing plots.

For more details, please go through the Jupyter Notebook attached above.

• Cafe Coffee Day dominates the restaurant chain landscape followed by Onesta and then Empire.
• Online orders are accepted by 64.4% of restaurants, whereas 35.6% of restaurants do not accept them.
• The city of Bangalore is known as a high-tech hub of India, and people who live a busy and modern life are inclined to choose Quick Bites.
• The most common cuisines are North Indian, Chinese, and South Indian. Bangalore is therefore influenced more by the cultures of the north than those of the south.
• Having reviewed the above scatterplot, we can conclude that most of the highest-rated restaurants accept online orders and are budget-friendly as well.
• In the box plot, it can be seen that there is a discrepancy between the median number of votes for both categories. The Zomato application gives customers the option to rate restaurants after they’ve ordered through it. This will lead to more votes for the restaurants accepting online orders.
• The majority of the restaurants are priced under 1000, which means they are affordable and few are luxurious.
• The most no. of eateries are found in BTM, HSR, and Koranmangala 5th block. BTM dominates the section by having more than 4000 restaurants.
• It is evident that eateries are primarily located in the central Bangalore region. As we get farther from the center of the city, the number of restaurants decreases. Therefore, prospective restaurateurs can consult this to identify suitable places for their business.

Check out the notebook above to learn more

• Space-Truckers: The Video Game
  • About the game
  • How to Play
    • Menus
    • Route Planning
    • Driving
  • Building the Application from Source
  • Concepts
  • Getting Help and Providing Feedback
  • Conceptual sketches

A game of getting stuff from Point A to Point B… IN SPAAAACCE!

Space-Truckers is an OSS project intended to demonstrate key concepts of integrating the BabylonJS WebGL/WebGPU framework into a web-based interactive application.

Gameplay in Space-Truckers is divided into three distinct phases: planning, driving, and scoring.

In the planning phase, your simulated cargo container (a.k.a. your trailer) starts in orbit around one of the system’s planets. The overall goal is to plan a course that will take the cargo pod to its’ destination – or at least close enough to intersect the destination planet’s retrieval systems, but you won’t have the benefit of being able to make course changes once you’ve launched on your journey – say a prayer to Sir Isaac Newton, because it is the gravitational forces of the star and its’ attendant planets that will bend and alter the ballistic path of your cargo post-launch!

Before launch though, you’ll be able to specify the precise direction, force, and timing of your cargo so you can line up the perfect route. Better routes are ones that have a higher potential score. The potential score is determined by a number of factors, including the length of the route (longer routes have more opportunity to gain score, but risk losing even more points in time penalties), the amount of time in transit, average speed, and more.

During planning, the simulation can be reset as many times as needed – that’s why it’s a simulation, after all! When you’ve launched on a successful route, you’ll have the option to either accept the route or reset and try again. Accepting the route takes you to the next game phase, where you’ll ride along with your cargo in your Space-Tractor, helping to nudge and guide it through a series of challenges encountered along the route.

Once it’s all said and done, your potential score will be displayed along with the actual score earned from the driving phase. Maybe you’ll make the leaderboards someday!

Although Space-Truckers is built to run in any browser capable of using WebGL and related JavaScript API’s, there are a few more requirements involved if you want to build the application and game from source code. You’ll need:

• NodeJS v14+
• NPM to match

Once you’ve cloned the source to your local machine, you should run an npm install to fetch and install needed dependencies. The /dist folder will contain the output of running npm run build, but for local development the npm run start command will run the webpack dev-server, which allows for module hot swapping and reloading, greatly speeding up the time between making a change and seeing it reflected in a browser!

Design docs and sketchs are located in the /design/ folder.

• Game design doc
• Technical specs doc

There are a number of different ways to get assistance with an issue you may encounter. Have a question about the game? Head over to the discussion boards and post your question there among the various topics available, or create your own.

If you encounter a bug or issue with the game or application you can create an Issue to help us track it, or add a comment to an existing issue that might help us understand the problem better.

Thanks for participating!

• Space-Truckers: The Video Game
  • About the game
  • How to Play
    • Menus
    • Route Planning
    • Driving
  • Building the Application from Source
  • Concepts
  • Getting Help and Providing Feedback
  • Conceptual sketches

A game of getting stuff from Point A to Point B… IN SPAAAACCE!

Space-Truckers is an OSS project intended to demonstrate key concepts of integrating the BabylonJS WebGL/WebGPU framework into a web-based interactive application.

Gameplay in Space-Truckers is divided into three distinct phases: planning, driving, and scoring.

In the planning phase, your simulated cargo container (a.k.a. your trailer) starts in orbit around one of the system’s planets.
The overall goal is to plan a course that will take the cargo pod to its’ destination – or at least close enough to intersect the destination
planet’s retrieval systems, but you won’t have the benefit of being able to make course changes once you’ve launched on your journey – say a prayer to
Sir Isaac Newton, because it is the gravitational forces of the star and its’ attendant planets that will bend and alter the ballistic path of your cargo post-launch!

Before launch though, you’ll be able to specify the precise direction, force, and timing of your cargo so you can line up the perfect route. Better routes are ones that have a higher potential score. The potential score is determined by a number of factors, including the length of the route (longer routes have more opportunity to gain score, but risk losing even more points in time penalties), the amount of time in transit, average speed, and more.

During planning, the simulation can be reset as many times as needed – that’s why it’s a simulation, after all! When you’ve launched on a successful route, you’ll have the option to either accept the route or reset and try again. Accepting the route takes you to the next game phase, where you’ll ride along with your cargo in your Space-Tractor, helping to nudge and guide it through a series of challenges encountered along the route.

Once it’s all said and done, your potential score will be displayed along with the actual score earned from the driving phase. Maybe you’ll make the leaderboards someday!

Although Space-Truckers is built to run in any browser capable of using WebGL and related JavaScript API’s, there are a few more requirements involved if you want to build the application and game from source code. You’ll need:

• NodeJS v14+
• NPM to match

Once you’ve cloned the source to your local machine, you should run an npm install to fetch and install needed dependencies. The /dist folder will contain the output of running npm run build, but for local development the npm run start command will run the webpack dev-server, which allows for module hot swapping and reloading, greatly speeding up the time between making a change and seeing it reflected in a browser!

Design docs and sketchs are located in the /design/ folder.

• Game design doc
• Technical specs doc

There are a number of different ways to get assistance with an issue you may encounter. Have a question about the game? Head over to the discussion boards and post your question there among the various topics available, or create your own.

If you encounter a bug or issue with the game or application you can create an Issue to help us track it, or add a comment to an existing issue that might help us understand the problem better.

Thanks for participating!

insta-extract is a command line application that scrapes instagram information.

[!] Instagram sometimes updates how its data is accessed, this script may be outdated.

Python must be installed.

# Clone project
git clone https://github.com/JavideSs/insta-extract.git
cd insta-extract

# Run insta-extract
python main.py -h

• User info:

python main.py -u <user_to_scraping>

If any other option has been used and you want to display the user information as well use the -i option:
python main.py -u <user_to_scraping> -i

To download the profile picture use the -dp option:
python main.py -u <user_to_scraping> -dp

• Login:

Required in some options according to limitations:
python main.py -l <user> <passw>

Logout:
python main.py -ld

• Posts info:

At index counting from the last post as 0:
python main.py -u <user_to_scraping> -p 1

Of all posts:
python main.py -l <user> <passw> -u <user_to_scraping> -p

To download the posts found use the -dp option:
python main.py -l <user> <passw> -u <user_to_scraping> -p -dp

• Followings usernames:

python main.py -l <user> <passw> -u <user_to_scraping> -f1 <file1.txt>

• Followers usernames:

python main.py -l <user> <passw> -u <user_to_scraping> -f2 <file2.txt>

• Compare usernames:

python main.py -c <file1.txt> <file2.txt>

Multiple options can be specified at the same time.
Example to know followings not followers and vice versa:
python main.py -l <user> <passw> -ld -u <user_to_scraping> -f1 <file1.txt> -f2 <file2.txt> -c <file1.txt> <file2.txt>

When you login with the -l option the session is saved in the usersession file, it will be used for the following extractions. So it is not necessary to use the option while the file exists.

If the account is private and you have not logged in or are not following him, you can only get user info.

The instagram api limits unlogged users to:

• The option of followings and followers will not be available.
• Post information will have a limit of a range of the last 12 posts.

Instagram blocks this script after many requests, be careful.

Username comparisons should be as the output format of the followings and followers options.

• Python (3.10) >= 3.6.
• Requests (2.32.3).

Your feedback is most welcomed by filling a
new issue.

Author:
Javier Mellado Sánchez
2021, 2023, 2025

TrackMania United Transformation Pack (TMUTP) is an unofficial visual enhancement modification for TrackMania United Forever (TMU). Its goal is to modernize the game’s graphics, bringing them closer to today’s standards.

• Updated Environment Textures

Each environment’s textures (except for Snow, Island, and Coast) have been replaced or modified to include textures from TrackMania² environments. This effectively gives TMU a fresh and modern look, going beyond what Nadeo envisioned originally.

Before	After

Before	After

Before	After

• Updated Menu Textures

All menu textures have been updated or replaced with the menu textures from Maniaplanet 3.

Before	After

• Updated HUD

The HUD now features the vignette overlay from TrackMania² & the removal of the level name background.

Before	After

• Updated Sounds

In addition to new menu, engine, surface, impact, and environment sounds, a few audio files have been replaced with their higher quality versions found in TrackMania². For environmental, surface, and impact sounds, I did my best to pick the sounds that most closely resembled the original ones found in TMU. The updated sounds are more detailed and improve feedback.

Overwrite the GameData directory in your TMU installation location with the GameData folder found in the archive.

Play the game and look at all the new changes.

Verify your game files through Steam.

All Rights Reserved. Ubisoft, Ubi.com, Maniaplanet, the Maniaplanet logo, Nadeo, the Ubisoft logo, and the Nadeo logo are trademarks of Ubisoft Entertainment in the U.S. and/or other countries.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.