(Please wait for 12-15 seconds for app to load initially)

A Kaggle competition turned project, this project is divided into 3 parts :

1. Exploratory Data Analysis - check here
   
2. Data Preparation - check here
   
3. Modelling - check here
   

Bike sharing systems are a means of renting bicycles where the process of obtaining membership, rental, and bike return is automated via a network of kiosk locations throughout a city. Using these systems, people are able rent a bike from a one location and return it to a different place on an as-needed basis.

You need to have the following dependecies before running the project:

• pandas pip install pandas
• numpy pip install numpy
• scipy pip install scipy
• scikit learn pip install scikit-learn
• streamlit pip install streamlit
• matplotlib pip install matplotlib
• seaborn pip install seaborn

Use the package manager to install Bike Sharing Demand Prediction project

You can clone the repo :

gitclone https://github.com/SarthakRana/Bike-Sharing-Demand-Prediction.git

GitHub CLI :

gh repo clone SarthakRana/Bike-Sharing-Demand-Prediction

You can also download the ZIP of this project and place on your working directory.

1. Install all dependencies mentioned in Prerequisites.
2. Open CLI/prompt and make sure Streamlit is installed by running the command streamlit --version. You should see something like this : Streamlit, version 0.67.1.
3. Do this for all other dependencies as well just to make sure everything is in right place and you are good to go.
4. Go to your working directory(where you have placed the .py file and other components) and open CLI/prompt there.
5. Type in the following command and press Enter :
   streamlit run app.py
   Please wait for 5-10 seconds for command to run.
6. A browser widow should open up with the app running.
7. Enjoy :)

1. Install all dependencies mentioned in Prerequisites.
2. Place the contents of project folder in your working directory.
3. Simply open Jupyter Notebooks/Jupyter Lab and run the .ipynb files.
4. All project related files like models, scalers and encoders will be saved in the same directory as you run the files.

For deployment on Heroku, we need to make 3 extra files.

Create a procfile and copy the below code :

web: sh setup.sh && streamlit run app.py

Run the below command to prepare the requirements.txt

pip freeze > requirements.txt

NOTE : I personally don't use the freeeze command as it creates some version conflicts while deploying on Heroku. I prefer writing doing the required packages name in the txt file. Heroku automatically makes use of the latest package version available while deploying.

Create a file setup.sh and copy the below code:

mkdir -p ~/.streamlit/
echo "\
[general]\n\
email = \"[email protected]\"\n\
" > ~/.streamlit/credentials.toml
echo "\
[server]\n\
headless = true\n\
enableCORS=false\n\
port = $PORT\n\
" > ~/.streamlit/config.toml

NOTE : To deploy on Heroku, you can either use Heroku CLI or Heroku Dashboard. You can follow the rest of the steps from here : https://www.youtube.com/watch?v=IWWu9M-aisA

Below are some screenshots from the project web app:

See the open issues for a list of proposed features (and known issues)(if any). If your issue is not listed in the already open issues, you can open up a new one.

• Add don't know option for Type of day field in Web app and code its functionality.
• Give option to skip Temperature, Humidity and Windspeed fields. In this case, we impute values from previous data.

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project.
2. Create your Feature Branch.
3. Commit your Changes.
4. Push to the Branch.
5. Open a Pull Request.

NOTE : Your name will be added here if I merge your pull request.

Sarthak Rana (https://www.linkedin.com/in/sarthakrana/)