AlmaBetter Verfied Project
Currently Rental bikes are introduced in many urban cities for the enhancement of mobility comfort. It is important to make the rental bike available and accessible to the public at the right time as it lessens the waiting time. Eventually, providing the city with a stable supply of rental bikes becomes a major concern. The crucial part is the prediction of bike count required at each hour for the stable supply of rental bikes.
The dataset contains weather information (Temperature, Humidity, Windspeed, Visibility, Dewpoint, Solar radiation, Snowfall, Rainfall), the number of bikes rented per hour and date information.
● Exploratory Data Analysis (EDA): In this part we have done some EDA on the features to see the trend.
● Data Processing: In this part we went through each attributes and encoded the categorical features.
● Model Creation: Finally in this part we created the various models. These various models are being analysed and we tried to study various models so as to get the best performing model for our project.
This Project includes 1 colab notebook and 1 Pdf of presentation.
Bike Sharing Demand Prediction - Includes Exploratory Data Analysis and all algorithms which are used in this project.
Bike Sharing Demand prediction.pdf - Includes pdf of the presentation of the project.
Google Colab - All the outputs are visible in the provided colab notebook.
The order of execution of the colab notebook is as follows:
1) Bike_Sharing_Demand_Prediction_Capstone_Project.ipynb
First, click on the open in colab button present on the top centre of the notebook.
In this .ipynb file, we have -
• EDA on Bike sharing demand prediction.
• Hypothesis.
• Fitting different models and cross validate them.
2) Kaggle Dataset
Downlaod the dataset from kaggle through provided link.Then, connect to the runtime and execute the cell to mount the drive or upload the data file to the current runtime.
3) Cell Path
Finally, delete the path in the dataset loading cell and replace it with the path of your current data file. Run each cell to see the output below it.
- Linear Regression
- Lasso and Ridge Regression
- Decision tree
- Random Forest
- Gradient Boosting
● When we compare the root mean squared error and mean absolute error of all the models, the random forest regression model has less root mean squared error and mean absolute error, ending with the R-squared of 99% . So, finally this model is best for predicting the bike rental count on daily basis.
● For all the models, temperature or hour was ranked as the most influential variable to predict the rental bike demand at each hour.
Soni Rani | Vivek Kumar | Suraj Singh
Random Forest Regressor - https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html Gradient Boosting Documentation - https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html