- Data Collection:
Importing libraries
Dataset Loading
About the dataset
- Data Cleaning:
Finding Duplicate values
Finding Missing/Null values
Understanding your variables
Handling Outliers
-
Data Wrangling and Feature Engineering
-
Exploratory Data Analysis(EDA):
Univariate Analysis
Bivariate Analysis
- Data Preprocessing:
Checking distribution of each feature and trensform it to normal distribution
Checking relationship between independent and dependent variables is linear
Checking multicollinearity in independent variables
Features encoding
Data Splitting
Data Scaling
- Supervise Machine learning algorithms and implementation:
Linear Regression
Lasso Regression
Ridge Regression
ElasticNet Regression
Decision Tree
Random Forest
XGBoost
7.Model Evaluation
- Model Explainability
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.
Attribute Information:
Date : year-month-day
Rented Bike count - Count of bikes rented at each hour
Hour - Hour of the day
Temperature-Temperature in Celsius
Humidity - %
Windspeed - m/s
Visibility - 10m
Dew point temperature - Celsius
Solar radiation - MJ/m2
Rainfall - mm
Snowfall - cm
Seasons - Winter, Spring, Summer, Autumn
Holiday - Holiday/No holiday
Functional Day - NoFunc(Non Functional Hours), Fun(Functional hours)
Observations :
Customers equally favour renting motorcycles in all seasons.
Customers choose to rent motorcycles when there are no holidays. They rarely use the bikes while traveling on vacation.
Nearly all consumers preferred to rent bikes during functional hours.
Bicycle rentals are popular throughout the month.
Renting bicycles was not very popular in 2017, but it increased by 83.02 percent in 2018.
Customers do not prefer to use rented bikes at night.
Customers do not prefer rented bikes in the early mornings hours of 4 and 5, but the usage increases from 7, 8, and 9, possibly due to people commuting to work. The same trend is observed in the evenings of 5, 6, and 7, as people travel from the work to home. Overall, the rented bike was the most frequently used during office commute times.
Customers mostly use rented bikes for transportation in the evening.
Observations :
Customers prefer to use rented bikes when the temperature is normal, but they do not use them when the temperature is below normal.
Obsevations :
Customers who travel most commonly use rented bikes in the morning at 8 a.m. and in the evening at 6 p.m.
People prefer to rent bikes when the humidity level is between 10% and 18%.
Customers consistently use rented bikes when the wind speed is between 2 m/s and 3.5 m/s, and usage is at its highest when the wind speed is normal, which is 3.2 m/s.
Renting a bike is the best option for customers when the dew point temperature ranges from 12°C to 18°C. Usage of rented bikes increases with increasing dew point temperatures, but it still peaks at normal dew point temperatures.
According to the graph, solar radiation has no effect on customer use of rented bikes.
People prefer to rent bikes the most when it is not raining.
When there is no snowfall, most people choose to rent bikes. However, the majority of customers prefer to rent bikes when it snows up to 4 cm.
In the first 10 days of the month, most rented bikes are used by customers. Customers consistently use rented bikes in the last 15 days of the month.
June has the highest usage of rented bikes throughout the year, followed by October. Customers usage of rented bikes is at its peak from April to September.
Observation :
The count of rented bikes on that day is unaffected by the day's visibility, but when visibility exceeds 1750, use of rented bikes increases more than usual.
Observations :
Most people rent bikes during the summer and autumn seasons. Fewer people choose to rent bikes during the winter.
People rent bikes even when there is no holiday, and usage on non-holiday days is higher than on holidays.
Almost every rented bike is used during its functional hours.
The use of rented bikes increased by three times in 2018 compared to 2017.
Customers equally prefer renting motorcycles in all seasons.
Customers choose to rent motorcycles when there are no holidays. They rarely use the bikes they rent while traveling on vacation.
Nearly all consumers prefer to rent bikes during functional hours.
Bicycle rentals are popular throughout the month.
Renting bicycles was not very popular in 2017, but it increased by 83.02 percent in 2018.
Customers do not prefer to use rented bikes at night.
Customers do not prefer rented bikes in the early mornings hours of 4 and 5, but uasage increases from 7, 8, and 9, possibly due to people commuting to work. The same trend is observed in the evenings 5, 6, and 7, as people travel from work to home. Overall, the rented bike was the most frequently used during office commute times.
Customers mostly use rented bikes for transportation in the evening.
Customers who travel most commonly use rented bikes in the morning at 8 a.m. and in the evening at 6 p.m.
Customers prefer to rent bikes When the humidity level is between 10% and 18%.
Customers consistently use rented bikes when the wind speed is between 2 m/s and 3.5 m/s, and usage is at its highest when wind speed is normal, which is 3.2 m/s.
Renting a bike is the best option for customers when the dew point temperature ranges from 12°C to 18°C. Usage of rented bike increases with increases with increasing dew point temperatures, but it still peaks at normal dew point temperatures.
According to the graph, solar radiation has no effect on customer use of rented bikes.
People prefer to rent bikes the most when it is not raining.
When there is no snowfall, most people choose to rent bikes. However, the majority of customers prefer to rent bikes when it snows up to 4 cm.
In the first 10 days of the month, most rented bikes are used by customers. Customers consistently use rented bikes in the last 15 days of the month.
June has the highest usage of rented bikes throughout the year, followed by October. Customers usage of rent bikes is at its peak from April to September.
The count of rented bikes on a day is not affected by the day's visibility. However, when visibility exceeds 1750, use of rented bikes increases more than usual.
Most people rent bikes during the summer and autumn seasons, while fewer people choose to rent bikes during the winter.
People rent bikes even when there is no holiday, and usage on non-holiday days is higher than on holiday.
Almost every rented bike is used during its functional hours.
The use of rented bikes increased by three times in 2018 compared to 2017.
The XGBoost regression model has the highest R-squared score, the lowest Root Mean Squared Error (RMSE), and has mean of residuals that are very close to zero.
Therefore, due to the model's high accuracy, low error, and near-zero mean of residuals, the XGBoost regression model is the ideal and well-trained model for forecasting the number of rented bikes required per hour.
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