GitHub

• Link: https://github.com/Seavleu/Amazon-Fine-Food-Review-Sentiment-Analysis/blob/main/Amazon_Fine__Food_Review.ipynb
• Demo:

Linear Regression

Assumption:

• Linearity: does our data follow the pattern?

, independance, normality, nomoskedasticity

Predicting the output from the continous data

Key takeaway: Residual/Error |yi - yi^|: sum the error and try to decrease them

MAE: sum all the error then divided how many data point we have to get the average MSE RMSE

R^2-coeff of determination RSS TSS

Dataset

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.

Variable Information

• Date : year-month-day
• Rented Bike count - Count of bikes rented at each hour
• Hour - Hour of he day
• Temperature-Temperature in Celsius