Bike Rental Prediction :

Click here: Bike Rental Prediction.

Problem Statement

The objective of this Case is to Predication of bike rental count on daily based on the environmental and seasonal settings.

1. Exploratory Data Analysis

   • Loading the dataset and libraries
   • Data cleaning
   • Typecasting the attributes
   • Missing value analysis

2. Attributes distributions and trends

   • Monthly distribution of counts
   • Yearly distribution of counts
   • Outliers analysis

3. Normality test

4. Correlation matrix

5. Split the dataset into train and test dataset

6. Encoding the categorical features

7. Modelling the training dataset

   • Linear Regression Model
   • Decision Tree Regressor Model
   • Random Forest Model

8. Cross Validation Prediction

   • Linear Regression CV Prediction
   • Decision Tree Regressor CV Prediction
   • Random Forest CV Prediction

9. Model performance on test dataset

   • Linear Regression Prediction
   • Decision Tree Regressor Prediction
   • Random Forest Prediction

10. Model Evaluation Metrics

    • R-squared score
    • Root mean square error
    • Mean absolute error

11.Choosing best model for predicting bike rental count