Building a Linear Regression Model for Bike Rental Prediction in Python
In this blog post, we will walk through the process of building a linear regression model in Python to predict the number of bike rentals based on weather conditions. We will use historical data to train our model and evaluate its performance using key metrics such as R-squared and mean absolute error.
Importing Data and Understanding the Dataset:
To start, we import the necessary libraries, such as Pandas, and load the dataset into a data frame called ‘bikes’. We then explore the structure and summary statistics of the data to gain insights into its characteristics.
Exploring Relationships Between Variables:
We investigate the relationships between weather features (temperature, humidity, and wind speed) and the number of bike rentals using scatter plots. The visualizations reveal the nature of the relationships, showing positive and negative linear associations between weather conditions and bike rentals.
Building and Evaluating the Linear Regression Model:
Before constructing the machine learning model, we split the data into training and test sets. We then separate the dependent variable ‘rentals’ from the independent variables. Using the LinearRegression class from scikit-learn, we train the model on the training data and obtain coefficients for each feature.
Evaluating Model Performance:
We calculate the coefficient of determination (R-squared) to assess how well the model explains the variability in the test data. Additionally, we compute the mean absolute error to quantify the average deviation between predicted and actual rental values.
Example Code:
Here is a snippet of Python code demonstrating the steps involved in building and evaluating the linear regression model:
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error
# Load data
bikes = pd.read_csv('bike_rental_data.csv')
# Split data into X (independent variables) and Y (dependent variable)
Y = bikes['rentals']
X = bikes.drop('rentals', axis=1)
# Split data into training and test sets
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42)
# Build and train the linear regression model
model = LinearRegression()
model.fit(X_train, Y_train)
# Evaluate the model
R_squared = model.score(X_test, Y_test)
Y_pred = model.predict(X_test)
MAE = mean_absolute_error(Y_test, Y_pred)
print(f"R-squared: {R_squared}")
print(f"Mean Absolute Error: {MAE}")
Conclusion:
By following the steps outlined in this blog post, you can develop a linear regression model in Python to predict bike rentals based on weather conditions. Understanding the relationships between variables and evaluating the model’s performance using metrics like R-squared and mean absolute error are essential for building accurate predictive models in machine learning.
By leveraging the power of linear regression and evaluating model performance effectively, you can create robust predictive models that provide valuable insights for decision-making in various domains. Experimenting with different features and refining the model can further enhance its predictive capabilities, leading to more accurate forecasts of bike rentals based on weather conditions.