The provided text appears to be a detailed transcript or script of a video or presentation. In this video, the speaker discusses a project related to Seoul City's bike demand prediction. Here is a concise summary:
The speaker introduces the project, which focuses on predicting bike demand in Seoul City. They mention being inspired by Korean dramas and express plans to work on projects related to other cities in future videos. The speaker provides an overview of the dataset, including its columns and data types. They then walk through data preprocessing steps, including handling missing data and converting categorical variables.
Next, the speaker conducts exploratory data analysis (EDA) by visualizing aspects like monthly bike demand, daily demand, and hourly demand. They also explore data skewness, correlation, and variance inflation factors. The speaker splits the data into training and testing sets, standardizes it, and applies linear regression and K-nearest neighbor models for prediction.
Finally, the speaker evaluates the models, saves them using pickle, and discusses the results, highlighting the K-nearest neighbor model as having the highest accuracy.
Please let me know if you need further details or have specific questions about this project.
Here are the key facts extracted from the text:
1. The project is about bike demand prediction in Seoul City.
2. The dataset contains 8760 rows and 14 columns.
3. The columns include date, rented bike count, hours, temperature, humidity, wind speed, visibility, dew point temperature, solar radiation, rainfall, snowfall, seasons, holiday, and functioning day.
4. The data is in a comma-separated value (CSV) format.
5. The dataset contains 9000 data points.
6. The data includes information about the date, time, year, month, and day of the week.
7. The dataset has been cleaned and preprocessed using pandas and NumPy.
8. The data has been visualized using seaborn and matplotlib.
9. The project uses a linear regression model to predict bike demand.
10. The model has been trained and tested using the train_test_split function from scikit-learn.
11. The model's performance has been evaluated using metrics such as mean squared error, mean absolute error, and R-squared.
12. The project also uses a random forest regression model to predict bike demand.
13. The random forest model has been trained and tested using the same data as the linear regression model.
14. The project uses a web application to allow users to input data and receive predictions.
15. The web application uses a pickle file to load the trained model and make predictions.
16. The project is open-source and can be found on GitHub.
17. The project's author is available to answer questions and provide support through the YouTube comment section.