Trajectory data from taxis can be use to predict the final destination of a journey. This is a valuable application of the Machine Learning Regression Project, as it can be used to improve traffic flow and reduce congestion. To train a machine learning model to predict the final destination from taxi trajectory data, we first need to collect a dataset of taxi trajectories. This dataset can be collected from a city’s transportation authority or from a private taxi company. Once we have this dataset, we can then use it to train a machine learning model.
What will you Learn in the Machine Learning Regression Project?
- You will learn about data loading and data pre-processing using sklearn libraries.
- You will learn to visualize data using libraries like Seaborn, Matplotlib, and Folium.
- You will learn to build models using machine learning algorithms like Random forest regressor and multi-out regressor.
- You will learn parameter optimization using grid search CV.
- You will learn about the model evaluation techniques using the confusion matrix, accuracy score, and recall score.
- Jupyter Notebook
- Scikit learn
- Matplotlib,Seaborn and folium
As part of this project, we will be performing the following tasks:
Task-1: Data loading and data checking for the null values and outliers, data visualization using techniques like boxplot, and scatterplot, handling the categorical values, and data pre-processing
Task 2: Splitting the data for training and testing.
Task-3: Model building using ensemble methods like random forest regressor.(Machine learning course – Topic name – Concept of the ensemble)
Task-4: Using the k-fold validation technique to fit the model in 3 splits to check for overfitting.(k fold validation)
Task-5: this Model evaluation s calculating the MSE and R2 score, and checking the accuracy of the model.(Model evaluation techniques like Man squared Error and R2 score)