 Paid Project , Project ,

# Taxi Trajectory and Predicting Final Destination

In this project, we are going to build a model that can predict the final destination of the taxi based on its trajectories. Hint videos, Q&A and step-by-step solution will also be available.
Course
Curriculum 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?

2. You will learn to visualize data using libraries like Seaborn, Matplotlib, and Folium.
3. You will learn to build models using machine learning algorithms like Random forest regressor and multi-out regressor.
4. You will learn parameter optimization using grid search CV.
5. You will learn about the model evaluation techniques using the confusion matrix, accuracy score, and recall score.

#### Tools Used

1. Jupyter Notebook
2. NumPy
3. Pandas
4. Scikit learn
5. 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)

## Course Content

Lesson Content
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#### Skills you will develop

Exploratory data analysis

Data pre-processing

Data visualization

Model evaluation using confusion matrix, accuracy score

Ensemble methods of machine learning

Parameter optimization

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