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.

What will you Learn in the Project?

  1. You will learn about data loading and data pre-processing using sklearn libraries.
  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

Tasks Performed

As part of this project we will be performing following tasks:

Task-1: Data loading and data checking for the null values, outliers, data visualization using techniques like boxplot, 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 ensemble)

Task-4: Using k fold validation technique to fit model in 3 splits, to check for overfitting.(k fold validation)

Task-5: Model evaluation, calculating the MSE and R2 score and checking the accuracy of the model.(Model evaluation techniques like Man squared Error and R2 score)

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Skills you will develop

Scikit learn

Exploratory data analysis


Matplotlib, Seaborn and folium

Data pre-processing

Data visualization

Model evaluation

Confusion matrix

Accuracy score

Ensemble methods

Parameter optimization

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