What will you Learn in the 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 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)