Answer / Loveleen Kaur

To work towards building a Random Forest, you can follow these steps:
1. Data Preprocessing: Clean and preprocess the data by handling missing values, encoding categorical variables, and scaling numerical features.
2. Train Decision Trees: Build individual decision trees from randomly selected subsets of the training data using a technique called bootstrap aggregating (or bagging).
3. Combine Decision Trees: Aggregate the individual decision trees to form a Random Forest by averaging their predictions or choosing the tree with the highest vote for each outcome.
4. Evaluate Performance: Assess the performance of the Random Forest using appropriate metrics such as accuracy, precision, recall, and F1 score.

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