### Summary of RandomForest Implementation Template

#### Data Preparation
1. **Load Data**: Read the dataset from a CSV file into a DataFrame.
2. **Drop Unnecessary Columns**: Remove columns that are not useful for the analysis, such as 'EmployeeCount', 'EmployeeNumber', 'Over18', and 'StandardHours'.
3. **Identify Categorical Columns**: Determine which columns are categorical by checking if they are of object type and have 50 or fewer unique values.
4. **Encode Target Variable**: Convert the 'Attrition' column into numerical format using category codes.
5. **Label Encoding**: Apply label encoding to the identified categorical columns to convert them into numerical format.

#### Data Manipulation
6. **Split Data**: Divide the dataset into features (X) and target (y), then split these into training and testing sets using a 70-30 split.

#### Model Implementation
7. **Initialize RandomForest**: Create a RandomForestClassifier with 100 estimators.
8. **Train Model**: Fit the RandomForest model using the training data (X_train, y_train).

#### Evaluation
9. **Evaluate Model on Training Data**:
   - Predict the target variable using the training data.
   - Calculate and print the accuracy score.
   - Generate and display a classification report.
   - Display the confusion matrix.
10. **Evaluate Model on Testing Data**:
    - Predict the target variable using the testing data.
    - Calculate and print the accuracy score.
    - Generate and display a classification report.
    - Display the confusion matrix.

#### Main Execution
11. **Execute Workflow**:
    - Prepare the data by calling the data preparation function.
    - Split the data into training and testing sets.
    - Implement the RandomForest model.
    - Evaluate the model on both training and testing datasets to assess performance.