### Summary of Prophet Implementation Pipeline

1. **Data Preparation:**
   - Load the dataset from a specified file path using `pandas` to read a CSV file.

2. **Data Manipulation:**
   - Rename the first two columns of the dataset to 'ds' (date) and 'y' (value) to fit the Prophet model's requirements.

3. **Model Implementation:**
   - Initialize a Prophet model with a specified interval width (default is 0.8).
   - Fit the model to the processed data to prepare it for forecasting.

4. **Visualization:**
   - Plot residuals and their density to assess the model's fit.
   - Generate and display plots comparing actual vs. fitted values using Prophet's built-in plotting functions.

5. **Evaluation:**
   - Use the model to predict future values and compare them against a test dataset.
   - Plot the forecasted values alongside actual values to visually assess the model's performance.
   - Display the model's components, such as trend and seasonality.

6. **Adjusting Trend & Adding Changepoints:**
   - Implement a Prophet model with additional parameters for changepoints and seasonality to capture more complex patterns.
   - Plot the forecast with changepoints to visualize where significant changes in trend occur.

7. **Save Results:**
   - Save the fitted model to a specified output path, typically using a method like pickling for later use.

8. **Main Execution:**
   - Execute the entire pipeline by preparing data, transforming it, fitting the model, visualizing results, evaluating the model, and saving the results.
   - Split the data into training and testing sets (70% train, 30% test) for model evaluation.
   - Call the main function to run the complete process.