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    For Windows:
            pip install pandas
            pip install numpy
            pip install matplotlib
            pip install scipy

    For Unix/MacOS:
            If you do not have pip installed already, use these commands to install it first:
                    sudo apt-get install python3-pip (for Unix)
                    sudo easy_install pip (for MacOS)
            Then run these commands:
                    pip3 install pandas
                    pip3 install numpy
                    pip3 install matplotlib
                    pip3 install scipy

    In the first part, we analyzed city-wise air traffic in India. Using the provided datasets, 
    we processed the data using pandas and performed various calculations and visualizations.
    
    The following insights were generated:
    
            Top 7 Cities with the most air traffic: We identified and displayed the top 7 cities in India 
            that experience the highest air traffic volume.
            
            Busiest Air Route in a month: We determined the busiest air route in terms of the 
            number of flights during a particular month.
            
            City-wise Arrival and Departures in Major Cities: We analyzed and visualized the 
            arrival and departure patterns in major cities, providing valuable insights into their air traffic dynamics.
            
    After successfully analyzing the domestic air traffic, we extended our analysis to international datasets. 
    By adapting the existing code, we obtained similar visualizations and insights for international cities.

    In this part, we focused on analyzing flight delays. 
    We explored datasets containing information on airline delays and airport delays. 
    Using pandas, we processed the data and calculated the percentage of delays for airlines and airports.
    By visualizing this information, we gained insights into the performance of airlines and airports in terms of flight delays.

    The growth analysis aimed to understand the increase in air passengers over time. 
    We analyzed the number of passengers traveling by air each year and plotted a graph illustrating the yearly growth trend. 
    
    Additionally, we calculated the rate of increase or decrease in air passengers on an annual basis.
    
    To further extend the analysis, we used linear regression to predict the number of passengers in upcoming years. 
    This predictive model utilized historical data to forecast future passenger numbers, providing 
    valuable insights into the expected growth of air travel.

    NOTE: You can download the dataset file for 'Growth Analysis' from this link https://data.world/makeovermonday/2021w16