In my latest update, I’ll continue from where I stopped before. I’ll show how I used Python to include the ADF and KPSS tests in my project. I’ll also talk about what I learned from the results of these tests and what they mean for my analysis. Here’s a piece of the code I used in Python
Stationarity matters because it affects how good our predictions about the future can be. When data isn’t stationary, our predictions might not be very accurate. To check if data is stationary or not, we use two tests: the ADF test and the KPSS test.
The ADF test helps us figure out if the data is changing too much over time. If the result is smaller than a certain number, it means the data is probably okay for making predictions. On the other hand, the KPSS test checks if there’s a pattern or trend in the data. If the result is higher than a specific number, it means there might be a trend that could mess up our predictions.
We also use something called the Autocorrelation Function (ACF) alongside these tests. ACF helps us see how data points relate to each other over time. This helps us pick the right tools to make predictions, especially for models that rely on understanding how data behaved in the past to guess what might happen next.
I present to you the main conclusions of our Time Series Analysis of the BPDA economic indicators dataset. Upon analyzing the data using statistical approaches, a few noteworthy patterns and trends have surfaced. We’ve included graphic representations of the patterns we’ve seen with this release.
we’ve covered the basics of time series analysis, I’m prepared to discuss how I’m using it for our BPDA economic indicators project.
Finding patterns and trends in Boston’s economy throughout time is my main objective. I’m doing this by examining data points like hotel occupancy rates, employment rates, housing costs, and traffic at Logan Airport. We can see how these data points have changed over time and spot any reoccurring trends by looking at them in chronological order.
I’ve attached a little bit of Python code that shows how I applied time series analysis.
Time Series Analysis helps us see patterns and trends in information ordered by time. Knowing when things happen helps us understand cycles, like when people buy more or when certain activities peak. This is super important in fields like economics because it helps us make smarter choices. We use Time Series Analysis by looking at data over time to find trends (things going up or down), seasonal patterns, and unusual events. Then, we can predict future trends using math based on what happened before. It’s like guessing what might happen next based on what we’ve seen.
This analysis is used in many areas: predicting stock prices, forecasting the weather, and helping businesses plan sales and marketing strategies. It’s a great tool to understand how things behaved in the past and make guesses about the future.
Our next step is to use this method to see how Boston’s economy changed over time. We’ll study jobs, housing prices, travel habits, and more to figure out where the economy has been and where it might be going. Watch for our next update where we’ll show how Time Series Analysis works in action!
we finished Descriptive Statistics. This helps us get a good understanding of things like jobs, house prices, and how busy airports and hotels are in Boston.
Descriptive_StatisticsToday I have learned about decision tree is a common tool in machine learning used for sorting data (classification) and predicting numbers (regression). It works by dividing the data into different areas, each with its own label or predicted number. Imagine the decision tree like an actual tree. Each point where the tree splits represents a decision made based on a certain piece of information. The branches show what happens after each decision, and the ends of the branches give the final answer or category.In learning about decision trees, a few main ideas stand out. The root node is at the very top of the tree and picks the best information to start splitting the data. The internal nodes are the splitting points where decisions are made based on different pieces of data. The branches are like pathways that show the results of these decisions. Lastly, the leaf nodes are where the tree ends, giving the final answer.
The dataset contains economic information that the Boston Planning and Development Authority (BPDA) collected every month from January 2013 to December 2019. It shows the BPDA’s work in keeping an eye on and understanding key figures that help them make smart choices in city planning and development. The data covers different economic areas like jobs, housing, transportation, and property development, and needs more analysis to understand it better.
Today I Discussed on few things with my team members that needs to be done and giving the title for the project and making the report and finalised the codes and continued to work on the dataset.
