Author: rrasireddy

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_Statistics

Today 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.

Project2

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.

Today, our group got together to talk about our second project, which looks at data concerning police shootings in Washington. We reviewed our progress and the questions we’ve been exploring. I’ve been using a statistical technique called logistic regression, tweaking it to get clearer insights. I plan to use more techniques to dig deeper into the data. Our team is keen on uncovering helpful insights from these numbers, We’ll keep trying different approaches to really understand these critical incidents.

We have closely examined the distribution of police shooting incidents in clusters around the United States. We discovered some places with higher numbers of these episodes and determined where they occur more frequently using two distinct methods: K-Means and DBSCAN. DBSCAN identified particular regions and odd patterns, whereas K-Means provided us with a clear division of the instances. Combined, these techniques improved our understanding of the geographic locations and patterns of these occurrences, which is a useful place to start for anyone wishing to learn more about this problem or develop solutions.

I completed a thorough Linear Discriminant Analysis (LDA) on the provided dataset concerning fatal police shootings.Linear Discriminant Analysis (LDA) on our recent dataset involving fatal police shootings. The LDA process was methodically carried out, beginning with a careful selection of features that influence the outcome of such incidents. We preprocessed the data, normalizing and scaling where necessary to ensure uniformity and fairness in analysis. The core of LDA involved calculating eigenvalues and eigenvectors to determine the linear combinations that best discriminate between the classes. We then projected the data onto a new axis to maximize the separation between various groups. The visualization of the results provided us with a stark representation of the distinct categories within the data, reinforcing the discriminative power of LDA in pattern recognition and classification.