Month: December 2023

EXPLORING_THE_EVOLUTION^J_DISTRIBUTION_AND_TRENDS_OF_BUILDING_PERMITS

Today my group and I worked to the final parts of our project report. Below are the findings that I contributed to the report:

• Permit Distributions over time
  • From 2009 to 2010, there was a steep increase in the number of permits issued. There was also a drop in the number of permits from 2019 to 2020.
  • Maximum number of permits are issued in October.
  • When we check the permit trends by its status, the number of active permits is above 20000 since 2007. The trend of expire permits is between 10000 and 20000 with a downward trend since 2015 (although there are minor fluctuations where it increases).
  • Since 2010, the largest number of permits issued was for ‘short form building permit’ followed by ‘Electrical Permit’.
  • The highest frequency declared valuations is of the range $ -1000000 to 1000.
  • The correlation of Sq_feet and declared valuations fluctuate a lot, but the highest correlation was between 2017-2019
  • The Kruska-Wallis test shows that there is a statistically significant difference in permit durations between cities.

To analyze the data set I did the following:

• First i plotted a trend chart to show how the number of permits vary over time.
• Next, I created a chart to check the status of permits over time.
• After this I created a heatmap to explore the type of permits over time
• For the same thing I also drew up a line graph.
• I will be trying to see now if there is any correlation between certain categories of permits as time passes.

THE_TOLL_OF_POLICE_SHOOTINGS_IN_THE_UNITED_STATES

Today after completing the preprocessing of the Boston building permit data my group decided to answer certain questions based in the following.

• Distribution of Permit Types:
  • Question: What is the distribution of different types of permits in the dataset?
  • Analysis: Generate a bar chart or pie chart to visualize the frequency of each permit type.
• Temporal Trends in Permit Issuance:
  • Question: How does the number of permits vary over time?
  • Analysis: Create a time series plot or bar chart to observe monthly or yearly trends in permit issuance.
• Geographical Patterns:
  • Question: Are there any geographical patterns in the distribution of permits?
  • Analysis: Plot the permits on a map to identify clusters or patterns based on location

Today I learnt about Simple Exponential Smoothing:

• The most basic of the exponentially smoothed methods goes by the name of simple exponential smoothing (SES).
• It is best suited for predictive modeling tasks involving data that exhibits little to no discernible long-term trends or recurring patterns.

   

• The central concept behind this approach is to presume that future market trends will closely resemble recent historical patterns observed in demand data.
• In other words, the model will primarily rely on historical data to predict future levels of demand without accounting for any potential changes or shifts in consumer behavior or broader economic factors.

   

• Compared to simpler forecasting methods like naive or moving average models, the exponential smoothing model possesses certain benefits.:
  •  Exponential smoothing techniques require only three inputs to operate effectively: the latest forecast, the actual value from that time period, and a smoothing constant (or weighting factor) that determines how much importance is assigned to recent data points.
  •  By using an exponential smoothing method, we can generate forecasts for future time periods based on past performance. These forecasts are deemed accurate because they take into account any discrepancies between predicted and actual outcomes.
  • When applying smoothing techniques, we tend to give more emphasis to recent observations compared to earlier ones, making it simpler to identify patterns within the data. This approach allows us to ignore the inherent unpredictability of certain events, resulting in more reliable predictions.