Our current task involves deploying anomaly detection techniques on a dataset of economic indicators. This statistical approach is aimed at identifying unusual patterns that differ…
In our analysis, we focus on the property assessment data from Boston, delving into the specifics of property features and types. We scrutinize aspects such…
https://jaswanthsaivmth522.sites.umassd.edu/files/2023/12/MTH-Project-2.pdf
In our project, we explore the complexities of the real estate market in Boston, emphasizing the advanced modeling techniques we use to predict property values.…
During today’s lesson, the graph depicting median home prices was a key reference, clearly showing the market’s highs and lows, thereby shedding light on economic…
I determined the settings for a Seasonal AutoRegressive Integrated Moving Average (SARIMA) model to forecast the “hotel_avg_daily_rate” time series. This process involved acknowledging the data’s…
I intend to leverage line charts and a variety of visual tools to conduct a detailed analysis of how earnings growth rates vary over time…
Time series analysis is a vital method for analyzing data that changes over time. This approach includes several important aspects, such as identifying trends, recognizing…
The AutoRegressive Integrated Moving Average (ARIMA) model stands out as an effective technique for predicting trends in time series data, comprising three key elements. First,…
In today’s learning session, we covered the concept of time series data, which is essentially a series of data points recorded at regular and uniform…
In our last session, we delved into the engaging field of time series analysis, a significant statistical method that allows us to track how data…
https://jaswanthsaivmth522.sites.umassd.edu/files/2023/11/MTH-Project-2.pdf
Every data point comes with a timestamp, like a chapter in a larger story waiting to be told. Time series analysis is our tool to…
In the domain of data analysis, a pivotal step involves the organization of raw data into coherent categories. This process is instrumental in enhancing our…
The process of making decisions using classification involves a series of essential steps. It begins with a clear and precise definition of the problem at…
In the realm of data analysis, Principal Component Analysis (PCA) stands out as a frequently employed statistical method for dimensionality reduction. To ensure that all…
An indispensable statistical approach for assessing the significance of differences in the average values between two groups is the t-test. Its primary purpose is to…
We are expanding our research to explore long-term trends and regional variations in fatalities related to police encounters. Additionally, we’re investigating how these incidents intersect…
We’re expanding our investigation into incidents involving the police to include an analysis of weapon types and their role in shaping the perceived threat level.…
In today’s session, we examined the age profile of individuals who lost their lives in police-related incidents, utilizing the Washington Post dataset. This examination led…
Clustering, a key method in unsupervised machine learning, uncovers patterns in datasets that lack labels by grouping similar data entities based on their inherent characteristics.…
In the realm of geographic information, the terms geo locations and geo positions refer to slightly different concepts. Geolocation refers to identifying particular locations or…
Several factors play a role in determining the significance of variables in data analysis and machine learning. The relevance of a variable is closely tied…
Performed multiple regression using CDC diabetes data involves statistical techniques to analyze the relationship between multiple independent variables and the dependent variable (diabetes prevalence). Below…
Cross validation is a technique for assessing how the statistical analysis generalizes to an independent data set. It is a technique for evaluating machine learning…
Watched the videos of resampling methods Estimation of prediction error and validation set approach, K-fold cross validation, cross validation: right and wrong ways. Estimating the…
In today’s discussion, We considered Crab Molt model as an example when independent variables are non-normally distributed. The data contains both pre-molt and post-molt sizes…
In interaction model, I didn’t understand after calculating R square values for both linear and quadratic model. Based on R square values from different models…
In today’s discussion, we learned about the significance of p-values and the null hypothesis in the context of simple linear regression: The p-value measures how…
In today’s discussion learned Linear regression which is a statistical approach allows us study relationship between two independent variables. mathematically we can write the expression…