The Kathmandu Durbar Square holds the palaces of the Malla and Shah kings who ruled over the city. Along with these palaces, the square surrounds quadrangles revealing courtyards and temples.
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We were discussing about the project report and thus far this is all that happened .
We’ve completed the visualization aspect of our research. These visualizations depict the connections between the predictor variables and the outcome variable using the test data. Here, obesity and inactivity serve as the predictor variables, while diabetes is the outcome variable. We’ve created plots to represent the relationships between obesity and diabetes, as well as inactivity and diabetes. Furthermore, I’ve determined the R^2 and mean squared error (MSE) values. An R^2 value of 0.395 suggests that approximately 39.5% of the variability in the diabetes data can be explained by the predictor variables. R^2 values can range between 0 and 1, with values closer to 1 implying a better model fit. Meanwhile, the MSE value stands at -0.400063, which measures the average squared difference between the estimated and observed values. A lower MSE typically indicates a better model, although its scale is dependent on the nature of the outcome variable.
Project 1
We finished our project ON police shooting analysis on Different scenarios.
we used different variables like Temperature, population and crime rate to run correlation check on the Police shooting data.
conclusion: Population has major impact on it and temperature doesn’t have and crime is little bit related.
Nikhil: Dataset gathering, word file and Helping in codes
Sowmya: data visualtion and writing code
27th nov
Today, I dedicated significant time and effort to my work with the crime incident report dataset. Its from the boston analyse dataset It’s been an ongoing project, and I’m currently in the process of developing a robust code to extract specific data related to individual crime from this extensive dataset spanning fewyears.
One of the exciting aspects of my work is the visualization part. I’m actively working on creating code that will allow me to plot graphs using latitude and longitude data parameters. These visualizations will provide a dynamic view of how crime incidents are distributed across the city of Boston. It’s a challenging task, but it promises to reveal valuable insights into crime patterns and hotspots within the city.
24th nov
I was actively engaged in analyzing a criminal dataset. Initially, we divided the dataset into a training set and a test set to facilitate our analysis. During this phase, I calculated various statistics, including crime rates, for each geographical region and identified key features and target variables for a predictive model.
Subsequently, we leveraged the test set to make predictions related to crime rates, employing a machine learning algorithm tailored for this purpose. Our script seamlessly integrated data visualization tools, such as matplotlib, to create informative plots and charts that showcased the relationships and trends within the criminal data.
22nd nov
Analyzing crime data through geospatial analysis is an essential approach for understanding and addressing crime-related challenges in various regions. This technique involves the examination and visualization of crime data on maps, harnessing technologies such as geographic information systems (GIS), satellite imagery, and GPS data. It enables law enforcement agencies, policymakers, and researchers to gain valuable insights, identify patterns, and make informed decisions to enhance public safety.
Crime data geospatial analysis plays a pivotal role in crime prevention and law enforcement. By plotting crime incidents on maps and analyzing their spatial distribution, law enforcement agencies can allocate resources more effectively. They can identify crime hotspots and deploy officers to areas with higher crime rates, thereby increasing the presence of law enforcement where it is most needed.
Moreover, geospatial analysis of crime data is invaluable for identifying trends and patterns in criminal behavior. It can reveal recurring crime patterns, modus operandi of criminals, and correlations between certain environmental factors and crime rates. This information is instrumental in developing proactive crime prevention strategies.
In addition to law enforcement, urban planners and city officials can use geospatial analysis to design safer neighborhoods. By understanding the spatial dynamics of crime, they can make urban planning decisions that contribute to crime reduction, such as improved street lighting, better public transportation, and the strategic placement of public facilities.
Furthermore, researchers and policymakers can use geospatial analysis to evaluate the effectiveness of crime prevention programs and policies. By mapping crime data over time, they can assess the impact of interventions and adjust strategies as needed.
In conclusion, geospatial analysis of crime data is a powerful tool that enhances our understanding of crime patterns, aids in resource allocation, and supports evidence-based decision-making in the realm of public safety and crime prevention. It is a valuable asset for law enforcement, urban planning, and policy development aimed at creating safer communities.
20th nov
Seasonal Autoregressive Integrated Moving Average (SARIMA) is an advanced model used for time series forecasting, building upon the ARIMA framework by incorporating seasonality. SARIMA consists of three main components: seasonal autoregressive (SAR), seasonal differencing (Seasonal I), and seasonal moving average (SMA). This combination allows for the modeling and prediction of time series data that exhibit recurring patterns over specific time intervals. SARIMA is particularly valuable in scenarios where seasonality plays a significant role in shaping data trends, such as in retail sales, climate patterns, or economic indicators. By addressing both non-seasonal and seasonal dynamics, SARIMA enhances the accuracy of forecasts, making it a versatile tool for analysts and researchers across various domains.
17th nov
Time series analysis, a foundational component of data science, focuses on examining data points recorded in sequence, offering valuable insights across various fields like economics and meteorology. This method is essential for predicting future trends based on historical data, allowing us to extract meaningful statistics, identify patterns, and make forecasts. Key concepts within this domain include trend analysis, which helps us spot long-term trends, seasonality for identifying patterns that repeat regularly, noise separation to isolate random variations, and stationarity, assuming consistent statistical properties over time.
Techniques like descriptive analysis provide visual insights, moving averages help smooth short-term fluctuations and emphasize longer-term trends, and ARIMA models aid in forecasting. Time series analysis plays a vital role in predicting market trends, improving weather forecasts, and supporting strategic business planning. As the field evolves, machine learning methods such as Random Forests and Neural Networks are increasingly integrated, offering robust solutions for complex time series forecasting challenges.
nov 13th
In today’s class, we delved into the fascinating field of time series analysis, which is a branch of advanced statistics that helps us understand how phenomena evolve over time. We explored the mysteries hidden within sequences of data points and learned how to predict future trends by using past data. We also discussed essential tools like moving averages and autoregressive models, which are like magical techniques that aid in understanding how numerical values change over time.
Recognizing patterns in things like weather patterns or stock market trends is just as important in time series analysis as the mathematical aspects. Our ability to spot trends, seasonal variations, and irregularities within data is incredibly valuable for making informed decisions. This skill can be seen as a superpower in the world of data science because it empowers us to forecast and plan for the future using historical data. Today’s lesson went beyond mathematics and highlighted the practical application of numerical insights in understanding the world around us.
10th nov
Logistic regression is a statistical method specifically designed for binary classification tasks, where it predicts the likelihood of an input belonging to one of two classes. It accomplishes this by using the logistic function to transform the result of a linear equation into a probability range spanning from 0 to 1. Logistic regression finds applications across various domains such as healthcare, marketing, and finance. It is used for purposes like predicting diseases, analyzing customer churn, and assessing credit risk.
The core concept of logistic regression revolves around assuming a linear relationship between the independent variables and the natural logarithm of the odds of the dependent variable. During model training, the goal is to determine the optimal coefficients, typically achieved through methods like Maximum Likelihood Estimation. The decision boundary generated by the model separates instances into their respective classes, and common performance metrics include accuracy, precision, recall, and the ROC curve.
Logistic regression’s appeal lies in its simplicity, interpretability, and effectiveness when dealing with data that can be linearly separated. These qualities have made it a widely used and enduringly popular technique in the realm of predictive modeling.
30th oct
In summary, to comprehensively understand police shootings, we can analyze victim demographics, map incident locations, study temporal trends, assess police training and policies, gather community perspectives, and conduct legal and policy analysis. These dimensions provide a holistic view of the complex factors contributing to this issue. Beyond age, we can delve into the demographics of the victims, including gender, ethnicity, and socioeconomic factors. This can help us identify disparities and potential biases in police shootings