How to make predictive modeling? Task Selected: {please write the task you have selected, either “Response Time” or “Ambulance Demand”? Task Description: {in your own words, please describe in 2-3 sentences what you see this task requiring you, as the analyst, to do}. Final Predictive Value: {Please state, in your own words, what you are ultimately trying to predict in this tasking. Please be specific, as both tasks could be done in a variety of ways. For example, if predicting response time, then are you trying to predict response times for different jurisdictions? For different times of day? For different days of the week? Ultimately, what are the specific values you are trying to predict in the end? Factors Considering: {Please list, based on your initial thinking and review of some of the relevant readings (as well as others) what factors you think would influence the prediction of your specific values. For example, if doing ambulance demand, maybe it is day of the week, or time of day, or season, or… What are those factors you initial think would be relevant to predicting your final values? You can list as few or as many factors as you would like, but please take some time to consider all the factors that may influence your predictive values, and take a look at the initial data} ,Day of the week (M, T, W, Th, F, Sa, Sn), {Factor 2}. {…}
How to make predictive modeling?
Task Selected: {please write the task you have selected, either “Response Time” or “Ambulance Demand”? Task Description: {in your own words, please describe in 2-3 sentences what you see this task requiring you, as the analyst, to do}. Final Predictive Value: {Please state, in your own words, what you are ultimately trying to predict in this tasking. Please be specific, as both tasks could be done in a variety of ways. For example, if predicting response time, then are you trying to predict response times for different jurisdictions? For different times of day? For different days of the week? Ultimately, what are the specific values you are trying to predict in the end? Factors Considering: {Please list, based on your initial thinking and review of some of the relevant readings (as well as others) what factors you think would influence the prediction of your specific values. For example, if doing ambulance demand, maybe it is day of the week, or time of day, or season, or… What are those factors you initial think would be relevant to predicting your final values? You can list as few or as many factors as you would like, but please take some time to consider all the factors that may influence your predictive values, and take a look at the initial data} ,Day of the week (M, T, W, Th, F, Sa, Sn), {Factor 2}. {…}
Predictive modeling is a process in data science and machine learning where a statistical or machine learning model is created to make predictions or forecasts about future events or outcomes based on historical data and patterns. It involves using algorithms to analyze historical data, identify patterns or relationships within the data, and then using these patterns to make predictions about new, unseen data.
Here are the key steps involved in predictive modeling:
1. Data Collection: The first step is to gather relevant data, which includes historical records of the phenomenon you want to predict and any related variables or features that may influence the predictions. Data can come from a variety of sources, including databases, surveys, sensors, and more.
2. Data Preprocessing: Data cleaning and preprocessing are essential steps. This involves handling missing values, outliers, and ensuring the data is in a suitable format for modeling. It may also include feature selection or engineering to create new variables that might improve prediction accuracy.
Feature Selection: Identifying and selecting the most important features (variables) that have the most significant impact on the prediction is crucial for building an efficient model.
Model Selection: Choosing the appropriate predictive model is a critical decision. The choice of model depends on the nature of the data and the problem you're trying to solve. Common models include linear regression, decision trees, random forests, support vector machines, neural networks, and more.
Model Training: The selected model is trained on the historical data, which involves adjusting its parameters to fit the patterns in the data. This step typically uses a portion of the data for training and another portion for testing to evaluate the model's performance.
Model Evaluation: The model's performance is assessed using evaluation metrics such as accuracy, precision, recall, F1-score, or mean squared error, depending on the type of prediction (classification or regression). The goal is to ensure the model's predictions are accurate and reliable.
Model Tuning: If the model's performance is not satisfactory, you may need to fine-tune its parameters or consider different algorithms to improve accuracy.
Deployment: Once the model is trained and evaluated satisfactorily, it can be deployed in a real-world environment to make predictions on new, unseen data. Deployment may involve integrating the model into an application, website, or decision-making system.
Monitoring and Maintenance: Predictive models may require ongoing monitoring and maintenance to ensure their predictions remain accurate over time. This can involve retraining the model with new data as it becomes available.
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Data Sources Considering: {since the ambulance data is not the only data you will need to use, what other data sources do you think you will need to use to help build and improve your predictive model? Take some time to look at various data sources for New York City (a good place to start is https://opendata.cityofnewyork.us/), but make sure you put data sources that are related to your task at hand. Don’t just put data here because it is listed on that site. What data would you need to actually predict either response times or ambulance demand? Please also list the website where these data sources can be found}
- NYC FDNY Emergency Medical Services Ambulance Calls, 2008-2016
- {Other dataset 1}
- {…}
Next Steps: {now that you have an initial set of factors you think are relevant, and greater clarity on what you need to predict, what are your next steps on this project? Think about what you will accomplish in the next month, and list out those steps. For example, do you need to acquire additional data sources? Do you need to link data sources based on some common variable? Do you need to do initial descriptive analysis to see how your data varies by one factor or another? In this section, list out and be specific, as this will help become your own task list for the next month as you work on this project.
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