Hello I need some help for a mid - project update assignment. I need to post 10 slides describing the current state of my project and explain it in a video. thanks My project: Predicting the cruelty of car accidents Description: The goal of this project is to predict the severity of car accidents based on several input features, including weather conditions, road conditions, and location. The project will use supervised learning algorithms to predict the severity of accidents. The output will be a severity score ranging from 1 to 5, with 1 being the least severe and 5 being the most severe. This project aims to help traffic control centers and emergency services respond more quickly and effectively to car accidents Workflow: 1. Data Gathering and Preprocessing 2. Exploratory Data Analysis 3. Feature Selection and Engineering 4. Model Selection and Training 5. Model Evaluation and Optimization 6. Final Model Deployment Project Design and Milestones: Technologies and Design Methods: Programming Language: Python Libraries: pandas, numpy, matplotlib, seaborn, scikit-learn Machine Learning Algorithm: Random Forest Classifier Web Development Framework: Flask Server-Side Configuration: AWS Elastic Beanstalk Client-Side Hardware/Software: Web Browser Milestones: 1. Data gathering and preprocessing: Collect and preprocess data from open- source traffic accident databases. 2. Exploratory data analysis: Conduct descriptive statistics, data visualization, and data cleaning. 3. Feature selection and engineering: Select relevant features and engineer new features if necessary. 4. Model selection and training: Choose the appropriate algorithm and train the model on the data. 5. Model evaluation and optimization: Evaluate the model's performance using various metrics and optimize the model. 6. Final model deployment: Deploy the model on a web application using Flask and AWS Elastic Beanstalk. Mid-Project Update Start thinking about executing your ideas and start creating code snippets so you can combine them together to show results. You should have already completed the following: a) choose a dataset and done exploratory data analysis on the data you are working with b) decide the type of learning method (supervised versus unsupervised etc) c) Conduct Feature analysis (are you engineering new features or using existing variables from the dataset?) d) Figure out the target feature(s) you are trying to predict if it is a prediction problem or insights if it is an unsupervised approach e) Plan for what visualization charts will be present in your final project presentation. The mid-project update presentation will lead to your final presentation. Having as many details in the project as possible is important to bring it to completion. You will post 10 slides describing the current state of your project (Project update). Your Project update slides must include all of the following 8 elements: -Problem statement(5 points) -Use case or target application(10 point.

1. Hello I need some help for a mid - project update assignment. I need to post 10 slides describing
the current state of my project and explain it in a video. thanks
My project: Predicting the cruelty of car accidents
Description: The goal of this project is to predict the severity of car accidents based on several
input features, including weather conditions, road conditions, and location. The project will use
supervised learning algorithms to predict the severity of accidents. The output will be a severity
score ranging from 1 to 5, with 1 being the least severe and 5 being the most severe. This project
aims to help traffic control centers and emergency services respond more quickly and effectively
to car accidents
Workflow:
1. Data Gathering and Preprocessing 2. Exploratory Data Analysis
3. Feature Selection and Engineering 4. Model Selection and Training
5. Model Evaluation and Optimization 6. Final Model Deployment
Project Design and Milestones:
Technologies and Design Methods:
Programming Language: Python
Libraries: pandas, numpy, matplotlib, seaborn, scikit-learn Machine Learning Algorithm:
Random Forest Classifier
Web Development Framework: Flask
Server-Side Configuration: AWS Elastic Beanstalk
Client-Side Hardware/Software: Web Browser
Milestones:
1. Data gathering and preprocessing: Collect and preprocess data from open- source traffic
accident databases.
2. Exploratory data analysis: Conduct descriptive statistics, data visualization, and data cleaning.
3. Feature selection and engineering: Select relevant features and engineer new features if
necessary.
4. Model selection and training: Choose the appropriate algorithm and train the model on the
data.
5. Model evaluation and optimization: Evaluate the model's performance using various metrics
and optimize the model.
6. Final model deployment: Deploy the model on a web application using Flask and AWS
Elastic Beanstalk.
Mid-Project Update
Start thinking about executing your ideas and start creating code snippets so you can combine

2. them together to show results. You should have already completed the following: a) choose a
dataset and done exploratory data analysis on the data you are working with b) decide the type of
learning method (supervised versus unsupervised etc) c) Conduct Feature analysis (are you
engineering new features or using existing variables from the dataset?) d) Figure out the target
feature(s) you are trying to predict if it is a prediction problem or insights if it is an unsupervised
approach e) Plan for what visualization charts will be present in your final project presentation.
The mid-project update presentation will lead to your final presentation. Having as many details
in the project as possible is important to bring it to completion.
You will post 10 slides describing the current state of your project (Project update).
Your Project update slides must include all of the following 8 elements:
-Problem statement(5 points)
-Use case or target application(10 points)
-Figure showing workflow (10 points)
-Dataset used (summarize the size, key features for prediction etc)(5 points)
-Initial data insights from exploratory data analysis(10 points)
-Machine learning strategy proposed for use (e.g. type of learning, algorithm details, details of
comparison if using more than one model) (10 points)
-Expected outcome of your model, model evaluation criteria and model performance metrics (10
points)
You must explain the presentation slides submitted above.It must include all of the following
elements:
-Title and Motivation-Why did you choose the project(10 points)
-Explain the Features of the dataset(10 points)
-Explain the algorithm implemented (10 points)
-Explain the expected outcome (10 points)