Tag Archives: fall prediction machine learning

Road Accident Severity Prediction Using Machine Learning Project

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The majority of fatalities and serious injuries occur as a result of incidents involving motor vehicles. If the traffic  management system is going to do its job of reducing the frequency and severity of traffic accidents, it needs a model for doing so. In this paper, we combine the results of three machine learning algorithms—logistic regression, decision tree, and random forest classifier—to build a predictive model. In order to forecast the severity of accidents in different regions, we used ML algorithms on a dataset of accidents from the United States. In addition, we examine vast quantities of traffic data, extracting  helpful accident patterns in order to pinpoint the factors that have a direct bearing on road accidents and make actionable suggestions for improvement. When compared to two other ML algorithms, random forest performed best on accuracy. The severity rating in this paper is not meant to reflect the severity of injuries sustained, but rather how the accident affects traffic flow. Accident severity, decision trees, random forests, and logistic regression are all terms that are often used to describe this area of study
Road accidents have become a major concern globally, causing a significant number of fatalities and injuries every year. The aim of this project is to predict road accidents severity using machine learning
techniques, in order to reduce their occurrence and mitigate the associated risks. The project uses data collected from various sources such as accident reports, weather conditions, and road infrastructure to train and evaluate various supervised learning algorithms and predict the accident severity. Four algorithms were compared, including Decision Tree, Naive Bayes, Random forest . Most probably occurring road accident locations are identified and that particular region is indicated as black pot. The proposed method can be used to provide real-time risk information to road users, helping them to make informed decisions and avoid potential accidents. The project highlights the importance of using machine learning techniques in road safety analysis, providing a foundation for further research in this field.

About Dataset

Dataset Link - https://www.kaggle.com/datasets/s3programmer/road-accident-severity-in-india

The data set has been prepared from manual records of road traffic accidents for the years 2017–22. All the sensitive information has been excluded during data encoding, and finally, it has 32 features and 12316 instances of the accident. Then it is preprocessed for the identification of major causes of the accident by analyzing it using different machine learning classification algorithms. Road.csv is the preprocessed dataset.

Running the web app

Locally

  • Install requirements
    pip install -r requirements.txt --user
  • Run flask web app
    python app.py

Download Link

Rainfall Prediction using LogisticRegression Flask Web App

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The Rainfall Prediction using LogisticRegression Flask Web App project is a web application that predicts the amount of rainfall based on historical weather data. The project involves building a logistic regression model that can predict the amount of rainfall based on several weather parameters, and integrating this model into a Flask web application.

Here's a general overview of the project:

  1. Data collection: Collect historical weather data from reliable sources such as the National Oceanic and Atmospheric Administration (NOAA) or India Meteorological Department (IMD).
  2. Data preprocessing: Clean and prepare the weather data for use in the logistic regression model.
  3. Feature selection: Select the most important features that can affect the rainfall prediction, such as temperature, humidity, wind speed, and cloud cover.
  4. Model training: Train a logistic regression model using the preprocessed weather data and the selected features.
  5. Model evaluation: Evaluate the performance of the logistic regression model to ensure it can accurately predict the amount of rainfall.
  6. Flask app development: Develop a Flask web application that allows users to input weather parameters and get a prediction of the amount of rainfall.
  7. Deployment: Deploy the web application to a server so that it can be accessed by users.

Overall, the Rainfall Prediction using LogisticRegression Flask Web App project can be a valuable tool for farmers and other industries that rely on weather predictions.

Explanation of the logistic regression model:

Logistic regression is a statistical model that is used for binary classification problems, where the goal is to predict whether an observation belongs to a particular class or not. The logistic regression model uses a logistic function to map the input features to a probability output. The logistic function is a sigmoid function that outputs a value between 0 and 1, which can be interpreted as the probability of the observation belonging to the positive class.

Here are the key components of the logistic regression model:

  1. Input features: The input features are the variables that are used to predict the outcome. In the case of rainfall prediction, the input features could include temperature, humidity, wind speed, and cloud cover.
  2. Weights: Each input feature is assigned a weight, which reflects the strength of the relationship between that feature and the outcome variable. The weights are learned during the training process and are used to make predictions.
  3. Bias term: The logistic regression model also includes a bias term, which is added to the weighted sum of the input features to produce the final prediction.
  4. Logistic function: The logistic function is a sigmoid function that is used to map the input features to a probability output. The logistic function has a characteristic S-shaped curve and outputs a value between 0 and 1.
  5. Decision boundary: The decision boundary is the threshold value that is used to determine whether an observation belongs to the positive class or the negative class. The decision boundary is typically set to 0.5, meaning that any observation with a predicted probability greater than 0.5 is classified as belonging to the positive class, while any observation with a predicted probability less than 0.5 is classified as belonging to the negative class.
  6. Training: During training, the logistic regression model is fed a set of labeled data and adjusts its weights to minimize the difference between the predicted output and the actual output. This process is typically done using an optimization algorithm such as gradient descent.

Overall, the logistic regression model is a simple and interpretable model that can be used for binary classification tasks, such as rainfall prediction.

Technology Used in the project :-

  1. We have developed this project using the below technology
  2. HTML : Page layout has been designed in HTML
  3. CSS : CSS has been used for all the desigining part
  4. JavaScript : All the validation task and animations has been developed by JavaScript
  5. Python : All the business logic has been implemented in Python
  6. Flask: Project has been developed over the Flask Framework

Supported Operating System :-

  1. We can configure this project on following operating system.
  2. Windows : This project can easily be configured on windows operating system. For running this project on Windows system, you will have to install
  3. Python 3.6.10, PIP, Django.
  4. Linux : We can run this project also on all versions of Linux operating systemMac : We can also easily configured this project on Mac operating system.

Installation Step : -

  1. python 3.6.8
  2. command 1 - python -m pip install --user -r requirements.txt
  3. command 2 - python app.py