In order to use our Yosnalab shopping services, you need to read carefully about our terms and conditions. If You disagree any part of terms then you cannot access our service.
I agree to pay the rental rate for the period we used the product and in transit and further agrees to promptly return the products at the end of the rental period in the same condition as received.
I will give alert to Yosnalab if my contact information changes.
I also agree to pay for any damages to, or loss of, the rented merchandise occurring during their time of possession or because of loss and or damage of the products. Upon return and inspection if any and all repairs necessary and or accessories missing that were itemized will be charged at our current rates and billed to you.
A full day rental is charged, even for a partial day use. There are absolutely no refunds for early returns.
The products can be used upto 4 weeks only, Cost will be calculated as per day of usage. We are not responsible for the damage of materials or other liability of any kind resulting from the use or malfunction of the equipment. We will not return the initial deposit of money until the product is returned back.
I am responsible for keeping track of my due dates. I understand that any notices sent out by Yosnalab are a courtesy only and failure to receive them does not excuse me from any charges.
A copy of both sides of their valid institution identification card need to be present. Depositing the money can be done only in the form of cash, not through credit/debit cards.
If you have questions or suggestions, please contact us.
Driving is considered to be one of the most difficult tasks of the day. All the drivers, it may be men or women will definitely experience tiredness or frustration whenever they have to drive through long traffic conditions. Nowadays, Road accidents play a major problem in public health and development. Road injuries and accidents are predicted to increase if road safety is not addressed adequately. Also, road traffic is the most complicated daily need. According to a report, more than 150,000 people are killed each year in traffic accidents, leading to around 400 deaths per day. Studies have released that road accidents and death- laceration ratio will keep on increasing. Designing and controlling traffic by advanced systems are available in order to fulfill the vital traffic needs. Assumption on the risks in traffic and the law and regulations will tend to reduce the road accidents.
Why: Problem statement
Nowadays traffic has been considered a difficult structure in designing and managing by the reason of increasing large number of vehicles. This situation has increased road accidents. Road accidents have influenced public health and country economy and many studies have been done to obtain a solution. Arising the need of accession to information from this large calibrated data obtained the cornerstone of the data mining. In this project, we will be using the most advanced machine learning classification techniques for road accident prediction by data mining.
There are a number of problems with trending practices for prevention of the accidents occurring in all areas. We will use few databases that are readily available officially by many sectors and government websites. The collected data will be analyzed, integrated and grouped together based on different constraints using the best-suited algorithm. This analysis will be useful to examine and identify the mistakes and the possible reasons for road accidents. It will also be helpful while construction roads and bridges. These predictions made will be very much helpful to plan and manage such problems.
How: Solution description
I collected the US border crossing entry data dataset for this problem. It is a multivariate dataset containing attributes that are: port name, state, portcode, border, measure, value and location. Here, we are not using any data cleaning process since there are no missing values.
The Block Diagram has been shown below:
Machine Learning Models:
First, I used the k-nearest-neighbor algorithm. Often abbreviated knn, it is an approach to data classification that estimates a data point is to be a member of one group or the other depending on the group the data points nearest. While training the model, I got the accuracy of 0.7894.
Then, I used the decision-tree classifier algorithm. It is a predictive modeling tool that has applications spanning a number of different areas, and decision trees are constructed via an algorithmic approach that identifies ways to split a data set based on different conditions. While training the model, I got the accuracy of 1.0.
How is it different from competition
Deep learning is the best method used for transportation or traffic-related predictions. But the existing projects fall short in different angles such as the use of the information, insufficient depth of machine learning tools, etc.
To analyze the raw data manually and predict a solution for traffic congestions form a tough process. But machine learning technology automatically detects the patterns or information from the available data, using the data mining algorithms. when using Data mining algorithms. Decision trees are the best-used approach for representing the data. Using Decision trees, data can be clearly understood in the most clear form of data. Every algorithm has a unique decision tree from the input data.
After using machine-learning models, I was able to conclude that the decision tree classifier algorithm is the best classification algorithm for the border crossing entry dataset.
Who are your customers
Traffic Controllers and Traffic Police are my Customers.
Project Phases and Schedule
Phase 1: Data Collection
Phase 2: Data Cleaning
Phase 3: Data Analysis
Phase 4: Prediction using machine learning techniques.