Social Distancing Detection System

Last Updated on May 3, 2021

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This is one of my academic projects in the domain of Computer Vision. The detection is done using the local GPU so Nvidia CUDA and CuDNN are used. In addition, the AlexyAB repository and Darknet framework were taken into account. For detection, I have used YOLOv4 which gives better accuracy and comprises more objects and classes.

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House Price Predictor

Last Updated on May 3, 2021

About

Here we have taken the information of a valid housing data set consisting of information of 500+ Houses. By taking all attributes as factors we will predict the price of the house. We are going to take advantage of all of the feature variables available to use and use it to analyze and predict house prices. Here we have to predict the price of the house on the basis of the following attributes:

~lot size – Square feet of the house I need. (Numerical)  

~Bedroom- How many bedrooms I need? (Numerical)

~bathroom – How many bathrooms I need? (Numerical)

~stories-How many stories building I need? (Numerical)

~driveway –Whether I need a driveway or not? (Binary)1 for yes and 0 for no.

~recreational room-Whether I need a rec room or not? (Binary)1 for yes and 0 for no.

~Gas hot water - Whether I need Gas Hot water or not? (Binary)1 for yes and 0 for no.

~full base- Whether I need a full base or not? (Binary)1 for yes and 0 for no.

~Air condition- Whether I need Air condition or not? (Binary)1 for yes and 0 for no.

By entering all these inputs of the attributes, and by using multivariate regression we will predict the house at price in $.

We have split the dataset into two parts training and testing set. Then by training the dataset we will use multivariate regression and predict the house of the price in the testing data set.

 

Here we have also compared actual and predicted price using Machine Learning  

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Classification

Last Updated on May 3, 2021

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What is classification?

In machine learning, classification refers to a predictive modeling problem where a class label is predicted for a given example of input data. Examples of classification problems include: Given an example, classify if it is spam or not. Given a handwritten character, classify it as one of the known characters.

Types of Classification 

}3.1 Logistic Regression


}3.2 K-Nearest Neighbors (K-NN)


}3.3 Support Vector Machine


}3.4 kernel svm


}3.5 Naïve bayes


}3.6 Decision tree classification


}3.7 Random forest classification 

Table of contents 

}Importing the libraries 

}Importing the dataset

}Splitting the dataset into the Training set and Test set

}Feature Scaling

}Training the model on the Training set 

}Predicting a new result

}Predicting the Test set results

} Making the Confusion Matrix

}Visualizing the Training set results

}Visualizing the Test set results   


}Problem description: A car company is releasing a new suv car model . we are given a dataset of 400 outcomes with customer’s age , salary and whether they have purchased it before or not I have to predict which customer is going to buy that suv .

Dataset 

RESULT FOR ALL:

Logistic Regression

K-Nearest Neighbors (K-NN)

Support Vector Machine

kernel svm