Charity Donor In Us

Last Updated on May 3, 2021

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A project which was made at the time of Udacity Nanodegree which includes the dataset of 1994 US Census.

The objective was to find the donors using supervised learning algorithms to be applied on the data.

Got to process the data with many encoding algorithms and pre processing of the same.

Implemented following algorithms on the data:

Gaussian Naïve Bayes

Decision Trees

Ensemble methods(Bagging, Adaboost, Gradient Boosting)

K-Nearest Neighbours

Support Vector Machines

Logistic Regression

Perfectly visualized the data and used the matplotlib library for the graph generation and for the better understanding of any individual.

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Book Recommendation System

Last Updated on May 3, 2021

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Book Recommendation System

Book recommendation is created and deployed in this approach of work, which helps in recommending books. Recommendation achieved by the users feedbacks and rating, this is the online which analyse the ratings, comments and reviews of user, negative positive nature of comments using opinion mining. User searching for the interested book will be displayed in top list and also can read feedback given by people about the book or any searched items. Whenever user search for any book from the large data available, he gets confused from the number of displayed item, which one to choose. In that case recommendation helps and displays on the interested items. This is the trustworthy approach, which is used in this project where selection is based on the dataset.

Clustering

This project used clustering as the central idea. A clustering approach is used. Clustering is based on similarity where similar elements are kept in a single group. Likewise similar element, the irrelevant elements are also reside in a group, which is another group, based on similarity value or maximum size of cluster. The clustering approach which is used in our work is K-mean clustering for grouping of similar users. It is the unsupervised and simplest learning algorithm, which simplifies mining work by grouping similar elements forming cluster. This is done using a parameter called K-centroids. Distance between each element is calculated for checking the similarity and forming a single cluster to reside the similar elements, after comparing with K-centroid parameter.

In this project, 6 clusters were made.

The project is made with 2 separate datsets in .csv format taken from Kaggle.

  1. Books dataset
  2. Ratings

This project is GUI based. The output page has 2 options:

  1. Rate books
  2. Recommend books

The user can chose either according to themselves.

Rate books

In this option, the user can rate books.

Recommend books

In this option the books are recommended to the user, according to their previous readings.

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Machine Learning Algorithms

Last Updated on May 3, 2021

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I have created this projects by learning some machine learning algorithm's

Different algorithms I have learned are :

  1. K-means : In k-means algorithm I learned the working of algorithm using a dataset and loaded it. I have used sklearn and used metrics function in it to predict best fit score for dataset.
  2. KNN : In this algorithm I have used a car.data csv file in order to perform operations on it. I have trained the data and then labels of columns in order to fit the data in model. Then have predicted the acurracy of model.
  3. Regression : In this algorithm I have used different libraries such as numpy, pandas, sklearn and matplotlib for working on excel file. numpy and pandas is used for reading the csv file form directory and also to use series and dataframes of pandas. Matplotlib is used for graphical representation of model and sklearn is used to import its linear model for the data and train the data.
  4. SVM(Support Vector Machine Algorithm): In this algorithm I have used a different data set. Loaded that dataset into the algorithm with help of sklearn. works for classification and for regression, svm uses hyperplay to divide data in straights(line, 4D). Its a linear way to divide data.
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Research Paper On Face Detection Using Haar Cascade Classifier

Last Updated on May 3, 2021

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Abstract:

In the last several years, face detection has been listed as one of the most engaging fields in research. Face detection algorithms are used for the detection of frontal human faces. Face detection finds use in many applications such as face tracking, face analysis, and face recognition. In this paper, we are going to discuss face detection using a haar cascade classifier and OpenCV. In this study, we would be focusing on some of the face detection technology in use.



Conclusion:

In this study, we covered and studied in detail about face detection technique using haar cascades classifier and OpenCV to get the desired output. Using the OpenCV library, the haar cascade classifier was able to perform successful face detection with high accuracy and efficiency. We also used the OpenCV package to extract some of the features of the face to compare them. Also, we discussed some popular face detection methods. Further, we discussed the scope of face detection in the future and some of its applications. At last, we conclude that the future of facial detection technology is bright Security and surveillance is the major segments that will be deeply influenced. Other areas that are now welcoming it are private industries, public buildings, and schools

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Heart Attack Prediction

Last Updated on May 3, 2021

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I did this project in the first semester of my MTech studies at Ahmedabad University. This project is all about predicting the heart attack based on different parameters such as cholesterol, bp, exercise, age, sex, chest pain type, slope, etc. The dataset size was 27 kb. It had 13 columns and 303 rows, I got this dataset from Kaggle. First I did data cleaning in which I removed outliers, null values, duplicate values. After that, I did some data visualization to get insight from the data. During the data visualization, some insights I got from the data were people mostly aged above 40 are suffering/ suffered from a heart attack once in their life, heart rate and chest pain are highly correlated with a heart attack, stress and cholesterol are also one of the main factors of a heart attack, we can see that the patient suffering from heart disease have high cholesterol as compared to the patient not suffering from heart disease. In this project, I have used different machine learning algorithms to predict the Heart attack. I used Logistic regression in which I got 85% accuracy, and decision tree I got 72% accuracy. In the end, there is a decision tree that shows the parameters affecting in order of correlation.

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Portfolio Website

Last Updated on May 3, 2021

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DIFFERENT PAGES IN IT:


  1. HOME PAGE : this is the home page and it greets the viewer and tells what this is about. and on the top of this page we can see the clicks for other pages which are discussed below.