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Siddhartha Kancharla 's Projects

-employee-attrition-retention icon -employee-attrition-retention

The HR of the company needed a model that can predict whether the employee will leave or not so that he can make appropriate arrangements to retain or replace based on the model. I built a model that can predict whether will leave or not based on the parameters entered

amazon-scraper icon amazon-scraper

This is a simple Amazon Product Scraper built using scrapy module in python

cereals-data-analytics- icon cereals-data-analytics-

Data Interpretation and Insights driven analytics , built Machine learning accuracy prediction model

data-analaytics-using-hadoop- icon data-analaytics-using-hadoop-

Documentation work on use case of worked done on the data of list consisting of employee’s details and analysis of data by partitioning as per the state and first name using pig query and pyspark

face_emotion_recognition- icon face_emotion_recognition-

The aim to classify the emotion on a person's face into one of seven categories, using deep constitutional neural networks. FER-2013 dataset consists of 35887 grayscale, 48x48 sized face images with seven emotions - angry, disgusted, fearful, happy, neutral, sad and surprised

handwritten-digit-recognition-using-tensorflow icon handwritten-digit-recognition-using-tensorflow

The handwritten digit recognition is the ability of computers to recognize human handwritten digits. It is a hard task for the machine because handwritten digits are not perfect and can be made with many different flavors. The handwritten digit recognition is the solution to this problem which uses the image of a digit and recognizes the digit present in the image

machine-learning-portfolio- icon machine-learning-portfolio-

Data Interpretation and Insights driven analytics of Various use cases worked as part of Machine learning portfolio

neuralet icon neuralet

Neuralet is an open-source platform for edge deep learning models on edge TPU, Jetson Nano, and more.

sms-backup-andriod-app- icon sms-backup-andriod-app-

While getting lot of SMS or Messages from the Family and Friends through Company or Sms Networking Sites. The main Problem is that some of our important or unread messages are getting deleted by knowing or unknowingly. To overcome such problem we want to develop a mobile application through which the user can backup the Message. The proposed application is to develop a system that offers news services through Mobile application. Once the application is downloaded and installed by the user, he/she should register to it and he can access the service by providing the unique username and password. User need to provide their username and password so that the SMS is Stored directly to your Database and Retrieve. The user gets a pop-up message while receiving message. These pop-up messages will inform the user about the existence of this application in the mobile device. This application provides more comfort for the users in receiving messages and to avoid internet need. This SMS BACKUP system is a type of application because here the process of receiving and storing takes place where service provider will provide the list of Messages that are available in the area selected by the user so that he/she can select the Message provided by that Application. Here in this application the user is provided with a wide range of messages to store with respective to the mobile number and their time. The android application on user’s mobile will have all details. The stored details from customer’s mobile are updated in central database and subsequently sent to Mobile Database. In this paper we present an automated sms backup system with real time customer feedback. This system is convenient, effective, easy and by implementing this project in the present system, the user can overcome the problems faced regarding their storage of SMS needs in an efficient manner.

url-classification-prediction-model- icon url-classification-prediction-model-

Malicious URL, a.k.a. malicious website, is a common and serious threat to cyber-security. Malicious URLs host unsolicited content (spam, phishing, drive-by downloads, etc.) and lure unsuspecting users to become victims of scams (monetary loss, theft of private information, and malware installation), and cause losses of billions of dollars every year. It is imperative to detect and act on such threats in a timely manner. Traditionally, this detection is done mostly through the usage of blacklists. However, blacklists cannot be exhaustive, and lack the ability to detect newly generated malicious URLs. To improve the generality of malicious URL detectors, machine learning techniques have been explored with increasing attention in recent years. This project aims to provide a comprehensive survey and a structural understanding of Malicious URL Detection techniques using machine learning. i present the formal formulation of Malicious URL Detection as a machine learning task, and categorize and review the contributions of literature studies that addresses different dimensions of this problem (feature representation, algorithm design, etc.).

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