praneeth300/Unsupervised-Learning

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praneeth300/Unsupervised-Learning

praneeth300

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Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. The most common unsupervised learning method is cluster analysis, which is used for exploratory data analysis to find hidden patterns or grouping in data.

Drowsiness-detection-system-using-Open-cv-

The majority of accidents happen due to the drowsiness of the driver. So, to prevent these accidents we will build a system using Python, OpenCV, and Keras which will alert the driver when he feels sleepy.

Flight-Price-Deployement-in-Streamlit

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Hyper-Parameter-tuning-techniques

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Python-Projects

Creating a games,gui,many more using python is really fun task. In this repository i've made a projects on python for beginner's to learn it.

AV-Jabathob-May2021

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Python

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Resume

Cardio-Vascular-Disease

Cardiovascular disease (CVD) is a class of diseases that involve the heart or blood vessels. CVD includes coronary artery diseases (CAD) such as angina and myocardial infarction (commonly known as a heart attack). In this repository i've made a project on Cardio Vascular Disease from scratch.

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Machine-Learning-Projects

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Natural-language-processing

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Streamlit

Stock-Price-Prediction

In this repository,The Apple stock price is predicted based on the certain criteria.

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Covid-Live-Tracker

In this repository,The live detaction of covid cases,deaths,Recoverd cases are visualize with help of different visualization techniques. Exploratory Data Analysis,Graphs,plots.

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Recommending-Products-and-their-Sales

In this project ,i worked on recommending the products, and thier sales to the sales team for better understanding which product is getting high popularity in various regions.

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Movie-recommendation-system

In this Repository contains a simple recommendation system by using the desciption of the movies, cast & crew of the movies.

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Review-Scrapper-

Scrapping the websites is a really fun, We don't have to go through the website for checking the which product getting high reviews and ratings, By using review scrapper u can scrape any product review that are needed. In this repository i made a project on review scrapping on flipkart website using python.

Wafer-fault-detection-system

Fake-News-using-keras-tensorflow

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Kaggle-competition-project

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Combination-of-all-Machine-learning-projects

This repository consists of all my machine learning projects around 12 projects deployed at one place to make simplicity for the end users.

Automated-EDA-libraries

Data cleaning and Exploratory data analysis is the challenging task for everyone. Around 80% of time is taken for the data cleaning and EDA and remaining 20% is for model building and all other process. Because of it's time complexity, reasearchers introduced a more Automated libraries for perform Automated EDA and data cleaning operations with few lines of code.It helps to makes our work easier within the time.

Wine-Quality-Prediction-End-to-End-Deployment

In this repository i've made a project on Wine quality prediction, With end to end deployment.

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Color-detection-Using-Opencv

Colour detection is the process of detecting the name of any color. Simple isn’t it? Well, for humans this is an extremely easy task but for computers, it is not straightforward. Human eyes and brains work together to translate light into color. Light receptors that are present in our eyes transmit the signal to the brain. Our brain then recognizes the color. Since childhood, we have mapped certain lights with their color names. We will be using the somewhat same strategy to detect color names.

Face-and-eye-detection-using-Opencv

The objective is to identify the human faces through the web cam by forming a rectangular shaped object around the face. It helps to identify the person's faces for security control. Soon i will post projects using Open cv

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Pandas-Visual-Analysis

Generates an interactive visual analysis widget to analyze a pandas DataFrame in Jupyter notebooks. It can display various different types of graphs with support for linked-brushing in interactive widgets. This allows data exploration and cognition to be simple, even with complex multivariate datasets. There is no need to create and style plots or interactivity - its all ready without any configuration.

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Covid19-Chatbot

Many of the questions patients have about Covid-19 are questions that other patients have as well, like What are symptoms of Covid-19? or What should I do if I fear I have been exposed? or Where can I get tested? While each patient has a unique medical history, many of these questions lend themselves to standardized answers that should reflect the most up-to-date knowledge and guidelines about the virus and be readily accessible for patients. Across a wide range of industries, chatbots have been an efficiency-enhancing way for business teams to interact with their customers. Chatbots are conversational agents that leverage machine learning and natural language processing to understand intent in order to reply with appropriate answers, and they have advantages relevant to the present crisis.1-3 First, they are accessible any time, allowing patients to obtain answers 24/7 and to avoid wait times on hold before reaching a human. Second, chatbots have a higher handling capacity than any human.4 A single chat bot can simultaneously have conversations with thousands of people no matter what time of day. Regardless of the volume of calls, every question may be answered immediately.

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Data-visualization

Visualizing statistical relationships Statistical analysis is a process of understanding how variables in a dataset relate to each other and how those relationships depend on other variables. Visualization can be a core component of this process because, when data are visualized properly, the human visual system can see trends and patterns that indicate a relationship. 1. Numerical Data Ploting relplot() scatterplot() lineplot() 2. Categorical Data Ploting catplot() boxplot() stripplot() swarmplot() etc... 3. Visualizing Distribution of the Data distplot() kdeplot() jointplot() rugplot() 4. Linear Regression and Relationship regplot() lmplot()

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Romeo-Chatbot

Chatbots, also called chatterbots, is a form of artificial intelligence (AI) used in messaging apps. This tool helps add convenience for customers—they are automated programs that interact with customers like a human would and cost little to nothing to engage with.

Predicting-Chances-of-Admission-in-to-UCLA-

In this repository consists of files regarding the Predicting chances of Admission in to UCLA based on the student performance on different attributes such as GRE Score , Toefl Score etc. on which the chances of student get a admission in to the UCLA is predicted.

Hotel-recommendation

In this project, collected or scraped a indian hotels from MakeMytrip website to recommend the best possible hotels based on the user preferences.

LazyPredict-Compare-ML-models-quickly

When building machine learning models we are not sure which algorithem is best for our usecase , also it takes so much of time taking building each and every model. For this, A __Lazypredict__ is introduced it is a open source python package which is created by shakar rao pandala, it predicts the output based on __30__ algorithems , Sound great..

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Exploratory-Data-Analysis-

in statistics, exploratory data analysis is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis testing task. Exploratory data analysis was promoted by John Tukey to encourage statisticians to explore the data, and possibly formulate hypotheses that could lead to new data collection and experiments. EDA is different from initial data analysis (IDA),[1] which focuses more narrowly on checking assumptions required for model fitting and hypothesis testing, and handling missing values and making transformations of variables as needed. EDA encompasses IDA.

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Forest-Cover-Classification

A short description I want to give of how I am going to solve this project before starting. Our goal in this project is to classify which forest type it is from the data given. This study area includes 4 Wilderness Areas located in the Roosevelt National Forest of Northern Colorado. These area represent forests with minimal human-caused disturbances, so that existing forest cover types are more a result of ecological process rather than forest management practices. Each observation is 30m x 30m forest cover type determined from US Forest Service (USFS) Region 2 Resource Information System (RIS) data. Independent variables were derived from the data originally obtained from US Geological Survey (USGS) and USFS data. I have been given a total of 54 attributes/features, (excluding 1 target variable) these attributes contain Binary and Quantative attributes, and I need to predict which Forest Cover-Type is it from the given features. I will first explore the data, visualize it, know what the data wants to tell us. Remove any missing values and features that have null values and scale the data within a specific range. Also perform dimensionality reduction procedure where I will use 4 models to tell us which are useful in order to predict the target variable, and then using features which gives us hgih score in the most models. Those 4 Models are: Extra Trees Classifier (ETC) Random Forest (RF) AdaBoost Classifier (ADBC) Gradient Boosting Classifier (GBC) Split the data 75%-25%, train-test set respectively. Will use 10 K-fold Cross Validation on train set. Feed the training data to the Naive Bayes (Our Benchmark Model) and evaluate the result. Training will be done on the Solution Models that I have chose, those are:- K-Nearest Neighbour (KNN), Random Forest (RF), Stochastic Gradient Descent Classifier (SGDC), Extra Trees Classifier (ETC), Logistic Regression (LG)

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Deep-learning-Projects

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