Chandradithya8

Automated_Libraries

In this repository I will be adding all the automated libraries with respect to data science.

FbProphet

Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well. Prophet is open source software released by Facebook’s Core Data Science team.

Feature_Engineering

Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.

Feature_Selection

Feature Selection is the process where you automatically or manually select those features which contribute most to your prediction variable or output in which you are interested in. Having irrelevant features in your data can decrease the accuracy of the models and make your model learn based on irrelevant features.

Handling_Imbalanced_Dataset

Imbalanced data sets are a special case for classification problem where the class distribution is not uniform among the classes. Typically, they are composed by two classes: The majority (negative) class and the minority (positive) class.

Heroku_Deployment

Heroku is a container-based cloud Platform as a Service (PaaS). Developers use Heroku to deploy, manage, and scale modern apps. Our platform is elegant, flexible, and easy to use, offering developers the simplest path to getting their apps to market.

Hyperparameter_Tuning_Techniques

Hyperparameter optimization or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process. By contrast, the values of other parameters (typically node weights) are learned. Hyperparameters are crucial as they control the overall behavior of a machine learning model. The ultimate goal is to find an optimal combination of hyperparameters that minimizes a predefined loss function to give better results.

Machine-Learning

In this repository I have done the implementation of all the machine learning concepts.

Machine_learning_Projects

This repository consist of machine learning projects.

Time_series_analysis

Time Series analysis is one of the important concept in python. So I have done the implementation of all the time series analysis techniques.

Chandradithya8/Automated_Libraries

In this repository I will be adding all the automated libraries with respect to data science.

Chandradithya8/Feature_Engineering

Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.

Chandradithya8/Feature_Selection

Feature Selection is the process where you automatically or manually select those features which contribute most to your prediction variable or output in which you are interested in. Having irrelevant features in your data can decrease the accuracy of the models and make your model learn based on irrelevant features.

Chandradithya8/Hyperparameter_Tuning_Techniques

Hyperparameter optimization or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process. By contrast, the values of other parameters (typically node weights) are learned. Hyperparameters are crucial as they control the overall behavior of a machine learning model. The ultimate goal is to find an optimal combination of hyperparameters that minimizes a predefined loss function to give better results.

Chandradithya8/Machine-Learning

In this repository I have done the implementation of all the machine learning concepts.

Chandradithya8/Handling_Imbalanced_Dataset

Imbalanced data sets are a special case for classification problem where the class distribution is not uniform among the classes. Typically, they are composed by two classes: The majority (negative) class and the minority (positive) class.

Chandradithya8/Heroku_Deployment

Heroku is a container-based cloud Platform as a Service (PaaS). Developers use Heroku to deploy, manage, and scale modern apps. Our platform is elegant, flexible, and easy to use, offering developers the simplest path to getting their apps to market.

Chandradithya8/Advanced_Regression

Chandradithya8/Algorithms-Pack

Chandradithya8/Artificial_Neural_network

Artificial neural networks are used in sequence and pattern recognition systems, data processing, robotics, modeling, etc. ANN acquires knowledge from their surroundings by adapting to internal and external parameters and they solve complex problems which are difficult to manage.

Chandradithya8/Cotton_leaf_Disease_classification_deep-learning

Chandradithya8/Covid-Prediction-using-chest-x-ray

Chandradithya8/Data_Augumentation

Chandradithya8/Diabetes_prediction_end_to_end

Chandradithya8/FarmApp

https://farmapp-chand.herokuapp.com/home

Chandradithya8/HealthApp

A Machine Learning and Deep Learning based application for disease detection.

Chandradithya8/Heart_disease_prediction-end_to_end

Chandradithya8/ipl_score_prediction

Chandradithya8/Loan-Prediction

Chandradithya8/Malaria_disease_prediction_deep-learning

Chandradithya8/Pnuemonia_disease_prediction_deep-learning

Chandradithya8/tic_tac_toe_app

Chandradithya8/Tomato_leaf_disease_classification

Chandradithya8/Trending-jobs-skills-analysis

Chandradithya8/classifier_mlops

Chandradithya8/Django-mongodb-crud

Chandradithya8/fashion-app

Chandradithya8/Pencil-sketching-using-python

Chandradithya8/rasa-nlu

Chandradithya8/Social_media_analysis