Rinester88

Affinity-Answers

Affinity-answers1

AIOT-SMARTER-ALEXA-WITH-CHATGPT

autogen

Enable Next-Gen Large Language Model Applications. Join our Discord: https://discord.gg/pAbnFJrkgZ

Blood-Bank

Small user input application for Blood Bank

cal.com

Scheduling infrastructure for absolutely everyone.

CurrencyConverter

A Basic CurrencyConverter Made Using Kotlin, Kodein, MVVM Pattern, Retrofit, CurrencyExchange API,Coroutines.

Decision-Tree

Decision Tree is a Supervised learning technique that can be used for both classification and Regression problems, but mostly it is preferred for solving Classification problems. It is a tree-structured classifier, where internal nodes represent the features of a dataset, branches represent the decision rules and each leaf node represents the outcome.

Hospital-Mortality-Predection-Using-Pycaret-and-ML

ML-Libraries

The libraries discussed here widely, one can do ML using python. And applying these libraries in a dataset discussed here widely. We can notice that using replace function we can easily change the values of column. Using sns.countplot() we can visualize the count of how many times repeated of all values of a particular column. Describe() function tells us columns count, mean, sd, min, max etc. Using seaborn boxplot we can visualize that a box plot is a way to show the spread and centers of a data set. Using seaborn histplot we can visualize that histogram represent the data distribution by forming bins along the range of the data and then drawing bars to show the number of observations that fall in each bin. Using pd.dataframe we can create a new dataframe of some features. Using train_test_split we can split the main dataset into training and testing dataset. We can transform all values of some columns using MinMaxScaler which scales all the data features in the range [0, 1] and StandardScaler which makes mean = 0 and scales the data to unit variance. Using seaborn histplot we can visualize that we use MinMaxScaler for directly normalizing the input variables and use StandardScaler for directly standardizing the input variables. Using count() function we count total number of values of particular column. Using value_counts() function we count column's all values how many times repeated. Using append() function we can add dataframe. Seaborn is a siimple graphical tool. Seaborn can be used for both exploration and presentation of findings. Basically here we learned about some machine learning libraries and we praticed on train_test split and normalisation.

Rinester88/Hospital-Mortality-Predection-Using-Pycaret-and-ML

Rinester88/ML-Libraries

The libraries discussed here widely, one can do ML using python. And applying these libraries in a dataset discussed here widely. We can notice that using replace function we can easily change the values of column. Using sns.countplot() we can visualize the count of how many times repeated of all values of a particular column. Describe() function tells us columns count, mean, sd, min, max etc. Using seaborn boxplot we can visualize that a box plot is a way to show the spread and centers of a data set. Using seaborn histplot we can visualize that histogram represent the data distribution by forming bins along the range of the data and then drawing bars to show the number of observations that fall in each bin. Using pd.dataframe we can create a new dataframe of some features. Using train_test_split we can split the main dataset into training and testing dataset. We can transform all values of some columns using MinMaxScaler which scales all the data features in the range [0, 1] and StandardScaler which makes mean = 0 and scales the data to unit variance. Using seaborn histplot we can visualize that we use MinMaxScaler for directly normalizing the input variables and use StandardScaler for directly standardizing the input variables. Using count() function we count total number of values of particular column. Using value_counts() function we count column's all values how many times repeated. Using append() function we can add dataframe. Seaborn is a siimple graphical tool. Seaborn can be used for both exploration and presentation of findings. Basically here we learned about some machine learning libraries and we praticed on train_test split and normalisation.

Rinester88/Affinity-Answers

Rinester88/Affinity-answers1

Rinester88/AIOT-SMARTER-ALEXA-WITH-CHATGPT

Rinester88/autogen

Enable Next-Gen Large Language Model Applications. Join our Discord: https://discord.gg/pAbnFJrkgZ

Rinester88/Blood-Bank

Small user input application for Blood Bank

Rinester88/cal.com

Scheduling infrastructure for absolutely everyone.

Rinester88/CurrencyConverter

A Basic CurrencyConverter Made Using Kotlin, Kodein, MVVM Pattern, Retrofit, CurrencyExchange API,Coroutines.

Rinester88/Decision-Tree

Decision Tree is a Supervised learning technique that can be used for both classification and Regression problems, but mostly it is preferred for solving Classification problems. It is a tree-structured classifier, where internal nodes represent the features of a dataset, branches represent the decision rules and each leaf node represents the outcome.

Rinester88/Drinking-Water-Potability-Prediction

The acceptability of water for human eating and other domestic needs without producing harm or health hazards is referred to as water potability. Potable water, also known as drinking water, must meet certain quality criteria in order to be safe for consumption and pose no health risks

Rinester88/ipl-win-probability-predictor

A machine learning project to find out the win probability of an IPL match

Rinester88/ML-Model-on-AirQuality-Dataset

Rinester88/Pan-Card-Tampering-

The purpose of this project is to detect tampering of PAN card using computer vision. This project will help different organization in detecting whether the Id i.e. the PAN card provided to them by thier employees or customers or anyone is original or not.

Rinester88/Prediction-of-Numeric-Values

Rinester88/Rinester88

Config files for my GitHub profile.

Rinester88/Sad-dev

Rinester88/Servo-motor-Using-pico

Rinester88/Servo-Motor-Usingpico

Rinester88/Simple-Menu-Driven-programme-Using-Java

Rinester88/Titanic---Machine-Learning-from-Disaster