vivek's repositories
Number-Plate-Detector
open cv this Detection of number plate and it save the image in jpg formate
Principle-Component-Analysis-PCA-
The principal component analysis is a technique that can transform higher dimensional data into lower dimensional data while keeping the essence of the data Benefits: i) fast execution of the algorithm ii) visualization is easy
Artificial-neural-network
Artificial neural network is a machine learning technique used for classification problems
Binning-Discretization-_-Quantile-Binning-_-KMeans-Binning
these concepts are useful for converting numerical data to categorical
cca-mean-mode-arbitary-value-end-of-distribution-missing-data-
complete case analysis drops the whole column if there are missing values, arbitrary value imputation in this we can use replace (mean or median) with -1 or 99.999, end of the distribution it replaces the values with "missing" term
Column-Transformer-
Column-Transformer is the method where you can use this feature and you can implement one-hot encoding and OrdinalEncoding both together
EDA-using-Bivariate-and-Multivariate-Analysis
EDA ON TIME OR DATE DISPLAY IN BEST VISUALATION
eda_Pandas-Profiling
eda_Pandas-Profiling
Feature-Construction-_-Feature-Splitting
The feature splitting technique used to split the column to extract more data from the column it is the part of feature construction it doesn't have mathematical rules eg: There are scores of students and we are splitting then as 0-35 one column and 35-80 one column and 80 - 100 one column
Feature_Scaling
Feature_Scaling_Normalization_MinMaxScaling_MaxAbsScaling_RobustScaling
Fetching-Data-From-an-API
extracting data from api(json)
firest-quesion-s
after getting the data question you have to ask yourself
grammar-correction-model
how to implement a grammar correction model that’s available on Hugging Face’s model hub. This video covers how to implement a T5 grammar correction model I recently trained and published on Hugging Face’s model hub with my very own Python package called Happy Transformer. I also briefly discuss how you could fine-tune your own grammar correction model with a dataset called JFLEG.
Mixed-Variables-Date-and-Time
Mixed Variables is the combination of numbers and object/Handling Date and Time
multivariate-analysis
the multivariate analysis compares different rows and columns for beat accuracy eg:knn imputer in univariate analysis it only compares with the same columns eg mean or median for numbers
Ordinal-Encoding---Label-Encoding
Ordinal Encoding - Label Encoding
Outlier-Detection-and-Removal
An outlier is a data point that is noticeably different from the rest. They represent errors in measurement, bad data collection, or simply show variables not considered when collecting the data.An outlier is a data point that is noticeably different from the rest. They represent errors in measurement, bad data collection, or simply show variables not considered when collecting the data.
Power-Transformer_Box-Cox-Transform-_Yeo-Johnson-Transform
Power Transformer works best on linear model and The Power Transformer actually automates this decision making by introducing a parameter called lambda. It decides on a generalized power transform by finding the best value of lambda
QQ-plot-Function-Transformer-Log-Transform-Reciprocal-Transform-Square-Root-Transform
Function Transformer is part of feature engineering it converts probability density function to normal distribution
standardization-feature-selection
feature selection is done before the model implementation like svm or logistic regression or .....
Virtual-Paint
an Virtual Paint using opencv in python credits: Murtaza's Workshop - Robotics and AI
Web-Scraping-
Web Scraping