Sengarofficial/ML_With_Logging

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Python
License
The Unlicense
Created over 1 year ago
Updated over 1 year ago

Sengarofficial/ML_With_Logging

Sengarofficial

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Understanding logging with machine learning implementation.

Target_Detection_SAR_Images

Recognition of Targets in SAR Images Based on a WVV Feature Using Various Machine And Deep Learning Models.

Inferential_Statistics

Statistical inference is the process of using data analysis to infer properties of an underlying distribution of probability. Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving estimates.

Jupyter Notebook

Predictive_Maintenance_With_MLops

Aircraft components are susceptible to degradation, which affects directly their reliability and performance. This machine learning project will be directed to provide a framework for predicting the aircraft’s remaining useful life (RUL) based on the entire life cycle data in order to provide the necessary maintenance behavior.

Handling_Imbalance_Dataset

A classification data set with skewed class proportions is called imbalanced. Classes that make up a large proportion of the data set are called majority classes. Those that make up a smaller proportion are minority classes.

Jupyter Notebook

Data-Analysis

Analyzing Data Through Python Using Pandas Library. Data analysis is a process of inspecting, cleansing, transforming, and modelling data with the goal of discovering useful information, informing conclusions, and supporting decision-making.

Jupyter Notebook

Outliers_Impact_Analysis

Outliers represent natural variations in the population, and they should be left as is in your dataset. These are called true outliers. Other outliers are problematic and should be removed because they represent measurement errors, data entry or processing errors, or poor sampling.

Jupyter Notebook

Machine_Learning_With_DVC

A practical approach for creating machine learning CI/CD Pipeline with DVC that is data version control.

Pandas_Tutorial

Pandas is an open-source library that is made mainly for working with relational or labeled data both easily and intuitively. It provides various data structures and operations for manipulating numerical data and time series. This library is built on top of the NumPy library. Pandas is fast and it has high performance & productivity for users.

Jupyter Notebook

Flask_RestApi

Building Flask RestApi.

Stock_Market_Volatility_Analysis

Time Series Analysis Using MA, ARIMA, SARIMA, ARCH & GARCH models.

Jupyter Notebook

Fraud_Transaction_Detection_With_Mlops

Exploratory_Data_Analysis

In statistics, exploratory data analysis is an approach of analyzing data sets to summarize their main characteristics, often using statistical graphics and other data visualization methods.

Jupyter Notebook

Induction_Furnace_Carbon_Sulphur_Response

This project is aimed to integrate artificial intelligence in steel Industry for better yielding and reducing production loss.

Jupyter Notebook

Normalization_Standardization

Feature transformation is a mathematical transformation in which we apply a mathematical formula to a particular column (feature) and transform the values, which are useful for our further analysis.

Jupyter Notebook

Movie_Recommender_System

Generalized recommendations for every user based on movie popularity and genre(Content Based)

Jupyter Notebook

BHP

Boston House Prediction With Docker Deployment

Jupyter Notebook

Algo_Trading_With_Python

Algorithmic trading is a method of executing orders using automated pre-programmed trading instructions accounting for variables such as time, price, and volume.[1] This type of trading attempts to leverage the speed and computational resources of computers relative to human traders.

Jupyter Notebook

Hyper_Parameter_Tuning

Hyperparameter tuning is an essential part of controlling the behavior of a machine learning model. If we don't correctly tune our hyperparameters, our estimated model parameters produce suboptimal results, as they don't minimize the loss function. This means our model makes more errors.

Jupyter Notebook

Handling_Categorical_Features

Categorical data is a type of data that is used to group information with similar characteristics, while numerical data is a type of data that expresses information in the form of numbers.

Jupyter Notebook

Feature_Selection_Engineering

Feature Selection is the method of reducing the input variable to your model by using only relevant data and getting rid of noise in data. It is the process of automatically choosing relevant features for your machine learning model based on the type of problem you are trying to solve.

Jupyter Notebook

South_German_Bank_MLops

NLP_With_Deployment

South_German_Bank_Credit_Risk_MLops

This project is an end to end machine learning project with mlops approach i.e github for code versioning and for versioning data, DVC is used and model tracking too.

Jupyter Notebook

E_Commerce_Customer_Behaviour

Chicken_Disease_Classification

Django_RestApi

Building Django RestApi.

MLFlow_Tutorial

MLFlow Tutorial

Jupyter Notebook

LSTM_Tutorial

Machine_Learning_Systematic_Approaches

This repo is defining the systematic approach for solving a machine learning problem along with concept of computer vision with python for image manipulations.

Jupyter Notebook

Python_Practice

Practical Approach of Python.

Jupyter Notebook