The Machine Learning project including ML/DL projects, notebooks, cheat codes of ML/DL, useful information on AI/AGI and codes or snippets/scripts/tasks with tips.

Github mirror of M. Zinkevich's "Rules of Machine Learning" style guide, with extra goodness.

This Project is a Final Year Project on Detection of Phishing Website Using Machine Learning, Copyright (c) 2021 Goodness Adediran All rights reserved.

Time series forecasting (TSF) is the task of predicting future values of a given sequence using historical data. Recently, this task has attracted the attention of researchers in the area of machine learning to address the limitations of traditional forecasting methods, which are time-consuming and full of complexity. With the increasing availability of extensive amounts of historical data along with the need of performing accurate production forecasting, particularly a powerful forecasting technique infers the stochastic dependency between past and future values is highly needed. In this research, we applied machine learning approach capable to address the limitations of traditional forecasting approaches and show accurate predictions and showed comparison of different machine learning models. For evaluation purpose, a case study from the petroleum industry domain is carried out using the production data of an actual gas field of Bangladesh. Toward a fair evaluation, the performance of the models were evaluated by measuring the goodness of fit through the coefficient of determination (R2 ) and Root Mean Square Error (RMSE), Mean Squared Error (MSE) , Mean Absolute Error(MAE) and model Accuracy

Anubis (formerly known as Benchmark AI), measures the goodness of machine learning workloads

Linear regression is one of the easiest and most popular Machine Learning algorithms. It is a statistical method that is used for predictive analysis. Linear regression makes predictions for continuous/real or numeric variables such as sales, salary, age, product price, etc. Linear regression algorithm shows a linear relationship between a dependent (y) and one or more independent (y) variables, hence called as linear regression. Since linear regression shows the linear relationship, which means it finds how the value of the dependent variable is changing according to the value of the independent variable. The linear regression model provides a sloped straight line representing the relationship between the variables. Consider the below image: Linear Regression in Machine Learning Mathematically, we can represent a linear regression as: y= a0+a1x+ ε Here, Y= Dependent Variable (Target Variable) X= Independent Variable (predictor Variable) a0= intercept of the line (Gives an additional degree of freedom) a1 = Linear regression coefficient (scale factor to each input value). ε = random error The values for x and y variables are training datasets for Linear Regression model representation. Types of Linear Regression Linear regression can be further divided into two types of the algorithm: Simple Linear Regression: If a single independent variable is used to predict the value of a numerical dependent variable, then such a Linear Regression algorithm is called Simple Linear Regression. Multiple Linear regression: If more than one independent variable is used to predict the value of a numerical dependent variable, then such a Linear Regression algorithm is called Multiple Linear Regression. Linear Regression Line A linear line showing the relationship between the dependent and independent variables is called a regression line. A regression line can show two types of relationship: Positive Linear Relationship: If the dependent variable increases on the Y-axis and independent variable increases on X-axis, then such a relationship is termed as a Positive linear relationship. Linear Regression in Machine Learning Negative Linear Relationship: If the dependent variable decreases on the Y-axis and independent variable increases on the X-axis, then such a relationship is called a negative linear relationship. Linear Regression in Machine Learning Finding the best fit line: When working with linear regression, our main goal is to find the best fit line that means the error between predicted values and actual values should be minimized. The best fit line will have the least error. The different values for weights or the coefficient of lines (a0, a1) gives a different line of regression, so we need to calculate the best values for a0 and a1 to find the best fit line, so to calculate this we use cost function. Cost function- The different values for weights or coefficient of lines (a0, a1) gives the different line of regression, and the cost function is used to estimate the values of the coefficient for the best fit line. Cost function optimizes the regression coefficients or weights. It measures how a linear regression model is performing. We can use the cost function to find the accuracy of the mapping function, which maps the input variable to the output variable. This mapping function is also known as Hypothesis function. For Linear Regression, we use the Mean Squared Error (MSE) cost function, which is the average of squared error occurred between the predicted values and actual values. It can be written as: For the above linear equation, MSE can be calculated as: Linear Regression in Machine Learning Where, N=Total number of observation Yi = Actual value (a1xi+a0)= Predicted value. Residuals: The distance between the actual value and predicted values is called residual. If the observed points are far from the regression line, then the residual will be high, and so cost function will high. If the scatter points are close to the regression line, then the residual will be small and hence the cost function. Gradient Descent: Gradient descent is used to minimize the MSE by calculating the gradient of the cost function. A regression model uses gradient descent to update the coefficients of the line by reducing the cost function. It is done by a random selection of values of coefficient and then iteratively update the values to reach the minimum cost function. Model Performance: The Goodness of fit determines how the line of regression fits the set of observations. The process of finding the best model out of various models is called optimization. It can be achieved by below method: 1. R-squared method: R-squared is a statistical method that determines the goodness of fit. It measures the strength of the relationship between the dependent and independent variables on a scale of 0-100%. The high value of R-square determines the less difference between the predicted values and actual values and hence represents a good model. It is also called a coefficient of determination, or coefficient of multiple determination for multiple regression. It can be calculated from the below formula: Linear Regression in Machine Learning Assumptions of Linear Regression Below are some important assumptions of Linear Regression. These are some formal checks while building a Linear Regression model, which ensures to get the best possible result from the given dataset. Linear relationship between the features and target: Linear regression assumes the linear relationship between the dependent and independent variables. Small or no multicollinearity between the features: Multicollinearity means high-correlation between the independent variables. Due to multicollinearity, it may difficult to find the true relationship between the predictors and target variables. Or we can say, it is difficult to determine which predictor variable is affecting the target variable and which is not. So, the model assumes either little or no multicollinearity between the features or independent variables. Homoscedasticity Assumption: Homoscedasticity is a situation when the error term is the same for all the values of independent variables. With homoscedasticity, there should be no clear pattern distribution of data in the scatter plot. Normal distribution of error terms: Linear regression assumes that the error term should follow the normal distribution pattern. If error terms are not normally distributed, then confidence intervals will become either too wide or too narrow, which may cause difficulties in finding coefficients. It can be checked using the q-q plot. If the plot shows a straight line without any deviation, which means the error is normally distributed. No autocorrelations: The linear regression model assumes no autocorrelation in error terms. If there will be any correlation in the error term, then it will drastically reduce the accuracy of the model. Autocorrelation usually occurs if there is a dependency between residual errors.

Wine Goodness Prediction

Machine learning tool for checking goodness of an anatomical scan (T1/T2)

Code for "Machine Learning-Based Estimation and Goodness-of-Fit for Large-Scale Confirmatory Item Factor Analysis"

Repository Containing different Concepts of Statistics for data science and machine learning Covered by me for the academic purpose and self Learning. This Repository includes Tests of Hypothesis such as One sample Z-test, Two sample Z-test, One sample T-test, Two sample T-test, Z-propotion test, Paired T-test, Unpaired T-test, Chi square test for goodness of fit & chi square test for Independence, One-way Anova and Two-way Anova test and test to check normality of the data using shapiro test, and to check variances using Levene test.

In this project I implemented a model described in the book "Machine Learning for Asset Managers" by Lopez de Prado (2019) called "Nested Clustered Optimisation". Some alterations to the original model were made and several backtesting techniques were applied in order to test the goodness of HNCO portfolio performance.

No description available

Shaolin.ai Classification learning module - machine learning goodness

Jupyter Notebook Containing Various Machine Learning Goodness

Machine learning model implementations with information about model "goodness"

No swipe dating app powered by the goodness of Machine Learning.

We firstly decompose a time series using Python language and some of the most relevant libraries for time series analysis and machine learning. We then perform data transformation processes, obtain different Machine Learning models and evaluate the goodness of each model. Our dataset features the consumption of electricity recorded by a counter.

This project aims at classifying the reuters news from 46 set of classes.Three different approaches have been used to solve the problem. And they are compared for their goodness. The three approaches being traditional machine learning models, Dense neural networks(DNNs) and RNNs.

As a part of course CS4048D(Mathematical Foundations of Machine Learning) end semester project we needed to create a presentation on Chi-Square Goodness of Fit and implement the code in python, cosidering both case wheen we reject accept Null hypothesis and when we reject it.

This project we have used the knowledge of machine learning to classify false new. To achieve the purpose I have used passive aggressive classifier. To summarize the goodness of the model, I have used confusion matrix. This project is implemented using various python libraries on Google Collab.

Linear regression model for the miles per gallon on the data at automobile [UCI](https://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/). Discard the categorical data. Analyze the data to get relevant insight. Get feature matrix X, and target variable y. Split data into training and testing. Normalize data using MinMaxScaler. Creat a LinearRegression object for modeling. Train the model with training data. Look at R^2 score for the goodness of fit for the train and test data. Present a graphical comparison of true and observed responses for the test data.

linear regression model for the miles per gallon on the data at automobile [UCI](https://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/). Discard the categorical data. >1. Analyze the data to get relevant insight. >1. Get feature matrix X, and target variable y. >1. Split data into training and testing. >1. Normalize data using MinMaxScaler. >1. Creat a LinearRegression object for modeling. >1. Train the model with training data. >1. Look at R^2 score for the goodness of fit for the train and test data. >1. Present a graphical comparison of true and observed responses for the test data.

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