Today we will attempt to predict absenteeism at work, machine learning dataset. Dataset can be downloaded from https://archive.ics.uci.edu/ml/datasets/Absenteeism+at+work #python #machinelearning #absenteeismfromwork #randomforest
In this video we will demonstrate python GUI application development using the tkinter library. We will create a basic calculator with buttons and display. Since the code is larger than usual, we have shared it via google drive: https://drive.google.com/file/d/1FUqpX8SYGNPoeH79QANb4jpPJi91wEPA/view?usp=sharing #pythongui #pythonguitutorial #python #tkinter #tkintertutorial 1. Creating screen/textbox: https://youtu.be/h_3jhD4SQfs?t=90 2. Creating tags to justify text to…
Today we will calculate expected returns from historical data. We will calculate Mean historical return, Exponential Moving Average (EMA) historical return, and historical return using Capital Asset Pricing Model (CAPM). We will also calculate Variance, Standard Deviation and Correlation using Statistics library. #expectedreturn #stockmarket #exponentialmovingaverage #capm #variance #standarddeviation #correlation #pypfopt
In this video we will demonstrate how a model predicting stock exchange data can be all wrong even though it predicts same patterns or scores over 90%. The reason I made this video is because I have seen similar videos online telling people how to predict stocks movements through Machine Learning using only historical data….
In this tutorial we will show you how to: – Read a csv file using pandas – Split training and testing data using sklearn – Create, train and test a neural network model (MLPRegressor) – Save, Load and Re-use a model using joblib – Plot error and predictions on scatter chart using pyplot #machinelearning #python…
In this video we will show you how to: – Extract data from CSV into a python dataframe using Pandas – Use Neighborhood Components Analysis (NCA) – Use K Neighbors Classifier (K Nearest Neighbors) – Use sklearn.pipeline for classification and prediction We will use the famous Iris plants dataset for this exercise. #python #spyder #csv…
In this tutorial, we will show you how to: – Read a csv file using Pandas library – Create and train a Nearest Centroid Classifier sklearn.neighbors – Use Principal component analysis (PCA) to compress dataframe – Predict #Iris dataset using Nearest Centroid Classifier – Create Visualizations using matplotlib #python #machinelearning #matplotlib #sklearn #scikitlearn #nearestcentroid #nearestcentroidclassifier…
In this video, we will show you how to: – Import html table into python dataframe using Pandas – Select appropriate columns from datagrame – Draw a pie chart using matplotlib.pyplot #webscraping #html #padas #python #matplotlib #pyplot #piechart
In this tutorial, we will show you how to: – Read a csv file using Pandas library – Create and train a Support Vector Machine (SVM) – Use Principal component analysis (PCA) to compress dataframe – Predict #Iris dataset using Support Vector Classification – Create Visualizations using matplotlib #python #machinelearning #matplotlib #sklearn #scikitlearn #supportvectormachine
Today we will show you how to perform loops in python. In python, loops can be performed through for loops, or while loops. Loops can also per performed within other loops. These are called nested loops. Loops can be performed on lists, arrays, ranges, etc. #python #loop #nestedforloop #nestedloop #nestedloops #range