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
Today we will to create decomposition trees and ribbon charts using DTS Sales data (https://docs.google.com/spreadsheets/d/1FbeFtMlB-CcrOnfWW39SdcIWXyLJm5qy/edit?usp=sharing&ouid=111966831820301688680&rtpof=true&sd=true). We will show Sales by Year, Region, Store, Product and Sale Type. #powerbi #decompositiontree #ribbonchart
In this power bi video, we will create a Power BI report using Financial Sample (https://go.microsoft.com/fwlink/?LinkID=521962) Excel Workbook by Microsoft. We show you how to create a Sales Report by Product and Segment and a Profit report by Date and Country.
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…
Today we will show you how to: – Import data from MS Excel – Create dimensional model – Create relational data model – Create hierarchies – Create charts, diagrams, slices, matrix and maps – Create measures using Data Analysis Expressions (DAX) We will use Adventure Works sample data available at GitHub for this tutorial https://github.com/microsoft/powerbi-desktop-samples/blob/main/AdventureWorks%20Sales%20Sample/AdventureWorks%20Sales.xlsx…
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
