Chance Insight – Data Analytics

Author: CB

  • DAX – Creating Multiple Measure With DAX Query Editor

    DAX – Creating Multiple Measure With DAX Query Editor

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    Creating multiple measures in DAX Query Editor It may be useful to create templates for creating DAX models, using commonly used DAX measures. Here we create a template for commonly used sales measures, such as Sales YTD, Sales Last Year, Sales Year on Year, and Sales Year on Year %, we can then apply the…

  • DAX – Using the Index Function

    DAX – Using the Index Function

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    Demonstrating the use of the Index function with the Contoso Retail Data warehouse. We can start off by building a virtual table in the DAX query editor, which we can use to apply the Index function. The table creates is a list of the first 10 customers by customer key from the DimCustomer table. If…

  • Building a Measure with the DAX RankX Function

    Building a Measure with the DAX RankX Function

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    Using the RANK() Function in DAX To demonstrate the use of the DAX function, we’ll start by creating a simple sales table with the sales amount and the year. Then we’ll add additional columns to create examples of using the RANK function. In the first example (SimpleSalesRank), we’ll just create a simple ranking. The default…

  • SQL Sales Report

    SQL Sales Report

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    We can create a more interesting sales report using Window functions and additional calculations in the query, including the gross margin %, total profit, and ranks for product price, sales quantity, and sales rank. The query uses a Common Table Expression (CTE) function to group the data first (as we can’t group using Windows functions).…

  • Simple Linear Regression

    Simple Linear Regression

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    Creating a simple Linear regression model and preparing for multi-linear regression. In this example, we use a sample of marketing spend data vs. sales and inspect the correlation between radio spend and total sales. The regression line is fitted using the ols function from statsmodels.formula.api Results return from the model.summary() method from the OLS (ordinary…

  • Graphing Assumptions

    Graphing Assumptions

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    Checking for assumptions (3 in this case), with sub-plotted seaborn graphics.The code for creating the linear regression model can be found in this post Linearity Assumption‘Each predictor variable (x) is linearly related to the outcome of variable y.’ Checking Homeoscedacity assumption with a scatterplot y_pred are the predicted y values from a regression line.The residuals…

  • Classification Models: Data Process

    Classification Models: Data Process

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    Introduction: Classification models are machine learning models that are used to predict binary outcome scenarios such as: Spam / Not SpamFraudulent Transaction / Non-Fraudulant Transationcustomer churn/ customer will not churncuster high value / customer low valueload approval /non-approval The Data Process Planning Exploratory Data Analysis (Python) Model Selection (Classification) Classification Models: Naive Bayes, Logistic Regression,…

  • K-Means

    K-Means

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    Supermarket Example Import libraries and read in CSV file to data frameThe data comes from kaggle here (mall data) First, we run some basic checks on the data to check for data integrity.Data includes 200 rows of data by 5 columns We can change Gender to numeric with the following Then we check for nulls…

  • Statistical Testing

    Statistical Testing

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    Significance Tests (normal distribution assumed) Groups Requirements Test Tails Statistic Distribution Degrees of freedom Example 1 normal dist, n > 30 and known variance z-test 1 and 2 z-score Normal NA A sample of the population is tested for height. Do they match the known population?Population mean and standard deviation are known, sample > 30…

  • Pandas Notes

    Pandas Notes

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    Notes from Getting Started Tutorial:https://pandas.pydata.org/docs/getting_started/intro_tutorials/03_subset_data.html 1. Creating a Dataframe and Series 2. Create Plots with Matplotlib 3. Add new columns from existing columns 4. Summary Statistics 5. Re-shape layout of tables 6. Sub-sets and Filtering import pandas as pd#read csv file to pandas DatafFrametitanic = pd.read_csv(“titanic.csv”)air_quality = pd.read_csv(“air_quality_no2.csv”, index_col=0, parse_dates=True) #save as excel, index=False removes…