May 21
Potential Models • Statistical Models • Parametric and Non-Parametric • Mathematical Model (Optimization) • Machine Learning • Data Mining • Deep Learning • Reinforcement Learning • Graph Mining • NLP • Optimization • Genetic Algorithm •Association •Basket Association •Apriori Algorithm •Supervised •Classification •Regression •Unsupervised •Clustering/Customer Segmentation •Reinforcement •Learn a policy (interactively) •Game Playing •Robot in …
May 21
• Examine relations • Test Hypothesis • Validate • Find groups/classes/rules • Learn a policy • Maximize Reward interactively • Predict (Class or Value) • Forecast (numeric, sales) • Compare • Classify • Cluster Data Analytics, Machine Learning, Data Science
May 21
Data Analytics, Machine Learning, Data Science
May 21
Regression Projects • R Square, Goodness of fit • RMSE Classification Projects • Confusion Matrix • ROC • Accuracy, Recall, Precision RL – Reinforcement • Reward – Cumulative Data Analytics, Machine Learning, Data Science
May 21
Some Approaches •Feature Selection •Feature Extraction •PCA •SVD •LDA Data Analytics, Machine Learning, Data Science
May 21
Initial Analysis of Text Data • Stop word filter • Lemma • POS • Vocabulary Analysis Image Data: Initial Analysis • Fix image size, ratios • Image Scaling • Transform to Gray • Standardize Data Analytics, Machine Learning, Data Science
May 21
Data • Dataset Characteristics •Large Scale, Real, Representative, Relevant Features, balanced classes, unit relevant • Adapting data/dataset for the project •Clean, normalize/standardize, bring more data, and bring more data of the missing type • Data Suitability for the project • Check for R Square Measure • Check for Bias, Variance, • Do Exploratory Analysis • …
May 21
To have a thorough understanding of the data. Two Types: • Initial Analysis • Exploratory Analysis Initial Analysis: Univariate Analysis • Deciding/Determining the dependent (target) variable • Assigning the correct data types, appropriate column names • Address: Inconsistencies, missing values, outliers • Categorical variables with too many levels (address the issue) • (understand) Distributions of …
May 20
Inductive and Deductive Methods for Data Analytics Projects Deductive: Top-down approach. Take existing theories and apply to data Inductive: Bottom-up approach. Observe data and derive a hypothesis. “ Examples in Data Analytics: “ Ref: Internet/Google AI In a data analytics project, you may use both of the approaches. Initially, you may use an inductive approach …
May 18
•MySQL Data Manipulation: •https://www.databasejournal.com/mysql/mysql-data-manipulation-and-query-statements/ •https://www.w3schools.com/sql/ •https://www.tutorialspoint.com/sql/index.htm •Workbench: https://www.tutorialspoint.com/create-a-new-database-with-mysql-workbench •SQL Server Data Manipulation •https://www.tutorialspoint.com/ms_sql_server/index.htm •Management Studio: •https://www.tutorialspoint.com/ms_sql_server/ms_sql_server_management_studio.htm •Power BI Data Manipulation •https://learn.microsoft.com/en-us/power-bi/connect-data/desktop-tutorial-importing-and-analyzing-data-from-a-web-page •Data Manipulation in Python •https://www.analyticsvidhya.com/blog/2021/06/data-manipulation-using-pandas-essential-functionalities-of-pandas-you-need-to-know/ Data Analytics, Machine Learning, Data Science
