/University-Assignment-Portfolio

Portfolio of all my assignments completed doing master of data analytics from Deakin university. This assignments are completed by me for academic and passing the units.

Primary LanguageJupyter NotebookMIT LicenseMIT

University-Assignment-Portfolio

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Portfolio of my data analytics assignments completed by me for Academic purposes.

Contents

Descriptive Analytics & Visualization

  1. Analysis of mobile phone usage in Australia:
  • Applied Confidence interval, hypothesis testing
  • Manipulate and summarize data, appraise statistical output, interpret
  • Perform descriptive statistics (summary measures, plots & suitable charts, or graphics, perform confidence interval, hypothesis test
  • Use contemporary data analysis &visualization tools and recognize
  • Apply quantitative reasoning skills to solve complex problems
  • Include commentary on the user’s expenditure, usages, patterns, satisfaction levels and demographic, social media engagement
  1. Mad Dog Craft Beer Sales Analysis:
  • Conducted predictive analysis of beer data and gave recommendations to improve sales from the historic data.
  • Performed basic statistic operations, outlier analysis tests, linear regression, and logistic regression in Excel.
  • Developed proposals on where to put effort and money to improve perceptions of product quality and brand image so as to increase the probability of being recommended.
  1. Sony Dashboard:
  • Created interactive dashboard using Sony Dataset for a upcoming online marketing campaign.
  • Insightful data-driven decision making with website
  • Using Dynamic filters for performing actions in the graph
  • Make interactive for visually appealing effects
  • Analyzed Profits & Sales for different types of movies

Predictive Analytics

Analysis of Wine Dataset:

  • Develop a data mining method of classifying imported wine based on price.
  • Create a wine origin and marketability
  • Best source of wine and optimum price rating ratio.
  • Clean-up and explore wine tasting data. Create models like k-nn, naïve Bayes, decision trees.
  • Develop a method of estimating rating (points) of wines based on their text attributes.
  • Create different models for structured text, unstructured text and mix of structured and unstructured text.
  • Create a deployment process.

Practical Machine Learning for Data Science

  1. Face Recognition:

  2. Word2Vec Model:

Machine Learning

  1. BBC Dataset:

  2. Breast Cancer Classification:

  • To predict whether a cancer is benign or malignant.
  • Developed an algorithm that uses SVM to accurately predict (~97 percent accuracy) if a breast cancer tumor is benign or malignant, basically teaching a machine to predict breast cancer.
  1. Human-Activity-Recognition-Using-Smartphones:
  • Predict a person’s actions based on a trace of their movement using sensors.
  • Applied supervised learning algorithms such as K-Nearest Neighbor Classification, Multiclass Logistic Regression with Elastic Net, Support Vector Machine (RBF Kernel), and Random Forest.
  • Optimized the best candidate model by hyper-parameter tuning using Grid Search and Cross-Validation; trained a random forest to predict with 99% accuracy.

Security & Privacy Issues in Analytic

  1. Cambridge Analytic and Facebook Studies:
  • Develop a privacy & security issues reporting relation to Cambridge analytical and Facebook situation.
  • Provide risks, recognize and apply the relevance Ethical, Regulatory And Governance Issues In Victoria.
  1. Dumnonia Corporation:
  • Implementation of K-Anonymity as a Model for Protecting Privacy For an organization.
  • Implementation of cloud technology.
  • Provide technological solutions.

Real World Analytics

Energy-Efficiency-for-Building-in-R: Analysis of Energy Efficiency Dataset for Building in R

Database and Information retreival

  1. SQL Query:

  2. Retreival:

Value of Information

IT Portfolio of Gap Inc.:

  • Make IT portfolio and business case of gap inc.
  • Identify it assets and business value,returns,risks
  • Apply RBV and competive advantage over the firm

Modern Data Science

  1. Wine Review Analysis:

  2. Banking-Marketing-Campaign-with-Spark:

Marketing Analytics

  1. Price and Promotion Analytics of Carman's Kitchen:

  2. Conjoint Analysis of Sony Curved Telivision:

Supply Chain Management and Logistics

  1. British Petroleum:

Statistical Data Analysis

  1. Assignment-1:

  2. Assignment-2:

License

MIT

Help

If you find any mistakes or you can't figure out something, raise a question. I will get back to you as soon as possible. If you liked what you saw, want to have a chat with me about the portfolio, work opportunities, or collaboration, shoot an email at gupta.sha@outlook.com