**Table Of Contents**

B.Tech AKTU Quantum Book will take you deep into the field of Data Analytics. To flourish in this dynamic sector, access important notes, repeated questions, and helpful insights. **Unit-2 Data Analysis**

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**Q1. What are the various types of regression analysis techniques ?**

**Ans. Various types of regression analysis techniques:**

**1. Linear regression:**Linear regressions are based on the assumption that the predictors (or factors) and the target variable have a linear relationship.**2. Non-linear regression:**Non-linear regression allows modeling of non-linear relationships.**3. Logistic regression:**When our target variable is binomial, logistic regression comes in handy (accept or reject).**4. Time series regression:**Based on previous time ordered data, time series regressions are used to forecast future behaviour of variables.

**Q2. Write short notes on Bayesian network.**

**Ans. **

- 1. Bayesian networks are a sort of probabilistic graphical model that employs Bayesian inference to compute probability.
- 2. A Bayesian network is a directed acyclic graph with each edge representing a conditional dependency and each node representing a distinct random variable.
- 3. Bayesian networks use edges in a directed graph to model conditional dependency.

- 3. By utilizing these linkages, one may rapidly execute inference on the random variables in the graph.
- 4. By taking advantage of conditional independence, we may generate a compact, factorised representation of the joint probability distribution using the relationships provided by our Bayesian network.
- 5. Technically, if an edge (A, B) connects random variables A and B in the graph, it signifies that P(B|A) is a factor in the joint probability distribution, and we must know P(B|A) for all values of B and A in order to conduct inference.
- 6. In the Fig. since Rain has an edge going into WetGrass, it means that P(WetGrass | Rain) will be a factor, whose probability values are specified next to the WetGrass node in a conditional probability table.
- 7. Bayesian networks satisfy the Markov property, which states that a node is conditionally independent of its non-descendants given its parents. In the given example, this means that

P(Sprinkler | Cloudy, Rain) = P(Sprinkler | Cloudy)

Since Sprinkler is conditionally independent of its non-descendant, Rain, given Cloudy.

**Q3. Explain the application of time series analysis.**

**Ans. Applications of time series analysis:**

**1. Retail sales:**- a. A clothes merchant wants to anticipate future monthly sales for several product lines.
- b. These estimates must take seasonal features of the customer’s purchase habits into account.
- c. A suitable time series model must account for varying demand over the calendar year.

**2. Spare parts planning:**- a. To maintain an adequate supply of parts to fix consumer products, companies’ service groups must estimate future spare part demand. Thousands of separate part numbers are frequently found in parts inventories.
- b. Complex models for each part number can be created to estimate future demand utilizing input variables such as expected part failure rates, service diagnostic efficacy, and forecasted new product shipments.
- c. Time series analysis, on the other hand, can produce reliable short-term estimates based solely on historical spare part demand history.

**3. Stock trading:**- a. Pairs trading is a practise used by some high-frequency stock traders.
- b. A strong positive correlation between the prices of two equities is utilized to spot a market opportunity in pairs trading.
- c. Assume that the stock prices of Company A and Company B move in lockstep.
- d. Time series analysis can be used to compare the stock values of various companies over time.
- e. A statistically higher than expected price difference implies that it is a good time to buy Company A stock and sell Company B shares, or vice versa.

**Q4. Explain rule induction.**

**Ans. **

- 1. Rule induction is the process of deducing if-then rules from a dataset in data mining.
- 2. These symbolic decision rules explain an intrinsic relationship between the dataset’s properties and class labels.
- 3. Intuitive rule induction underpins many real-life situations.
- 4. Rule induction provides a sophisticated classification approach that is simple to understand for end users.
- 5. It is utilized in predictive analytics to categorize unknown data.
- 6. Rule induction is often used to characterize data patterns.
- 7. The simplest technique to derive rules from a data collection is to use a decision tree built on the same data set.

**Q5. Define fuzzy logic and its importance in our daily life. What is role of crisp sets in fuzzy logic ? **

**Ans. **

- 1. Fuzzy logic is a computing approach focused on “degrees of truth” rather than “true or false” (1 or 0).
- 2. Fuzzy logic encompasses the extreme situations of truth, 0 and l, as well as the different states of truth in between.
- 3. Fuzzy logic enables the incorporation of human judgements into computing problems.
- 4. It provides an effective method for resolving numerous criterion conflicts and better assessing options.

**Importance of fuzzy logic in daily life: **

- 1. Fuzzy logic is required for the development of human-like AI capabilities.
- 2. It is used to create intelligent systems for decision making, identification, optimisation, and control.
- 3. Many persons interested in research and development, such as engineers, mathematicians, computer software developers, and researchers, find fuzzy logic incredibly beneficial.
- 4. Fuzzy logic has been employed in a wide range of applications, including facial recognition, air conditioners, hoover cleaners, weather forecasting systems, medical diagnostics, and stock trading.

**Role of crisp sets in fuzzy logic: **

- 1. It contains the precise location of the set boundaries.
- 2. It provides the membership value of the set.

**Q6. Compare and contrast classical logic and fuzzy logic.**

**Ans. **

S. No. | Crisp (classical) logic | Fuzzy logic |

1. | An element in classical logic either belongs to or does not belong to a set. | Fuzzy logic allows for a more flexible understanding of set membership. |

2. | Crisp logic is built on a 2-state truth values (True/False). | Fuzzy logic is built on a multistate truth values. |

3. | The statement which is either ‘True’ or ‘False’ but not both is called a proposition in crisp logic. | A fuzzy proposition is a statement which acquires a fuzzy truth value. |

4. | Law of excluded middle and law of non-contradiction holds good in crisp logic. | Law of excluded middle and law of contradiction are violated. |

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